https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2026.1686157/full https://www.frontiersin.org/files/Articles/1686157/fphy-14-1686157-HTML/image_m/fphy-14-1686157-g004.jpg Figure 4. Analysis chart of prediction experiment based on MEP-3M. https://www.frontiersin.org/files/Articles/1686157/fphy-14-1686157-HTML/image_m/fphy-14-1686157-g005.jpg Figure 5. Experimental analysis of time characteristics on purchase prediction under Olist. https://www.frontiersin.org/files/Articles/1686157/fphy-14-1686157-HTML/image_m/fphy-14-1686157-g006.jpg Figure 6. Experimental analysis of time characteristics on purchase prediction under User Behavior. https://www.frontiersin.org/files/Articles/1686157/fphy-14-1686157-HTML/image_m/fphy-14-1686157-t003.jpg Table 3. Experimental results of text type splitting. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1791886/full https://www.frontiersin.org/files/Articles/1791886/fmed-13-1791886-HTML/image_m/fmed-13-1791886-g001.jpg Figure 1. Study flowchart. Flow diagram illustrating patient selection from Affiliated Jinhua Hospit https://www.frontiersin.org/files/Articles/1791886/fmed-13-1791886-HTML/image_m/fmed-13-1791886-t001.jpg Table 1. Patient’s clinical characteristics according to HCHR quartiles. https://www.frontiersin.org/files/Articles/1791886/fmed-13-1791886-HTML/image_m/fmed-13-1791886-t002.jpg Table 2. Relationship between HCHR and 28-day mortality in septic patients. https://www.frontiersin.org/files/Articles/1791886/fmed-13-1791886-HTML/image_m/fmed-13-1791886-g002.jpg Figure 2. Subgroup analyses examining the association between HCHR and 28-day mortality. https://www.frontiersin.org/files/Articles/1791886/fmed-13-1791886-HTML/image_m/fmed-13-1791886-g003.jpg Figure 3. Association between HCHR and 28-day mortality using restricted cubic spline (RCS) analysis https://www.frontiersin.org/files/Articles/1791886/fmed-13-1791886-HTML/image_m/fmed-13-1791886-g004.jpg Figure 4. The ROC curve of HCHR in predicting 28-day mortality in septic patients. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1762903/full https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t001.jpg Table 1. The coding results of Xin and its structure in Chinese culture. https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-g001.jpg Figure 1. The model of Xin and its structure in Chinese culture. https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t002.jpg Table 2. The structural factor characteristics, eigenvalues, and cumulative contribution rates of Xi https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t003.jpg Table 3. The structural matrix of Xin in Chinese culture after rotation (N = 231). https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t004.jpg Table 4. Confirmatory factor analysis results of Xin in Chinese culture (N = 814). https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-g002.jpg Figure 2. The validation path diagram of Xin and its structure in Chinese culture. https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t005.jpg Table 5. Descriptive statistical results (N = 807). https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t006.jpg Table 6. Results of paired sample t-test (N = 807). https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t007.jpg Table 7. Results of independent samples t-test by gender (N = 807). https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t008.jpg Table 8. Results of homogeneity of variance test (N = 807). https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t009.jpg Table 9. Results of one-way ANOVA on educational level (N = 807). https://www.frontiersin.org/files/Articles/1762903/fpsyg-17-1762903-HTML-r1/image_m/fpsyg-17-1762903-t010.jpg Table 10. Results of independent samples t-test by ethnicity (N = 807). https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1645814/full https://www.frontiersin.org/files/Articles/1645814/fneur-16-1645814-HTML/image_m/fneur-16-1645814-t001.jpg Table 1. EEG correlation table of laparoscopic surgery specificity and anesthesia-related mechanisms https://www.frontiersin.org/files/Articles/1645814/fneur-16-1645814-HTML/image_m/fneur-16-1645814-t002.jpg Table 2. Comparison of POD and POCD. https://www.frontiersin.org/files/Articles/1645814/fneur-16-1645814-HTML/image_m/fneur-16-1645814-t003.jpg Table 3. Framework of perioperative EEG monitoring optimized nursing mode. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1585799/full https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t002.jpg Table 2. mALFF differences. https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t003.jpg Table 3. zALFF differences. https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g001.jpg Figure 1. mALFF analysis. Two-sample t-test results are presented. Areas in red indicate significant https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g002.jpg Figure 2. zALFF analysis. Two-sample t-test results are presented. Areas in red indicate significant https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t004.jpg Table 4. ReHo differences (SMKCC method). https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g003.jpg Figure 3. SMKCCREHO analysis. Two-sample t-test results are presented. Areas in blue indicate signif https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t005.jpg Table 5. Functional connection with the left postcentral gyrus as the seed point for patient group c https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t006.jpg Table 6. Functional connection with the right Postcentral gyrus as the seed point for patient group https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t007.jpg Table 7. Functional connection with the left precentral gyrus as the seed point for patient group co https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t008.jpg Table 8. Functional connection with the right precentral gyrus as the seed point for patient group c https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g004.jpg Figure 4. Functional connection with the left Postcentral gyrus as the seed point for patient group https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g005.jpg Figure 5. Functional connection with the right Postcentral gyrus as the seed point for patient group https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g006.jpg Figure 6. Functional connection with the left Precentral gyrus as the seed point for patient group c https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g007.jpg Figure 7. Functional connection with the right Precentral gyrus as the seed point for patient group https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t009.jpg Table 9. DegreeCentrality (Bi-SmDegreeCentrality). https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t010.jpg Table 10. DegreeCentrality (Bi-SzDegreeCentrality). https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t011.jpg Table 11. DegreeCentrality (weighted-SmDegreeCentrality). https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-t012.jpg Table 12. DegreeCentrality (weighted-SzDegreeCentrality). https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g008.jpg Figure 8. DegreeCentrality(Bi-SmDegreeCentrality). Areas in blue indicate significantly decreased va https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g009.jpg Figure 9. DegreeCentrality(Bi-SzDegreeCentrality). Areas in blue indicate significantly decreased va https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g010.jpg Figure 10. DegreeCentrality(weighted-SmDegreeCentrality). Areas in blue indicate significantly decre https://www.frontiersin.org/files/Articles/1585799/fmed-12-1585799-HTML-r1/image_m/fmed-12-1585799-g011.jpg Figure 11. DegreeCentrality(weighted-SzDegreeCentrality). Areas in blue indicate significantly decre https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1665484/full https://www.frontiersin.org/files/Articles/1665484/fphar-16-1665484-HTML/image_m/fphar-16-1665484-g001.jpg Figure 1. Flow diagram of the selection of studies for inclusion. https://www.frontiersin.org/files/Articles/1665484/fphar-16-1665484-HTML/image_m/fphar-16-1665484-t001.jpg Table 1. General data of 24 patients reported in case series/reports. https://www.frontiersin.org/files/Articles/1665484/fphar-16-1665484-HTML/image_m/fphar-16-1665484-t002.jpg Table 2. Clinical information of 24 included patients. https://www.frontiersin.org/files/Articles/1665484/fphar-16-1665484-HTML/image_m/fphar-16-1665484-t003.jpg Table 3. Treatment and prognosis of 24 patients reported in case series/reports. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1613093/full https://www.frontiersin.org/files/Articles/1613093/fonc-15-1613093-HTML/image_m/fonc-15-1613093-g001.jpg Figure 1. The workflow of this study. https://www.frontiersin.org/files/Articles/1613093/fonc-15-1613093-HTML/image_m/fonc-15-1613093-t001.jpg Table 1. Demographic and tumor characteristics of patients with colorectal liver metastasis. https://www.frontiersin.org/files/Articles/1613093/fonc-15-1613093-HTML/image_m/fonc-15-1613093-g002.jpg Figure 2. The schematic of colorectal-liver-metastases database. https://www.frontiersin.org/files/Articles/1613093/fonc-15-1613093-HTML/image_m/fonc-15-1613093-g003.jpg Figure 3. The results of lasso feature selection. (A) The cross-validation curve of LASSO. (B) The r https://www.frontiersin.org/files/Articles/1613093/fonc-15-1613093-HTML/image_m/fonc-15-1613093-g004.jpg Figure 4. The feature contribution of selected features. https://www.frontiersin.org/files/Articles/1613093/fonc-15-1613093-HTML/image_m/fonc-15-1613093-t002.jpg Table 2. The result of radiomics models. https://www.frontiersin.org/files/Articles/1613093/fonc-15-1613093-HTML/image_m/fonc-15-1613093-g005.jpg Figure 5. The visualization of model result of radiomics models. (A) The ROC curve of radiomics mode https://www.frontiersin.org/files/Articles/1613093/fonc-15-1613093-HTML/image_m/fonc-15-1613093-t003.jpg Table 3. The result of radiomics-clinical models. https://www.frontiersin.org/files/Articles/1613093/fonc-15-1613093-HTML/image_m/fonc-15-1613093-g006.jpg Figure 6. The visualization of model result of radiomics-clinical models. (A) The ROC curve of radio https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1745491/full https://www.frontiersin.org/files/Articles/1745491/fpsyg-17-1745491-HTML-r1/image_m/fpsyg-17-1745491-t001.jpg Table 1. Descriptive statistics of study variables. https://www.frontiersin.org/files/Articles/1745491/fpsyg-17-1745491-HTML-r1/image_m/fpsyg-17-1745491-t002.jpg Table 2. Pearson correlation matrix. https://www.frontiersin.org/files/Articles/1745491/fpsyg-17-1745491-HTML-r1/image_m/fpsyg-17-1745491-g001.jpg Figure 1. Scatterplots of mathematics achievement vs. key predictors: MATHEFF (top-left), ESCS (bott https://www.frontiersin.org/files/Articles/1745491/fpsyg-17-1745491-HTML-r1/image_m/fpsyg-17-1745491-t003.jpg Table 3. Full model regression coefficients (robust standard errors). https://www.frontiersin.org/files/Articles/1745491/fpsyg-17-1745491-HTML-r1/image_m/fpsyg-17-1745491-t004.jpg Table 4. Final parsimonious model coefficients (robust standard errors). https://www.frontiersin.org/files/Articles/1745491/fpsyg-17-1745491-HTML-r1/image_m/fpsyg-17-1745491-g002.jpg Figure 2. Observed vs. predicted mathematics achievement: Parsimonious model (left) and full model ( https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1710963/full https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-g001.jpg Figure 1. CONSORT diagram. https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-t001.jpg Table 1. Participants’ demographic characteristics and screening scores. https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-g002.jpg Figure 2. Study design overview. https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-t002.jpg Table 2. Schedule. https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-g003.jpg Figure 3. Imagery scenarios framework and examples (A) Framework of criticism scenario (B) Framework https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-t003.jpg Table 3. ITT analysis intention-to-treatment analysis results. https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-g004.jpg Figure 4. SCL results during anticipation phases of Recall, Renewal, Reinstatement and during Hotspo https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-t004.jpg Table 4. ITT descriptive intention-to-treatment descriptive analysis. https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-g005.jpg Figure 5. Rating of anticipation phase of treated criticism across groups and time points. https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-g006.jpg Figure 6. Hotspot rating of treated criticism across groups and time points. https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-t005.jpg Table 5. ITT contrast analysis. https://www.frontiersin.org/files/Articles/1710963/fpsyg-16-1710963-HTML-r1/image_m/fpsyg-16-1710963-g007.jpg Figure 7. Course of SCL during first intervention session with PE visible during 4th part of scenari https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1664598/full https://www.frontiersin.org/files/Articles/1664598/fpsyg-16-1664598-HTML/image_m/fpsyg-16-1664598-g001.jpg Figure 1. Chronotype classification flowchart. https://www.frontiersin.org/files/Articles/1664598/fpsyg-16-1664598-HTML/image_m/fpsyg-16-1664598-t001.jpg Table 1. Age, cumulative grade average, absence from classrooms, and time preference for lectures an https://www.frontiersin.org/files/Articles/1664598/fpsyg-16-1664598-HTML/image_m/fpsyg-16-1664598-t002.jpg Table 2. Chronotypes among clinical phase medical students at the University of Tabuk, Saudi Arabia. https://www.frontiersin.org/files/Articles/1664598/fpsyg-16-1664598-HTML/image_m/fpsyg-16-1664598-t003.jpg Table 3. Morningness/Eveningness Questionnaire components. https://www.frontiersin.org/files/Articles/1664598/fpsyg-16-1664598-HTML/image_m/fpsyg-16-1664598-t004.jpg Table 4. Correlation between cumulative grade average, absence from classrooms, age, and study time https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1609403/full https://www.frontiersin.org/files/Articles/1609403/fpsyg-16-1609403-HTML/image_m/fpsyg-16-1609403-t001.jpg Table 1. Sociodemographic and clinical data. https://www.frontiersin.org/files/Articles/1609403/fpsyg-16-1609403-HTML/image_m/fpsyg-16-1609403-g001.jpg Figure 1. Neuropsychological and neuropsychiatric differences between groups at baseline and 1 year https://www.frontiersin.org/files/Articles/1609403/fpsyg-16-1609403-HTML/image_m/fpsyg-16-1609403-g002.jpg Figure 2. Motor status and neuropsychological performance over time. Parameter estimates from linear https://www.frontiersin.org/files/Articles/1609403/fpsyg-16-1609403-HTML/image_m/fpsyg-16-1609403-t002.jpg Table 2. Linear mixed-effect models. https://www.frontiersin.org/files/Articles/1609403/fpsyg-16-1609403-HTML/image_m/fpsyg-16-1609403-g003.jpg Figure 3. Neuropsychiatric symptomatology and general health variables over time. Parameter estimate https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1710562/full https://www.frontiersin.org/files/Articles/1710562/fpubh-13-1710562-HTML-r1/image_m/fpubh-13-1710562-t001.jpg Table 1. Socio-demographic and academic characteristics of study participants (N = 751). https://www.frontiersin.org/files/Articles/1710562/fpubh-13-1710562-HTML-r1/image_m/fpubh-13-1710562-t002.jpg Table 2. Univariate linear regression analysis of predictors for cumulative percentage among medical https://www.frontiersin.org/files/Articles/1710562/fpubh-13-1710562-HTML-r1/image_m/fpubh-13-1710562-t003.jpg Table 3. Multivariable linear regression analysis of predictors for cumulative percentage among medi https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1712622/full https://www.frontiersin.org/files/Articles/1712622/fmed-12-1712622-HTML-r1/image_m/fmed-12-1712622-t001.jpg Table 1. Overview of the main mind–brain philosophies [adapted from Cheniaux and Lyra (12)]. https://www.frontiersin.org/files/Articles/1712622/fmed-12-1712622-HTML-r1/image_m/fmed-12-1712622-g001.jpg Figure 1. Predictive coding in the Bayesian brain [adapted from Haker et al. (60)] used with license https://www.frontiersin.org/files/Articles/1712622/fmed-12-1712622-HTML-r1/image_m/fmed-12-1712622-g002.jpg Figure 2. (A) Schematic illustration of the ascending and descending pathways where core affective n https://www.frontiersin.org/files/Articles/1712622/fmed-12-1712622-HTML-r1/image_m/fmed-12-1712622-t002.jpg Table 2. Affective systems as described by Panksepp and Biven (16) [adapted from Penner and Stoddard https://www.frontiersin.org/files/Articles/1712622/fmed-12-1712622-HTML-r1/image_m/fmed-12-1712622-t003.jpg Table 3. Case vignettes. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1693796/full https://www.frontiersin.org/files/Articles/1693796/fcimb-15-1693796-HTML/image_m/fcimb-15-1693796-g001.jpg Figure 1. Schematic illustrates the balanced virus–phage–host immunity network in health, contrasted https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1719386/full https://www.frontiersin.org/files/Articles/1719386/fnut-12-1719386-HTML-r1/image_m/fnut-12-1719386-g001.jpg Figure 1. Flow of patients recruitment. https://www.frontiersin.org/files/Articles/1719386/fnut-12-1719386-HTML-r1/image_m/fnut-12-1719386-t001.jpg Table 1. Baseline of patient characteristics. https://www.frontiersin.org/files/Articles/1719386/fnut-12-1719386-HTML-r1/image_m/fnut-12-1719386-t002.jpg Table 2. The characteristics of initiating EN in patients in the RICU. https://www.frontiersin.org/files/Articles/1719386/fnut-12-1719386-HTML-r1/image_m/fnut-12-1719386-t003.jpg Table 3. EN intolerance and discontinuation during EN feeding. https://www.frontiersin.org/files/Articles/1719386/fnut-12-1719386-HTML-r1/image_m/fnut-12-1719386-t004.jpg Table 4. Aspiration precautions for high aspiration risk patients. https://www.frontiersin.org/files/Articles/1719386/fnut-12-1719386-HTML-r1/image_m/fnut-12-1719386-t005.jpg Table 5. The correlation between feeding adequacy and non-social infections in patients with respira https://www.frontiersin.org/files/Articles/1719386/fnut-12-1719386-HTML-r1/image_m/fnut-12-1719386-g002.jpg Figure 2. Cox analysis of overfeeding and 28-day mortality in patients with respiratory critical ill https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1629988/full https://www.frontiersin.org/files/Articles/1629988/fcell-13-1629988-HTML/image_m/fcell-13-1629988-g001.jpg Figure 1. Cardiac Organoids. (A) Schematic representation of the cardiac organoid differentiation pr https://www.frontiersin.org/files/Articles/1629988/fcell-13-1629988-HTML/image_m/fcell-13-1629988-g002.jpg Figure 2. Cardiomyocyte Maturation in Organoids. (A) Immature cardiomyocytes in day 8 organoids. (A′ https://www.frontiersin.org/files/Articles/1629988/fcell-13-1629988-HTML/image_m/fcell-13-1629988-g003.jpg Figure 3. Vasculogenesis and Hematopoiesis in Cardiac Organoids. (A) PECAM1+ cell islands and endoth https://www.frontiersin.org/files/Articles/1629988/fcell-13-1629988-HTML/image_m/fcell-13-1629988-g004.jpg Figure 4. Single-cell RNA-sequencing analysis of organoids at day 35 of development. (A) Single-cell https://www.frontiersin.org/files/Articles/1629988/fcell-13-1629988-HTML/image_m/fcell-13-1629988-g005.jpg Figure 5. Single-cell RNA-sequencing analysis of hematopoietic cells derived from cardiac organoids https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1661952/full https://www.frontiersin.org/files/Articles/1661952/fcell-13-1661952-HTML/image_m/fcell-13-1661952-g001.jpg Figure 1. Placental development. (A) Around 4–5 days post-fertilization, the blastocyst forms with a https://www.frontiersin.org/files/Articles/1661952/fcell-13-1661952-HTML/image_m/fcell-13-1661952-t001.jpg Table 1. Summary of single-cell and single-nucleus RNA-seq studies and key findings in human trophob https://www.frontiersin.org/files/Articles/1661952/fcell-13-1661952-HTML/image_m/fcell-13-1661952-g002.jpg Figure 2. The cis-regulatory elements: enhancers and silencers. (A) The cis-regulatory elements (CRE https://www.frontiersin.org/files/Articles/1661952/fcell-13-1661952-HTML/image_m/fcell-13-1661952-t002.jpg Table 2. Summary of cis-regulatory elements, TFs, and their depletion effects in trophoblast. https://www.frontiersin.org/files/Articles/1661952/fcell-13-1661952-HTML/image_m/fcell-13-1661952-g003.jpg Figure 3. TFs can act as activators and repressors. A Trophoblast cell fate decisions are orchestrat https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1768797/full https://www.frontiersin.org/files/Articles/1768797/fmicb-17-1768797-HTML/image_m/fmicb-17-1768797-t001.jpg Table 1. Physiochemical characteristics of potato rhizosphere soil under different rotation systems. https://www.frontiersin.org/files/Articles/1768797/fmicb-17-1768797-HTML/image_m/fmicb-17-1768797-g001.jpg Figure 1. The agronomic traits of potatoes in different rotation systems. Error bars indicate the st https://www.frontiersin.org/files/Articles/1768797/fmicb-17-1768797-HTML/image_m/fmicb-17-1768797-g002.jpg Figure 2. Nonmetric multidimensional scaling ordination for rhizosphere soil metabolites of potato i https://www.frontiersin.org/files/Articles/1768797/fmicb-17-1768797-HTML/image_m/fmicb-17-1768797-g003.jpg Figure 3. The OTUs and diversity index of bacteria (a) and fungi (b) in rhizosphere soils of potato https://www.frontiersin.org/files/Articles/1768797/fmicb-17-1768797-HTML/image_m/fmicb-17-1768797-t002.jpg Table 2. ANOVA of the relative abundance (%) of the dominant bacterial and fungal phyla in rhizosphe https://www.frontiersin.org/files/Articles/1768797/fmicb-17-1768797-HTML/image_m/fmicb-17-1768797-g004.jpg Figure 4. Linear discriminant analysis effect size identified the significantly different abundant b https://www.frontiersin.org/files/Articles/1768797/fmicb-17-1768797-HTML/image_m/fmicb-17-1768797-g005.jpg Figure 5. Pearson’s correlation analysis of differential metabolites and soil physicochemical charac https://www.frontiersin.org/files/Articles/1768797/fmicb-17-1768797-HTML/image_m/fmicb-17-1768797-g006.jpg Figure 6. Variation partitioning analysis of the effects of primary metabolites, secondary metabolit https://www.frontiersin.org/files/Articles/1768797/fmicb-17-1768797-HTML/image_m/fmicb-17-1768797-g007.jpg Figure 7. Two-way orthogonal partial least squares (O2PLS) score plot showing the association betwee https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1749610/full https://www.frontiersin.org/files/Articles/1749610/fpubh-14-1749610-HTML-r1/image_m/fpubh-14-1749610-t001.jpg Table 1. Sample characteristics. https://www.frontiersin.org/files/Articles/1749610/fpubh-14-1749610-HTML-r1/image_m/fpubh-14-1749610-t002.jpg Table 2. Mean value of dependent and independent variables with difference between genders. https://www.frontiersin.org/files/Articles/1749610/fpubh-14-1749610-HTML-r1/image_m/fpubh-14-1749610-t003.jpg Table 3. Ordinary least squares regression toward attitudes toward homosexuality. https://www.frontiersin.org/files/Articles/1749610/fpubh-14-1749610-HTML-r1/image_m/fpubh-14-1749610-g001.jpg Figure 1. The moderating effect of traditional gender role beliefs in acceptance premarital sex and https://www.frontiersin.org/files/Articles/1749610/fpubh-14-1749610-HTML-r1/image_m/fpubh-14-1749610-g002.jpg Figure 2. The moderating effect of traditional gender role beliefs in filial piety and acceptance ho https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1721076/full https://www.frontiersin.org/files/Articles/1721076/fneur-17-1721076-HTML/image_m/fneur-17-1721076-t001.jpg Table 1. The regulatory effect and mechanism of BECs on T cells. https://www.frontiersin.org/files/Articles/1721076/fneur-17-1721076-HTML/image_m/fneur-17-1721076-g001.jpg Figure 1. Activated BECs dynamically regulate T cells in multiple sclerosis, including (a) recruitme https://www.frontiersin.org/files/Articles/1721076/fneur-17-1721076-HTML/image_m/fneur-17-1721076-t002.jpg Table 2. The regulatory effect and mechanism of T cells on BECs. https://www.frontiersin.org/files/Articles/1721076/fneur-17-1721076-HTML/image_m/fneur-17-1721076-g002.jpg Figure 2. T cell impair BEC barrier function and reprogram immunomodulatory function. (Created with https://www.frontiersin.org/files/Articles/1721076/fneur-17-1721076-HTML/image_m/fneur-17-1721076-g003.jpg Figure 3. Mechanism diagram of interaction between BECs and T cells. (a) Under inflammatory conditio https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1748634/full https://www.frontiersin.org/files/Articles/1748634/fimmu-16-1748634-HTML-r1/image_m/fimmu-16-1748634-g001.jpg Figure 1. Trogocytosis is initiated upon the interaction between CAR-T cells and tumor cells. (A, B) https://www.frontiersin.org/files/Articles/1748634/fimmu-16-1748634-HTML-r1/image_m/fimmu-16-1748634-g002.jpg Figure 2. The killing activity of trog+ CAR-T cells changed after trogocytosis. (A) The levels of IL https://www.frontiersin.org/files/Articles/1748634/fimmu-16-1748634-HTML-r1/image_m/fimmu-16-1748634-g003.jpg Figure 3. The expression level of CD20 in tumor cells impacts the activation of CD20 CAR-T cells. (A https://www.frontiersin.org/files/Articles/1748634/fimmu-16-1748634-HTML-r1/image_m/fimmu-16-1748634-g004.jpg Figure 4. Bryostatin can significantly increase the expression of CD20 in tumor cells and normal B c https://www.frontiersin.org/files/Articles/1748634/fimmu-16-1748634-HTML-r1/image_m/fimmu-16-1748634-g005.jpg Figure 5. Bryostatin could enhance the killing activity of CD20 CAR-T cells in vitro. (A) The killin https://www.frontiersin.org/files/Articles/1748634/fimmu-16-1748634-HTML-r1/image_m/fimmu-16-1748634-g006.jpg Figure 6. Bryostatin enhanced the growth inhibitory effect of CAR-T on tumors in tumor-bearing mice. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1742654/full https://www.frontiersin.org/files/Articles/1742654/fimmu-16-1742654-HTML/image_m/fimmu-16-1742654-g001.jpg Figure 1. Immunosuppressive myeloid–stromal crosstalk underlying PD-1/PD-L1 resistance in the CRC tu https://www.frontiersin.org/files/Articles/1742654/fimmu-16-1742654-HTML/image_m/fimmu-16-1742654-t001.jpg Table 1. Single-cell and spatial proteomic platforms for delineating immunosuppressive niches in col https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1696792/full https://www.frontiersin.org/files/Articles/1696792/fimmu-17-1696792-HTML/image_m/fimmu-17-1696792-g001.jpg Figure 1. Study design baseline characteristics. (a) Experimental design process. (b) Representative https://www.frontiersin.org/files/Articles/1696792/fimmu-17-1696792-HTML/image_m/fimmu-17-1696792-t001.jpg Table 1. The patient characteristics. https://www.frontiersin.org/files/Articles/1696792/fimmu-17-1696792-HTML/image_m/fimmu-17-1696792-g002.jpg Figure 2. Patients in the MPR group showed a significant increase in immune cell infiltration post-N https://www.frontiersin.org/files/Articles/1696792/fimmu-17-1696792-HTML/image_m/fimmu-17-1696792-g003.jpg Figure 3. Immune profiling identifies a subset of immune hot tumors with favorable outcomes from NCI https://www.frontiersin.org/files/Articles/1696792/fimmu-17-1696792-HTML/image_m/fimmu-17-1696792-g004.jpg Figure 4. Screening for DEGs in NCI. (a) volcano diagram of DEGs in the cold tumor-related gene modu https://www.frontiersin.org/files/Articles/1696792/fimmu-17-1696792-HTML/image_m/fimmu-17-1696792-g005.jpg Figure 5. The expression of seven gene pre-treatment is associated with NCI. (a) Heatmap of seven ge https://www.frontiersin.org/files/Articles/1696792/fimmu-17-1696792-HTML/image_m/fimmu-17-1696792-g006.jpg Figure 6. Validation of external data showing association of seven gene expression levels with immun https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1667835/full https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-t001.jpg Table 1. Primer sequences for qRT–PCR. https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-t002.jpg Table 2. siRNA sequences used for transfection. https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g001.jpg Figure 1. Expression of the PRNP gene in different cell lines treated with gemcitabine (A) UMAP plot https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g002.jpg Figure 2. Evaluation of FHL2, PRNP, and RRM1 genes with pathway enrichment (A) Venn diagram displayi https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g003.jpg Figure 3. Associations between PRNP gene expression, sensitivity to immune checkpoint therapy, and c https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g004.jpg Figure 4. scRNA-seq analysis of gemcitabine-treated mouse pancreatic cancer models (A) Heatmap of PR https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g005.jpg Figure 5. Single-cell and spatial transcriptome analysis of chemotherapy-treated populations (A) Exp https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g006.jpg Figure 6. Effects of GEM and PRNP expression on cell proliferation (A) MTT assay to assess the survi https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g007.jpg Figure 7. Effects of GEM and PRNP expression on cell migration and invasion (A, B) Wound-healing ass https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g008.jpg Figure 8. Relationship between GEM and PRNP expression ferroptosis (A) DCFH-DA fluorescence detectio https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g009.jpg Figure 9. Relationship between Ferrostatin-1 and PRNP expression and ferroptosis (A). MTT assay to a https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g010.jpg Figure 10. Relationship between PRNP expression, cellular autophagy, and apoptosis (A, B). AO/EB flu https://www.frontiersin.org/files/Articles/1667835/fimmu-16-1667835-HTML/image_m/fimmu-16-1667835-g011.jpg Figure 11. Mechanism diagram. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1599223/full https://www.frontiersin.org/files/Articles/1599223/fimmu-16-1599223-HTML/image_m/fimmu-16-1599223-g001.jpg Figure 1. Integrative analysis of ACACA expression and survival outcome. (A) ACACA mRNA expression i https://www.frontiersin.org/files/Articles/1599223/fimmu-16-1599223-HTML/image_m/fimmu-16-1599223-g002.jpg Figure 2. Prognostic value of ACACA mRNA expression across cancers. (A) Kaplan-Meier survival curves https://www.frontiersin.org/files/Articles/1599223/fimmu-16-1599223-HTML/image_m/fimmu-16-1599223-g003.jpg Figure 3. Pan-cancer immune landscape correlated with ACACA mRNA expression. (A) Correlation of ACAC https://www.frontiersin.org/files/Articles/1599223/fimmu-16-1599223-HTML/image_m/fimmu-16-1599223-g004.jpg Figure 4. Functional annotation and therapeutic implications of ACACA in pan-cancer. (A) Gene Set En https://www.frontiersin.org/files/Articles/1599223/fimmu-16-1599223-HTML/image_m/fimmu-16-1599223-g005.jpg Figure 5. Comprehensive analysis of ACACA in lung adenocarcinoma (LUAD). (A) Kaplan-Meier survival p https://www.frontiersin.org/files/Articles/1599223/fimmu-16-1599223-HTML/image_m/fimmu-16-1599223-g006.jpg Figure 6. Single-cell RNA sequencing analysis of ACACA in LUAD (GEO: GSE131907). (A) UMAP projection https://www.frontiersin.org/files/Articles/1599223/fimmu-16-1599223-HTML/image_m/fimmu-16-1599223-g007.jpg Figure 7. Validation of ACACA expression levels in clinical tissue samples and cell lines. (A) Repre https://www.frontiersin.org/files/Articles/1599223/fimmu-16-1599223-HTML/image_m/fimmu-16-1599223-g008.jpg Figure 8. ACACA drives proliferation, migration, and EGFR-TKI resistance in lung cancer cells. (A) R https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1684168/full https://www.frontiersin.org/files/Articles/1684168/fpsyt-16-1684168-HTML-r1/image_m/fpsyt-16-1684168-t001.jpg Table 1. Counts and age-standardized incidence, prevalence, and DALYs rates of schizophrenia in 2021 https://www.frontiersin.org/files/Articles/1684168/fpsyt-16-1684168-HTML-r1/image_m/fpsyt-16-1684168-g001.jpg Figure 1. Global trends in counts and age-standardized rates of incidence (A), prevalence (B), and d https://www.frontiersin.org/files/Articles/1684168/fpsyt-16-1684168-HTML-r1/image_m/fpsyt-16-1684168-g002.jpg Figure 2. Age-specific trends in counts and rates of incidence (A), prevalence (B), and DALYs (C) fo https://www.frontiersin.org/files/Articles/1684168/fpsyt-16-1684168-HTML-r1/image_m/fpsyt-16-1684168-g003.jpg Figure 3. Trends in age-standardized incidence (A), prevalence (B), and DALYs (C) rates per 100, 000 https://www.frontiersin.org/files/Articles/1684168/fpsyt-16-1684168-HTML-r1/image_m/fpsyt-16-1684168-g004.jpg Figure 4. Global distribution in ASIR and trends in estimated annual percentage change (EAPC) of sch https://www.frontiersin.org/files/Articles/1684168/fpsyt-16-1684168-HTML-r1/image_m/fpsyt-16-1684168-g005.jpg Figure 5. Joinpoint regression analysis of sex-specific age-standardized incidence (A), prevalence ( https://www.frontiersin.org/files/Articles/1684168/fpsyt-16-1684168-HTML-r1/image_m/fpsyt-16-1684168-g006.jpg Figure 6. Bayesian age-period-cohort (BAPC) model sex-specific predictions of ASIR, ASPR, and ASDR. https://www.frontiersin.org/files/Articles/1684168/fpsyt-16-1684168-HTML-r1/image_m/fpsyt-16-1684168-g007.jpg Figure 7. Machine learning sex-specific predictions of ASIR, ASPR, and ASDR. (A) ASIR of both. (B) A https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1780720/full https://www.frontiersin.org/files/Articles/1780720/fimmu-17-1780720-HTML/image_m/fimmu-17-1780720-g001.jpg Figure 1. Immune Infiltration and Structural Cells Depletion in AAA. (A) Uniform manifold approximat https://www.frontiersin.org/files/Articles/1780720/fimmu-17-1780720-HTML/image_m/fimmu-17-1780720-g002.jpg Figure 2. Immune-Inflammatory Activation and Vascular Structural Disruption in AAA. (A, B) PCA plot https://www.frontiersin.org/files/Articles/1780720/fimmu-17-1780720-HTML/image_m/fimmu-17-1780720-g003.jpg Figure 3. Macrophage Polarization Correlates with AAA Progression. (A, B) Results of AAA-related cel https://www.frontiersin.org/files/Articles/1780720/fimmu-17-1780720-HTML/image_m/fimmu-17-1780720-g004.jpg Figure 4. Macrophages Exhibit Altered Gene Expression and Secrete Abundant CCL20. (A) The volcano pl https://www.frontiersin.org/files/Articles/1780720/fimmu-17-1780720-HTML/image_m/fimmu-17-1780720-t001.jpg Table 1. Demographic characteristics of participants from UK Biobank database. https://www.frontiersin.org/files/Articles/1780720/fimmu-17-1780720-HTML/image_m/fimmu-17-1780720-t002.jpg Table 2. High circulating CCL20 level predicted higher risk of AAA. https://www.frontiersin.org/files/Articles/1780720/fimmu-17-1780720-HTML/image_m/fimmu-17-1780720-g005.jpg Figure 5. Macrophages Recruit Significant Numbers of Immune Cells through the CCL20-CCR6 Axis. (A) C https://www.frontiersin.org/files/Articles/1780720/fimmu-17-1780720-HTML/image_m/fimmu-17-1780720-g006.jpg Figure 6. Targeting the CCL20-CCR6 Axis Suppresses AAA Progression. (A) Schematic representation of https://www.frontiersin.org/files/Articles/1780720/fimmu-17-1780720-HTML/image_m/fimmu-17-1780720-g007.jpg Figure 7. The role of CCL20-CCR6 axis in AAA formation. Macrophage polarization was imbalanced, with https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1680290/full https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-g001.jpg Figure 1. Study participant selection flowchart. https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-t001.jpg Table 1. Baseline characteristics of participants by AFT. https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-t002.jpg Table 2. Performance comparison of seven machine learning models in predicting cognitive impairment https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-g002.jpg Figure 2. Machine learning model performance on AFT-based cognitive impairment prediction. (A) Recei https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-g003.jpg Figure 3. SHAP analysis for cognitive impairment prediction using the AFT. (A) Top 15 features ranke https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-t003.jpg Table 3. Model performance comparison on CERAD-WL cognitive impairment screening. https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-g004.jpg Figure 4. Performance metrics of machine learning models for CERAD-WL-based cognitive impairment det https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-g005.jpg Figure 5. Interpretable machine learning analysis for CERAD-WL-based cognitive impairment prediction https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-t004.jpg Table 4. DSST-based cognitive impairment prediction: model performance comparison. https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-g006.jpg Figure 6. Clinical performance assessment of machine learning models using DSST. (A) Diagnostic disc https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-g007.jpg Figure 7. Clinically interpretable features of DSST-based cognitive screening models. (A) Key predic https://www.frontiersin.org/files/Articles/1680290/fnut-12-1680290-HTML-r1/image_m/fnut-12-1680290-g008.jpg Figure 8. Effects of different treatments on ROS, SIRT1, and BDNF levels. (A) Relative level of ROS https://www.frontiersin.org/journals/ocean-sustainability/articles/10.3389/focsu.2025.1643289/full https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g001.jpg Figure 1. Maps of the study area. (A) Southern and central North Sea (blue, ICES areas 4.bc), northe https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g002.jpg Figure 2. Flow diagram of the data used and the results of the analyses presented herein. The shape https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-t001.jpg Table 1. The issue identified by the DPSIR (Drivers, Pressures, State, Impact, Response), to decide https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g003.jpg Figure 3. Total EU and national annual landings of cod (A, C, E, G) and plaice (B, D, F, H) from the https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g004.jpg Figure 4. Abundance index of density of cod at different spatial scales covering the Southern and ce https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g005.jpg Figure 5. Abundance index of density of plaice at different spatial scales covering the Southern and https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g006.jpg Figure 6. Danish fleet cod landings (in kg) by year and métiers in the Jammer Bay area 2014–2022. GN https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g007.jpg Figure 7. Danish fleet cod landings in the Jammer Bay area 2014–2022. Estimated partial effect of bo https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g008.jpg Figure 8. Danish fleet plaice landings (in kg) by year and métiers in the Jammer Bay area 2014–2022. https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g009.jpg Figure 9. Danish fleet plaice landings in the Jammer Bay area 2014–2022. Estimated partial effect of https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g010.jpg Figure 10. Danish fleet plaice landings by month in the Jammer Bay area 2014–2022. Estimated partial https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g011.jpg Figure 11. Distribution of the Danish fleet cod discards and landings (tons) in the Skagerrak 2015–2 https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-g012.jpg Figure 12. Distribution of the Danish fleet plaice landings and discards (tons) in the Skagerrak 201 https://www.frontiersin.org/files/Articles/1643289/focsu-03-1643289-HTML/image_m/focsu-03-1643289-t002.jpg Table 2. Summary of the outputs presented in Figure 2, with reference to the relevant figure numbers https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1697026/full https://www.frontiersin.org/files/Articles/1697026/fmars-12-1697026-HTML/image_m/fmars-12-1697026-g001.jpg Figure 1. Map of Denmark. Indicating the main area (Limfjorden) where bivalve fishery is taken place https://www.frontiersin.org/files/Articles/1697026/fmars-12-1697026-HTML/image_m/fmars-12-1697026-g002.jpg Figure 2. Schematic overview of the electronic Black Box monitoring system, mounted on all Danish bi https://www.frontiersin.org/files/Articles/1697026/fmars-12-1697026-HTML/image_m/fmars-12-1697026-g003.jpg Figure 3. A screen dump from the Black Box analyzer software visualising dredge tracks (yellow lines https://www.frontiersin.org/files/Articles/1697026/fmars-12-1697026-HTML/image_m/fmars-12-1697026-g004.jpg Figure 4. Area affected by blue mussel fishery in Lovns Bredning assessed by BB data per year within https://www.frontiersin.org/files/Articles/1697026/fmars-12-1697026-HTML/image_m/fmars-12-1697026-t001.jpg Table 1. The cumulative overlap (km2) between the fishing activities (2013-2020) of the actual eelgr https://www.frontiersin.org/files/Articles/1697026/fmars-12-1697026-HTML/image_m/fmars-12-1697026-g005.jpg Figure 5. The swept area dredged in 2018 in Lovns Bredning using VMS squares (top) or black box squa https://www.frontiersin.org/files/Articles/1697026/fmars-12-1697026-HTML/image_m/fmars-12-1697026-g006.jpg Figure 6. The overlap (km2) between fishing activities in 2018 and areas with actual and potential e https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1763791/full https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g001.jpg Figure 1. Overview of the design and methods used in this Mendelian randomization study. BMI, Body M https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-t001.jpg Table 1. Clinical characteristics and dietary patterns in IVDD patients and controls. https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-t002.jpg Table 2. GWAS data sources for instrumental variables selection. https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g002.jpg Figure 2. Forest plot to visualize the causal effect of BMI on IVDD. IVDD, intervertebral disc degen https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g003.jpg Figure 3. Forest plot to visualize the causal effect of waist traits on IVDD. IVDD, intervertebral d https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g004.jpg Figure 4. Forest plot to visualize the causal effect of hip traits on IVDD. IVDD, intervertebral dis https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g005.jpg Figure 5. Forest plot to visualize the reverse Mendelian randomization causal effect of IVDD on BMI. https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g006.jpg Figure 6. Forest plot to visualize the reverse Mendelian randomization causal effect of IVDD on wais https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g007.jpg Figure 7. Forest plot to visualize the reverse Mendelian randomization causal effect of IVDD on hip https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g008.jpg Figure 8. Forest plot to visualize the positive causal effect of dietary habits on IVDD. IVDD, inter https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g009.jpg Figure 9. Forest plot to visualize the negative causal effect of dietary habits on IVDD. IVDD, inter https://www.frontiersin.org/files/Articles/1763791/fmed-13-1763791-HTML-r1/image_m/fmed-13-1763791-g010.jpg Figure 10. Forest plot to visualize the reverse Mendelian randomization causal effect of IVDD on die https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1740559/full https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-g001.jpg Figure 1. Factors impeding diabetic wound healing. https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-g002.jpg Figure 2. Overview of different stimulus-responsive hydrogel biomaterials applied in diabetic wound https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-t001.jpg Table 1. Summary of representative stimuli-responsive hydrogel systems for diabetic wound healing. https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-g003.jpg Figure 3. Construction process and structure of representative glucose-responsive hydrogels. (A) Fab https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-g004.jpg Figure 4. Construction process and structure of representative pH-responsive hydrogels. (A) Preparat https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-g005.jpg Figure 5. Construction process and structure of representative temperature-responsive hydrogels. (A) https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-g006.jpg Figure 6. Construction process and structure of representative electro-responsive hydrogels. (A) The https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-g007.jpg Figure 7. Construction process and structure of representative ROS-responsive hydrogels. (A) Fabrica https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-g008.jpg Figure 8. Construction process and structure of representative enzyme-responsive hydrogels. (A) Outl https://www.frontiersin.org/files/Articles/1740559/fmed-12-1740559-HTML/image_m/fmed-12-1740559-g009.jpg Figure 9. Construction process and structure of representative multi -responsive hydrogels. (A) Synt https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1718554/full https://www.frontiersin.org/files/Articles/1718554/fmed-12-1718554-HTML/image_m/fmed-12-1718554-gr0001.jpg Graphical Abstract. Diagram illustrating the integration of AI in healthcare. Central AI symbol conn https://www.frontiersin.org/files/Articles/1718554/fmed-12-1718554-HTML/image_m/fmed-12-1718554-g001.jpg Figure 1. AI advances understanding of osteoporosis biomarkers and drug discovery. AI has significan https://www.frontiersin.org/files/Articles/1718554/fmed-12-1718554-HTML/image_m/fmed-12-1718554-t001.jpg Table 1. AI enhances the comprehension of osteoporosis molecular biomarkers. https://www.frontiersin.org/files/Articles/1718554/fmed-12-1718554-HTML/image_m/fmed-12-1718554-t002.jpg Table 2. AI enhances osteoporosis drug discovery. https://www.frontiersin.org/files/Articles/1718554/fmed-12-1718554-HTML/image_m/fmed-12-1718554-g002.jpg Figure 2. AI improves osteoporosis diagnosis and treatment. AI integrates multimodal imaging, bone m https://www.frontiersin.org/files/Articles/1718554/fmed-12-1718554-HTML/image_m/fmed-12-1718554-t003.jpg Table 3. Application of AI in the risk prediction and diagnosis of osteoporosis. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1707588/full https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g001.jpg Figure 1. Methodology flow diagram of proposed work. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g002.jpg Figure 2. Some random samples from the original dataset. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g003.jpg Figure 3. Preprocessing steps visualization. (a) Original Image, (b) Fixed Pixels, (c) Binary Thresh https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g004.jpg Figure 4. Clipped knees with padded slices. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g005.jpg Figure 5. Splitting of the dataset after augmentation. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g006.jpg Figure 6. Effect of different augmentation techniques on a sample image. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g007.jpg Figure 7. Architecture diagram of proposed work. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-t001.jpg Table 1. Summary of the proposed model. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-t002.jpg Table 2. Proposed 3D model test results. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-t003.jpg Table 3. ML algorithms results and description of parameters. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g008.jpg Figure 8. Accuracies of trained models with (a) DenseNet201, (b) MobileNet and (c) DenseNet121 as Ba https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g009.jpg Figure 9. AUC of trained models with (a) DenseNet201, (b) MobileNet and (c) DenseNet121 as BaseNet. https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g010.jpg Figure 10. Recall of trained models with (a) DenseNet201, (b) MobileNet and (c) DenseNet121 as BaseN https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g011.jpg Figure 11. Precision of trained models with (a) DenseNet201, (b) MobileNet and (c) DenseNet121 as Ba https://www.frontiersin.org/files/Articles/1707588/fmed-12-1707588-HTML-r1/image_m/fmed-12-1707588-g012.jpg Figure 12. Loss of trained models with (a) DenseNet201, (b) MobileNet and (c) DenseNet121 as BaseNet https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1700752/full https://www.frontiersin.org/files/Articles/1700752/fmed-12-1700752-HTML/image_m/fmed-12-1700752-t001.jpg Table 1. Baseline characteristics grouped by all-cause mortality in patients with osteoporosis compl https://www.frontiersin.org/files/Articles/1700752/fmed-12-1700752-HTML/image_m/fmed-12-1700752-t002.jpg Table 2. Association between Hs-CRP and all-cause mortality in patients with osteoporosis complicate https://www.frontiersin.org/files/Articles/1700752/fmed-12-1700752-HTML/image_m/fmed-12-1700752-t003.jpg Table 3. Multivariate subgroup association between Hs-CRP and all-cause mortality in patients with o https://www.frontiersin.org/files/Articles/1700752/fmed-12-1700752-HTML/image_m/fmed-12-1700752-t004.jpg Table 4. Association between Hs-CRP and all-cause mortality in patients with osteoporosis complicate https://www.frontiersin.org/files/Articles/1700752/fmed-12-1700752-HTML/image_m/fmed-12-1700752-g001.jpg Figure 1. ROC curve showing the predictive value of Hs-CRP for all-cause mortality in patients with https://www.frontiersin.org/files/Articles/1700752/fmed-12-1700752-HTML/image_m/fmed-12-1700752-g002.jpg Figure 2. Restricted cubic spline plot showing the linear association between Hs-CRP and all-cause m https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1697423/full https://www.frontiersin.org/files/Articles/1697423/fmed-12-1697423-HTML-r1/image_m/fmed-12-1697423-t001.jpg Table 1. MNBAC DALYs ASR ASDR and ASIR in 1990 and 2021, and EAPC from 1990 to 2021 in global, 5 SDI https://www.frontiersin.org/files/Articles/1697423/fmed-12-1697423-HTML-r1/image_m/fmed-12-1697423-g001.jpg Figure 1. The scatter plot of polynomial regression analysis between SDI values and age-standardized https://www.frontiersin.org/files/Articles/1697423/fmed-12-1697423-HTML-r1/image_m/fmed-12-1697423-g002.jpg Figure 2. The DALYs, deaths, and incidence rate of MNBAC across different age groups and genders in https://www.frontiersin.org/files/Articles/1697423/fmed-12-1697423-HTML-r1/image_m/fmed-12-1697423-g003.jpg Figure 3. The effects of age, period, and birth cohort on the incidence of MNBAC in global and 5 SDI https://www.frontiersin.org/files/Articles/1697423/fmed-12-1697423-HTML-r1/image_m/fmed-12-1697423-g004.jpg Figure 4. The BAPC model predicts the DALYs, deaths, incidence cases, and ASR of MNBAC from 2021 to https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1691328/full https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-t001.jpg Table 1. Cartilage evaluation according to the Mankin score system. https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-t002.jpg Table 2. Primers for real-time polymerase chain reaction (PCR) analysis of gene expression https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-t003.jpg Table 3. Composition of test food. https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-g001.jpg Figure 1. Joint diameter during pre-intervention (A) and post-intervention (B) of MIA-induced OA rat https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-g002.jpg Figure 2. Histological evaluation of joint activity with different groups of H&E staining of MIA-ind https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-g003.jpg Figure 3. The content of HA (A), IL-1β (B), and TNF-α (C) in the serum of MIA-induced OA rats. #### https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-g004.jpg Figure 4. The content of HA (A), IL-1β (B), TNF-α (C), NO (D), and PGE2 (E) in the synovial fluid of https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-g005.jpg Figure 5. HA changes the frequency of CD45+ subpopulation cells in synovial fluid. (A) Gating strate https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-g006.jpg Figure 6. The mRNA expression of (A) Mmp-3, (B) Mmp-9, (C) Mmp-13, (D) iNos, and (E) Cox2 in the art https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-g007.jpg Figure 7. Flow chart of the study design. https://www.frontiersin.org/files/Articles/1691328/fnut-12-1691328-HTML-r1/image_m/fnut-12-1691328-g008.jpg Figure 8. Changes in (A) WOMAC score, (B) Pain score, (C) Stiffness score, (D) Difficulty score, and https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1727187/full https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-g001.jpg Figure 1. Study flow diagram for patient selection and analysis. https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-g002.jpg Figure 2. A heatmap of the correlation between different variables. Color intensity reflects correla https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-t002.jpg Table 2. Multivariate Cox regression analysis of the predictors of post-PCI MACCEs. https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-t003.jpg Table 3. Multivariable Cox regression for the risk of MACCEs post-PCI. https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-g003.jpg Figure 3. Kaplan–Meier curves for MACCE-free survival. A. Patients stratified by SYNTAX score: low-r https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-g004.jpg Figure 4. A nomogram predicting 6- and 12-month MACCE-free survival post-PCI. Assign points for each https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-g005.jpg Figure 5. ROC curves for HGI’s MACCEs prediction at six and 12 months post-PCI. AUC: area under the https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-g006.jpg Figure 6. Restricted cubic spline curves for the association between HGI and SYNTAX score with MACCE https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-t004.jpg Table 4. Mediation analysis of the relationship between HGI and MACCEs via SYNTAX score. https://www.frontiersin.org/files/Articles/1727187/fendo-16-1727187-HTML/image_m/fendo-16-1727187-g007.jpg Figure 7. The results of mediation analysis. A mediation pathway diagram of the relationship between https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1781543/full https://www.frontiersin.org/files/Articles/1781543/fpsyg-17-1781543-HTML/image_m/fpsyg-17-1781543-t001.jpg Table 1. Information regarding participants’ individual characteristics. https://www.frontiersin.org/files/Articles/1781543/fpsyg-17-1781543-HTML/image_m/fpsyg-17-1781543-g001.jpg Figure 1. Research model. https://www.frontiersin.org/files/Articles/1781543/fpsyg-17-1781543-HTML/image_m/fpsyg-17-1781543-t002.jpg Table 2. Interrelationships between variables, reliability coefficients (α, ω, CR), average variance https://www.frontiersin.org/files/Articles/1781543/fpsyg-17-1781543-HTML/image_m/fpsyg-17-1781543-t003.jpg Table 3. Mediating effect analysis (empathic self-efficacy). https://www.frontiersin.org/files/Articles/1781543/fpsyg-17-1781543-HTML/image_m/fpsyg-17-1781543-t004.jpg Table 4. Mediating effect analysis (social self-efficacy). https://www.frontiersin.org/files/Articles/1781543/fpsyg-17-1781543-HTML/image_m/fpsyg-17-1781543-g002.jpg Figure 2. First-level CFA for the perceived empathetic self-efficacy and social self-efficacy scale. https://www.frontiersin.org/files/Articles/1781543/fpsyg-17-1781543-HTML/image_m/fpsyg-17-1781543-g003.jpg Figure 3. First-order CFA for the job satisfaction subfactor of the quality of life scale for employ https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1637976/full https://www.frontiersin.org/files/Articles/1637976/fimmu-16-1637976-HTML/image_m/fimmu-16-1637976-g001.jpg Figure 1. Normal vs. dysbiosis states of the gut microbiome and their effects on microglial activati https://www.frontiersin.org/files/Articles/1637976/fimmu-16-1637976-HTML/image_m/fimmu-16-1637976-g002.jpg Figure 2. Schematic of the gut–brain axis in ALS. Loss of beneficial microbes reduces neuroprotectiv https://www.frontiersin.org/files/Articles/1637976/fimmu-16-1637976-HTML/image_m/fimmu-16-1637976-g003.jpg Figure 3. Evidence from ALS mouse models. In SOD1-G93A mice, gut dysbiosis correlates with reduced t https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1651316/full https://www.frontiersin.org/files/Articles/1651316/fcimb-15-1651316-HTML-r1/image_m/fcimb-15-1651316-t001.jpg Table 1. Demographic and clinical characteristics of study population. https://www.frontiersin.org/files/Articles/1651316/fcimb-15-1651316-HTML-r1/image_m/fcimb-15-1651316-g001.jpg Figure 1. Comparison of alpha diversity between the overweight/obese and normal weight groups. (A) O https://www.frontiersin.org/files/Articles/1651316/fcimb-15-1651316-HTML-r1/image_m/fcimb-15-1651316-g002.jpg Figure 2. Comparison of beta diversity among the overweight, obese, and normal weight groups. (A) Br https://www.frontiersin.org/files/Articles/1651316/fcimb-15-1651316-HTML-r1/image_m/fcimb-15-1651316-g003.jpg Figure 3. Relative abundance of the microbial community and differentially abundant taxa. Stacked ba https://www.frontiersin.org/files/Articles/1651316/fcimb-15-1651316-HTML-r1/image_m/fcimb-15-1651316-g004.jpg Figure 4. Functional prediction analysis using phylogenetic investigation of communities by reconstr https://www.frontiersin.org/journals/insect-science/articles/10.3389/finsc.2026.1700002/full https://www.frontiersin.org/files/Articles/1700002/finsc-06-1700002-HTML-r1/image_m/finsc-06-1700002-g001.jpg Figure 1. Relative DcCathL gene expression of healthy (CLas−) and infected (CLas+) D. citri. The box https://www.frontiersin.org/files/Articles/1700002/finsc-06-1700002-HTML-r1/image_m/finsc-06-1700002-g002.jpg Figure 2. Fluorescence in situ hybridization (FISH) to detect DcCathL transcripts in gut tissues dis https://www.frontiersin.org/files/Articles/1700002/finsc-06-1700002-HTML-r1/image_m/finsc-06-1700002-g003.jpg Figure 3. Immunostaining to detect DcCathL protein in gut tissues dissected from healthy (CLas−) and https://www.frontiersin.org/files/Articles/1700002/finsc-06-1700002-HTML-r1/image_m/finsc-06-1700002-g004.jpg Figure 4. Immunostaining to detect DcCathL protein in ovary tissues dissected from healthy (CLas−) a https://www.frontiersin.org/files/Articles/1700002/finsc-06-1700002-HTML-r1/image_m/finsc-06-1700002-g005.jpg Figure 5. Immunostaining to detect DcCathL protein in salivary gland tissues dissected from healthy https://www.frontiersin.org/files/Articles/1700002/finsc-06-1700002-HTML-r1/image_m/finsc-06-1700002-g006.jpg Figure 6. Uptake of recombinant GFP in the artificial diet of D. citri. (A) rGFP detected in the nym https://www.frontiersin.org/files/Articles/1700002/finsc-06-1700002-HTML-r1/image_m/finsc-06-1700002-g007.jpg Figure 7. Cox proportional hazards regression showing the survival probability at 72 h of evaluation https://www.frontiersin.org/files/Articles/1700002/finsc-06-1700002-HTML-r1/image_m/finsc-06-1700002-g008.jpg Figure 8. Cox proportional hazards regression showing the survival probability at 264 h of evaluatio https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1755851/full https://www.frontiersin.org/files/Articles/1755851/fimmu-17-1755851-HTML-r1/image_m/fimmu-17-1755851-t001.jpg Table 1. Demographic and clinicopathological characteristics of patients. https://www.frontiersin.org/files/Articles/1755851/fimmu-17-1755851-HTML-r1/image_m/fimmu-17-1755851-t002.jpg Table 2. The number and proportion of PD-1/PD-L1 inhibitors used in this study. https://www.frontiersin.org/files/Articles/1755851/fimmu-17-1755851-HTML-r1/image_m/fimmu-17-1755851-g001.jpg Figure 1. (a) Distribution of serum ferritin levels across SCLC molecular subtypes (SCLC-A, n=295; S https://www.frontiersin.org/files/Articles/1755851/fimmu-17-1755851-HTML-r1/image_m/fimmu-17-1755851-g002.jpg Figure 2. (a) Histogram of ORR in 425 patients from the experimental group, stratified by pre-treatm https://www.frontiersin.org/files/Articles/1755851/fimmu-17-1755851-HTML-r1/image_m/fimmu-17-1755851-g003.jpg Figure 3. Survival curves for PFS. (a) The 425 patients in the experimental group were divided into https://www.frontiersin.org/files/Articles/1755851/fimmu-17-1755851-HTML-r1/image_m/fimmu-17-1755851-g004.jpg Figure 4. Survival curves for PFS. (a) Using a cutoff of NLR = 4.2, within the high ΔSF group, patie https://www.frontiersin.org/files/Articles/1755851/fimmu-17-1755851-HTML-r1/image_m/fimmu-17-1755851-t003.jpg Table 3. Details of the Cox proportional hazard model for 425 patients. https://www.frontiersin.org/files/Articles/1755851/fimmu-17-1755851-HTML-r1/image_m/fimmu-17-1755851-t004.jpg Table 4. Details of the Cox proportional hazard model for 380 patients. https://www.frontiersin.org/journals/chemical-biology/articles/10.3389/fchbi.2026.1741100/full https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g001.jpg Figure 1. Molecular representation of the predicted binding pose of the Roscovitine derivative LGR14 https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g002.jpg Figure 2. Chemical structure of Roscovitine and its four derivatives CR8, DRF053, N&N1, and LGR1406. https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-t001.jpg Table 1. Overview of the data files generated in this study and their availability on Figshare. https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-t002.jpg Table 2. Best binding stability (kcal/mol) from molecular docking simulations of CDK family proteins https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-t003.jpg Table 3. RMSD values (nm) for the c-∝ of the protein of the CDK-ligand complexes and the Apoprotein https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-t004.jpg Table 4. Root mean square deviation (RMSD) values for ligands in CDK-ligand complexes over 1 μs mole https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g003.jpg Figure 3. Root mean square deviation (RMSD) profiles over 1 μs molecular dynamics simulations of CDK https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g004.jpg Figure 4. Root mean square deviation (RMSD) profiles over 1 μs molecular dynamics simulations of CDK https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g005.jpg Figure 5. Root mean square deviation (RMSD) profiles over 1 μs molecular dynamics simulations of CDK https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g006.jpg Figure 6. Root mean square deviation (RMSD) profiles over 1 μs molecular dynamics simulations of CDK https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g007.jpg Figure 7. Root mean square deviation (RMSD) profiles over 1 μs molecular dynamics simulations of CDK https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g008.jpg Figure 8. (Left) Root mean square fluctuation (RMSF) analysis identifying flexible regions in CDK2. https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g009.jpg Figure 9. (Left) Root mean square fluctuation (RMSF) analysis highlighting flexible regions in CDK4. https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g010.jpg Figure 10. (Left) Root mean square fluctuation (RMSF) analysis identifying flexible regions in CDK5. https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g011.jpg Figure 11. (Left) Root mean square fluctuation (RMSF) analysis highlighting flexible regions in CDK6 https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g012.jpg Figure 12. (Left) Root mean square fluctuation (RMSF) analysis highlighting flexible regions in CDK1 https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-t005.jpg Table 5. Gibbs free binding energy values (ΔG) obtained from MM/PBSA calculations. https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-t006.jpg Table 6. Persistent protein-ligand contacts identified through molecular dynamics simulations. https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g013.jpg Figure 13. Comparative interaction profiling of ligand-kinase complexes. The 3D bar charts display t https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g014.jpg Figure 14. PMF profiles of ligand unbinding from CDK5. The plot shows the free energy change along t https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-g015.jpg Figure 15. Binding interface of Roscovitine (colored green) and, in turn: LGR1406 (colored purple), https://www.frontiersin.org/files/Articles/1741100/fchbi-05-1741100-HTML-r1/image_m/fchbi-05-1741100-t007.jpg Table 7. In silico drug-likeness and early ADME profiling of CDK ligands, Roscovitine and analogues https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1741725/full https://www.frontiersin.org/files/Articles/1741725/fmed-13-1741725-HTML-r1/image_m/fmed-13-1741725-t001.jpg Table 1. Characteristics of participants according to frailty status. https://www.frontiersin.org/files/Articles/1741725/fmed-13-1741725-HTML-r1/image_m/fmed-13-1741725-g001.jpg Figure 1. Functional trajectories decline in frail and no-frail patients. 6MWT, 6-min walk test; DLc https://www.frontiersin.org/files/Articles/1741725/fmed-13-1741725-HTML-r1/image_m/fmed-13-1741725-t002.jpg Table 2. Adverse events due to antifibrotic therapy in frail and no-frail patients. https://www.frontiersin.org/files/Articles/1741725/fmed-13-1741725-HTML-r1/image_m/fmed-13-1741725-t003.jpg Table 3. Fixed effects from the linear mixed-effects model for longitudinal FVC%. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1767821/full https://www.frontiersin.org/files/Articles/1767821/fmolb-13-1767821-HTML/image_m/fmolb-13-1767821-g001.jpg Figure 1. Overview of AI-based protein structure prediction frameworks and emerging trends. https://www.frontiersin.org/files/Articles/1767821/fmolb-13-1767821-HTML/image_m/fmolb-13-1767821-t001.jpg Table 1. Selected AI-based methods and resources related to protein structure prediction and downstr https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1741875/full https://www.frontiersin.org/files/Articles/1741875/fimmu-17-1741875-HTML/image_m/fimmu-17-1741875-t001.jpg Table 1. Biomarkers and risk factors connecting OSA and lung cancer. https://www.frontiersin.org/files/Articles/1741875/fimmu-17-1741875-HTML/image_m/fimmu-17-1741875-g001.jpg Figure 1. IH-mediated effects on LC and its microenvironment. IH acts by promoting cell alterations https://www.frontiersin.org/files/Articles/1741875/fimmu-17-1741875-HTML/image_m/fimmu-17-1741875-g002.jpg Figure 2. Proposed mechanistic link between OSA-related intermittent hypoxia and immune-related adve https://www.frontiersin.org/files/Articles/1741875/fimmu-17-1741875-HTML/image_m/fimmu-17-1741875-g003.jpg Figure 3. The complex mechanistic interconnection between OSA and Lung Cancer (A) and between OSA an https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2025.1686252/full https://www.frontiersin.org/files/Articles/1686252/fnbeh-19-1686252-HTML/image_m/fnbeh-19-1686252-g001.jpg Figure 1. Hippocampal tri-synaptic loop. EC, entorhinal cortex; DG, dentate gyrus; CA1 and CA3, corn https://www.frontiersin.org/files/Articles/1686252/fnbeh-19-1686252-HTML/image_m/fnbeh-19-1686252-g002.jpg Figure 2. Schematic showing the re-entrant loops of the Papez circuit. PRH CTX, perirhinal cortex; H https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1717138/full https://www.frontiersin.org/files/Articles/1717138/fonc-15-1717138-HTML/image_m/fonc-15-1717138-t001.jpg Table 1. Common therapeutic approaches for gastrointestinal tumors. https://www.frontiersin.org/files/Articles/1717138/fonc-15-1717138-HTML/image_m/fonc-15-1717138-g001.jpg Figure 1. The mechanism of mitophagy. Legend of Figure 1: This figure illustrates the mechanism of m https://www.frontiersin.org/files/Articles/1717138/fonc-15-1717138-HTML/image_m/fonc-15-1717138-g002.jpg Figure 2. Methods for mitochondrial isolation. Legend of Figure 2: This figure summarizes four commo https://www.frontiersin.org/files/Articles/1717138/fonc-15-1717138-HTML/image_m/fonc-15-1717138-g003.jpg Figure 3. The role of mitophagy in various cellular processes. Legend of Figure 3: This figure illus https://www.frontiersin.org/files/Articles/1717138/fonc-15-1717138-HTML/image_m/fonc-15-1717138-t002.jpg Table 2. Mitophagy in different gastrointestinal tumor cell types: mechanisms, roles, and challenges https://www.frontiersin.org/files/Articles/1717138/fonc-15-1717138-HTML/image_m/fonc-15-1717138-g004.jpg Figure 4. Impact of PINK1/Parkin activator and BNIP3/NIX inhibitor on tumor growth and metastasis. L https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1722540/full https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-g001.jpg Figure 1. PRISMA flow diagram of the study selection process (2019–2024). https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-t001.jpg Table 1. Summary of methodological quality assessment of included studies (MMAT). https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-t002.jpg Table 2. Distribution of 40 papers by research area. https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-g002.jpg Figure 2. Frequently used research methods in gamification studies (VOSviewer visualization). https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-t003.jpg Table 3. Distribution of 40 papers by theme. https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-t004.jpg Table 4. Distribution of 40 papers by publishers. https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-t005.jpg Table 5. Publication distribution and overlap between Google scholar and scopus databases. https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-g003.jpg Figure 3. Annual publication trend of the selected articles (2019-2024). https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-g004.jpg Figure 4. WordCloud visualization of author occurrence in the reviewed studies. https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-g005.jpg Figure 5. Co-occurrence map of keywords in selected gamification literature (VOSviewer visualization https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-t006.jpg Table 6. Types of gamification interventions used in school geography education. https://www.frontiersin.org/files/Articles/1722540/feduc-11-1722540-HTML-r1/image_m/feduc-11-1722540-t007.jpg Table 7. Mapping gamification interventions to geography-specific learning outcomes. https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2026.1753325/full https://www.frontiersin.org/files/Articles/1753325/frph-08-1753325-HTML/image_m/frph-08-1753325-g001.jpg Figure 1. CAMeN symptothermal chart used to register fertility biomarkers in the FAM group. https://www.frontiersin.org/files/Articles/1753325/frph-08-1753325-HTML/image_m/frph-08-1753325-t001.jpg Table 1. Characteristics of the studied populations. https://www.frontiersin.org/files/Articles/1753325/frph-08-1753325-HTML/image_m/frph-08-1753325-g002.jpg Figure 2. Pregnancy rate in the general population and after stratification for female age. ART, ass https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1761601/full https://www.frontiersin.org/files/Articles/1761601/fdgth-08-1761601-HTML/image_m/fdgth-08-1761601-g001.jpg Figure 1. Checklist evolution. https://www.frontiersin.org/files/Articles/1761601/fdgth-08-1761601-HTML/image_m/fdgth-08-1761601-g002.jpg Figure 2. MEDAI-LLM-SUMM checklist structure. Total: 24 items (20 core + 4 optional). Items marked w https://www.frontiersin.org/files/Articles/1761601/fdgth-08-1761601-HTML/image_m/fdgth-08-1761601-t001.jpg Table 1. Feature comparison across checklists. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1739981/full https://www.frontiersin.org/files/Articles/1739981/fphar-17-1739981-HTML/image_m/fphar-17-1739981-g001.jpg Figure 1. NSUN2 is overexpressed in CRC and associated with poor prognosis. (A) Heatmap of different https://www.frontiersin.org/files/Articles/1739981/fphar-17-1739981-HTML/image_m/fphar-17-1739981-g002.jpg Figure 2. Knockdown of NSUN2 inhibits CRC progression in vitro and in vivo. (A) NSUN2 knockdown effi https://www.frontiersin.org/files/Articles/1739981/fphar-17-1739981-HTML/image_m/fphar-17-1739981-g003.jpg Figure 3. NSUN2 promotes oxaliplatin resistance in CRC. (A) Box plots of NSUN2 expression in CRC pat https://www.frontiersin.org/files/Articles/1739981/fphar-17-1739981-HTML/image_m/fphar-17-1739981-g004.jpg Figure 4. NSUN2 knockdown induces ferroptosis in CRC. (A) KEGG enrichment analysis based on DEGs bet https://www.frontiersin.org/files/Articles/1739981/fphar-17-1739981-HTML/image_m/fphar-17-1739981-g005.jpg Figure 5. NSUN2 regulates ferroptosis resistance in CRC through stabilization of DHODH mRNA. (A) Cor https://www.frontiersin.org/files/Articles/1739981/fphar-17-1739981-HTML/image_m/fphar-17-1739981-g006.jpg Figure 6. NSUN2 promotes oxaliplatin resistance via DHODH-mediated ferroptosis suppression. (A) West https://www.frontiersin.org/files/Articles/1739981/fphar-17-1739981-HTML/image_m/fphar-17-1739981-g007.jpg Figure 7. Clinical relevance of DHODH expression in colorectal cancer. (A) Box plot of DHODH express https://www.frontiersin.org/files/Articles/1739981/fphar-17-1739981-HTML/image_m/fphar-17-1739981-g008.jpg Figure 8. Schematic illustration of NSUN2 function in CRC. Proposed model of NSUN2-mediated m5C modi https://www.frontiersin.org/journals/microbiomes/articles/10.3389/frmbi.2025.1676639/full https://www.frontiersin.org/files/Articles/1676639/frmbi-04-1676639-HTML/image_m/frmbi-04-1676639-g001.jpg Figure 1. Selection may operate on multiple levels of biological organization resulting in different https://www.frontiersin.org/files/Articles/1676639/frmbi-04-1676639-HTML/image_m/frmbi-04-1676639-g002.jpg Figure 2. (A) Biological levels may fulfill one or more roles of the three units of selection, inclu https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1785156/full https://www.frontiersin.org/files/Articles/1785156/fmed-13-1785156-HTML/image_m/fmed-13-1785156-g001.jpg Figure 1. Abnormal pulmonary computed tomography images in patients with bronchiectasis complicated https://www.frontiersin.org/files/Articles/1785156/fmed-13-1785156-HTML/image_m/fmed-13-1785156-g002.jpg Figure 2. Clinical impact of non-tuberculous mycobacteria and P. aeruginosa infections, both as sing https://www.frontiersin.org/files/Articles/1785156/fmed-13-1785156-HTML/image_m/fmed-13-1785156-g003.jpg Figure 3. The P. aeruginosa and M. avium complex co-infect dendritic cells and human peripheral bloo https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1734560/full https://www.frontiersin.org/files/Articles/1734560/fendo-17-1734560-HTML/image_m/fendo-17-1734560-g001.jpg Figure 1. CONSORT flow diagram of participant progression through the trial. Flow of participants th https://www.frontiersin.org/files/Articles/1734560/fendo-17-1734560-HTML/image_m/fendo-17-1734560-t001.jpg Table 1. The 12-week whole-body vibration training (WBV) protocol. https://www.frontiersin.org/files/Articles/1734560/fendo-17-1734560-HTML/image_m/fendo-17-1734560-t002.jpg Table 2. The 12-week treadmill walking training protocol. https://www.frontiersin.org/files/Articles/1734560/fendo-17-1734560-HTML/image_m/fendo-17-1734560-t003.jpg Table 3. List of potential adverse events. https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2025.1679706/full https://www.frontiersin.org/files/Articles/1679706/fdmed-06-1679706-HTML/image_m/fdmed-06-1679706-g001.jpg Figure 1. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) flow diagram s https://www.frontiersin.org/files/Articles/1679706/fdmed-06-1679706-HTML/image_m/fdmed-06-1679706-t001.jpg Table 1. Summary of main characteristics of the 16 included studies. https://www.frontiersin.org/files/Articles/1679706/fdmed-06-1679706-HTML/image_m/fdmed-06-1679706-t002.jpg Table 2. GRADE quality assessment summary. https://www.frontiersin.org/files/Articles/1679706/fdmed-06-1679706-HTML/image_m/fdmed-06-1679706-g002.jpg Figure 2. (A) review the authors’ judgments of each risk area of bias for included studies, expresse https://www.frontiersin.org/files/Articles/1679706/fdmed-06-1679706-HTML/image_m/fdmed-06-1679706-g003.jpg Figure 3. The heterogeneity analysis results for the 16 outcomes. https://www.frontiersin.org/files/Articles/1679706/fdmed-06-1679706-HTML/image_m/fdmed-06-1679706-g004.jpg Figure 4. Results of pulpitis subgroup analysis. https://www.frontiersin.org/files/Articles/1679706/fdmed-06-1679706-HTML/image_m/fdmed-06-1679706-g005.jpg Figure 5. The bias testing results for anesthesia among the 16 outcome measures. https://www.frontiersin.org/files/Articles/1679706/fdmed-06-1679706-HTML/image_m/fdmed-06-1679706-g006.jpg Figure 6. Sensitivity and influence analyses of the meta-analysis on hepatoblastoma treatment. (A) L https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1680115/full https://www.frontiersin.org/files/Articles/1680115/fmed-13-1680115-HTML-r1/image_m/fmed-13-1680115-g001.jpg Figure 1. Clinical trial sources and inclusion and exclusion flowchart. (A) Clinical trial sources. https://www.frontiersin.org/files/Articles/1680115/fmed-13-1680115-HTML-r1/image_m/fmed-13-1680115-g002.jpg Figure 2. Comprehensive analysis of clinical trials of JAK kinase inhibitors for the treatment of rh https://www.frontiersin.org/files/Articles/1680115/fmed-13-1680115-HTML-r1/image_m/fmed-13-1680115-t001.jpg Table 1. Distribution of experiments across the country. https://www.frontiersin.org/files/Articles/1680115/fmed-13-1680115-HTML-r1/image_m/fmed-13-1680115-t002.jpg Table 2. Distribution of experiments in the region. https://www.frontiersin.org/files/Articles/1680115/fmed-13-1680115-HTML-r1/image_m/fmed-13-1680115-t003.jpg Table 3. Safety and efficacy of JAK Inhibitor. https://www.frontiersin.org/files/Articles/1680115/fmed-13-1680115-HTML-r1/image_m/fmed-13-1680115-t004.jpg Table 4. Common adverse events and serious adverse events of JAK inhibitor [affected/at risk (%)]. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1629149/full https://www.frontiersin.org/files/Articles/1629149/frai-08-1629149-HTML/image_m/frai-08-1629149-g001.jpg Figure 1. The flow chart of this study. https://www.frontiersin.org/files/Articles/1629149/frai-08-1629149-HTML/image_m/frai-08-1629149-g002.jpg Figure 2. The basic characteristics of AI’s responses to CNO-related questions. (A–C) Three sets of https://www.frontiersin.org/files/Articles/1629149/frai-08-1629149-HTML/image_m/frai-08-1629149-t001.jpg Table 1. Comparison of the response times of three AIs to CNO-related questions. https://www.frontiersin.org/files/Articles/1629149/frai-08-1629149-HTML/image_m/frai-08-1629149-t002.jpg Table 2. Comparison of the word counts of the answers given by three AIs to CNO-related questions. https://www.frontiersin.org/files/Articles/1629149/frai-08-1629149-HTML/image_m/frai-08-1629149-g003.jpg Figure 3. Comparison of ratings between two reviewers by the three AI language models to questions r https://www.frontiersin.org/files/Articles/1629149/frai-08-1629149-HTML/image_m/frai-08-1629149-g004.jpg Figure 4. Comparison among Deepseek V3, Doubao, and Kimi 1.5 in answering CNO-related questions. (A– https://www.frontiersin.org/files/Articles/1629149/frai-08-1629149-HTML/image_m/frai-08-1629149-t003.jpg Table 3. The results of three trials conducted by two reviewers in asking CNO-related questions to t https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1677320/full https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g001.jpg Figure 1. (a) Total phosphorus and the concentration of (b) Pi, Po, and (c) stable P, mod-labile P, https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g002.jpg Figure 2. The contents of different soil P forms in the four treatments. (a) Resin-Pi, (b) NaHCO₃-Pi https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g003.jpg Figure 3. The effects of wetland degradation on (a) soil water content (SW), (b) soil pH (pH), (c) s https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g004.jpg Figure 4. (a) PCA analysis of phosphorus cycling genes across wetlands with varying degradation leve https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g005.jpg Figure 5. The impacts of wetland degradation on the abundances of (a) organic P mineralization, (b) https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g006.jpg Figure 6. Major phosphorus metabolic pathways in soil. The boxes with different colors represent dif https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g007.jpg Figure 7. Redundancy analysis (RDA, a) and Spearman correlation analysis (b) depicting the relations https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g008.jpg Figure 8. Results of mean square error (MSE, %) from a random forest aiming to identify the main dri https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g009.jpg Figure 9. Partial least squares path modeling (PLS-PM) of the effects of wetland degradation, SW, pH https://www.frontiersin.org/files/Articles/1677320/fmicb-16-1677320-HTML-r1/image_m/fmicb-16-1677320-g010.jpg Figure 10. Conceptual diagram of the effects of wetland degradation on soil phosphorus fractions and https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1703970/full https://www.frontiersin.org/files/Articles/1703970/fnut-13-1703970-HTML/image_m/fnut-13-1703970-g001.jpg Figure 1. Flow-chart of articles selection. https://www.frontiersin.org/files/Articles/1703970/fnut-13-1703970-HTML/image_m/fnut-13-1703970-t001.jpg Table 1. Summary of included studies and main results. https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2026.1764703/full https://www.frontiersin.org/files/Articles/1764703/fnbeh-20-1764703-HTML-r1/image_m/fnbeh-20-1764703-g001.jpg Figure 1. Experimental procedure: Experimental setup for the Dyadic Moral Evaluation Task (DMET). Pa https://www.frontiersin.org/files/Articles/1764703/fnbeh-20-1764703-HTML-r1/image_m/fnbeh-20-1764703-g002.jpg Figure 2. Head rendering showing the fNIRS montage configuration, with emitters (AF3, AF4, F5, and F https://www.frontiersin.org/files/Articles/1764703/fnbeh-20-1764703-HTML-r1/image_m/fnbeh-20-1764703-g003.jpg Figure 3. Boxplot illustrating the distribution of normalized Euclidean distance (EuDist, mmol·mm) v https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1715485/full https://www.frontiersin.org/files/Articles/1715485/fpls-17-1715485-HTML/image_m/fpls-17-1715485-g001.jpg Figure 1. The analytical workflow: extraction and analysis procedure for the characterization of roo https://www.frontiersin.org/files/Articles/1715485/fpls-17-1715485-HTML/image_m/fpls-17-1715485-g002.jpg Figure 2. Molecule structure of the novel-sterol compound observed in 1H-NMR lipophilic spectrum of https://www.frontiersin.org/files/Articles/1715485/fpls-17-1715485-HTML/image_m/fpls-17-1715485-g003.jpg Figure 3. Key HMBC correlations of the novel-sterol compound, observed in 1H NMR lipophilic spectrum https://www.frontiersin.org/files/Articles/1715485/fpls-17-1715485-HTML/image_m/fpls-17-1715485-g004.jpg Figure 4. Boxplot of metabolites identified and quantified in co-cultures and individual cultures of https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1737065/full https://www.frontiersin.org/files/Articles/1737065/fcell-13-1737065-HTML-r1/image_m/fcell-13-1737065-g001.jpg Figure 1. Conserved cellular and molecular mechanisms of neuron genesis and patterning. Throughout e https://www.frontiersin.org/files/Articles/1737065/fcell-13-1737065-HTML-r1/image_m/fcell-13-1737065-g002.jpg Figure 2. Astrocyte development and heterogeneity. Schematic coronal sections illustrate the organiz https://www.frontiersin.org/files/Articles/1737065/fcell-13-1737065-HTML-r1/image_m/fcell-13-1737065-g003.jpg Figure 3. Dynamics of neurogenic activation in the fish pallium and mouse striatum. (A) The upper ro https://www.frontiersin.org/files/Articles/1737065/fcell-13-1737065-HTML-r1/image_m/fcell-13-1737065-g004.jpg Figure 4. A comparative framework of striatal neurogenesis in mammals. Schematic coronal brain secti https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1670639/full https://www.frontiersin.org/files/Articles/1670639/fonc-16-1670639-HTML-r1/image_m/fonc-16-1670639-g001.jpg Figure 1. Case 1 timeline of clinical events and treatments (A) with associated radiographic progres https://www.frontiersin.org/files/Articles/1670639/fonc-16-1670639-HTML-r1/image_m/fonc-16-1670639-g002.jpg Figure 2. Diagnostic work-up from Case 1. (A) Histological micrograph showing glial neoplasm of low https://www.frontiersin.org/files/Articles/1670639/fonc-16-1670639-HTML-r1/image_m/fonc-16-1670639-g003.jpg Figure 3. Diagnostic work-up from Case 2. (A) Histological micrographs showing glial neoplasm with h https://www.frontiersin.org/files/Articles/1670639/fonc-16-1670639-HTML-r1/image_m/fonc-16-1670639-t001.jpg Table 1. Comparison of demographic, clinical, and radiological features of Case 1 and Case 2 compare https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2026.1731876/full https://www.frontiersin.org/files/Articles/1731876/fsoc-11-1731876-HTML-r1/image_m/fsoc-11-1731876-t001.jpg Table 1. Coding trajectory from raw data to macro-level subjective theories. https://www.frontiersin.org/files/Articles/1731876/fsoc-11-1731876-HTML-r1/image_m/fsoc-11-1731876-t002.jpg Table 2. Intermediate level subjective theories. https://www.frontiersin.org/files/Articles/1731876/fsoc-11-1731876-HTML-r1/image_m/fsoc-11-1731876-g001.jpg Figure 1. Relational model between 3 macro-level subjective theories. https://www.frontiersin.org/journals/animal-science/articles/10.3389/fanim.2025.1672528/full https://www.frontiersin.org/files/Articles/1672528/fanim-06-1672528-HTML/image_m/fanim-06-1672528-g001.jpg Figure 1. Map of Uganda showing the location of the study districts. https://www.frontiersin.org/files/Articles/1672528/fanim-06-1672528-HTML/image_m/fanim-06-1672528-t001.jpg Table 1. Number and type of interviews in the Eight (8) main case study districts. https://www.frontiersin.org/files/Articles/1672528/fanim-06-1672528-HTML/image_m/fanim-06-1672528-g002.jpg Figure 2. Using checkers game pieces to visualize the influence of actors. https://www.frontiersin.org/files/Articles/1672528/fanim-06-1672528-HTML/image_m/fanim-06-1672528-g003.jpg Figure 3. A network of actors involved in the production of Safety Beef in Uganda. The numbers encir https://www.frontiersin.org/files/Articles/1672528/fanim-06-1672528-HTML/image_m/fanim-06-1672528-t002.jpg Table 2. Mean and standard deviation of the influence scores of actors in the production of safe bee https://www.frontiersin.org/files/Articles/1672528/fanim-06-1672528-HTML/image_m/fanim-06-1672528-g004.jpg Figure 4. Beef value chain actor level of influence in the production of safe beef by Sub-region. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1737616/full https://www.frontiersin.org/files/Articles/1737616/fbioe-14-1737616-HTML-r1/image_m/fbioe-14-1737616-g001.jpg Figure 1. 3D-bioprinted CAD file that was used to print the fibroblast construct. The final dome-sha https://www.frontiersin.org/files/Articles/1737616/fbioe-14-1737616-HTML-r1/image_m/fbioe-14-1737616-g002.jpg Figure 2. Schematic showing 3D bioprinting of human fibroblasts with the antibiotic clindamycin in S https://www.frontiersin.org/files/Articles/1737616/fbioe-14-1737616-HTML-r1/image_m/fbioe-14-1737616-g003.jpg Figure 3. (a,b) SiNPs were prepared and imaged on TEM in 10 mL of ethanol. (c,d) SiNPs prepared by s https://www.frontiersin.org/files/Articles/1737616/fbioe-14-1737616-HTML-r1/image_m/fbioe-14-1737616-g004.jpg Figure 4. (a) Vogel and Johnson agar (VJA) culture of S. aureus (Thermo Fisher Scientific). (b) Top https://www.frontiersin.org/files/Articles/1737616/fbioe-14-1737616-HTML-r1/image_m/fbioe-14-1737616-t001.jpg Table 1. HPLC detection of clindamycin post-incubation and washing of the SiNP supernatant. https://www.frontiersin.org/files/Articles/1737616/fbioe-14-1737616-HTML-r1/image_m/fbioe-14-1737616-g005.jpg Figure 5. S. aureus bacterial fluorescence imaging using BacLight®. Bacteria were imaged in LB broth https://www.frontiersin.org/files/Articles/1737616/fbioe-14-1737616-HTML-r1/image_m/fbioe-14-1737616-g006.jpg Figure 6. Imaging of 3D-bioprinted construct of fibroblasts infected with S. epidermidis (AH852) and https://www.frontiersin.org/files/Articles/1737616/fbioe-14-1737616-HTML-r1/image_m/fbioe-14-1737616-g007.jpg Figure 7. Investigations in the “dermis” model of 3D-bioprinted construct inoculated with S. epiderm https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1695101/full https://www.frontiersin.org/files/Articles/1695101/fpsyg-16-1695101-HTML-r1/image_m/fpsyg-16-1695101-g001.jpg Figure 1. Hypothesized model. https://www.frontiersin.org/files/Articles/1695101/fpsyg-16-1695101-HTML-r1/image_m/fpsyg-16-1695101-t001.jpg Table 1. Participant demographics and professional characteristics (N = 352). https://www.frontiersin.org/files/Articles/1695101/fpsyg-16-1695101-HTML-r1/image_m/fpsyg-16-1695101-t002.jpg Table 2. Construct reliability and confirmatory factor analysis of the measurement model. https://www.frontiersin.org/files/Articles/1695101/fpsyg-16-1695101-HTML-r1/image_m/fpsyg-16-1695101-t003.jpg Table 3. Convergent and discriminant validity of the measurement model. https://www.frontiersin.org/files/Articles/1695101/fpsyg-16-1695101-HTML-r1/image_m/fpsyg-16-1695101-t004.jpg Table 4. Direct, indirect, and total effects. https://www.frontiersin.org/files/Articles/1695101/fpsyg-16-1695101-HTML-r1/image_m/fpsyg-16-1695101-t005.jpg Table 5. Effect size (Cohen’s f2) of structural paths. https://www.frontiersin.org/files/Articles/1695101/fpsyg-16-1695101-HTML-r1/image_m/fpsyg-16-1695101-g002.jpg Figure 2. Simple slopes of LI predicting CDM at low (−1 SD), mean, and high (+1 SD) levels of TD. https://www.frontiersin.org/files/Articles/1695101/fpsyg-16-1695101-HTML-r1/image_m/fpsyg-16-1695101-g003.jpg Figure 3. Johnson–Neyman (J–N) analysis of the moderating effect of TD on the relationship between L https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1681868/full https://www.frontiersin.org/files/Articles/1681868/fspor-07-1681868-HTML/image_m/fspor-07-1681868-t001.jpg Table 1. Lesson principles and instructional approaches. https://www.frontiersin.org/files/Articles/1681868/fspor-07-1681868-HTML/image_m/fspor-07-1681868-g001.jpg Figure 1. HAM with axis of orientation (leftmost) (x, upward–downward), anteroposterior (z, forward– https://www.frontiersin.org/files/Articles/1681868/fspor-07-1681868-HTML/image_m/fspor-07-1681868-g002.jpg Figure 2. Topmost: raw data obtained via the HAM. Bottommost: 2D SPLYZA motion data. https://www.frontiersin.org/files/Articles/1681868/fspor-07-1681868-HTML/image_m/fspor-07-1681868-t002.jpg Table 2. Anonymous student responses (n = 14) of SALG scores of pre-post F.I.T. intervention. Where https://www.frontiersin.org/files/Articles/1681868/fspor-07-1681868-HTML/image_m/fspor-07-1681868-g003.jpg Figure 3. Topmost: SALG scores post F.I.T implementation. Bottommost: SALG score pre–F.I.T implement https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1707688/full https://www.frontiersin.org/files/Articles/1707688/fphar-17-1707688-HTML/image_m/fphar-17-1707688-g001.jpg Figure 1. Diagram for the pathogenesis of AD. https://www.frontiersin.org/files/Articles/1707688/fphar-17-1707688-HTML/image_m/fphar-17-1707688-t001.jpg Table 1. Mechanisms of baicalin and baicalein in combating Alzheimer’s disease. https://www.frontiersin.org/files/Articles/1707688/fphar-17-1707688-HTML/image_m/fphar-17-1707688-g002.jpg Figure 2. Baicalin and baicalein targeting Alzheimer's disease: key mechanisms of action. https://www.frontiersin.org/files/Articles/1707688/fphar-17-1707688-HTML/image_m/fphar-17-1707688-g003.jpg Figure 3. The impact of gut microbiota-derived short-chain fatty acids on Alzheimer's disease pathol https://www.frontiersin.org/files/Articles/1707688/fphar-17-1707688-HTML/image_m/fphar-17-1707688-t002.jpg Table 2. Approved pharmacotherapy for Alzheimer’s disease: Mechanisms, efficacy, and safety profiles https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1762009/full https://www.frontiersin.org/files/Articles/1762009/fonc-16-1762009-HTML/image_m/fonc-16-1762009-g001.jpg Figure 1. Preoperative MRI and PET-CT findings. (A) Axial T2-weighted MRI showing a heterogeneous 9 https://www.frontiersin.org/files/Articles/1762009/fonc-16-1762009-HTML/image_m/fonc-16-1762009-g002.jpg Figure 2. Multidisciplinary feasibility assessment for complete cytoreduction. This framework summar https://www.frontiersin.org/files/Articles/1762009/fonc-16-1762009-HTML/image_m/fonc-16-1762009-g003.jpg Figure 3. Intraoperative images of the multidisciplinary surgical procedure performed. (A) The diagr https://www.frontiersin.org/files/Articles/1762009/fonc-16-1762009-HTML/image_m/fonc-16-1762009-g004.jpg Figure 4. The “Four Surgical Limits” conceptual framework for complex oncologic surgery. A visual mo https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1684994/full https://www.frontiersin.org/files/Articles/1684994/fpsyt-16-1684994-HTML-r1/image_m/fpsyt-16-1684994-g001.jpg Figure 1. The details of the selection process. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1732372/full https://www.frontiersin.org/files/Articles/1732372/fmicb-16-1732372-HTML/image_m/fmicb-16-1732372-g001.jpg Figure 1. Alpha diversity shows significant differences across compartments. (A) Alpha diversity Cha https://www.frontiersin.org/files/Articles/1732372/fmicb-16-1732372-HTML/image_m/fmicb-16-1732372-g002.jpg Figure 2. Beta diversity based on UniFrac distances showing significant differences across tissues. https://www.frontiersin.org/files/Articles/1732372/fmicb-16-1732372-HTML/image_m/fmicb-16-1732372-g003.jpg Figure 3. Composition of the 10 most abundant taxa for fungal and bacterial communities in different https://www.frontiersin.org/files/Articles/1732372/fmicb-16-1732372-HTML/image_m/fmicb-16-1732372-g004.jpg Figure 4. Spearman’s correlation (ρ) between microbial genera and soil physicochemical parameters ac https://www.frontiersin.org/files/Articles/1732372/fmicb-16-1732372-HTML/image_m/fmicb-16-1732372-g005.jpg Figure 5. dbRDA based on UniFrac distances showing significant differences across tissues. Vector di https://www.frontiersin.org/files/Articles/1732372/fmicb-16-1732372-HTML/image_m/fmicb-16-1732372-g006.jpg Figure 6. Upset plot of overlap of different compartments; it was calculated with presence/absence m https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1701902/full https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-t001.jpg Table 1. Demographic characteristics of participants (N = 847). https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-t002.jpg Table 2. Means, standard deviations, and correlations among main variables (N = 758). https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-t003.jpg Table 3. Results of longitudinal measurement invariance testing. https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-g001.jpg Figure 1. Cross-lagged analysis between physical exercise and social self-efficacy. ***statistical s https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-t004.jpg Table 4. Fit indices of competing models. https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-t005.jpg Table 5. Longitudinal mediation effects of peer acceptance in the cross-lagged model. https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-g002.jpg Figure 2. Autoregressive model M1 of physical exercise, peer acceptance, and social self-efficacy. * https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-g003.jpg Figure 3. Autoregressive model M2 of physical exercise, peer acceptance, and social self-efficacy. * https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-g004.jpg Figure 4. Autoregressive model M3 of physical exercise, peer acceptance, and social self-efficacy. * https://www.frontiersin.org/files/Articles/1701902/fpsyg-16-1701902-HTML/image_m/fpsyg-16-1701902-g005.jpg Figure 5. Autoregressive model M4 of physical exercise, peer acceptance, and social self-efficacy. * https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1753827/full https://www.frontiersin.org/files/Articles/1753827/fpsyt-17-1753827-HTML/image_m/fpsyt-17-1753827-t001.jpg Table 1. Scores obtained during the most recent assessment according to the Barthel index. https://www.frontiersin.org/files/Articles/1753827/fpsyt-17-1753827-HTML/image_m/fpsyt-17-1753827-t002.jpg Table 2. Results of the Mann–Whitney U nonparametric statistical test by gender of respondents. https://www.frontiersin.org/files/Articles/1753827/fpsyt-17-1753827-HTML/image_m/fpsyt-17-1753827-t003.jpg Table 3. Mean results of the MNA questionnaire (N = 234). https://www.frontiersin.org/files/Articles/1753827/fpsyt-17-1753827-HTML/image_m/fpsyt-17-1753827-t004.jpg Table 4. Associations between the beck depression inventory (BDI) score and MNA scale scores (Spearm https://www.frontiersin.org/files/Articles/1753827/fpsyt-17-1753827-HTML/image_m/fpsyt-17-1753827-t005.jpg Table 5. Correlations between the barthel index, beck depression inventory (BDI), MNA scores and age https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1644675/full https://www.frontiersin.org/files/Articles/1644675/fvets-12-1644675-HTML-r1/image_m/fvets-12-1644675-t001.jpg Table 1. General characterization of study sample (n = 29). https://www.frontiersin.org/files/Articles/1644675/fvets-12-1644675-HTML-r1/image_m/fvets-12-1644675-t002.jpg Table 2. Fatty acids profiling identifies by GC-MS after alkaline trimethylation and expressed as re https://www.frontiersin.org/files/Articles/1644675/fvets-12-1644675-HTML-r1/image_m/fvets-12-1644675-t003.jpg Table 3. Total fatty acid contents in control, gingivitis and periodontitis groups. https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1723329/full https://www.frontiersin.org/files/Articles/1723329/fphy-13-1723329-HTML/image_m/fphy-13-1723329-t001.jpg Table 1. The healthy BBB: an engineering blueprint for transport and measurement. https://www.frontiersin.org/files/Articles/1723329/fphy-13-1723329-HTML/image_m/fphy-13-1723329-t002.jpg Table 2. GBM reprograms the BBB: drivers, consequences, readouts, levers. https://www.frontiersin.org/files/Articles/1723329/fphy-13-1723329-HTML/image_m/fphy-13-1723329-g001.jpg Figure 1. State-resolved BBB/BTB in GBM: Barrier phenotypes orchestrate delivery and immunity. This https://www.frontiersin.org/files/Articles/1723329/fphy-13-1723329-HTML/image_m/fphy-13-1723329-t003.jpg Table 3. Matching intervention to BBB state, payload, and endpoints. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1734705/full https://www.frontiersin.org/files/Articles/1734705/fimmu-17-1734705-HTML/image_m/fimmu-17-1734705-t001.jpg Table 1. Summary of DSA trend done at initial screen, after daratumumab, pre- and post-desensitizati https://www.frontiersin.org/files/Articles/1734705/fimmu-17-1734705-HTML/image_m/fimmu-17-1734705-g001.jpg Figure 1. DSA trend done at initial screen, after daratumumab, pre- and post-desensitization. https://www.frontiersin.org/files/Articles/1734705/fimmu-17-1734705-HTML/image_m/fimmu-17-1734705-g002.jpg Figure 2. Desensitization protocol. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1673156/full https://www.frontiersin.org/files/Articles/1673156/fped-13-1673156-HTML/image_m/fped-13-1673156-g001.jpg Figure 1. The role of AKK in UC. First, AKK may reduce colonic infiltrating macrophages through the https://www.frontiersin.org/files/Articles/1673156/fped-13-1673156-HTML/image_m/fped-13-1673156-g002.jpg Figure 2. The model demonstrates the protective effect of targeting AKK in the treatment of ulcerati https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1758001/full https://www.frontiersin.org/files/Articles/1758001/fmars-13-1758001-HTML/image_m/fmars-13-1758001-g001.jpg Figure 1. Principal component analysis (PCA) of cumulative morphological traits in males (A) and fem https://www.frontiersin.org/files/Articles/1758001/fmars-13-1758001-HTML/image_m/fmars-13-1758001-g002.jpg Figure 2. Relationships between carapace width and carapace length with other morphological traits i https://www.frontiersin.org/files/Articles/1758001/fmars-13-1758001-HTML/image_m/fmars-13-1758001-g003.jpg Figure 3. Surface topography and spine patterns of the outer exoskeleton in males (i) and females (i https://www.frontiersin.org/files/Articles/1758001/fmars-13-1758001-HTML/image_m/fmars-13-1758001-g004.jpg Figure 4. Surface topography of the eyestalk in males (i) and females (ii) X. testudinatus (A, D), s https://www.frontiersin.org/files/Articles/1758001/fmars-13-1758001-HTML/image_m/fmars-13-1758001-g005.jpg Figure 5. Cuticular microstructure of males (I) and females (II) X. testudinatus. Panels show (i) su https://www.frontiersin.org/files/Articles/1758001/fmars-13-1758001-HTML/image_m/fmars-13-1758001-g006.jpg Figure 6. Transmission electron microscopy images illustrating crystallinity patterns in males (A) a https://www.frontiersin.org/files/Articles/1758001/fmars-13-1758001-HTML/image_m/fmars-13-1758001-g007.jpg Figure 7. X-ray diffraction (XRD) patterns illustrating differences in crystallinity between males a https://www.frontiersin.org/files/Articles/1758001/fmars-13-1758001-HTML/image_m/fmars-13-1758001-g008.jpg Figure 8. SEM-EDX analyses of chemical compositions in males (i) females (ii) X. testudinatus crab s https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2025.1655500/full https://www.frontiersin.org/files/Articles/1655500/fnsys-19-1655500-HTML/image_m/fnsys-19-1655500-t001.jpg Table 1. Key disorders, affected networks, and dsr component disruptions. https://www.frontiersin.org/files/Articles/1655500/fnsys-19-1655500-HTML/image_m/fnsys-19-1655500-g001.jpg Figure 1. Conceptual illustration of the dynamic self-representation (DSR) framework contrasted with https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1666549/full https://www.frontiersin.org/files/Articles/1666549/fimmu-16-1666549-HTML/image_m/fimmu-16-1666549-g001.jpg Figure 1. Flow chart of this study (Figdraw, ID:UPSYS00f00). https://www.frontiersin.org/files/Articles/1666549/fimmu-16-1666549-HTML/image_m/fimmu-16-1666549-g002.jpg Figure 2. Single cell RNA analysis of radiation-induced skin injury. (A) All cells in 8 samples were https://www.frontiersin.org/files/Articles/1666549/fimmu-16-1666549-HTML/image_m/fimmu-16-1666549-g003.jpg Figure 3. Single-cell RNA analysis of fibroblast subtypes. (A) Dendrogram visualizing different thre https://www.frontiersin.org/files/Articles/1666549/fimmu-16-1666549-HTML/image_m/fimmu-16-1666549-g004.jpg Figure 4. Gene expression and enrichment analysis of MMP3 FIB subtypes and validation. (A) Heatmap d https://www.frontiersin.org/files/Articles/1666549/fimmu-16-1666549-HTML/image_m/fimmu-16-1666549-g005.jpg Figure 5. Radiation induces HSF senescence and apoptosis by upregulating TGFBR2 (A) Schematic diagra https://www.frontiersin.org/files/Articles/1666549/fimmu-16-1666549-HTML/image_m/fimmu-16-1666549-g006.jpg Figure 6. Molecular docking and molecular dynamic results. (A) TGFBR2 - Berberine molecular docking. https://www.frontiersin.org/files/Articles/1666549/fimmu-16-1666549-HTML/image_m/fimmu-16-1666549-g007.jpg Figure 7. Schematic of the mechanism of TGFBR2 in mediating radiation induced HSF apoptosis. (Figdra https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2025.1688694/full https://www.frontiersin.org/files/Articles/1688694/fnmol-18-1688694-HTML/image_m/fnmol-18-1688694-g001.jpg Figure 1. PGC-1α is overexpressed in the hippocampal DG of AD brain. (A) APP/PS1 offspring genotypes https://www.frontiersin.org/files/Articles/1688694/fnmol-18-1688694-HTML/image_m/fnmol-18-1688694-t001.jpg Table 1. Primers used for RT-qPCR analyses. https://www.frontiersin.org/files/Articles/1688694/fnmol-18-1688694-HTML/image_m/fnmol-18-1688694-g002.jpg Figure 2. PGC-1α promotes the differentiation and survival of newborn neurons in the DG of AD hippoc https://www.frontiersin.org/files/Articles/1688694/fnmol-18-1688694-HTML/image_m/fnmol-18-1688694-g003.jpg Figure 3. PGC-1α promotes newborn neuron proliferation in the AD hippocampus via the FNDC5/BDNF/TrkB https://www.frontiersin.org/files/Articles/1688694/fnmol-18-1688694-HTML/image_m/fnmol-18-1688694-g004.jpg Figure 4. Generation of PGC-1α conditional knockout (CKO) mice and confirmation of reduced hippocamp https://www.frontiersin.org/files/Articles/1688694/fnmol-18-1688694-HTML/image_m/fnmol-18-1688694-g005.jpg Figure 5. Pgc-1α gene deletion reduces immature and mature neuron numbers by inhibiting the FNDC5/BD https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1767144/full https://www.frontiersin.org/files/Articles/1767144/fpls-17-1767144-HTML/image_m/fpls-17-1767144-g001.jpg Figure 1. Impact of chemical treatments on photosynthetic rate across different leaf positions and l https://www.frontiersin.org/files/Articles/1767144/fpls-17-1767144-HTML/image_m/fpls-17-1767144-g002.jpg Figure 2. Effect of different chemical treatments on photosynthetic rate across varying light intens https://www.frontiersin.org/files/Articles/1767144/fpls-17-1767144-HTML/image_m/fpls-17-1767144-g003.jpg Figure 3. The enzyme activities of (A) acetylcholinesterase (AChE), (B) carboxylesterase (CarE), (C) https://www.frontiersin.org/files/Articles/1767144/fpls-17-1767144-HTML/image_m/fpls-17-1767144-g004.jpg Figure 4. The expression levels of various insecticide resistance genes were measured following the https://www.frontiersin.org/files/Articles/1767144/fpls-17-1767144-HTML/image_m/fpls-17-1767144-t001.jpg Table 1. Olfactory responses of gravid female S. frugiperda in Y-tube olfactometer bioassays. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1663718/full https://www.frontiersin.org/files/Articles/1663718/feduc-10-1663718-HTML/image_m/feduc-10-1663718-t001.jpg Table 1. Summary of primary study themes. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1724853/full https://www.frontiersin.org/files/Articles/1724853/fpsyt-17-1724853-HTML-r1/image_m/fpsyt-17-1724853-t001.jpg Table 1. Characteristics of the interview participants (N = 15). https://www.frontiersin.org/files/Articles/1724853/fpsyt-17-1724853-HTML-r1/image_m/fpsyt-17-1724853-t002.jpg Table 2. Semi-structured interview topic guide. https://www.frontiersin.org/files/Articles/1724853/fpsyt-17-1724853-HTML-r1/image_m/fpsyt-17-1724853-t003.jpg Table 3. Professional roles involved in suicide prevention and their perceived functions, as describ https://www.frontiersin.org/files/Articles/1724853/fpsyt-17-1724853-HTML-r1/image_m/fpsyt-17-1724853-g001.jpg Figure 1. Perceived network of IPC in suicide prevention as described by interview participants. Nod https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1682362/full https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-t001.jpg Table 1. Clinical and laboratorial characteristics of participants in this study. https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-g001.jpg Figure 1. Evaluation of Experimental Data Quality and Establishment of Intergroup Difference Models. https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-g002.jpg Figure 2. Differential Metabolite Screening and Enrichment and Topology Analyses of Pathways Between https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-t002.jpg Table 2. Differentially expressed and most important metabolites between VI and VF groups and their https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-g003.jpg Figure 3. Differential Metabolite Screening and Enrichment and Topology Analyses of Pathways Between https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-g004.jpg Figure 4. Differential Expression of Metabolites Derived from Kidney and Adrenal Glands Among the VI https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-g005.jpg Figure 5. Differential Expression of Metabolites Derived from Kidney and Adrenal Glands Among the VI https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-t003.jpg Table 3. Detected xenobiotics in this research. https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-g006.jpg Figure 6. Differential Expression of Metabolites with Oxidative Stress and Inflammatory Functions Am https://www.frontiersin.org/files/Articles/1682362/fendo-16-1682362-HTML/image_m/fendo-16-1682362-g007.jpg Figure 7. Differential Expression of Metabolites with Oxidative Stress and Inflammatory Functions Am https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1671964/full https://www.frontiersin.org/files/Articles/1671964/fimmu-16-1671964-HTML/image_m/fimmu-16-1671964-g001.jpg Figure 1. Changes in cardiac injury markers, myoglobin, and liver function. https://www.frontiersin.org/files/Articles/1671964/fimmu-16-1671964-HTML/image_m/fimmu-16-1671964-g002.jpg Figure 2. Trend changes in MG-ADL and QMG scores. https://www.frontiersin.org/files/Articles/1671964/fimmu-16-1671964-HTML/image_m/fimmu-16-1671964-g003.jpg Figure 3. Trend changes in IgG. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1649420/full https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-g001.jpg Figure 1. Flowchart of the screening process for non-invasive diagnostic methods of bladder cancer. https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-t001.jpg Table 1. Study characteristics. https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-t002.jpg Table 2. Efficacy analysis of urine-derived biomarkers. https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-t003.jpg Table 3. Urine microRNA biomarkers. https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-t004.jpg Table 4. Urine-based combined diagnosis. https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-t005.jpg Table 5. Efficacy analysis of blood-derived biomarkers. https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-g002.jpg Figure 2. Summary of risk of bias in the studies included. (A) Risk of bias in the studies included. https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-g003.jpg Figure 3. Efficacy analysis of urine-derived biomarkers. https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-g004.jpg Figure 4. Efficacy analysis of blood-derived biomarkers. https://www.frontiersin.org/files/Articles/1649420/fonc-15-1649420-HTML/image_m/fonc-15-1649420-t006.jpg Table 6. Blood microRNA biomarkers. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1709097/full https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g001.jpg Figure 1. (A) Location of site 2A-1 on the Scotian Slope. (B) Push core transect sample locations us https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-t001.jpg Table 1. Push core sample summary. https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g002.jpg Figure 2. Sedimentary carbon and porewater ion survey. Dashed lines indicate discontinuous down core https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g003.jpg Figure 3. Cross-seep diffusion flux and core bottom hydrocarbon gases (HCG) concentrations measured https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g004.jpg Figure 4. Simpson’s diversity index of (A) IPL and (B) CL transect heatmaps as well as (C) the avera https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g005.jpg Figure 5. 1G-GDGT and GDGT relative abundance and concentrations across site 2A-1 transect. Addition https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g006.jpg Figure 6. Transect heatmaps of archaeal IPLs, CLs, and pigments. Red, black, and pink dotted lines a https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g007.jpg Figure 7. (A) PCA biplot of lipid classes and samples. (B) Combined PCA analysis of seep lipid class https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g008.jpg Figure 8. (A) Cross-plot of percent SO42− lost calculated as the difference between normal marine co https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g009.jpg Figure 9. Heatmaps of (A) MIIPL and (B) MICL marking the range of ANME-1 activity. Shaded regions ma https://www.frontiersin.org/files/Articles/1709097/fmicb-17-1709097-HTML-r2/image_m/fmicb-17-1709097-g010.jpg Figure 10. (A) Mean Simpson’s index changes across the 2A-1 seep transect. (B) The reconstructed see https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1692638/full https://www.frontiersin.org/files/Articles/1692638/fcvm-12-1692638-HTML/image_m/fcvm-12-1692638-g001.jpg Figure 1. Temporal progression of macrophage-mediated mechanisms in AF. This diagram illustrates the https://www.frontiersin.org/files/Articles/1692638/fcvm-12-1692638-HTML/image_m/fcvm-12-1692638-t001.jpg Table 1. Summary of Key macrophage-mediated signaling pathways in atrial fibrillation. https://www.frontiersin.org/files/Articles/1692638/fcvm-12-1692638-HTML/image_m/fcvm-12-1692638-g002.jpg Figure 2. Inflammatory and fibrotic signaling axes mediated by cardiac macrophages in AF. This schem https://www.frontiersin.org/files/Articles/1692638/fcvm-12-1692638-HTML/image_m/fcvm-12-1692638-g003.jpg Figure 3. Cardiac macrophage recruitment, polarization and effector pathways in atrial fibrillation https://www.frontiersin.org/files/Articles/1692638/fcvm-12-1692638-HTML/image_m/fcvm-12-1692638-t002.jpg Table 2. Summary of quantitative findings from animal, clinical studies on macrophages in AF. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1456142/full https://www.frontiersin.org/files/Articles/1456142/fpubh-13-1456142-HTML/image_m/fpubh-13-1456142-g001.jpg Figure 1. Analytical framework of UDSR policy in Beijing. This framework was developed by the author https://www.frontiersin.org/files/Articles/1456142/fpubh-13-1456142-HTML/image_m/fpubh-13-1456142-t001.jpg Table 1. Hospital characteristics of counterpart support hospitals. https://www.frontiersin.org/files/Articles/1456142/fpubh-13-1456142-HTML/image_m/fpubh-13-1456142-t002.jpg Table 2. Variable setting and coding based on policy implementation model. https://www.frontiersin.org/files/Articles/1456142/fpubh-13-1456142-HTML/image_m/fpubh-13-1456142-t003.jpg Table 3. Necessity analysis of individual condition variable. https://www.frontiersin.org/files/Articles/1456142/fpubh-13-1456142-HTML/image_m/fpubh-13-1456142-t004.jpg Table 4. The condition configuration of the effect of the UDSR policy. https://www.frontiersin.org/files/Articles/1456142/fpubh-13-1456142-HTML/image_m/fpubh-13-1456142-g002.jpg Figure 2. Influencing factors and implementation path of the UDSR policy in Beijing. https://www.frontiersin.org/files/Articles/1456142/fpubh-13-1456142-HTML/image_m/fpubh-13-1456142-t005.jpg Table 5. The condition configuration of the effect of the UDSR policy in multiple periods. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1693688/full https://www.frontiersin.org/files/Articles/1693688/fmed-12-1693688-HTML/image_m/fmed-12-1693688-g001.jpg Figure 1. Imaging findings, staining of pathological sections, and the flowchart of diagnosis, thera https://www.frontiersin.org/files/Articles/1693688/fmed-12-1693688-HTML/image_m/fmed-12-1693688-t001.jpg Table 1. Chemotherapy regimen: drugs, dosage, and days. https://www.frontiersin.org/files/Articles/1693688/fmed-12-1693688-HTML/image_m/fmed-12-1693688-g002.jpg Figure 2. Schematic representation of the BCR-ABL1 gene fusion in this patient. Next-generation sequ https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1710379/full https://www.frontiersin.org/files/Articles/1710379/fendo-17-1710379-HTML/image_m/fendo-17-1710379-g001.jpg Figure 1. cBMD measurement. Example from one subject (L2 vertebra). Three axial 1-mm slices (superio https://www.frontiersin.org/files/Articles/1710379/fendo-17-1710379-HTML/image_m/fendo-17-1710379-t001.jpg Table 1. Demographic and clinical characteristics of the study cohort stratified by QCT−vBMD status. https://www.frontiersin.org/files/Articles/1710379/fendo-17-1710379-HTML/image_m/fendo-17-1710379-g002.jpg Figure 2. Age- and sex-related variations in lumbar QCT-vBMD in PHPT. Points show group means; error https://www.frontiersin.org/files/Articles/1710379/fendo-17-1710379-HTML/image_m/fendo-17-1710379-g003.jpg Figure 3. Correlation between vertebral cBMD and QCT-vBMD by level (T12–L3). Scatter plots with line https://www.frontiersin.org/files/Articles/1710379/fendo-17-1710379-HTML/image_m/fendo-17-1710379-t002.jpg Table 2. Pearson correlations between vertebral cBMD and QCT-vBMD, and inter-vertebral cBMD correlat https://www.frontiersin.org/files/Articles/1710379/fendo-17-1710379-HTML/image_m/fendo-17-1710379-g004.jpg Figure 4. ROC curves for vertebral cBMD by level (T12–L3) diagnosing (A) osteoporosis and (B) low bo https://www.frontiersin.org/files/Articles/1710379/fendo-17-1710379-HTML/image_m/fendo-17-1710379-t003.jpg Table 3. Diagnostic performance of vertebral cBMD for osteoporosis and low bone mass. https://www.frontiersin.org/files/Articles/1710379/fendo-17-1710379-HTML/image_m/fendo-17-1710379-g005.jpg Figure 5. ROC curves of stepwise logistic models for osteoporosis. Model 1: age, sex, BMI; Model 2: https://www.frontiersin.org/files/Articles/1710379/fendo-17-1710379-HTML/image_m/fendo-17-1710379-t004.jpg Table 4. Multivariable linear regression analysis of QCT-vBMD and L2-cBMD in PHPT patients. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1758617/full https://www.frontiersin.org/files/Articles/1758617/fphar-17-1758617-HTML/image_m/fphar-17-1758617-t001.jpg Table 1. Targeted regulation of the gut microbiota microenvironment by nanomaterials. https://www.frontiersin.org/files/Articles/1758617/fphar-17-1758617-HTML/image_m/fphar-17-1758617-g001.jpg Figure 1. Schematic overview of nanotechnology for the diagnosis, delivery, modulation and therapy i https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1713722/full https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-t001.jpg Table 1. Formulation and nutrient content of the basal diet (DM base). https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-g001.jpg Figure 1. Components of Vitex negundo L. var. cannabifolia. The total ion chromatograms (TIC) for th https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-t002.jpg Table 2. Identification results of Vitex negundo L. var. cannabifolia extract. https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-t003.jpg Table 3. Effect of VNE supplementation on the growth performance of broilers1. https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-g002.jpg Figure 2. Effect of VNE supplementation on the intestine morphology of broilers. Representative imag https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-t004.jpg Table 4. Effect of VNE supplementation on the intestine morphology of broilers1. https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-t005.jpg Table 5. Effect of VNE supplementation on the blood biochemistry of broilers1. https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-t006.jpg Table 6. Effect of VNE supplementation on the serum cytokine of broilers1. https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-g003.jpg Figure 3. Effect of VNE supplementation on the diversity of the cecal microbiota of broilers. (A) A https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-g004.jpg Figure 4. Effect of dietary supplementation with VNE on the cecal microbiota of broilers. (A) Microb https://www.frontiersin.org/files/Articles/1713722/fvets-12-1713722-HTML/image_m/fvets-12-1713722-g005.jpg Figure 5. Correlation analysis between the gut microbiome and the serum development-related index. H https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1714086/full https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g001.jpg Figure 1. An EEG image containing bad trials displayed using the EPAT toolbox. Each image contains t https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g002.jpg Figure 2. The pipeline of the proposed YOLOBT. Different colors represent distinct functional module https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g003.jpg Figure 3. The structure of CLAB. S1...Sn represent the spatial attention maps generated by the nth b https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g004.jpg Figure 4. The structure of HFGM. High-level features first guide low-level features, and then dynami https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g005.jpg Figure 5. The structure of GICM, consisting of global and local branches. It integrates local inform https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g006.jpg Figure 6. The structure of the local branch. The branch includes five sub-branches, each using convo https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-t001.jpg Table 1. Training hyperparameters. https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-t002.jpg Table 2. Ablation experiment idea on our dataset. https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-t003.jpg Table 3. The results of ablation experiments on our dataset. https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g007.jpg Figure 7. Precision, recall, mAP, and F1 scores from the ablation experiments. https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-t004.jpg Table 4. Comparison with object detection models on our dataset. https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g008.jpg Figure 8. Precision, recall, mAP, F1, and FPS results from comparison experiments with object detect https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g009.jpg Figure 9. Representative comparison of different models for EEG bad trial detection: SSD, Faster-RCN https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-t005.jpg Table 5. Comparison with traditional and temporal deep learning methods. https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g010.jpg Figure 10. Quantitative characterization of artifact severity categories. (Left) Distribution of max https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-t006.jpg Table 6. Model performance under different artifact and signal conditions. https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g011.jpg Figure 11. EEG bad trial detection results from two subjects with different signal qualities. (Left) https://www.frontiersin.org/files/Articles/1714086/fnhum-20-1714086-HTML/image_m/fnhum-20-1714086-g012.jpg Figure 12. Attention heatmap visualization of YOLOBT using Grad-CAM++. Warmer colors (red/yellow) in https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1766642/full https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g001.jpg Figure 1. Effects of salt stress on the growth morphology of B. balsamifera. (A) Phenotypic response https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g002.jpg Figure 2. Effects of salt stress on photosynthesis-related parameters of B. balsamifera. (A) Net pho https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g003.jpg Figure 3. Effects of salt stress on leaf physiological and biochemical indices of B. balsamifera. (A https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g004.jpg Figure 4. Metabolome expression analysis. (A) Principal component analysis: Each point represents a https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g005.jpg Figure 5. Transcriptome expression analysis. (A) Principal component analysis: Each point represents https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g006.jpg Figure 6. Association analysis between metabolome and transcriptome. (A) Correlation analysis of DAM https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g007.jpg Figure 7. Expression of DAMs and DEGs related to the oxidative phosphorylation pathway. The schemati https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g008.jpg Figure 8. Expression of DAMs and DEGs related to the flavonoid and flavonol biosynthesis pathways. I https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g009.jpg Figure 9. Expression of DAMs and DEGs related to the cutin, cork and wax biosynthesis pathways. In t https://www.frontiersin.org/files/Articles/1766642/fpls-17-1766642-HTML-r1/image_m/fpls-17-1766642-g010.jpg Figure 10. Comparative analysis of RNA-seq and RT-qPCR. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1747560/full https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g001.jpg Figure 1. Workflow graph. https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g002.jpg Figure 2. Construction of the disulfidptosis-related prognostic model. (a) Venn diagram showing 86 o https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g003.jpg Figure 3. Survival prediction and validation of the prognostic model. (a) Kaplan-Meier survival curv https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g004.jpg Figure 4. Clinical correlation analysis and diagnostic nomogram construction. (a, b) Forest plots fr https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g005.jpg Figure 5. Functional enrichment analysis of the prognostic model. (a) Volcano plot of DEGs between r https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g006.jpg Figure 6. Immune characterization based on the prognostic model. (a–d) Violin plots comparing stroma https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g007.jpg Figure 7. Identification and functional validation of UBASH3B as a prognostic biomarker. (a) Correla https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g008.jpg Figure 8. Single-cell profiling of disulfidptosis-related features. (a) Cell type annotation and com https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g009.jpg Figure 9. Reverse pseudotime analysis reveals the association of UBASH3B and disulfidptosis with the https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g010.jpg Figure 10. Spatial transcriptomic profiling of disulfidptosis activity and core gene expression in t https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g011.jpg Figure 11. Silencing UBASH3B suppresses proliferation and metastasis in pancreatic cancer. (a) IHC s https://www.frontiersin.org/files/Articles/1747560/fimmu-17-1747560-HTML/image_m/fimmu-17-1747560-g012.jpg Figure 12. Potential regulatory mechanisms and transformation strategies of UABSH3B as a core biomar https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1779401/full https://www.frontiersin.org/files/Articles/1779401/fvets-13-1779401-HTML-r1/image_m/fvets-13-1779401-g001.jpg Figure 1. Aggregated mean incidence of pelvic organ prolapse in North American sows by month, 2019-2 https://www.frontiersin.org/files/Articles/1779401/fvets-13-1779401-HTML-r1/image_m/fvets-13-1779401-g002.jpg Figure 2. Time series for mean incidence of pelvic organ prolapse in sows in North America, 2019-202 https://www.frontiersin.org/files/Articles/1779401/fvets-13-1779401-HTML-r1/image_m/fvets-13-1779401-g003.jpg Figure 3. Mean annual incidence of pelvic organ prolapse in sows by state, 2019–2024. Color intensit https://www.frontiersin.org/files/Articles/1779401/fvets-13-1779401-HTML-r1/image_m/fvets-13-1779401-t001.jpg Table 1. Conditional model results from the zero-inflated beta regression analysis. https://www.frontiersin.org/files/Articles/1779401/fvets-13-1779401-HTML-r1/image_m/fvets-13-1779401-t002.jpg Table 2. Zero-inflated model results from the zero-inflated beta regression analysis. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1728785/full https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-t001.jpg Table 1. Questions in the interview form. https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-t002.jpg Table 2. Interviewee information sheet. https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-t003.jpg Table 3. Selective coding. https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-g001.jpg Figure 1. Theoretical model of college students’ AI literacy. https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-t004.jpg Table 4. Measurement scale. https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-t005.jpg Table 5. Cronbach’s α coefficient. https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-t006.jpg Table 6. Kaiser–Meyer–Olkin (KMO) value and Bartlett’s test of sphericity. https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-t007.jpg Table 7. Factor loading coefficients. https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-t008.jpg Table 8. Pearson correlations and the square root values of Average Variance Extracted (AVE). https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-t009.jpg Table 9. Factor loading coefficients. https://www.frontiersin.org/files/Articles/1728785/fpsyg-17-1728785-HTML/image_m/fpsyg-17-1728785-g002.jpg Figure 2. The structural equation model of college students’ intelligence literacy. https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1526455/full https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-g001.jpg Figure 1. Flow diagram of the study. https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-t001.jpg Table 1. Baseline characteristics of subjects. https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-t002.jpg Table 2. The baseline characteristics of the patients in the training and validation cohort. https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-g002.jpg Figure 2. LASSO regression analysis with 10-fold cross-validation for predicting motor functional im https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-t003.jpg Table 3. Original model coefficients. https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-t004.jpg Table 4. Final model coefficients. https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-g003.jpg Figure 3. The nomogram. This nomogram is based on three independent risk factors: HbD levels in the https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-g004.jpg Figure 4. Receiver operating characteristic (ROC) curves for nomogram. (A) Training ROC. This graph https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-g005.jpg Figure 5. Calibration curves for the training cohort and the validation cohort. (A) Training cohort. https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-g006.jpg Figure 6. Decision-curve analysis. https://www.frontiersin.org/files/Articles/1526455/fnhum-19-1526455-HTML/image_m/fnhum-19-1526455-t005.jpg Table 5. Correlation results between model variables and ADL. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1560225/full https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-g001.jpg Figure 1. GDQ positive and negative BPC plot. (A) BPC plot in positive ion mode of GDQ - standard pe https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-t001.jpg Table 1. Category and quantity of compound in GDQ. https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-g002.jpg Figure 2. GDQ inhibit systemic inflammatory response induced by PM2.5 exposure. (A) Concentrations o https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-g003.jpg Figure 3. GDQ rescued Th17/Treg imbalance in PM2.5-induced lung injury. (A) Representative FACS plot https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-g004.jpg Figure 4. GDQ protected against PM2.5-induced lung injury. (A) Pulmonary function instruments were u https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-g005.jpg Figure 5. GDQ attenuates lung tissue damage induced by PM2.5. (A) HE staining of lung tissue in each https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-g006.jpg Figure 6. The effect of GDQ on the composition of the lung microbiota induced by PM2.5 exposure in r https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-g007.jpg Figure 7. The effect of GDQ on the composition of the lung microbiota induced by PM2.5 exposure in r https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-g008.jpg Figure 8. The effect of GDQ on the serum metabolome induced by PM2.5 exposure in rats. (A) PCA score https://www.frontiersin.org/files/Articles/1560225/fmed-12-1560225-HTML/image_m/fmed-12-1560225-g009.jpg Figure 9. The effect of GDQ on the serum metabolome induced by PM2.5 exposure in rats. (A) Z-score p https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1791705/full https://www.frontiersin.org/files/Articles/1791705/fpsyg-17-1791705-HTML-r1/image_m/fpsyg-17-1791705-t001.jpg Table 1. Mean, standard deviations, and correlations between the independent and dependent variables https://www.frontiersin.org/files/Articles/1791705/fpsyg-17-1791705-HTML-r1/image_m/fpsyg-17-1791705-t002.jpg Table 2. Regression model summary for the relationship between work burnout and practice of communit https://www.frontiersin.org/files/Articles/1791705/fpsyg-17-1791705-HTML-r1/image_m/fpsyg-17-1791705-t003.jpg Table 3. Coefficients of the relationship between work burnout and practice of community policing am https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1763868/full https://www.frontiersin.org/files/Articles/1763868/fpls-17-1763868-HTML/image_m/fpls-17-1763868-g001.jpg Figure 1. Schematic comparison between Process-Based Models (PBMs), intermediate organ-level represe https://www.frontiersin.org/files/Articles/1763868/fpls-17-1763868-HTML/image_m/fpls-17-1763868-g002.jpg Figure 2. Schematic representation of carbon (C) and nitrogen (N) assimilation and allocation in a w https://www.frontiersin.org/files/Articles/1763868/fpls-17-1763868-HTML/image_m/fpls-17-1763868-g003.jpg Figure 3. Conceptual model of resource allocation in wheat under stress. Each box represents a funct https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1683221/full https://www.frontiersin.org/files/Articles/1683221/fimmu-17-1683221-HTML/image_m/fimmu-17-1683221-t001.jpg Table 1. Comparison of clinical and laboratory parameters in patients with PFAPA syndrome and identi https://www.frontiersin.org/files/Articles/1683221/fimmu-17-1683221-HTML/image_m/fimmu-17-1683221-g001.jpg Figure 1. Serum levels of cytokines in patients wit PFAPA and bacterial infection. https://www.frontiersin.org/files/Articles/1683221/fimmu-17-1683221-HTML/image_m/fimmu-17-1683221-t002.jpg Table 2. Multiple logistic regression analysis of parameters in PFAPA syndrome and identified bacter https://www.frontiersin.org/files/Articles/1683221/fimmu-17-1683221-HTML/image_m/fimmu-17-1683221-t003.jpg Table 3. Receiver operating characteristic curve analysis of laboratory parameters in two groups. https://www.frontiersin.org/files/Articles/1683221/fimmu-17-1683221-HTML/image_m/fimmu-17-1683221-g002.jpg Figure 2. Receiver operating characteristic curves of combined model and the role of IFN-γ/IL-6, PLT https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1732845/full https://www.frontiersin.org/files/Articles/1732845/fpubh-13-1732845-HTML-r1/image_m/fpubh-13-1732845-t001.jpg Table 1. Non-uptake of COVID-19 vaccination by social characteristics (row percentages). https://www.frontiersin.org/files/Articles/1732845/fpubh-13-1732845-HTML-r1/image_m/fpubh-13-1732845-t002.jpg Table 2. Experiences of discrimination in the past 5 years by social characteristics (% by row). https://www.frontiersin.org/files/Articles/1732845/fpubh-13-1732845-HTML-r1/image_m/fpubh-13-1732845-t003.jpg Table 3. Reported grounds for discrimination by type of discriminatory experience (% by column)*. https://www.frontiersin.org/files/Articles/1732845/fpubh-13-1732845-HTML-r1/image_m/fpubh-13-1732845-t004.jpg Table 4. Factors associated with non-vaccination according to the inclusion of type of discriminatio https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1737790/full https://www.frontiersin.org/files/Articles/1737790/frai-09-1737790-HTML/image_m/frai-09-1737790-g001.jpg Figure 1. PRISMA flowchart. https://www.frontiersin.org/files/Articles/1737790/frai-09-1737790-HTML/image_m/frai-09-1737790-t001.jpg Table 1. Summary of emerging themes in the current state of knowledge. https://www.frontiersin.org/files/Articles/1737790/frai-09-1737790-HTML/image_m/frai-09-1737790-t002.jpg Table 2. Comparative summary of detection approaches and research perspectives. https://www.frontiersin.org/files/Articles/1737790/frai-09-1737790-HTML/image_m/frai-09-1737790-g002.jpg Figure 2. Conceptual framework. https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1666476/full https://www.frontiersin.org/files/Articles/1666476/fnhum-19-1666476-HTML/image_m/fnhum-19-1666476-g001.jpg Figure 1. Illustration of the protocol and angular errors during the adaptation phase. (A) Participa https://www.frontiersin.org/files/Articles/1666476/fnhum-19-1666476-HTML/image_m/fnhum-19-1666476-g002.jpg Figure 2. Subjective line center in the group exposed to abrupt (ABR) and gradual (GRA) perturbation https://www.frontiersin.org/files/Articles/1666476/fnhum-19-1666476-HTML/image_m/fnhum-19-1666476-g003.jpg Figure 3. The width of the interval in which participants provide reliable responses to the line bis https://www.frontiersin.org/files/Articles/1666476/fnhum-19-1666476-HTML/image_m/fnhum-19-1666476-g004.jpg Figure 4. Simulation of logistic functions. Functions f and g are parallel (same slope) but have dif https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1705734/full https://www.frontiersin.org/files/Articles/1705734/fnhum-20-1705734-HTML/image_m/fnhum-20-1705734-g001.jpg Figure 1. Slant board mounted on the force plate in different experimental conditions. Horizontal (H https://www.frontiersin.org/files/Articles/1705734/fnhum-20-1705734-HTML/image_m/fnhum-20-1705734-t001.jpg Table 1. Angular position (°) of ankle and knee joints under all slope conditions. https://www.frontiersin.org/files/Articles/1705734/fnhum-20-1705734-HTML/image_m/fnhum-20-1705734-g002.jpg Figure 2. Angular position (°) of right (Ankle_R) and left (Ankle_L) ankles (A) and right (Knee_R) a https://www.frontiersin.org/files/Articles/1705734/fnhum-20-1705734-HTML/image_m/fnhum-20-1705734-t002.jpg Table 2. Normalized EMG (% of the MVC) under all slope conditions. https://www.frontiersin.org/files/Articles/1705734/fnhum-20-1705734-HTML/image_m/fnhum-20-1705734-g003.jpg Figure 3. Normalized EMG (%) of rectus femoris. EMGs of the right (RecF-R) and left (RecF-L) rectus https://www.frontiersin.org/files/Articles/1705734/fnhum-20-1705734-HTML/image_m/fnhum-20-1705734-g004.jpg Figure 4. Representative traces of 30-s CP trajectory in three lateral slope conditions. From left t https://www.frontiersin.org/files/Articles/1705734/fnhum-20-1705734-HTML/image_m/fnhum-20-1705734-t003.jpg Table 3. Posturographic parameters under all slope conditions. https://www.frontiersin.org/files/Articles/1705734/fnhum-20-1705734-HTML/image_m/fnhum-20-1705734-g005.jpg Figure 5. Mean deviation of the CP along the anterior-posterior axis (Xm) (A) and medial-lateral axi https://www.frontiersin.org/files/Articles/1705734/fnhum-20-1705734-HTML/image_m/fnhum-20-1705734-g006.jpg Figure 6. Mean velocity of the CP along the medial-lateral axis (Vm-tr) (A) and anterior-posterior ( https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2026.1735652/full https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-g001.jpg Figure 1. Flow diagram of identification and eligibility of publications. https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-g002.jpg Figure 2. Proportion of timely medical visit for testicular torsion in an international comparison. https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-g003.jpg Figure 3. Mean symptom durations (h) and orchiectomy rate for testicular torsion in an international https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-t001.jpg Table 1. Distribution of studies included for the meta-analysis of delayed medical consultations. https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-g004.jpg Figure 4. Distribution of factors associated with delayed medical consultation in patients with test https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-g005.jpg Figure 5. Pooled risk ratios (95% CI) for factors associated with delayed medical consultation in pa https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-t002.jpg Table 2. Summary of study characteristics and quality analysis of mean duration meta-analysis. https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-g006.jpg Figure 6. Forest plot comparisons of mean symptom durations between (A) pre-pandemic and during pand https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-t003.jpg Table 3. Results of factors of mean duration meta-analysis. https://www.frontiersin.org/files/Articles/1735652/frph-08-1735652-HTML/image_m/frph-08-1735652-g007.jpg Figure 7. (A) Comparison of misdiagnosis rate in different countries and in different provinces of C https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1692333/full https://www.frontiersin.org/files/Articles/1692333/fendo-16-1692333-HTML/image_m/fendo-16-1692333-g001.jpg Figure 1. Effect of BPS and/or PFOS on the growth of brain organoids. (A) Schematic of the cerebral https://www.frontiersin.org/files/Articles/1692333/fendo-16-1692333-HTML/image_m/fendo-16-1692333-g002.jpg Figure 2. (A) Hematoxylin/eosin staining of cerebral organoids at day 40. Scale bar, 150 µm, origina https://www.frontiersin.org/files/Articles/1692333/fendo-16-1692333-HTML/image_m/fendo-16-1692333-g003.jpg Figure 3. Effect of BPS and/or PFOS on brain organoid architectures. (A) Experimental procedures and https://www.frontiersin.org/files/Articles/1692333/fendo-16-1692333-HTML/image_m/fendo-16-1692333-g004.jpg Figure 4. Effect of BPS and/or PFOS on NMDAR2B and VGluT1 expression in 40-days organoids. (A) Exper https://www.frontiersin.org/files/Articles/1692333/fendo-16-1692333-HTML/image_m/fendo-16-1692333-g005.jpg Figure 5. Effect of BPS and/or PFOS on the ETC complexes proteins. (A) Schematics of the experimenta https://www.frontiersin.org/files/Articles/1692333/fendo-16-1692333-HTML/image_m/fendo-16-1692333-g006.jpg Figure 6. Effects of BPS and/or PFOS on genomic and non-genomic cascades. Western blot images and ba https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1693753/full https://www.frontiersin.org/files/Articles/1693753/fphar-16-1693753-HTML/image_m/fphar-16-1693753-g001.jpg Figure 1. Therapeutic approaches targeting metabolic and immune alterations in MASLD. (A) Strategies https://www.frontiersin.org/files/Articles/1693753/fphar-16-1693753-HTML/image_m/fphar-16-1693753-t001.jpg Table 1. Ongoing strategies for MASLD considering immunoregulatory effects. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1702773/full https://www.frontiersin.org/files/Articles/1702773/fmed-13-1702773-HTML-r1/image_m/fmed-13-1702773-g001.jpg Figure 1. Study design. BMND; Bacopa monnieri (L.) Wettst normal decoction, BMFD; Bacopa monnieri (L https://www.frontiersin.org/files/Articles/1702773/fmed-13-1702773-HTML-r1/image_m/fmed-13-1702773-t001.jpg Table 1. Investigational products. https://www.frontiersin.org/files/Articles/1702773/fmed-13-1702773-HTML-r1/image_m/fmed-13-1702773-t002.jpg Table 2. Study procedures. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1662354/full https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-t001.jpg Table 1. Clinical characteristics of CRLM patients in the training and test sets. https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-g001.jpg Figure 1. Subject selection flowchart for this experiments. https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-g002.jpg Figure 2. Radiomics analysis and machine learning workflow for predicting the recurrence risk in CRL https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-t002.jpg Table 2. The details of different classifiers. https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-t003.jpg Table 3. The key radiomics features selected out through ROIIntra. https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-t004.jpg Table 4. The key radiomics features selected out through ROIIntra+Peri2mm. https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-t005.jpg Table 5. The key radiomics features selected out through ROIIntra+Peri4mm. https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-t006.jpg Table 6. The key radiomics features selected out through ROIIntra+Peri6mm. https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-t007.jpg Table 7. Performance metrics SVM, RF, and MLP models across different ROI radiomics signature in the https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-g003.jpg Figure 3. ROC of the predictive models constructed by different radiomics signatures in the test set https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-g004.jpg Figure 4. DCA of the predictive models constructed by different radiomics signatures in the test set https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-g005.jpg Figure 5. Calibration curve of the predictive models constructed by different radiomics signatures i https://www.frontiersin.org/files/Articles/1662354/fonc-15-1662354-HTML/image_m/fonc-15-1662354-t008.jpg Table 8. Performance metrics SVM, RF, and MLP models across different ROI radiomics signature in the https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1745043/full https://www.frontiersin.org/files/Articles/1745043/fpsyg-17-1745043-HTML-r1/image_m/fpsyg-17-1745043-t001.jpg Table 1. Perceived impact of tutoring by faculty. https://www.frontiersin.org/files/Articles/1745043/fpsyg-17-1745043-HTML-r1/image_m/fpsyg-17-1745043-t002.jpg Table 2. Multiple regression analysis: predictors of academic performance. https://www.frontiersin.org/files/Articles/1745043/fpsyg-17-1745043-HTML-r1/image_m/fpsyg-17-1745043-t003.jpg Table 3. Measurement model: standardized factor loadings for tutor perception and satisfaction. https://www.frontiersin.org/files/Articles/1745043/fpsyg-17-1745043-HTML-r1/image_m/fpsyg-17-1745043-t004.jpg Table 4. Standardized structural coefficients (perception → satisfaction model). https://www.frontiersin.org/files/Articles/1745043/fpsyg-17-1745043-HTML-r1/image_m/fpsyg-17-1745043-t005.jpg Table 5. Standardized structural coefficients (satisfaction → perceived impact model). https://www.frontiersin.org/files/Articles/1745043/fpsyg-17-1745043-HTML-r1/image_m/fpsyg-17-1745043-t006.jpg Table 6. Moderating effects on the satisfaction → perceived impact path. https://www.frontiersin.org/files/Articles/1745043/fpsyg-17-1745043-HTML-r1/image_m/fpsyg-17-1745043-t007.jpg Table 7. Relationship between tutoring use and perceived quality. https://www.frontiersin.org/files/Articles/1745043/fpsyg-17-1745043-HTML-r1/image_m/fpsyg-17-1745043-t008.jpg Table 8. Comparison of perceived quality of tutoring service by tutoring participation. https://www.frontiersin.org/files/Articles/1745043/fpsyg-17-1745043-HTML-r1/image_m/fpsyg-17-1745043-t009.jpg Table 9. Proposed components of the University Tutoring Support Program. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1727544/full https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-t001.jpg Table 1. Demographic information of questionnaire participants (N = 78). https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-g001.jpg Figure 1. Schematic diagram of the research design. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-g002.jpg Figure 2. Sample of student translation task. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-g003.jpg Figure 3. AI-powered translation feedback platform. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-t002.jpg Table 2. Students’ proficiency by academic level and course grade. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-t003.jpg Table 3. Demographic information of interview participants (n = 18). https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-t004.jpg Table 4. Alignment of research questions, data, instruments, and analyses. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-t005.jpg Table 5. Text types for translation tasks. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-g004.jpg Figure 4. The built-in prompt used to generate ChatGPT feedback. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-t006.jpg Table 6. Acceptance rate by student proficiency and genre. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-g005.jpg Figure 5. Model-predicted vs. observed acceptance probabilities. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-t007.jpg Table 7. Three-way ANOVA results. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-t008.jpg Table 8. Spearman’s rank correlation matrix. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-g006.jpg Figure 6. Correlation scatter plot. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-g007.jpg Figure 7. Diagram of factors influencing the acceptance rate of LLM-generated feedback. https://www.frontiersin.org/files/Articles/1727544/frai-09-1727544-HTML/image_m/frai-09-1727544-g008.jpg Figure 8. Core criteria for students’ judgment of LLM-generated feedback. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1686323/full https://www.frontiersin.org/files/Articles/1686323/fphar-16-1686323-HTML/image_m/fphar-16-1686323-g007.jpg Scheme 1. Schematic diagram of the synthesis of CAT@MnS and its mechanism of alleviating AKI through https://www.frontiersin.org/files/Articles/1686323/fphar-16-1686323-HTML/image_m/fphar-16-1686323-g001.jpg Figure 1. Structural characterization of CAT@MnS. (A) and (B) TEM images of CAT@MnS. (C) DLS results https://www.frontiersin.org/files/Articles/1686323/fphar-16-1686323-HTML/image_m/fphar-16-1686323-g002.jpg Figure 2. Antioxidative activities of CAT@MnS. (A–C) Influence of different pH, metal ion concentrat https://www.frontiersin.org/files/Articles/1686323/fphar-16-1686323-HTML/image_m/fphar-16-1686323-g003.jpg Figure 3. Antioxidant effects of CAT@MnS. (A) Drug toxicity gradient verification of CAT@MnS, n = 3. https://www.frontiersin.org/files/Articles/1686323/fphar-16-1686323-HTML/image_m/fphar-16-1686323-g004.jpg Figure 4. The preparation of cisplatin induced acute kidney injury mouse model and the correlation i https://www.frontiersin.org/files/Articles/1686323/fphar-16-1686323-HTML/image_m/fphar-16-1686323-g005.jpg Figure 5. Fluorescence of mouse kidney and Drug distribution change in vivo. (A) Immunofluorescence https://www.frontiersin.org/files/Articles/1686323/fphar-16-1686323-HTML/image_m/fphar-16-1686323-g006.jpg Figure 6. The preparation of glycerin induced acute kidney injury mouse model and the correlation in https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1761271/full https://www.frontiersin.org/files/Articles/1761271/fmed-13-1761271-HTML/image_m/fmed-13-1761271-g001.jpg Figure 1. Flow chart of the study. The flowchart shows the patient selection process in this retrosp https://www.frontiersin.org/files/Articles/1761271/fmed-13-1761271-HTML/image_m/fmed-13-1761271-t001.jpg Table 1. Comparison of baseline data between the two groups. https://www.frontiersin.org/files/Articles/1761271/fmed-13-1761271-HTML/image_m/fmed-13-1761271-g002.jpg Figure 2. Comparison of the clinical data between two groups at baseline and follow-up. (A) Increase https://www.frontiersin.org/files/Articles/1761271/fmed-13-1761271-HTML/image_m/fmed-13-1761271-g003.jpg Figure 3. Comparison of the 6-month remission rates between the two groups. ns, no significant diffe https://www.frontiersin.org/files/Articles/1761271/fmed-13-1761271-HTML/image_m/fmed-13-1761271-t002.jpg Table 2. Non-remission risk factors in patients with PMN. https://www.frontiersin.org/files/Articles/1761271/fmed-13-1761271-HTML/image_m/fmed-13-1761271-g004.jpg Figure 4. Comparison of RTX dosing (A) and immunosuppressive therapy costs (B) between the two group https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2025.1641898/full https://www.frontiersin.org/files/Articles/1641898/fsoc-10-1641898-HTML/image_m/fsoc-10-1641898-t001.jpg Table A1. Empirical data sources. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1666547/full https://www.frontiersin.org/files/Articles/1666547/fnut-12-1666547-HTML/image_m/fnut-12-1666547-g001.jpg Figure 1. PubMed, Embase, Web of Science, and the Cochrane Library were systematically searched for https://www.frontiersin.org/files/Articles/1666547/fnut-12-1666547-HTML/image_m/fnut-12-1666547-t001.jpg Table 1. Basic characteristics of the included literature. https://www.frontiersin.org/files/Articles/1666547/fnut-12-1666547-HTML/image_m/fnut-12-1666547-t002.jpg Table 2. Efficacy comparison of CRS intervention categories. https://www.frontiersin.org/files/Articles/1666547/fnut-12-1666547-HTML/image_m/fnut-12-1666547-t003.jpg Table 3. Risk of bias in studies. https://www.frontiersin.org/files/Articles/1666547/fnut-12-1666547-HTML/image_m/fnut-12-1666547-g002.jpg Figure 2. The results were analyzed by a content analysis. The interventions for CRS comprised four https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1674964/full https://www.frontiersin.org/files/Articles/1674964/fimmu-16-1674964-HTML/image_m/fimmu-16-1674964-g001.jpg Figure 1. Early dynamics of memory T cells during T. cruzi infection. (A) Representative dotplot of https://www.frontiersin.org/files/Articles/1674964/fimmu-16-1674964-HTML/image_m/fimmu-16-1674964-g002.jpg Figure 2. Cytotoxic activity of TMEM and TVM cells against enriched T. cruzi-infected macrophages. ( https://www.frontiersin.org/files/Articles/1674964/fimmu-16-1674964-HTML/image_m/fimmu-16-1674964-g003.jpg Figure 3. RAE expression in peritoneal macrophages and NKG2D in effector T cells remains unchanged e https://www.frontiersin.org/files/Articles/1674964/fimmu-16-1674964-HTML/image_m/fimmu-16-1674964-g004.jpg Figure 4. TVM cells produce IFNγ and activate STAT1 signaling in T. cruzi-infected macrophages. (A) https://www.frontiersin.org/files/Articles/1674964/fimmu-16-1674964-HTML/image_m/fimmu-16-1674964-g005.jpg Figure 5. IFNγ neutralization impairs parasite control but not T cell degranulation. Cytotoxic assay https://www.frontiersin.org/files/Articles/1674964/fimmu-16-1674964-HTML/image_m/fimmu-16-1674964-g006.jpg Figure 6. IFNγ produced by TVM cells and TMEM cells drives the production of reactive oxygen species https://www.frontiersin.org/files/Articles/1674964/fimmu-16-1674964-HTML/image_m/fimmu-16-1674964-g007.jpg Figure 7. IFNγ produced by TVM cells and TMEM cells promote the production of nitric oxide (NO) in T https://www.frontiersin.org/files/Articles/1674964/fimmu-16-1674964-HTML/image_m/fimmu-16-1674964-g008.jpg Figure 8. Enhanced functional profile of KIR+ and NKG2A+ human TVM cells in chronic T. cruzi infecte https://www.frontiersin.org/files/Articles/1674964/fimmu-16-1674964-HTML/image_m/fimmu-16-1674964-g009.jpg Figure 9. Functional markers in KIR+ TVM cells from healthy donors correlate with the expression of https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1698237/full https://www.frontiersin.org/files/Articles/1698237/fbioe-13-1698237-HTML/image_m/fbioe-13-1698237-g001.jpg Figure 1. Diagram of the PET artificial ligament with BP/SF coating to improve the graft-bone healin https://www.frontiersin.org/files/Articles/1698237/fbioe-13-1698237-HTML/image_m/fbioe-13-1698237-g002.jpg Figure 2. Material characterization. (A) TEM images of the morphology of BP, (B,C) SEM images of the https://www.frontiersin.org/files/Articles/1698237/fbioe-13-1698237-HTML/image_m/fbioe-13-1698237-g003.jpg Figure 3. In vitro experiments. (A) CCK-8 assay of MC3T3-E1 cells cultured for 1, 3, 5 and 14 days i https://www.frontiersin.org/files/Articles/1698237/fbioe-13-1698237-HTML/image_m/fbioe-13-1698237-g004.jpg Figure 4. Micro-CT analysis of the PET and BP/SF-PET groups at 12 weeks postoperatively. (A,B) The c https://www.frontiersin.org/files/Articles/1698237/fbioe-13-1698237-HTML/image_m/fbioe-13-1698237-g005.jpg Figure 5. Histological and immunofluorescent results of the PET and BP/SF-PET groups. (A) Hematoxyli https://www.frontiersin.org/files/Articles/1698237/fbioe-13-1698237-HTML/image_m/fbioe-13-1698237-g006.jpg Figure 6. Biomechanical results for the PET and BP/SF-PET groups at 6 and 12 weeks postoperatively. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1729835/full https://www.frontiersin.org/files/Articles/1729835/fmed-13-1729835-HTML-r1/image_m/fmed-13-1729835-gr0001.jpg Graphical Abstract. (a) The figure shows the effect of HCV on disrupting mTORC2 and AMPK-dependent c https://www.frontiersin.org/files/Articles/1729835/fmed-13-1729835-HTML-r1/image_m/fmed-13-1729835-g001.jpg Figure 1. Comparison of serum concentrations of selected parameters in the entire patient population https://www.frontiersin.org/files/Articles/1729835/fmed-13-1729835-HTML-r1/image_m/fmed-13-1729835-g002.jpg Figure 2. Comparison of serum concentrations of MCP-1 and FABP-1 in the entire patient population (f https://www.frontiersin.org/files/Articles/1729835/fmed-13-1729835-HTML-r1/image_m/fmed-13-1729835-t001.jpg Table 1. Changes in parameters concentration of separated G/P-treated and Sofosbuvir/Velpatasvir-tre https://www.frontiersin.org/files/Articles/1729835/fmed-13-1729835-HTML-r1/image_m/fmed-13-1729835-g003.jpg Figure 3. Comparison of selected serum concentrations of ANGPTL-6, FGF-19, ghrelin, MCP-1, FABP-1, t https://www.frontiersin.org/files/Articles/1729835/fmed-13-1729835-HTML-r1/image_m/fmed-13-1729835-g004.jpg Figure 4. (a) Comparison of serum concentrations of ANGPTL-6, ghrelin, FABP-1, total cholesterol, an https://www.frontiersin.org/files/Articles/1729835/fmed-13-1729835-HTML-r1/image_m/fmed-13-1729835-g005.jpg Figure 5. (a) Comparison of ANGPTL-6, ghrelin, FABP-1, total cholesterol, and HDL concentrations in https://www.frontiersin.org/files/Articles/1729835/fmed-13-1729835-HTML-r1/image_m/fmed-13-1729835-t002.jpg Table A1. Changes in concentration of analysed parameters of G/P -treated and S/V -treated patients https://www.frontiersin.org/files/Articles/1729835/fmed-13-1729835-HTML-r1/image_m/fmed-13-1729835-t003.jpg Table A2. Changes in parameters concentration of G/P-treated and Sofosbuvir/Velpatasvir-treated pati https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1717948/full https://www.frontiersin.org/files/Articles/1717948/fpsyg-17-1717948-HTML-r1/image_m/fpsyg-17-1717948-g001.jpg Figure 1. Procedure and steps for evaluating the psychometric properties of the Arabic version of th https://www.frontiersin.org/files/Articles/1717948/fpsyg-17-1717948-HTML-r1/image_m/fpsyg-17-1717948-t001.jpg Table 1. Sociodemographic and clinical characteristics of participants (N = 100). https://www.frontiersin.org/files/Articles/1717948/fpsyg-17-1717948-HTML-r1/image_m/fpsyg-17-1717948-t002.jpg Table 2. Mean scores, internal consistency, test–retest reliability, and intra-class correlation of https://www.frontiersin.org/files/Articles/1717948/fpsyg-17-1717948-HTML-r1/image_m/fpsyg-17-1717948-t003.jpg Table 3. Factor structure, item–total correlation, internal consistency, and eigenvalues of the Arab https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1632285/full https://www.frontiersin.org/files/Articles/1632285/fpubh-13-1632285-HTML-r1/image_m/fpubh-13-1632285-t001.jpg Table 1. Trauma scales, questions, and response options. https://www.frontiersin.org/files/Articles/1632285/fpubh-13-1632285-HTML-r1/image_m/fpubh-13-1632285-g001.jpg Figure 1. Process of survey development. LEC-5, Life Events Checklist for DSM-5; SLESQ, Stressful Li https://www.frontiersin.org/files/Articles/1632285/fpubh-13-1632285-HTML-r1/image_m/fpubh-13-1632285-t002.jpg Table 2. Descriptive statistics for a sample of transgender women in Birmingham, AL (N = 105). https://www.frontiersin.org/files/Articles/1632285/fpubh-13-1632285-HTML-r1/image_m/fpubh-13-1632285-t003.jpg Table 3. Trauma instrument reliability. https://www.frontiersin.org/files/Articles/1632285/fpubh-13-1632285-HTML-r1/image_m/fpubh-13-1632285-t004.jpg Table 4. Results of the trauma survey administered to TGW, stratified by self-reported racial identi https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1709141/full https://www.frontiersin.org/files/Articles/1709141/fimmu-16-1709141-HTML/image_m/fimmu-16-1709141-g001.jpg Figure 1. Identification of a TME-associated gene set driving TNBC progression. (A) WGCNA identifyin https://www.frontiersin.org/files/Articles/1709141/fimmu-16-1709141-HTML/image_m/fimmu-16-1709141-g002.jpg Figure 2. Construction and validation of the 11-gene prognostic signature. (A, B) LASSO Cox regressi https://www.frontiersin.org/files/Articles/1709141/fimmu-16-1709141-HTML/image_m/fimmu-16-1709141-g003.jpg Figure 3. The prognostic signature reflects the tumor immune microenvironment. (A) Volcano plot of D https://www.frontiersin.org/files/Articles/1709141/fimmu-16-1709141-HTML/image_m/fimmu-16-1709141-g004.jpg Figure 4. Identification of MFAP4 as the key prognostic gene. (A) Expression levels of the 11 signat https://www.frontiersin.org/files/Articles/1709141/fimmu-16-1709141-HTML/image_m/fimmu-16-1709141-g005.jpg Figure 5. MFAP4 overexpression inhibits TNBC cell migration. (A, B) Validation of MFAP4 overexpressi https://www.frontiersin.org/files/Articles/1709141/fimmu-16-1709141-HTML/image_m/fimmu-16-1709141-g006.jpg Figure 6. MFAP4 overexpression suppresses TNBC cell proliferation. (A) CCK-8 proliferation curves fo https://www.frontiersin.org/files/Articles/1709141/fimmu-16-1709141-HTML/image_m/fimmu-16-1709141-g007.jpg Figure 7. MFAP4 negatively regulates the PI3K/AKT/mTOR pathway. (A) Volcano plot of DEGs between MFA https://www.frontiersin.org/files/Articles/1709141/fimmu-16-1709141-HTML/image_m/fimmu-16-1709141-g008.jpg Figure 8. Pharmacological activation of mTOR rescues the anti-tumor effects of MFAP4. Hs-578T cells https://www.frontiersin.org/files/Articles/1709141/fimmu-16-1709141-HTML/image_m/fimmu-16-1709141-g009.jpg Figure 9. A hypothetical working model for MFAP4-mediated suppression of TNBC progression. Based on https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1682405/full https://www.frontiersin.org/files/Articles/1682405/fmicb-16-1682405-HTML/image_m/fmicb-16-1682405-g001.jpg Figure 1. Experimental design. The workflow illustrates which materials, placental tissue or superna https://www.frontiersin.org/files/Articles/1682405/fmicb-16-1682405-HTML/image_m/fmicb-16-1682405-t001.jpg Table 1. RT-qPCR primer sequences and target gene information. https://www.frontiersin.org/files/Articles/1682405/fmicb-16-1682405-HTML/image_m/fmicb-16-1682405-g002.jpg Figure 2. Viability and observation of Mycobacterium tuberculosis in placental explants. Panel 1. Co https://www.frontiersin.org/files/Articles/1682405/fmicb-16-1682405-HTML/image_m/fmicb-16-1682405-g003.jpg Figure 3. Immunofluorescence images of placental explants infected with Mycobacterium tuberculosis. https://www.frontiersin.org/files/Articles/1682405/fmicb-16-1682405-HTML/image_m/fmicb-16-1682405-g004.jpg Figure 4. Histopathological alterations induced by Mycobacterium tuberculosis in human placental exp https://www.frontiersin.org/files/Articles/1682405/fmicb-16-1682405-HTML/image_m/fmicb-16-1682405-g005.jpg Figure 5. Differential expression profiles of reactivated rNRP1 and rNRP2 phases relative to the log https://www.frontiersin.org/files/Articles/1682405/fmicb-16-1682405-HTML/image_m/fmicb-16-1682405-g006.jpg Figure 6. Expression of secreted metalloproteinases in placental explants infected with M. tuberculo https://www.frontiersin.org/files/Articles/1682405/fmicb-16-1682405-HTML/image_m/fmicb-16-1682405-g007.jpg Figure 7. Expression of proinflammatory and immunomodulatory cytokines in placental explants infecte https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2025.1712242/full https://www.frontiersin.org/files/Articles/1712242/fpain-06-1712242-HTML/image_m/fpain-06-1712242-g001.jpg Figure 1. Pathophysiological cascade of fascia-related mechanisms in myofascial pain syndrome. Propo https://www.frontiersin.org/files/Articles/1712242/fpain-06-1712242-HTML/image_m/fpain-06-1712242-g002.jpg Figure 2. Biopsychosocial model of myofascial pain syndrome. Peripheral mechanisms encompass not onl https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1754353/full https://www.frontiersin.org/files/Articles/1754353/fpubh-14-1754353-HTML/image_m/fpubh-14-1754353-g001.jpg Figure 1. Map of Brazil (A) and Salvador showing the location of the three marginalized communities https://www.frontiersin.org/files/Articles/1754353/fpubh-14-1754353-HTML/image_m/fpubh-14-1754353-t001.jpg Table 1. Baseline profile of study communities included in the interdisciplinary project (November 2 https://www.frontiersin.org/files/Articles/1754353/fpubh-14-1754353-HTML/image_m/fpubh-14-1754353-g002.jpg Figure 2. Schematic diagram of the project design. https://www.frontiersin.org/files/Articles/1754353/fpubh-14-1754353-HTML/image_m/fpubh-14-1754353-t002.jpg Table 2. Profile of community groups across the study communities included in the interdisciplinary https://www.frontiersin.org/files/Articles/1754353/fpubh-14-1754353-HTML/image_m/fpubh-14-1754353-t003.jpg Table 3. List of interventions developed in Alto do Cabrito community. https://www.frontiersin.org/files/Articles/1754353/fpubh-14-1754353-HTML/image_m/fpubh-14-1754353-t004.jpg Table 4. List of Interventions developed in Marechal Rondon community. https://www.frontiersin.org/files/Articles/1754353/fpubh-14-1754353-HTML/image_m/fpubh-14-1754353-t005.jpg Table 5. List of interventions developed in Pau da Lima community. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1704325/full https://www.frontiersin.org/files/Articles/1704325/fpsyt-16-1704325-HTML-r1/image_m/fpsyt-16-1704325-g001.jpg Figure 1. Logic model. CEO, Chief executive officer; MPOC-20, Measures of Processes of Care; QoL, Qu https://www.frontiersin.org/files/Articles/1704325/fpsyt-16-1704325-HTML-r1/image_m/fpsyt-16-1704325-t001.jpg Table 1. Extensive needs service – measures. https://www.frontiersin.org/files/Articles/1704325/fpsyt-16-1704325-HTML-r1/image_m/fpsyt-16-1704325-g002.jpg Figure 2. Total number of ENS clients. This figure represents the total number of clients at each mo https://www.frontiersin.org/files/Articles/1704325/fpsyt-16-1704325-HTML-r1/image_m/fpsyt-16-1704325-g003.jpg Figure 3. BFDS, PGH-7, RUQ & BASC-3 Completion rates (Quarterly). This figure shows the response rat https://www.frontiersin.org/files/Articles/1704325/fpsyt-16-1704325-HTML-r1/image_m/fpsyt-16-1704325-g004.jpg Figure 4. Outcome measures timeline. BFDS, Brief Family Distress Scale; PGH-7, Pediatric Global Heal https://www.frontiersin.org/files/Articles/1704325/fpsyt-16-1704325-HTML-r1/image_m/fpsyt-16-1704325-t002.jpg Full list of potential measures. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1514445/full https://www.frontiersin.org/files/Articles/1514445/fphar-16-1514445-HTML/image_m/fphar-16-1514445-g001.jpg Figure 1. Methodology for LLM-assessment of comprehensive medication management Created with biorend https://www.frontiersin.org/files/Articles/1514445/fphar-16-1514445-HTML/image_m/fphar-16-1514445-t001.jpg Table 1. Pooled rate of medication continuation per LLM. https://www.frontiersin.org/files/Articles/1514445/fphar-16-1514445-HTML/image_m/fphar-16-1514445-t002.jpg Table 2. Pooled median Likert scores expressing clinician agreement with each LLM-generated medicati https://www.frontiersin.org/files/Articles/1514445/fphar-16-1514445-HTML/image_m/fphar-16-1514445-t003.jpg Table 3. Reason for discontinuation of medications by the clinician panel. https://www.frontiersin.org/files/Articles/1514445/fphar-16-1514445-HTML/image_m/fphar-16-1514445-t004.jpg Table 4. Medication errors. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1546150/full https://www.frontiersin.org/files/Articles/1546150/fpsyt-16-1546150-HTML-r1/image_m/fpsyt-16-1546150-g001.jpg Figure 1. Efficacy evaluation process, patient interviews. https://www.frontiersin.org/files/Articles/1546150/fpsyt-16-1546150-HTML-r1/image_m/fpsyt-16-1546150-t001.jpg Table 1. Characteristics of the sample and the interviews; T1-core CFI, T2, and T3. https://www.frontiersin.org/files/Articles/1546150/fpsyt-16-1546150-HTML-r1/image_m/fpsyt-16-1546150-t002.jpg Table 2. Patients’ experience with the CFI (drawing from the DIP instrument). https://www.frontiersin.org/files/Articles/1546150/fpsyt-16-1546150-HTML-r1/image_m/fpsyt-16-1546150-t003.jpg Table 3. Thematic overview of main themes and subthemes. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1729846/full https://www.frontiersin.org/files/Articles/1729846/fmicb-17-1729846-HTML/image_m/fmicb-17-1729846-t001.jpg Table 1. Genomic characteristics and gene discovery dynamics for Corynebacterium pseudotuberculosis https://www.frontiersin.org/files/Articles/1729846/fmicb-17-1729846-HTML/image_m/fmicb-17-1729846-g001.jpg Figure 1. Pairwise genomic similarity among 788 Corynebacterium pseudotuberculosis isolates. The hea https://www.frontiersin.org/files/Articles/1729846/fmicb-17-1729846-HTML/image_m/fmicb-17-1729846-g002.jpg Figure 2. Gene presence-absence matrix of 788 Corynebacterium pseudotuberculosis genomes. The heatma https://www.frontiersin.org/files/Articles/1729846/fmicb-17-1729846-HTML/image_m/fmicb-17-1729846-g003.jpg Figure 3. Organization of the nar Operon in Biovar equi. Syntenic arrangement of genes involved in n https://www.frontiersin.org/files/Articles/1729846/fmicb-17-1729846-HTML/image_m/fmicb-17-1729846-g004.jpg Figure 4. Core genome SNP phylogenies of Corynebacterium pseudotuberculosis biovars. (A) Phylogeneti https://www.frontiersin.org/files/Articles/1729846/fmicb-17-1729846-HTML/image_m/fmicb-17-1729846-t002.jpg Table 2. Important SNPs contributing to host species adaptation identified using XGBoost. https://www.frontiersin.org/files/Articles/1729846/fmicb-17-1729846-HTML/image_m/fmicb-17-1729846-g005.jpg Figure 5. Functional enrichment and interaction network analysis of genes associated with host-speci https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1690552/full https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-g000.jpg Graphical Abstract. Epigenetic mechanisms driving tumor immune evasion. The schematic summarizes key https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-t001.jpg Table 1. Key epigenetic alterations in glioblastoma and their immunological consequences. https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-g001.jpg Figure 1. Comparative overview of epigenetic and immunological mechanisms in peripheral vs. CNS tumo https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-t002.jpg Table 2. Key epigenetic regulators in GBM, their principal molecular targets, and their immunologica https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-g002.jpg Figure 2. Tumor-intrinsic epigenetic mechanisms suppressing antigen presentation and T-Cell function https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-g003.jpg Figure 3. Epigenetic crosstalk between tumor, glial, and infiltrating immune cells in the glioblasto https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-t003.jpg Table 3. Current landscape of epigenetic therapeutics in glioblastoma (GBM). https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-g004.jpg Figure 4. Epigenetic reprogramming strategies to convert immune-cold glioblastomas into immune-hot t https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-g005.jpg Figure 5. The continuum of epigenetic immune regulation in glioblastoma. The immuno-epigenetic state https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-t004.jpg Table 4. Opportunities and risks in emerging immuno-epigenetic therapeutic strategies for glioblasto https://www.frontiersin.org/files/Articles/1690552/fimmu-16-1690552-HTML/image_m/fimmu-16-1690552-g006.jpg Figure 6. Future roadmap for precision immuno-epigenetic therapy in glioblastoma. The next generatio https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1687327/full https://www.frontiersin.org/files/Articles/1687327/fpubh-13-1687327-HTML/image_m/fpubh-13-1687327-t001.jpg Table 1. Model input parameters of transition probabilities. https://www.frontiersin.org/files/Articles/1687327/fpubh-13-1687327-HTML/image_m/fpubh-13-1687327-t002.jpg Table 2. Patient demographics of included studies. https://www.frontiersin.org/files/Articles/1687327/fpubh-13-1687327-HTML/image_m/fpubh-13-1687327-t003.jpg Table 3. Cost-effectiveness results. https://www.frontiersin.org/files/Articles/1687327/fpubh-13-1687327-HTML/image_m/fpubh-13-1687327-t004.jpg Table 4. Subgroup analysis results. https://www.frontiersin.org/files/Articles/1687327/fpubh-13-1687327-HTML/image_m/fpubh-13-1687327-g001.jpg Figure 1. Costeffectiveness acceptability curves presenting the results of the probabilistic sensiti https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1772129/full https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-g001.jpg Figure 1. Hypothesized research model. Solid arrows represent hypothesized direct effects. Dashed ar https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-t001.jpg Table 1. Exploratory factor analysis (EFA) results and item purification for each construct. https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-t002.jpg Table 2. Confirmatory factor analysis (CFA) results for the final measurement model. https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-t003.jpg Table 3. Descriptive statistics and correlations among key variables (dimension means). https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-t004.jpg Table 4. Means and standard deviations of key variables by English proficiency level (dimension mean https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-t005.jpg Table 5. Results of hierarchical regression analysis for testing direct effects (H1, H2, H3). https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-t006.jpg Table 6. Results of moderation analysis for H4, H5, and H6. https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-g002.jpg Figure 2. Simple slopes of PEOU on PU at high (+1 SD) and low (−1 SD) English proficiency. Near-para https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-g003.jpg Figure 3. Simple slopes of PU on BI moderated by EP. Simple-slopes plot shows near-parallel lines at https://www.frontiersin.org/files/Articles/1772129/fpsyg-17-1772129-HTML/image_m/fpsyg-17-1772129-g004.jpg Figure 4. Simple slopes of PU on BI at high (+1 SD) and low (−1 SD) English proficiency. Near-parall https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1569179/full https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g001.jpg Figure 1. (A) The ASR of incidence in 1990, 2005, 2021. (B) The ASR of prevalence in 1990, 2005, 202 https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g002.jpg Figure 2. (A) The trend in ASR of incidence (EAPC) from 1990 to 2021; (B) The trend in ASR of preval https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g003.jpg Figure 3. (A) The global age accumulation map of case number of IHD from 1990 to 2021. (B) The globa https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g004.jpg Figure 4. Changes in the incidence (A), prevalence (B), DALYs (C), deaths (D) of IHD according to ag https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g005.jpg Figure 5. (A) Frontier analysis based on SDI and Ischemic heart disease incidence rate from 1990 to https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g006.jpg Figure 6. Analysis of risk factors of DALYs and deaths for IHD by global and SDI quintile regions in https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g007.jpg Figure 7. (A) The joinpoint regression analysis on the case number of incidence; (B) the joinpoint r https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g008.jpg Figure 8. The effects of age, period, and birth cohort on the relative risk of IHD incidence (A), pr https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-t001.jpg Table 1. RRs of IHD incidence, prevalence, and deaths for both sexes due to age, period, and birth c https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g009.jpg Figure 9. Global trends of IHD in ASR of incidence (A), prevalence (B), deaths (C), and DALYs (D) ra https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g010.jpg Figure 10. Global trends of IHD in case number and ASR of incidence (A), prevalence (B), deaths (C), https://www.frontiersin.org/files/Articles/1569179/fpubh-13-1569179-HTML/image_m/fpubh-13-1569179-g011.jpg Figure 11. Global trends of IHD in case number and ASR of incidence (A), prevalence (B), deaths (C), https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1734619/full https://www.frontiersin.org/files/Articles/1734619/fmed-13-1734619-HTML/image_m/fmed-13-1734619-t001.jpg Table 1. Primers used in this study (Sangon Biotech). https://www.frontiersin.org/files/Articles/1734619/fmed-13-1734619-HTML/image_m/fmed-13-1734619-g001.jpg Figure 1. Single-cell landscape of cellular composition in bleomycin-induced pulmonary fibrosis. (a) https://www.frontiersin.org/files/Articles/1734619/fmed-13-1734619-HTML/image_m/fmed-13-1734619-g002.jpg Figure 2. Differential gene expression and functional enrichment of lung epithelial cells in bleomyc https://www.frontiersin.org/files/Articles/1734619/fmed-13-1734619-HTML/image_m/fmed-13-1734619-g003.jpg Figure 3. ESRP1 and Rap1a were both upregulated in the lung tissues of BLM-induced pulmonary fibrosi https://www.frontiersin.org/files/Articles/1734619/fmed-13-1734619-HTML/image_m/fmed-13-1734619-g004.jpg Figure 4. ESRP1 and Rap1a were both upregulated in the lung tissues of IPF patients. (a) IPF was eva https://www.frontiersin.org/files/Articles/1734619/fmed-13-1734619-HTML/image_m/fmed-13-1734619-g005.jpg Figure 5. ESRP1 regulates Rap1a to promote EMT in MLE-12 cells. (a) The protein expression levels of https://www.frontiersin.org/files/Articles/1734619/fmed-13-1734619-HTML/image_m/fmed-13-1734619-g006.jpg Figure 6. ESRP1 promote EMT via Epac-Rap1a in MLE-12 cells. (a) Lentiviral esrp1 knockdown or overex https://www.frontiersin.org/files/Articles/1734619/fmed-13-1734619-HTML/image_m/fmed-13-1734619-t002.jpg Table 2. siRNA sequence used in this study (Sangon Biotech). https://www.frontiersin.org/files/Articles/1734619/fmed-13-1734619-HTML/image_m/fmed-13-1734619-t003.jpg Table 3. FGFR2-IIIb and FGFR2-IIIc sequence used in this study (Sangon Biotech). https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2026.1717794/full https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-g001.jpg Figure 1. The research model of media affordance and its impact on audience acceptance. https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-t001.jpg Table 1. Frequency analysis of sample background information. https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-t002.jpg Table 2. Reliability and convergent validity analysis. https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-t003.jpg Table 3. Discriminant validity analysis. https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-t004.jpg Table 4. Correlation analysis of variables. https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-t005.jpg Table 5. Results of original multiple linear regression analysis on audience acceptance. https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-t006.jpg Table 6. ANOVA results of the optimized regression model. https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-t007.jpg Table 7. Model summary of the regression analysis. https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-t008.jpg Table 8. Results of optimized multiple linear regression analysis on audience acceptance. https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-g002.jpg Figure 2. Mean values of evaluation dimensions across different age groups, illustrating the generat https://www.frontiersin.org/files/Articles/1717794/fcomp-08-1717794-HTML/image_m/fcomp-08-1717794-g003.jpg Figure 3. Mean values of evaluation dimensions across different occupational groups, highlighting th https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1612945/full https://www.frontiersin.org/files/Articles/1612945/fmed-12-1612945-HTML-r2/image_m/fmed-12-1612945-g001.jpg Figure 1. The flow diagram of the trial. https://www.frontiersin.org/files/Articles/1612945/fmed-12-1612945-HTML-r2/image_m/fmed-12-1612945-t001.jpg Table 1. Drug composition. https://www.frontiersin.org/files/Articles/1612945/fmed-12-1612945-HTML-r2/image_m/fmed-12-1612945-t002.jpg Table 2. Community-acquired pneumonia symptom severity assessment scale. https://www.frontiersin.org/files/Articles/1612945/fmed-12-1612945-HTML-r2/image_m/fmed-12-1612945-t003.jpg Table 3. TCM syndrome points. https://www.frontiersin.org/files/Articles/1612945/fmed-12-1612945-HTML-r2/image_m/fmed-12-1612945-t004.jpg Table 4. Time schedule of enrolment, intervention and outcome measures of the trial. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1794442/full https://www.frontiersin.org/files/Articles/1794442/fpubh-14-1794442-HTML/image_m/fpubh-14-1794442-t001.jpg Table 1. Injury incidence rate by gender, age, residence, and education status among children in Hen https://www.frontiersin.org/files/Articles/1794442/fpubh-14-1794442-HTML/image_m/fpubh-14-1794442-t002.jpg Table 2. Univariate analysis of factors associated with injury outcome among children. https://www.frontiersin.org/files/Articles/1794442/fpubh-14-1794442-HTML/image_m/fpubh-14-1794442-t003.jpg Table 3. Proportion and event rate of different injury causes for childhood by gender and age groups https://www.frontiersin.org/files/Articles/1794442/fpubh-14-1794442-HTML/image_m/fpubh-14-1794442-t004.jpg Table 4. Circumstances and clinical characteristics of childhood injuries by gender. https://www.frontiersin.org/files/Articles/1794442/fpubh-14-1794442-HTML/image_m/fpubh-14-1794442-t005.jpg Table 5. Supervision status at the time of childhood injury occurrence. https://www.frontiersin.org/files/Articles/1794442/fpubh-14-1794442-HTML/image_m/fpubh-14-1794442-g001.jpg Figure 1. Average hospital stay and days off due to injury by demographic group (a) and injury type https://www.frontiersin.org/files/Articles/1794442/fpubh-14-1794442-HTML/image_m/fpubh-14-1794442-t006.jpg Table 6. Economic loss due to injuries among children aged 0–17 years, by gender, age group, and maj https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1763078/full https://www.frontiersin.org/files/Articles/1763078/fpls-17-1763078-HTML/image_m/fpls-17-1763078-g001.jpg Figure 1. Overview of the tea plant pan-genome based on homology (A) Number and distribution of orth https://www.frontiersin.org/files/Articles/1763078/fpls-17-1763078-HTML/image_m/fpls-17-1763078-g002.jpg Figure 2. Phylogenetic analysis, PAV, and CNV of CsUGT genes (A) Phylogenetic tree of UGT genes from https://www.frontiersin.org/files/Articles/1763078/fpls-17-1763078-HTML/image_m/fpls-17-1763078-g003.jpg Figure 3. Synteny and gene duplication types of UGT genes in 22 tea plant varieties (A) Synteny of U https://www.frontiersin.org/files/Articles/1763078/fpls-17-1763078-HTML/image_m/fpls-17-1763078-g004.jpg Figure 4. Distribution of Ka/Ks values of CsUGT genes in 22 tea plant varieties. https://www.frontiersin.org/files/Articles/1763078/fpls-17-1763078-HTML/image_m/fpls-17-1763078-g005.jpg Figure 5. shows structural variations (SVs) affecting gene expression, structure, motifs, and promot https://www.frontiersin.org/files/Articles/1763078/fpls-17-1763078-HTML/image_m/fpls-17-1763078-g006.jpg Figure 6. Widespread expression of typical and atypical CsUGT genes (A) Heatmap showing the classifi https://www.frontiersin.org/files/Articles/1763078/fpls-17-1763078-HTML/image_m/fpls-17-1763078-g007.jpg Figure 7. Expression profiles of the UGT gene family in different tissues and under stress condition https://www.frontiersin.org/journals/tuberculosis/articles/10.3389/ftubr.2026.1713844/full https://www.frontiersin.org/files/Articles/1713844/ftubr-04-1713844-HTML-r1/image_m/ftubr-04-1713844-g001.jpg Figure 1. Study arm description. https://www.frontiersin.org/files/Articles/1713844/ftubr-04-1713844-HTML-r1/image_m/ftubr-04-1713844-g002.jpg Figure 2. Components of an integrated self-efficacy driven intervention for individuals with TB and https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1695776/full https://www.frontiersin.org/files/Articles/1695776/fonc-16-1695776-HTML/image_m/fonc-16-1695776-t001.jpg Table 1. Baseline demographic, cardiometabolic, tumor, and treatment characteristics by endocrine re https://www.frontiersin.org/files/Articles/1695776/fonc-16-1695776-HTML/image_m/fonc-16-1695776-t002.jpg Table 2. Longitudinal lipid profiles over 24 months according to endocrine therapy regimen. https://www.frontiersin.org/files/Articles/1695776/fonc-16-1695776-HTML/image_m/fonc-16-1695776-g001.jpg Figure 1. Model-adjusted lipid trajectories over 24 months by endocrine regimen. (A) Total cholester https://www.frontiersin.org/files/Articles/1695776/fonc-16-1695776-HTML/image_m/fonc-16-1695776-t003.jpg Table 3. Incidence of metabolic and vascular events within 24 months according to endocrine therapy https://www.frontiersin.org/files/Articles/1695776/fonc-16-1695776-HTML/image_m/fonc-16-1695776-g002.jpg Figure 2. Kaplan–Meier curves for clinical events over 24 months by endocrine regimen. (A) New-onset https://www.frontiersin.org/files/Articles/1695776/fonc-16-1695776-HTML/image_m/fonc-16-1695776-t004.jpg Table 4. Baseline lipid abnormalities according to endocrine therapy regimen. https://www.frontiersin.org/files/Articles/1695776/fonc-16-1695776-HTML/image_m/fonc-16-1695776-g003.jpg Figure 3. Model-predicted lipid trajectories over 24 months stratified by baseline lipid status. (A) https://www.frontiersin.org/files/Articles/1695776/fonc-16-1695776-HTML/image_m/fonc-16-1695776-t005.jpg Table 5. Patterns of concomitant Sanhuang Decoction (SHD) use during endocrine therapy. https://www.frontiersin.org/files/Articles/1695776/fonc-16-1695776-HTML/image_m/fonc-16-1695776-g004.jpg Figure 4. Model-predicted lipid trajectories over 24 months by concomitant SHD use and endocrine reg https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1682717/full https://www.frontiersin.org/files/Articles/1682717/fnut-13-1682717-HTML-r1/image_m/fnut-13-1682717-t001.jpg Table 1. Comparison of the demographics of patients between the different prognoses groups. https://www.frontiersin.org/files/Articles/1682717/fnut-13-1682717-HTML-r1/image_m/fnut-13-1682717-t002.jpg Table 2. Comparison of the prognosis of patients between the case and control groups. https://www.frontiersin.org/files/Articles/1682717/fnut-13-1682717-HTML-r1/image_m/fnut-13-1682717-g001.jpg Figure 1. Graph of changes in serum phosphorus of patients before and after EN in the two groups. https://www.frontiersin.org/files/Articles/1682717/fnut-13-1682717-HTML-r1/image_m/fnut-13-1682717-g002.jpg Figure 2. Forest plot of prognostic risk factors in patients with severe stroke. https://www.frontiersin.org/files/Articles/1682717/fnut-13-1682717-HTML-r1/image_m/fnut-13-1682717-g003.jpg Figure 3. ROC curve of RFS for predicting prognosis in patients with severe stroke. https://www.frontiersin.org/files/Articles/1682717/fnut-13-1682717-HTML-r1/image_m/fnut-13-1682717-g004.jpg Figure 4. Survival curves for patients with severe stroke. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1620533/full https://www.frontiersin.org/files/Articles/1620533/fphar-16-1620533-HTML-r2/image_m/fphar-16-1620533-g001.jpg Figure 1. BHD mitigates IS-induced injury through multiple pathways. These pathways include suppress https://www.frontiersin.org/files/Articles/1620533/fphar-16-1620533-HTML-r2/image_m/fphar-16-1620533-g002.jpg Figure 2. Molecular mechanisms of BHD in suppressing neuroinflammation. BHD alleviates neuroinflamma https://www.frontiersin.org/files/Articles/1620533/fphar-16-1620533-HTML-r2/image_m/fphar-16-1620533-g003.jpg Figure 3. Molecular mechanisms of BHD in restoring mitochondrial function. BHD regulates mitochondri https://www.frontiersin.org/files/Articles/1620533/fphar-16-1620533-HTML-r2/image_m/fphar-16-1620533-g004.jpg Figure 4. Molecular mechanisms of BHD in suppressing oxidative stress. BHD restores mitochondrial me https://www.frontiersin.org/files/Articles/1620533/fphar-16-1620533-HTML-r2/image_m/fphar-16-1620533-g005.jpg Figure 5. Molecular mechanisms of BHD in restoring mitochondrial function. BHD modulates the PKCε/Na https://www.frontiersin.org/files/Articles/1620533/fphar-16-1620533-HTML-r2/image_m/fphar-16-1620533-g006.jpg Figure 6. Molecular mechanisms of BHD in inhibiting apoptosis. BHD potentially inhibits Bad via the https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1765074/full https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-g001.jpg Figure 1. Flowchart of the publications selection. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-g002.jpg Figure 2. Trends in annual publication outputs on MD in metabolic syndrome from 2015 to 2025. (A) Tr https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-t001.jpg Table 1. Most relevant countries by corresponding authors of MD in metabolic syndrome. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-g003.jpg Figure 3. Map of countries/regions and institutions involved in MD in metabolic syndrome research fr https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-t002.jpg Table 2. Top 10 most relevant affiliations of MD in metabolic syndrome. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-g004.jpg Figure 4. Journals with the highest publication volume and citation counts. (A) Journals with the hi https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-t003.jpg Table 3. Top 10 journals with the most published articles. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-g005.jpg Figure 5. Co-cited journals related to MD in metabolic syndrome from 2015 to 2025. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-t004.jpg Table 4. Top 10 journals with the most cited journals. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-t005.jpg Table 5. Top 25 cited references related to MD in metabolic syndrome. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-g006.jpg Figure 6. Top 25 references with the strongest citation bursts in MD in metabolic syndrome. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-t006.jpg Table 6. Top 10 keywords related to MD in metabolic syndrome. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-g007.jpg Figure 7. Keyword co-occurrence map of publications on MD in metabolic syndrome. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-g008.jpg Figure 8. Trend topics in MD in metabolic syndrome. https://www.frontiersin.org/files/Articles/1765074/fnut-13-1765074-HTML/image_m/fnut-13-1765074-t007.jpg Table 7. Core findings of similar bibliometric studies. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1777008/full https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-g001.jpg Figure 1. Flowchart of this study. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t001.jpg Table 1. Cronbach’s alpha reliability statistics. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t002.jpg Table 2. Kaiser–Meyer–Olkin measure and Bartlett’s test of sphericity. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t003.jpg Table 3. Sociodemographic characteristics of participants. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-g002.jpg Figure 2. Smoking status of participants. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-g003.jpg Figure 3. Smoking duration among participants. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-g004.jpg Figure 4. Alcohol consumption status of participants. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t004.jpg Table 4. Types of alcoholic beverages consumed among drinkers. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t005.jpg Table 5. Cross-tabulation of drinking frequency and main type of alcoholic beverage consumed. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t006.jpg Table 6. Dietary patterns of participants. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t007.jpg Table 7. Physical activity patterns of participants. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t008.jpg Table 8. Scores of proactive health literacy. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t009.jpg Table 9. Spearman’s rank correlations between sociodemographic characteristics, lifestyle factors, a https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t010.jpg Table 10. Multiple linear regression analysis results for the health knowledge score. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t011.jpg Table 11. Multiple linear regression analysis results for the health belief and attitude score. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t012.jpg Table 12. Multiple linear regression analysis results for the health behavior score. https://www.frontiersin.org/files/Articles/1777008/fpubh-14-1777008-HTML/image_m/fpubh-14-1777008-t013.jpg Table 13. Multiple linear regression analysis results for the overall proactive health literacy scor https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1526110/full https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-g007.jpg Graphical Abstract. QRHXD may reduce the disease activity of RA, attenuate the inflammatory response https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-t001.jpg Table 1. Specific ingredients of QRHXD. https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-t002.jpg Table 2. Results of LC-MS/MS analysis of QRHXD. https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-g001.jpg Figure 1. Total ion chromatogram of QRHXD in positive ion mode (A) and negative ion mode (B) of LC-M https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-t003.jpg Table 3. Basic information characteristics of subjects. https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-t004.jpg Table 4. Comparison of clinical indicators before and after treatment with QRHXD. https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-g002.jpg Figure 2. Proteomic analysis of RA patients before treatment with QRHXD (LQ Group), RA patients afte https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-g003.jpg Figure 3. Metabolomics analysis of RA patients before treatment with QRHXD (LQ Group), RA patients a https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-g004.jpg Figure 4. Integrated analysis of differentially expressed proteins (DEPs) and differential metabolit https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-t005.jpg Table 5. Integrated analysis of differentially expressed proteins and differential metabolites. https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-g005.jpg Figure 5. Effect of QRHXD on arthritis, toe swelling, synovial proliferation and inflammatory cytoki https://www.frontiersin.org/files/Articles/1526110/fimmu-16-1526110-HTML/image_m/fimmu-16-1526110-g006.jpg Figure 6. Assessment of bone destruction in mice and WB validation of target proteins. (A) Micro-CT https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1714639/full https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-i001.jpg Graphical Abstract. Network pharmacological snalysis, mendelian randomization analysis, and animal e https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-t001.jpg Table 1. Specific ingredients of WXG. https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-t002.jpg Table 2. Data sources in mendelian randomization analyse. https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-t003.jpg Table 3. qRCR primer sequence. https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-g001.jpg Figure 1. Analysis of the in vitro material basis and blood-absorbed components of WXG. (A) Total io https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-g002.jpg Figure 2. Network pharmacology analysis and enrichment analysis. (A) Potential targets for WXG in th https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-g003.jpg Figure 3. Molecular docking diagram. (A) Docking energy heatmap of top 6 components with top 6 targe https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-g004.jpg Figure 4. The results of Mendelian randomization analyse. (A–C) Co-localization site maps for the ex https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-g005.jpg Figure 5. WXG attenuated cardiac dysfunction and structural remodeling in mice with MI-HF. (A) Echoc https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-g006.jpg Figure 6. WXG reduces cardiac damage and apoptosis, and ameliorated inflammation in mice with MI-HF. https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-g007.jpg Figure 7. Western blotting and quantitative real-time polymerase chain reaction validation of main t https://www.frontiersin.org/files/Articles/1714639/fcvm-12-1714639-HTML/image_m/fcvm-12-1714639-g008.jpg Figure 8. Immunofluorescence and western blotting of apoptosis-related proteins. (A) immunofluoresce https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1720842/full https://www.frontiersin.org/files/Articles/1720842/fgene-16-1720842-HTML/image_m/fgene-16-1720842-g001.jpg Figure 1. The mechanism of m6A readers, writers, and erasers. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1724597/full https://www.frontiersin.org/files/Articles/1724597/fcell-14-1724597-HTML-r1/image_m/fcell-14-1724597-t001.jpg Table 1. Characteristics of MSCs from different sources and their potential in endometrial repair. https://www.frontiersin.org/files/Articles/1724597/fcell-14-1724597-HTML-r1/image_m/fcell-14-1724597-g001.jpg Figure 1. Schematic diagram of the multifaceted mechanisms by which MSCs improve endometrial recepti https://www.frontiersin.org/files/Articles/1724597/fcell-14-1724597-HTML-r1/image_m/fcell-14-1724597-t002.jpg Table 2. Overview of representative clinical studies on MSC therapy for endometrial receptivity-rela https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1766157/full https://www.frontiersin.org/files/Articles/1766157/fphar-17-1766157-HTML-r1/image_m/fphar-17-1766157-g001.jpg Figure 1. Flow plot of the literature selection process. https://www.frontiersin.org/files/Articles/1766157/fphar-17-1766157-HTML-r1/image_m/fphar-17-1766157-t001.jpg Table 1. Study and patient characteristics https://www.frontiersin.org/files/Articles/1766157/fphar-17-1766157-HTML-r1/image_m/fphar-17-1766157-g002.jpg Figure 2. Forest plots summarizing the efficacy of ICIs plus CCRT versus CCRT plus placebo in locall https://www.frontiersin.org/files/Articles/1766157/fphar-17-1766157-HTML-r1/image_m/fphar-17-1766157-g003.jpg Figure 3. Forest plots summarizing AEs associated with ICIs in combination with CCRT for locally adv https://www.frontiersin.org/files/Articles/1766157/fphar-17-1766157-HTML-r1/image_m/fphar-17-1766157-g004.jpg Figure 4. Forest plot of treatment discontinuation due to AEs in patients receiving “ICIs plus CCRT” https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1715946/full https://www.frontiersin.org/files/Articles/1715946/fcvm-13-1715946-HTML/image_m/fcvm-13-1715946-t001.jpg Table 1. Primers for qRT-PCR. https://www.frontiersin.org/files/Articles/1715946/fcvm-13-1715946-HTML/image_m/fcvm-13-1715946-g001.jpg Figure 1. Identification and determination of DE-CM-FRGs. (A) Volcano plot of DEGs between AS and co https://www.frontiersin.org/files/Articles/1715946/fcvm-13-1715946-HTML/image_m/fcvm-13-1715946-g002.jpg Figure 2. Enrichment analysis of DE-CM-FRGs. (A,B): GO (A) and KEGG (B) enrichment analyses of DE-CM https://www.frontiersin.org/files/Articles/1715946/fcvm-13-1715946-HTML/image_m/fcvm-13-1715946-g003.jpg Figure 3. AS subtype identification based on DE-CM-FRGs and immune microenvironment characteristics. https://www.frontiersin.org/files/Articles/1715946/fcvm-13-1715946-HTML/image_m/fcvm-13-1715946-g004.jpg Figure 4. Machine learning-based screening of candidate hub genes and diagnostic value assessment. ( https://www.frontiersin.org/files/Articles/1715946/fcvm-13-1715946-HTML/image_m/fcvm-13-1715946-g005.jpg Figure 5. Validation of key diagnostic genes. (A,B) ROC curves of the key diagnostic genes in the tr https://www.frontiersin.org/files/Articles/1715946/fcvm-13-1715946-HTML/image_m/fcvm-13-1715946-g006.jpg Figure 6. Gene-transcription factor (TF) regulatory network and protein-chemical interaction network https://www.frontiersin.org/files/Articles/1715946/fcvm-13-1715946-HTML/image_m/fcvm-13-1715946-g007.jpg Figure 7. HMOX1 promotes ox-LDL-induced dysfunction of foam macrophages by regulating ferroptosis. ( https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1748784/full https://www.frontiersin.org/files/Articles/1748784/fbioe-14-1748784-HTML/image_m/fbioe-14-1748784-g001.jpg Figure 1. Structural, mechanical, and functional characterization of the PBM. PVA hydrogel. (a) Repr https://www.frontiersin.org/files/Articles/1748784/fbioe-14-1748784-HTML/image_m/fbioe-14-1748784-g002.jpg Figure 2. Adhesion, hemocompatibility, and injectability of PBM. PVA hydrogels. (a) Adhesion perform https://www.frontiersin.org/files/Articles/1748784/fbioe-14-1748784-HTML/image_m/fbioe-14-1748784-g003.jpg Figure 3. In vitro immunomodulatory and endothelial-related responses to PBM. PVA hydrogels. (a–c) I https://www.frontiersin.org/files/Articles/1748784/fbioe-14-1748784-HTML/image_m/fbioe-14-1748784-g004.jpg Figure 4. In vivo wound healing and scar-related outcomes in diabetic mice. (a) Representative photo https://www.frontiersin.org/files/Articles/1748784/fbioe-14-1748784-HTML/image_m/fbioe-14-1748784-g005.jpg Figure 5. In vitro cytocompatibility and migration behavior of PBM. PVA hydrogels. (a–b) Live/dead s https://www.frontiersin.org/files/Articles/1748784/fbioe-14-1748784-HTML/image_m/fbioe-14-1748784-g006.jpg Figure 6. Histological and immunofluorescence analysis of wound tissues. (a–e) Immunostaining of IL- https://www.frontiersin.org/files/Articles/1748784/fbioe-14-1748784-HTML/image_m/fbioe-14-1748784-g007.jpg Scheme 1. Schematic illustration of the wound-healing mechanism mediated by PBM. PVA hydrogel. An in https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1733074/full https://www.frontiersin.org/files/Articles/1733074/fpubh-14-1733074-HTML/image_m/fpubh-14-1733074-g001.jpg Figure 1. Study flow diagram. https://www.frontiersin.org/files/Articles/1733074/fpubh-14-1733074-HTML/image_m/fpubh-14-1733074-t001.jpg Table 1. Socio-demographic characteristics of sample. https://www.frontiersin.org/files/Articles/1733074/fpubh-14-1733074-HTML/image_m/fpubh-14-1733074-t002.jpg Table 2. The effectiveness of primary outcome. https://www.frontiersin.org/files/Articles/1733074/fpubh-14-1733074-HTML/image_m/fpubh-14-1733074-t003.jpg Table 3. The effectiveness of children’s secondary outcome. https://www.frontiersin.org/files/Articles/1733074/fpubh-14-1733074-HTML/image_m/fpubh-14-1733074-g002.jpg Figure 2. Comparison of baseline and follow-up between children in the intervention and control grou https://www.frontiersin.org/files/Articles/1733074/fpubh-14-1733074-HTML/image_m/fpubh-14-1733074-t004.jpg Table 4. The effectiveness of parents’ secondary outcome. https://www.frontiersin.org/files/Articles/1733074/fpubh-14-1733074-HTML/image_m/fpubh-14-1733074-g003.jpg Figure 3. Comparison of baseline and follow-up between parents in the intervention and control group https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1698512/full https://www.frontiersin.org/files/Articles/1698512/fped-13-1698512-HTML/image_m/fped-13-1698512-g001.jpg Figure 1. Gating strategy of mesenchymal cells. https://www.frontiersin.org/files/Articles/1698512/fped-13-1698512-HTML/image_m/fped-13-1698512-g002.jpg Figure 2. Gating strategy of hemopoietic progenitor cells. https://www.frontiersin.org/files/Articles/1698512/fped-13-1698512-HTML/image_m/fped-13-1698512-t001.jpg Table 1. Perinatal characteristics of the neonatal cohort, and between neonates with DCC and ICC. https://www.frontiersin.org/files/Articles/1698512/fped-13-1698512-HTML/image_m/fped-13-1698512-t002.jpg Table 2. Outcomes of the neonatal cohort, and between neonates with DCC and ICC. https://www.frontiersin.org/files/Articles/1698512/fped-13-1698512-HTML/image_m/fped-13-1698512-t003.jpg Table 3. Hematological parameters between neonates with DCC and ICC on the first, 10th and the 30th https://www.frontiersin.org/files/Articles/1698512/fped-13-1698512-HTML/image_m/fped-13-1698512-t004.jpg Table 4. Hematological parameters between neonates with and without LOS and BPD on the first, 10th a https://www.frontiersin.org/files/Articles/1698512/fped-13-1698512-HTML/image_m/fped-13-1698512-t005.jpg Table 5. Linear regression analysis of the association between DCC and MSCs, HPCs, VSELs, early and https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1768117/full https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g001.jpg Figure 1. Flowchart of literature retrieval and data processing. https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g002.jpg Figure 2. Annual publication volume and cumulative publication volume trend chart. The abscissa is t https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-t001.jpg Table 1. Top 10 productive countries regarding the research of phage regulation of gut microbiota. https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g003.jpg Figure 3. (A) International research collaboration map. The color depth of countries corresponds to https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-t002.jpg Table 2. Top 10 related institutions regarding the research of phage regulation of gut microbiota. https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g004.jpg Figure 4. Evolution of the institutional co-citation collaboration network. Nodes represent research https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-t003.jpg Table 3. Top 10 authors related to phage regulation of gut microbiota. https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g005.jpg Figure 5. (A) Evolution of author production over time. The abscissa is the year, and the ordinate i https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-t004.jpg Table 4. Top 10 productive journals related to phage regulation of gut microbiota. https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g006.jpg Figure 6. (A) Core sources identified by Bradford’s law. The abscissa is the source log rank, and th https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g007.jpg Figure 7. Top 25 keywords with the strongest citation bursts. The abscissa is the year (2005–2024), https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g008.jpg Figure 8. (A) Keyword co-occurrence network map. Nodes represent keywords, node size corresponds to https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-t005.jpg Table 5. Top 15 co-cited references related to phage regulation of gut microbiota. https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g009.jpg Figure 9. Top 25 references with the strongest citation bursts. The abscissa is the year (2005–2024) https://www.frontiersin.org/files/Articles/1768117/fmicb-17-1768117-HTML/image_m/fmicb-17-1768117-g010.jpg Figure 10. Co-cited references network map. Nodes represent individual high-impact references, node https://www.frontiersin.org/journals/language-sciences/articles/10.3389/flang.2025.1637387/full https://www.frontiersin.org/files/Articles/1637387/flang-04-1637387-HTML/image_m/flang-04-1637387-g001.jpg Figure 1. Hypotheses in the current study. https://www.frontiersin.org/files/Articles/1637387/flang-04-1637387-HTML/image_m/flang-04-1637387-g002.jpg Figure 2. Processing fluency by language type and presentation (Study 1). The error bars show standa https://www.frontiersin.org/files/Articles/1637387/flang-04-1637387-HTML/image_m/flang-04-1637387-g003.jpg Figure 3. Boxplots of processing fluency (Study 1). https://www.frontiersin.org/files/Articles/1637387/flang-04-1637387-HTML/image_m/flang-04-1637387-g004.jpg Figure 4. Processing fluency by language type and presentation (Study 2). The error bars show standa https://www.frontiersin.org/files/Articles/1637387/flang-04-1637387-HTML/image_m/flang-04-1637387-g005.jpg Figure 5. Product attitude by language type and presentation (Study 3). The error bars show standard https://www.frontiersin.org/files/Articles/1637387/flang-04-1637387-HTML/image_m/flang-04-1637387-g006.jpg Figure 6. Boxplots of attitudes by language (Study 3). https://www.frontiersin.org/files/Articles/1637387/flang-04-1637387-HTML/image_m/flang-04-1637387-g007.jpg Figure 7. Mediation analysis (Study 3). **p < 0.01, *p < 0.05, †p < 0.10. The congruency of language https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1751005/full https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-g001.jpg Figure 1. Screening of strain Y31 for phosphate-solubilizing activity. (A) Formation of a clear phos https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-g002.jpg Figure 2. Growth-promoting and antifungal activities of strain Y31. (A) Protease production; (B) amy https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-g003.jpg Figure 3. Identification of strain Y31. (A) Phylogenetic analysis of strain Y31 based on 16S rDNA se https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-g004.jpg Figure 4. Effects of different concentrations of strain Y31 on the growth of potted cucumber plants. https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-g005.jpg Figure 5. Effects of strain Y31 on cucumber growth and productivity in a greenhouse experiment. (A) https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-t001.jpg Table 1. Soil properties of CK and Y31 treatment. https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-g006.jpg Figure 6. Microbial community characteristics and beta diversity index in rhizosphere soils under CK https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-t002.jpg Table 2. Microbial alpha diversity index in the soils of CK and Y31 treatments. https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-g007.jpg Figure 7. Taxonomic composition of rhizosphere soil microorganisms at the phylum and genus levels un https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-t003.jpg Table 3. Relative abundance of differential species at the phylum and genus levels. https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-t004.jpg Table 4. General features of strain Y31 genome. https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-g008.jpg Figure 8. Genomic features of strain Y31. The circular genome map shows concentric layers from cente https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-t005.jpg Table 5. Genes related to phosphate metabolism. https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-g009.jpg Figure 9. Predictive analysis of carbohydrate-active enzymes within the Y31 genome was conducted thr https://www.frontiersin.org/files/Articles/1751005/fmicb-16-1751005-HTML/image_m/fmicb-16-1751005-t006.jpg Table 6. The secondary metabolism gene clusters in the B. subtilis Y31 genome identified using AntiS https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1628904/full https://www.frontiersin.org/files/Articles/1628904/fpubh-13-1628904-HTML/image_m/fpubh-13-1628904-g001.jpg Figure 1. Flowchart of the study selection process. From 3240 studies identified, 1159 remained afte https://www.frontiersin.org/files/Articles/1628904/fpubh-13-1628904-HTML/image_m/fpubh-13-1628904-t001.jpg Table 1. Characteristics and findings of the included studies. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1622234/full https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-t001.jpg Table 1. Structure and key components of the survey on meat consumption and sustainable diets in the https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-t002.jpg Table 2. Motivations for reducing red-meat consumption among participants (N = 1,371). https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-g001.jpg Figure 1. Environmental awareness, perceptions, and experience with plant-based meals among particip https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-t003.jpg Table 3. Sociodemographic characteristics and nutritional status of the participants (n = 1,371). https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-t004.jpg Table 4. Consumption pattern and frequency of red meat. https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-t005.jpg Table 5. Intake of meat alternatives and frequency of plant-based meals. https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-g002.jpg Figure 2. Distribution of willingness to reduce meat consumption on a 5-point Likert scale (1 = not https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-t006.jpg Table 6. Perceived barriers and attitudes toward reducing meat consumption* (Likert-scale items, N = https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-t007.jpg Table 7. Awareness and attitudes toward sustainable diets and meat consumption about health and envi https://www.frontiersin.org/files/Articles/1622234/fsufs-10-1622234-HTML/image_m/fsufs-10-1622234-t008.jpg Table 8. Bivariate analysis of socio-demographic and health factors in relation to willingness to re https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1688720/full https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g001.jpg Figure 1. Preferred reporting items for systematic reviews and meta-analyses flow diagram. https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-t001.jpg Table 1. Top five countries in the web of science database for the number of publications in the fie https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g002.jpg Figure 2. The number of research literature on children's palliative care at home and abroad is tren https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g003.jpg Figure 3. Visualization of the authors of the child hospice study in the web of science database. https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g004.jpg Figure 4. Visualization of the authors of the child hospice study in the CNKI database. https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g005.jpg Figure 5. Visualization of the child hospice research institution in the web of science database. https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g006.jpg Figure 6. Visualization map of child hospice research institutions in the CNKI database. https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g007.jpg Figure 7. Visualization map of keyword clustering of child hospice research in the web of Science da https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g008.jpg Figure 8. In the CNKI database, the keyword clustering visualization map of child hospice care. https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g009.jpg Figure 9. Keyword emergence analysis of child hospice care research in the Web of science database. https://www.frontiersin.org/files/Articles/1688720/fped-14-1688720-HTML/image_m/fped-14-1688720-g010.jpg Figure 10. Keyword emergence analysis of children's hospice care research in CNKI database. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1775512/full https://www.frontiersin.org/files/Articles/1775512/fimmu-17-1775512-HTML/image_m/fimmu-17-1775512-t001.jpg Table 1. Main reagents used in the experiment. https://www.frontiersin.org/files/Articles/1775512/fimmu-17-1775512-HTML/image_m/fimmu-17-1775512-t002.jpg Table 2. Main experimental instruments and consumables. https://www.frontiersin.org/files/Articles/1775512/fimmu-17-1775512-HTML/image_m/fimmu-17-1775512-g001.jpg Figure 1. Body weight changes in mice of each group during the modeling period. (A) Body weight chan https://www.frontiersin.org/files/Articles/1775512/fimmu-17-1775512-HTML/image_m/fimmu-17-1775512-g002.jpg Figure 2. Effects of tobacco concentration on inflammatory cytokine expression in BALF of mice with https://www.frontiersin.org/files/Articles/1775512/fimmu-17-1775512-HTML/image_m/fimmu-17-1775512-g003.jpg Figure 3. Wright-Giemsa staining showing the effect of tobacco concentration on inflammatory cells i https://www.frontiersin.org/files/Articles/1775512/fimmu-17-1775512-HTML/image_m/fimmu-17-1775512-g004.jpg Figure 4. Statistical analysis of inflammatory cell counts in BALF of mice with different inflammato https://www.frontiersin.org/files/Articles/1775512/fimmu-17-1775512-HTML/image_m/fimmu-17-1775512-g005.jpg Figure 5. H&E staining showing the effect of tobacco concentration on lung histopathological changes https://www.frontiersin.org/files/Articles/1775512/fimmu-17-1775512-HTML/image_m/fimmu-17-1775512-g006.jpg Figure 6. FPAS staining to observe the effect of tobacco concentration on goblet cell hyperplasia in https://www.frontiersin.org/files/Articles/1775512/fimmu-17-1775512-HTML/image_m/fimmu-17-1775512-g007.jpg Figure 7. Masson trichrome staining showing the effect of tobacco concentration on pulmonary fibrosi https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1637548/full https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-g001.jpg Figure 1. Timeline of DSS treatment in rats and schematic workflow for stereological counting of TH- https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-t001.jpg Table 1. Primary and secondary antibodies used for immunohistochemistry (IH) and immunofluorescence https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-t002.jpg Table 2. Primers used for real-time PCR. https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-g002.jpg Figure 2. Volcano plot of the fold change (FC, DSS/water) of females and males. The x-axis represent https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-g003.jpg Figure 3. Histological analysis of the colon from DSS-treated and untreated female and male rats. (A https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-g004.jpg Figure 4. mRNA relative expression of pro-inflammatory cytokines in the colon of DSS-treated and con https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-g005.jpg Figure 5. Immunolocalization of P-α-syn in the colon of female rats under subchronic DSS treatment. https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-g006.jpg Figure 6. Expression of pro- and anti-inflammatory cytokines and intercellular adhesion molecule (IC https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-g007.jpg Figure 7. Expression of α-synuclein (α-syn), microtubule-associated protein (MAP)-2, tyrosine hydrox https://www.frontiersin.org/files/Articles/1637548/fimmu-16-1637548-HTML/image_m/fimmu-16-1637548-g008.jpg Figure 8. Dopaminergic neurons in the SN of DSS-treated and control rats. Coronal brain sections sho https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1695102/full https://www.frontiersin.org/files/Articles/1695102/fcimb-16-1695102-HTML/image_m/fcimb-16-1695102-g001.jpg Figure 1. Host and microbial EV proteins co-exist in CF airways. (A) MS-based spectra generated from https://www.frontiersin.org/files/Articles/1695102/fcimb-16-1695102-HTML/image_m/fcimb-16-1695102-g002.jpg Figure 2. Airway cultures stimulated with P. aeruginosa EVs demonstrated changes in EV concentration https://www.frontiersin.org/files/Articles/1695102/fcimb-16-1695102-HTML/image_m/fcimb-16-1695102-g003.jpg Figure 3. P. aeruginosa EVs are enriched in metabolic pathways and secretory, which can be modified https://www.frontiersin.org/files/Articles/1695102/fcimb-16-1695102-HTML/image_m/fcimb-16-1695102-g004.jpg Figure 4. (A) Schematic of the experimental protocol. (B) Representative 2D plots of CD66b vs. CD16 https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1716415/full https://www.frontiersin.org/files/Articles/1716415/fphar-17-1716415-HTML/image_m/fphar-17-1716415-g001.jpg Figure 1. Formation of Trapped Protein Complexes and Interstrand Crosslinks at Abasic Sites. Schemat https://www.frontiersin.org/files/Articles/1716415/fphar-17-1716415-HTML/image_m/fphar-17-1716415-g002.jpg Figure 2. Mechanisms of Chemotherapy-Induced DNA-Protein Crosslinks (DPCs). Schematic illustration o https://www.frontiersin.org/files/Articles/1716415/fphar-17-1716415-HTML/image_m/fphar-17-1716415-g003.jpg Figure 3. Overview of the Major DNA-Protein Crosslink (DPC) Repair Pathways. Illustration of the thr https://www.frontiersin.org/files/Articles/1716415/fphar-17-1716415-HTML/image_m/fphar-17-1716415-g004.jpg Figure 4. Proteolysis-Dependent Repair of DNA-Protein Crosslinks. Schematic representation of the SU https://www.frontiersin.org/files/Articles/1716415/fphar-17-1716415-HTML/image_m/fphar-17-1716415-t001.jpg Table 1. Categories of trapped protein–DNA complexes, their sources, and repair pathways. https://www.frontiersin.org/files/Articles/1716415/fphar-17-1716415-HTML/image_m/fphar-17-1716415-t002.jpg Table 2. The biochemical functions of key repair factors. https://www.frontiersin.org/files/Articles/1716415/fphar-17-1716415-HTML/image_m/fphar-17-1716415-g005.jpg Figure 5. Genetic Alterations in Core DPC Repair Components in Human Cancers. Analysis of genetic al https://www.frontiersin.org/files/Articles/1716415/fphar-17-1716415-HTML/image_m/fphar-17-1716415-t003.jpg Table 3. Representative clinical trials of therapies targeting the DNA-protein crosslink response. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1726012/full https://www.frontiersin.org/files/Articles/1726012/fnins-20-1726012-HTML/image_m/fnins-20-1726012-g001.jpg Figure 1. Representative listener examples are shown for the experimental groups. (A) NH (normal hea https://www.frontiersin.org/files/Articles/1726012/fnins-20-1726012-HTML/image_m/fnins-20-1726012-g002.jpg Figure 2. Associated azimuth target-response plots for the representative listener examples in Figur https://www.frontiersin.org/files/Articles/1726012/fnins-20-1726012-HTML/image_m/fnins-20-1726012-g003.jpg Figure 3. Azimuth localization outcomes for (A) gain, (B) bias, (C) r2, and (D) MAE for each present https://www.frontiersin.org/files/Articles/1726012/fnins-20-1726012-HTML/image_m/fnins-20-1726012-g004.jpg Figure 4. Elevation gain (A) and response variability (B) as a function of target azimuth. Mean prom https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1772269/full https://www.frontiersin.org/files/Articles/1772269/fspor-08-1772269-HTML/image_m/fspor-08-1772269-t001.jpg Table 1. Basic information about the subjects (N = 15). https://www.frontiersin.org/files/Articles/1772269/fspor-08-1772269-HTML/image_m/fspor-08-1772269-g001.jpg Figure 1. Experimental procedure. CMJ, Countermovement jump, min, minutes. https://www.frontiersin.org/files/Articles/1772269/fspor-08-1772269-HTML/image_m/fspor-08-1772269-g002.jpg Figure 2. CMJ performance metrics across time points under two VL thresholds. (A: Jump height; B: Re https://www.frontiersin.org/files/Articles/1772269/fspor-08-1772269-HTML/image_m/fspor-08-1772269-t002.jpg Table 2. CMJ and 30 m sprint test results before and after the two VL-inducing exercise intervention https://www.frontiersin.org/files/Articles/1772269/fspor-08-1772269-HTML/image_m/fspor-08-1772269-t003.jpg Table 3. Effect sizes (Cohen's d, 95% CI) for changes from PRE in CMJ and 30 m sprint outcomes under https://www.frontiersin.org/files/Articles/1772269/fspor-08-1772269-HTML/image_m/fspor-08-1772269-g003.jpg Figure 3. Sprint performance following VL-controlled PAPE protocols. (D: Total time used; E: Average https://www.frontiersin.org/files/Articles/1772269/fspor-08-1772269-HTML/image_m/fspor-08-1772269-t004.jpg Table 4. Statistics of total deep squats. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1629994/full https://www.frontiersin.org/files/Articles/1629994/fvets-12-1629994-HTML/image_m/fvets-12-1629994-g001.jpg Figure 1. Image depicting how the cross- (A) and the tangential trimming (B) were performed on the s https://www.frontiersin.org/files/Articles/1629994/fvets-12-1629994-HTML/image_m/fvets-12-1629994-t001.jpg Table 1. Histopathological evaluation of the surgical margins of 20 cutaneous tumors in 13 dogs and https://www.frontiersin.org/files/Articles/1629994/fvets-12-1629994-HTML/image_m/fvets-12-1629994-g002.jpg Figure 2. Cross- (A,C,E) and tangential- (B,D,F) H&E histological sections from a grade II STS in a https://www.frontiersin.org/journals/gastroenterology/articles/10.3389/fgstr.2026.1747118/full https://www.frontiersin.org/files/Articles/1747118/fgstr-05-1747118-HTML/image_m/fgstr-05-1747118-g001.jpg Figure 1. Timeline of FDA-approved FDA therapies for ulcerative colitis. Created with Biorender.com. https://www.frontiersin.org/files/Articles/1747118/fgstr-05-1747118-HTML/image_m/fgstr-05-1747118-g002.jpg Figure 2. Breaking the therapeutic ceiling in ulcerative colitis. ‘conceptual framework adapted from https://www.frontiersin.org/files/Articles/1747118/fgstr-05-1747118-HTML/image_m/fgstr-05-1747118-t001.jpg Table 1. Emerging therapeutic agents for moderate to severe ulcerative colitis: classification, mech https://www.frontiersin.org/files/Articles/1747118/fgstr-05-1747118-HTML/image_m/fgstr-05-1747118-g003.jpg Figure 3. Therapeutic agents in development for ulcerative colitis by mechanism of action and clinic https://www.frontiersin.org/files/Articles/1747118/fgstr-05-1747118-HTML/image_m/fgstr-05-1747118-g004.jpg Figure 4. Mechanistic targets of emerging therapies in the ulcerative colitis pipeline. Created with https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1726118/full https://www.frontiersin.org/files/Articles/1726118/fendo-17-1726118-HTML/image_m/fendo-17-1726118-t001.jpg Table 1. Summary of eating behavior questionnaires and their behavioral domains. https://www.frontiersin.org/files/Articles/1726118/fendo-17-1726118-HTML/image_m/fendo-17-1726118-t002.jpg Table 2. Comparison of sociodemographic and clinical characteristics across study groups. https://www.frontiersin.org/files/Articles/1726118/fendo-17-1726118-HTML/image_m/fendo-17-1726118-t003.jpg Table 3. Comparison of scale and subscale scores across study groups. https://www.frontiersin.org/files/Articles/1726118/fendo-17-1726118-HTML/image_m/fendo-17-1726118-g001.jpg Figure 1. Associations between cortisol biomarkers and eating behavior domains: LNSC vs. MEQ-Interfe https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1694773/full https://www.frontiersin.org/files/Articles/1694773/fneur-16-1694773-HTML/image_m/fneur-16-1694773-g001.jpg Figure 1. (A) Normal slow-wave sleep EEG/EMG sample epochs in two control mice (C57 and OLA) and a s https://www.frontiersin.org/files/Articles/1694773/fneur-16-1694773-HTML/image_m/fneur-16-1694773-g002.jpg Figure 2. Spectral features of spike–wave discharges (SWDs) in JAX mice. (A) Example of a 10-s epoch https://www.frontiersin.org/files/Articles/1694773/fneur-16-1694773-HTML/image_m/fneur-16-1694773-g003.jpg Figure 3. Sleep–wake analysis in C57, OLA and JAX mice. Left- and right-hand side charts show result https://www.frontiersin.org/files/Articles/1694773/fneur-16-1694773-HTML/image_m/fneur-16-1694773-g004.jpg Figure 4. Occurrence of spike–wave discharges (SWDs) by time of day and vigilance state in JAX mice. https://www.frontiersin.org/files/Articles/1694773/fneur-16-1694773-HTML/image_m/fneur-16-1694773-g005.jpg Figure 5. Effects of spike–wave discharges (SWDs) on phase transitions. (A) Correlation between the https://www.frontiersin.org/files/Articles/1694773/fneur-16-1694773-HTML/image_m/fneur-16-1694773-g006.jpg Figure 6. Summary of findings. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1652898/full https://www.frontiersin.org/files/Articles/1652898/fendo-16-1652898-HTML/image_m/fendo-16-1652898-t001.jpg Table 1. Primers and their sequences for qRT-PCR analysis. https://www.frontiersin.org/files/Articles/1652898/fendo-16-1652898-HTML/image_m/fendo-16-1652898-g001.jpg Figure 1. Flowchart and dataset. (A) Workflow for Identifying Mitophagy-Related Signatures in Prolif https://www.frontiersin.org/files/Articles/1652898/fendo-16-1652898-HTML/image_m/fendo-16-1652898-t002.jpg Table 2. Overview of the dataset. https://www.frontiersin.org/files/Articles/1652898/fendo-16-1652898-HTML/image_m/fendo-16-1652898-g002.jpg Figure 2. Panoramic view of disease immunocyte infiltration in the training dataset and correlation https://www.frontiersin.org/files/Articles/1652898/fendo-16-1652898-HTML/image_m/fendo-16-1652898-g003.jpg Figure 3. GO, KEGG, DO Enrichment Analysis and GSEA and GSVA analysis of DEMRGs. (A) Differentially https://www.frontiersin.org/files/Articles/1652898/fendo-16-1652898-HTML/image_m/fendo-16-1652898-g004.jpg Figure 4. Identification of genes characterized by the PDR model. (A) Relationship between Lambada, https://www.frontiersin.org/files/Articles/1652898/fendo-16-1652898-HTML/image_m/fendo-16-1652898-g005.jpg Figure 5. Hub-gene expression and mitophagy markers were measured in ARPE-19 cells. (A–H) The mRNA l https://www.frontiersin.org/files/Articles/1652898/fendo-16-1652898-HTML/image_m/fendo-16-1652898-g006.jpg Figure 6. WGCNA analysis and PPI analysis. (A) Elimination of outlier samples by cut height. (B) Det https://www.frontiersin.org/files/Articles/1652898/fendo-16-1652898-HTML/image_m/fendo-16-1652898-g007.jpg Figure 7. Correlation between hub genes and both the degree of immune cell infiltration and the immu https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2026.1750586/full https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g001.jpg Figure 1. 3D-printed nesting aids for solitary cavity-nesting bees. Experimental nesting aids fabric https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g002.jpg Figure 2. Drop retention in biological and 3D-printed porous structures. Comparison between water dr https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g003.jpg Figure 3. Geometric variation of TPMS and ADMS porous samples. Four 3D-printed porous samples: TPMS https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g004.jpg Figure 4. TPMS diamond-graded geometry and experimental setup. (a) Section of the diamond-type tripl https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g005.jpg Figure 5. 3D-printed shader panels with pore-scale modulation for evaporative cooling. Panels fabric https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g006.jpg Figure 6. Real-building setup and longitudinal section of the 3D-printed panel. The left diagram ill https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g007.jpg Figure 7. Installation of WS and C panels on the building Facade. Water-supplied (pWS) and control ( https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g008.jpg Figure 8. Climatic-chamber test of pWS and pC Facade components. Climatic-chamber setup used to test https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g009.jpg Figure 9. Temperature profiles of small-scale samples (sWS, sC) and outdoor air (Taᵢr). Temperature https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g010.jpg Figure 10. Infrared images showing surface-temperature differences between the water-supplied WS and https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-t001.jpg Table 1. Temperature range of traditional nesting materials and 3D-printed samples under heatwave co https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g011.jpg Figure 11. Diurnal temperature variation of tested nesting materials under outdoor conditions. Tempe https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g012.jpg Figure 12. Temperature variation of pWS and pC panels in real-building setup. Temperature profiles o https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g013.jpg Figure 13. Thermal-imaging comparison of evaporatively cooled and control panels. Infrared thermal i https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-g014.jpg Figure 14. Temperature distribution of Facade-mounted panels and ambient references. Box plots showi https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-t002.jpg Table 2. Summary of temperature statistics across all experiments (7 days). Mean, minimum, and maxim https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-t003.jpg Table 3. Duration of exposure above biological temperature thresholds for pWS and pC panels (data co https://www.frontiersin.org/files/Articles/1750586/fbuil-12-1750586-HTML/image_m/fbuil-12-1750586-t004.jpg Table 4. Summary statistics for climatic-chamber tests. Overview of temperature (T) and relative hum https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2026.1788657/full https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-t001.jpg Table 1. Comparative overview of representative PVSD studies and the gap addressed in this study. https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g001.jpg Figure 1. KineticSKIN module concept with independently adjustable upper and lower wings (split-cont https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g002.jpg Figure 2. Performance-based design methodology using multi-criteria analysis. https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-t002.jpg Table 2. Office model and façade infill material properties. https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g003.jpg Figure 3. The baseline scenario (BS-7r) considering the wings as horizontal louvres. https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g004.jpg Figure 4. (a) A0: closed; (b) A1s: perpendicular to sun’s altitude in summer and A1s,c: lower wing c https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-t003.jpg Table 3. Definitions of performance aspects and quantitative indicators used in this study. https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g005.jpg Figure 5. KineticSKIN’s user-preferences scenarios and daylighting strategies: (a) S1 (individual ad https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g006.jpg Figure 6. Upper and lower wings independently actuated by two linear actuators (left) and alternativ https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-t004.jpg Table 4. Illuminance mean values for upper and lower wing configurations. https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-t005.jpg Table 5. Comparative evaluation on A500 (12:00), light uniformity and SHG between BS-7r and kinetic https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g007.jpg Figure 7. Glare results at 12:00 on the summer: (a) BS-7r; (f) A1s; (g) A1s,c and winter solstices: https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g008.jpg Figure 8. Illuminance (left), glare (middle) and radiance render (right) at 12:00 on the winter sols https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g009.jpg Figure 9. Solar irradiance on the upper wings of all façade rows on the summer (left) and winter (ri https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g010.jpg Figure 10. Solar irradiance on the upper wings of the top row and the two bottom rows on the summer https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-t006.jpg Table 6. Quantitative indicators for KineticSKIN configurations at 12:00 on the winter solstice (Dec https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-t007.jpg Table 7. Quantitative indicators for KineticSKIN configurations at 12:00 on the summer solstice (Jun https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-g011.jpg Figure 11. S1: indoor visualization at 12:00 on the summer solstice. https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-t008.jpg Table 8. TFPV yield, façade actuation, artificial lighting and heating demand in Stuttgart. https://www.frontiersin.org/files/Articles/1788657/fbuil-12-1788657-HTML/image_m/fbuil-12-1788657-t009.jpg Table 9. Cooling demand in Cairo, Egypt and Dubai, UAE. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1674533/full https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-g001.jpg Figure 1. The role of gut microbiota in obesity development. Notes: Obesity is an imbalance of energ https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-t001.jpg Table 1. Summary of the study of gut microbiota and SCFAs on energy metabolism. https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-t002.jpg Table 2. Summary of the study of gut microbiota on response of host inflammation. https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-t003.jpg Table 3. Summary of the study of gut microbiota participates in bile acid metabolism. https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-t004.jpg Table 4. Summary of the study of gut microbiota involved in the gut-brain axis. https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-g002.jpg Figure 2. The Occurrence of Obesity under the Understanding of TCM. Notes: TCM believes that spleen https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-t005.jpg Table 5. Comparison table of TCM concepts and indicators. https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-t006.jpg Table 6. Summary of pre-clinical studies of TCM formulas. https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-t007.jpg Table 7. Summary of clinical studies of TCM formulas. https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-t008.jpg Table 8. Summary of pre-clinical studies of botanical drugs. https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-t009.jpg Table 9. Summary of pre-clinical studies of metabolites. https://www.frontiersin.org/files/Articles/1674533/fphar-17-1674533-HTML/image_m/fphar-17-1674533-g003.jpg Figure 3. Common Potential Mechanism of TCM formulas and botanical drugs that strengthen the spleen https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1672330/full https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t001.jpg Table 1. Demographic characteristics of respondents. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t002.jpg Table 2. Coding results. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-g001.jpg Figure 1. Research model. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t003.jpg Table 3. Demographic characteristics of the respondents. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t004.jpg Table 4. Reliability and validity of the measurement instruments. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t005.jpg Table 5. Discriminant validity test. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t006.jpg Table 6. CFA model fit, ULMF test, and SEM model fit. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t007.jpg Table 7. Results of hypothesis testing. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-g002.jpg Figure 2. Results of model analysis (***p < 0.001, the dashed line indicates that the hypothesis is https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t008.jpg Table 8. Data calibration. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t009.jpg Table 9. Results of necessary condition analysis. https://www.frontiersin.org/files/Articles/1672330/fpsyg-16-1672330-HTML/image_m/fpsyg-16-1672330-t010.jpg Table 10. Configurations of conditions. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1743120/full https://www.frontiersin.org/files/Articles/1743120/fnins-20-1743120-HTML/image_m/fnins-20-1743120-g001.jpg Figure 1. (A) Non-contrast CT scan on admission showing hyperdense blood filling the ventricular sys https://www.frontiersin.org/files/Articles/1743120/fnins-20-1743120-HTML/image_m/fnins-20-1743120-g002.jpg Figure 2. Follow-up non-contrast axial CT scan obtained on the sixth postoperative day after endosco https://www.frontiersin.org/files/Articles/1743120/fnins-20-1743120-HTML/image_m/fnins-20-1743120-g003.jpg Figure 3. Timeline of clinical events. The timeline illustrates the sequence from high-altitude expo https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1753853/full https://www.frontiersin.org/files/Articles/1753853/fcvm-13-1753853-HTML/image_m/fcvm-13-1753853-g001.jpg Figure 1. PRISMA flowchart. https://www.frontiersin.org/files/Articles/1753853/fcvm-13-1753853-HTML/image_m/fcvm-13-1753853-t001.jpg Table 1. Basic characteristics of included studies (n = 27). https://www.frontiersin.org/files/Articles/1753853/fcvm-13-1753853-HTML/image_m/fcvm-13-1753853-t002.jpg Table 2. Results of guideline quality evaluation. https://www.frontiersin.org/files/Articles/1753853/fcvm-13-1753853-HTML/image_m/fcvm-13-1753853-t003.jpg Table 3. Results of expert consensus quality evaluation. https://www.frontiersin.org/files/Articles/1753853/fcvm-13-1753853-HTML/image_m/fcvm-13-1753853-t004.jpg Table 4. Results of systematic review and meta-analysis quality evaluation . https://www.frontiersin.org/files/Articles/1753853/fcvm-13-1753853-HTML/image_m/fcvm-13-1753853-t005.jpg Table 5. Results of randomized controlled trial quality evaluation. https://www.frontiersin.org/files/Articles/1753853/fcvm-13-1753853-HTML/image_m/fcvm-13-1753853-t006.jpg Table 6. Summary of evidence for non-pharmacological interventions of dyslipidemia in patients with https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1727546/full https://www.frontiersin.org/files/Articles/1727546/fcvm-13-1727546-HTML-r1/image_m/fcvm-13-1727546-g001.jpg Figure 1. Twelve-lead electrocardiograms. (A) Shows the tracing obtained at admission. (B,C) Depict https://www.frontiersin.org/files/Articles/1727546/fcvm-13-1727546-HTML-r1/image_m/fcvm-13-1727546-g002.jpg Figure 2. Echocardiographic findings. (A) Illustrates reduced global longitudinal strain. (B) Demons https://www.frontiersin.org/files/Articles/1727546/fcvm-13-1727546-HTML-r1/image_m/fcvm-13-1727546-g003.jpg Figure 3. Coronary angiography. Coronary angiography showed unobstructed epicardial vessels with Thr https://www.frontiersin.org/files/Articles/1727546/fcvm-13-1727546-HTML-r1/image_m/fcvm-13-1727546-g004.jpg Figure 4. Diagnostic and management timeline. α-Gal A, α-galactosidase A; Lyso-Gb3, globotriaosylsph https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1628828/full https://www.frontiersin.org/files/Articles/1628828/fpsyg-16-1628828-HTML/image_m/fpsyg-16-1628828-t001.jpg Table 1. Descriptive statistics for TEC components and age in two groups of children. https://www.frontiersin.org/files/Articles/1628828/fpsyg-16-1628828-HTML/image_m/fpsyg-16-1628828-t002.jpg Table 2. Differences in emotion understanding components in two groups of children. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1791881/full https://www.frontiersin.org/files/Articles/1791881/fmed-13-1791881-HTML-r1/image_m/fmed-13-1791881-g001.jpg Figure 1. Flowchart of the overall study design. https://www.frontiersin.org/files/Articles/1791881/fmed-13-1791881-HTML-r1/image_m/fmed-13-1791881-t001.jpg Table 1. Characteristics of PRP formulations and absolute platelet counts. https://www.frontiersin.org/files/Articles/1791881/fmed-13-1791881-HTML-r1/image_m/fmed-13-1791881-g002.jpg Figure 2. Preparation of the two types of PRP. Created with BioRender.com. https://www.frontiersin.org/files/Articles/1791881/fmed-13-1791881-HTML-r1/image_m/fmed-13-1791881-g003.jpg Figure 3. Ultrasound-guided PRP injection. (A) Injection with the shoulder in the neutral position. https://www.frontiersin.org/files/Articles/1791881/fmed-13-1791881-HTML-r1/image_m/fmed-13-1791881-t002.jpg Table 2. Schedules for follow-up assessments and data collection. https://www.frontiersin.org/files/Articles/1791881/fmed-13-1791881-HTML-r1/image_m/fmed-13-1791881-t003.jpg Table 3. Summary of outcomes. https://www.frontiersin.org/journals/insect-science/articles/10.3389/finsc.2026.1762540/full https://www.frontiersin.org/files/Articles/1762540/finsc-06-1762540-HTML-r1/image_m/finsc-06-1762540-g001.jpg Figure 1. (A) Worker ant, Ectatomma edentatum Roger, 1893 (Formicidae: Ectatomminae), with a hinged https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2026.1754569/full https://www.frontiersin.org/files/Articles/1754569/fragi-07-1754569-HTML/image_m/fragi-07-1754569-g001.jpg Figure 1. Evaluation of Ectoine cytotoxicity and its effects on cell morphology. (A,B) Viability of https://www.frontiersin.org/files/Articles/1754569/fragi-07-1754569-HTML/image_m/fragi-07-1754569-g002.jpg Figure 2. Effect of H2O2 on the viability of HaCaT and EA. hy926 cells. (A) Morphology of HaCaT cell https://www.frontiersin.org/files/Articles/1754569/fragi-07-1754569-HTML/image_m/fragi-07-1754569-g003.jpg Figure 3. Ectoine promotes proliferation and reduces senescence in HaCaT and EA. hy926 cells. (A–D) https://www.frontiersin.org/files/Articles/1754569/fragi-07-1754569-HTML/image_m/fragi-07-1754569-g004.jpg Figure 4. Ectoine attenuates intracellular ROS levels and maintains Lamin B1 expression in HaCaT and https://www.frontiersin.org/files/Articles/1754569/fragi-07-1754569-HTML/image_m/fragi-07-1754569-g005.jpg Figure 5. Ectoine downregulates senescence-related gene expression in H2O2-induced HaCaT and EA. hy9 https://www.frontiersin.org/files/Articles/1754569/fragi-07-1754569-HTML/image_m/fragi-07-1754569-g006.jpg Figure 6. Ectoine downregulates senescence-related protein expression and reduces apoptosis in HaCaT https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1686082/full https://www.frontiersin.org/files/Articles/1686082/fendo-17-1686082-HTML/image_m/fendo-17-1686082-g001.jpg Figure 1. Flowchart of the study design and data analysis. BMI, body mass index; SBP, systolic blood https://www.frontiersin.org/files/Articles/1686082/fendo-17-1686082-HTML/image_m/fendo-17-1686082-t001.jpg Table 1. Comparison of clinical indicators between participants with prediabetes and normoglycemia i https://www.frontiersin.org/files/Articles/1686082/fendo-17-1686082-HTML/image_m/fendo-17-1686082-g002.jpg Figure 2. Use LASSO regression to screen for differential variables in the training set. (A) Variabl https://www.frontiersin.org/files/Articles/1686082/fendo-17-1686082-HTML/image_m/fendo-17-1686082-t002.jpg Table 2. Multivariate Cox proportional hazards model analysis of feature variables in the training s https://www.frontiersin.org/files/Articles/1686082/fendo-17-1686082-HTML/image_m/fendo-17-1686082-g003.jpg Figure 3. ROC curves for predicting glucose reversal at 3, 4, and 5 years in participants with predi https://www.frontiersin.org/files/Articles/1686082/fendo-17-1686082-HTML/image_m/fendo-17-1686082-g004.jpg Figure 4. ROC curves for six machine learning algorithms predicting glucose reversal in participants https://www.frontiersin.org/files/Articles/1686082/fendo-17-1686082-HTML/image_m/fendo-17-1686082-t003.jpg Table 3. Performance evaluation of six machine learning algorithms and the Cox proportional hazards https://www.frontiersin.org/files/Articles/1686082/fendo-17-1686082-HTML/image_m/fendo-17-1686082-g005.jpg Figure 5. Feature variable importance ranking and interpretation of feature variables using SHAP ana https://www.frontiersin.org/files/Articles/1686082/fendo-17-1686082-HTML/image_m/fendo-17-1686082-g006.jpg Figure 6. Kaplan - Meier curves for the top six most important feature variables. (A) Kaplan-Meier c https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1765843/full https://www.frontiersin.org/files/Articles/1765843/fimmu-17-1765843-HTML-r1/image_m/fimmu-17-1765843-g001.jpg Figure 1. Flowchart of study design. This flowchart describes the study design, number of samples in https://www.frontiersin.org/files/Articles/1765843/fimmu-17-1765843-HTML-r1/image_m/fimmu-17-1765843-t001.jpg Table 1. Description of the different immunopeptidomics batches with varying sample conditions and i https://www.frontiersin.org/files/Articles/1765843/fimmu-17-1765843-HTML-r1/image_m/fimmu-17-1765843-t002.jpg Table 2. CL patient characteristics. https://www.frontiersin.org/files/Articles/1765843/fimmu-17-1765843-HTML-r1/image_m/fimmu-17-1765843-g002.jpg Figure 2. MHC-presented peptide counts and lengths of peptides exclusively mapping to L. aethiopica https://www.frontiersin.org/files/Articles/1765843/fimmu-17-1765843-HTML-r1/image_m/fimmu-17-1765843-g003.jpg Figure 3. Sharing of exact MHC-presented epitopes between samples/patients. Every column represents https://www.frontiersin.org/files/Articles/1765843/fimmu-17-1765843-HTML-r1/image_m/fimmu-17-1765843-g004.jpg Figure 4. Sharing of source proteins (from which MHC-presented L. aethiopica epitopes are derived) b https://www.frontiersin.org/files/Articles/1765843/fimmu-17-1765843-HTML-r1/image_m/fimmu-17-1765843-g005.jpg Figure 5. Antigen epitope-richness. The number of identified MHC-presented high-confidence L. aethio https://www.frontiersin.org/files/Articles/1765843/fimmu-17-1765843-HTML-r1/image_m/fimmu-17-1765843-t003.jpg Table 3. Biological description of the top 10 antigens that are shared across multiple (>=3) patient https://www.frontiersin.org/files/Articles/1765843/fimmu-17-1765843-HTML-r1/image_m/fimmu-17-1765843-g006.jpg Figure 6. Overlap between experimentally observed peptides and in silico predicted peptides. Overlap https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1665539/full https://www.frontiersin.org/files/Articles/1665539/fsufs-10-1665539-HTML/image_m/fsufs-10-1665539-g001.jpg Figure 1. Visualization of the sensitivity measure S = φ(1−λ) within the feasible domain 0 https://www.frontiersin.org/files/Articles/1665539/fsufs-10-1665539-HTML/image_m/fsufs-10-1665539-g002.jpg Figure 2. Estimated correlation matrix from 1,000 copula samples reveal a strong empirical link betw https://www.frontiersin.org/files/Articles/1665539/fsufs-10-1665539-HTML/image_m/fsufs-10-1665539-g003.jpg Figure 3. A distribution of poverty risk scenarios derived from observed historical dependence patte https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1766722/full https://www.frontiersin.org/files/Articles/1766722/fimmu-17-1766722-HTML-r2/image_m/fimmu-17-1766722-t001.jpg Table 1. Serum IgG ELISA and ADCC endpoint titers of individual HSV-2 HCS. https://www.frontiersin.org/files/Articles/1766722/fimmu-17-1766722-HTML-r2/image_m/fimmu-17-1766722-g001.jpg Figure 1. Establishing the CD107a NK cell (ADCC) assay with HSV-2 gC2, gD2, and gE2 as targets. Sche https://www.frontiersin.org/files/Articles/1766722/fimmu-17-1766722-HTML-r2/image_m/fimmu-17-1766722-g002.jpg Figure 2. NK cell CD107a expression using seropositive HCS or seronegative sera. Transfection effici https://www.frontiersin.org/files/Articles/1766722/fimmu-17-1766722-HTML-r2/image_m/fimmu-17-1766722-g003.jpg Figure 3. SPR graphs demonstrating binding of HSV-1/2 seronegative human IgG Fc. HSV-1 gE1, gE1/gI1, https://www.frontiersin.org/files/Articles/1766722/fimmu-17-1766722-HTML-r2/image_m/fimmu-17-1766722-g004.jpg Figure 4. HSV-2 gE2 inhibits NK cell CD107a expression. Experimental model: Left side: Full-length g https://www.frontiersin.org/files/Articles/1766722/fimmu-17-1766722-HTML-r2/image_m/fimmu-17-1766722-t002.jpg Table 2. HSV-2 HCS ADCC endpoint titers for gD2/gE2WT/gI2 and gD2/gE2MUT/gI2. https://www.frontiersin.org/files/Articles/1766722/fimmu-17-1766722-HTML-r2/image_m/fimmu-17-1766722-g005.jpg Figure 5. Identifying a gE2 mAb that blocks IgG Fc binding to gE2/gI2. The gE2/gI2 complex was added https://www.frontiersin.org/files/Articles/1766722/fimmu-17-1766722-HTML-r2/image_m/fimmu-17-1766722-g006.jpg Figure 6. Spiking human sera with B1E6, a gE2 mAb that blocks IgG Fc binding, or E1, a gE2 mAb that https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1786399/full https://www.frontiersin.org/files/Articles/1786399/fmolb-13-1786399-HTML/image_m/fmolb-13-1786399-g001.jpg Figure 1. PRISMA-style flow diagram of study identification, screening, eligibility assessment and i https://www.frontiersin.org/files/Articles/1786399/fmolb-13-1786399-HTML/image_m/fmolb-13-1786399-t001.jpg Table 1. Characteristics and key findings of studies investigating RNA biomarkers in resistant hyper https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1640508/full https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-t001.jpg Table 1. Demographic information of the participants in this study (n = 68). https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-t002.jpg Table 2. Overlapping differentially expressed protein profiles. https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-g001.jpg Figure 1. Results from quantitative proteomic analysis screened from iTRAQ. (A) Volcano plot of DEPs https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-g002.jpg Figure 2. Functional enrichment analysis and PPI of DEPs in GC. (A) GO analysis of DEPs. (B) KEGG pa https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-t003.jpg Table 3. Top GO and KEGG enrichment terms associated with differentially expressed salivary proteins https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-g003.jpg Figure 3. PPI network of DEPs. The size of the nodes was proportional to the degree of centrality de https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-g004.jpg Figure 4. Comparison between the iTRAQ-based results and the PRM-based results. https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-g005.jpg Figure 5. Comparison of protein expression between the GC and non-GC groups using PRM ( *P < 0.05, * https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-t004.jpg Table 4. Summary of differential expression and diagnostic potential of validated biomarkers (PRM ph https://www.frontiersin.org/files/Articles/1640508/fmolb-12-1640508-HTML-r1/image_m/fmolb-12-1640508-g006.jpg Figure 6. Kaplan–Meier survival analysis of (A) CST4, (B) CST5, (C) S100A8, and (D) S100A9. Survival https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1749957/full https://www.frontiersin.org/files/Articles/1749957/fonc-16-1749957-HTML/image_m/fonc-16-1749957-t001.jpg Table 1. Patient demographic and clinical characteristics. https://www.frontiersin.org/files/Articles/1749957/fonc-16-1749957-HTML/image_m/fonc-16-1749957-g001.jpg Figure 1. (A) The cumulative catheter survival; (B) The cumulative hematoma-free catheter survival; https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1737713/full https://www.frontiersin.org/files/Articles/1737713/fendo-16-1737713-HTML/image_m/fendo-16-1737713-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1737713/fendo-16-1737713-HTML/image_m/fendo-16-1737713-g001.jpg Figure 1. Restricted cubic spline (RCS) curves of ApoB, gTyG index and TyG index levels across diffe https://www.frontiersin.org/files/Articles/1737713/fendo-16-1737713-HTML/image_m/fendo-16-1737713-t002.jpg Table 2. Risk ratios of depression across ApoB levels in different models. https://www.frontiersin.org/files/Articles/1737713/fendo-16-1737713-HTML/image_m/fendo-16-1737713-g002.jpg Figure 2. Subgroup analyses of the associations among ApoB, the gTyG index, and depression in CHF pa https://www.frontiersin.org/files/Articles/1737713/fendo-16-1737713-HTML/image_m/fendo-16-1737713-g003.jpg Figure 3. Variable selection via the LGBM model. (A) Variable importance ranking. (B) Contribution o https://www.frontiersin.org/files/Articles/1737713/fendo-16-1737713-HTML/image_m/fendo-16-1737713-g004.jpg Figure 4. Confusion matrices of eight ML models. (A) Confusion matrix for the ANN. (B) Confusion mat https://www.frontiersin.org/files/Articles/1737713/fendo-16-1737713-HTML/image_m/fendo-16-1737713-g005.jpg Figure 5. Comparison of eight ML models on training and test sets. (A) AUC values on the training se https://www.frontiersin.org/files/Articles/1737713/fendo-16-1737713-HTML/image_m/fendo-16-1737713-g006.jpg Figure 6. Global model interpretation via the SHAP method. (A) Bar chart of variable contributions. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1701529/full https://www.frontiersin.org/files/Articles/1701529/fpsyg-16-1701529-HTML/image_m/fpsyg-16-1701529-g001.jpg Figure 1. Proposed model. https://www.frontiersin.org/files/Articles/1701529/fpsyg-16-1701529-HTML/image_m/fpsyg-16-1701529-t001.jpg Table 1. Descriptive statistics. https://www.frontiersin.org/files/Articles/1701529/fpsyg-16-1701529-HTML/image_m/fpsyg-16-1701529-t002.jpg Table 2. Results of hypothesis testing. https://www.frontiersin.org/files/Articles/1701529/fpsyg-16-1701529-HTML/image_m/fpsyg-16-1701529-t003.jpg Table 3. Results of mediation and moderated mediation effect analyses. https://www.frontiersin.org/files/Articles/1701529/fpsyg-16-1701529-HTML/image_m/fpsyg-16-1701529-g002.jpg Figure 2. Moderating role of POS. https://www.frontiersin.org/files/Articles/1701529/fpsyg-16-1701529-HTML/image_m/fpsyg-16-1701529-t004.jpg Table 4. Summary of hypotheses testing results. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1684030/full https://www.frontiersin.org/files/Articles/1684030/fcimb-15-1684030-HTML/image_m/fcimb-15-1684030-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1684030/fcimb-15-1684030-HTML/image_m/fcimb-15-1684030-t001.jpg Table 1. Four major algorithms used for signal detection. https://www.frontiersin.org/files/Articles/1684030/fcimb-15-1684030-HTML/image_m/fcimb-15-1684030-g001.jpg Figure 1. FAERS Data analysis workflow. https://www.frontiersin.org/files/Articles/1684030/fcimb-15-1684030-HTML/image_m/fcimb-15-1684030-g002.jpg Figure 2. Reporting odds ratios (ROR) for durvalumab and atezolizumab in relation to different types https://www.frontiersin.org/files/Articles/1684030/fcimb-15-1684030-HTML/image_m/fcimb-15-1684030-g003.jpg Figure 3. Causal effect of monocyte subset characteristics on the risk of early TB progression. Inve https://www.frontiersin.org/files/Articles/1684030/fcimb-15-1684030-HTML/image_m/fcimb-15-1684030-g004.jpg Figure 4. (A) Pathway interaction network of GSEA-KEGG results. Black circles indicate clusters of s https://www.frontiersin.org/files/Articles/1684030/fcimb-15-1684030-HTML/image_m/fcimb-15-1684030-t002.jpg Table 2. Prioritized small-molecule compounds potentially repurposed for LTBI intervention, with kno https://www.frontiersin.org/files/Articles/1684030/fcimb-15-1684030-HTML/image_m/fcimb-15-1684030-g005.jpg Figure 5. Molecular docking experiment. (A) Molecular docking model of Lycorine and TOCF1. (B) Molec https://www.frontiersin.org/files/Articles/1684030/fcimb-15-1684030-HTML/image_m/fcimb-15-1684030-g006.jpg Figure 6. Lycorine effects on L-Mtb–infected BMDMs. (A) Cell viability assessed by CCK-8 assay follo https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1492531/full https://www.frontiersin.org/files/Articles/1492531/fimmu-15-1492531-HTML/image_m/fimmu-15-1492531-g001.jpg Figure 1. RPA1-ETAA1 Association Linked to PD-L1 in Hepatocellular Carcinoma (A) Immunohistochemistr https://www.frontiersin.org/files/Articles/1492531/fimmu-15-1492531-HTML/image_m/fimmu-15-1492531-g002.jpg Figure 2. ETAA1 Shapes Immunophenotypes in Liver Hepatocellular Carcinoma. (A) Immune scores from th https://www.frontiersin.org/files/Articles/1492531/fimmu-15-1492531-HTML/image_m/fimmu-15-1492531-g003.jpg Figure 3. RPA1-ETAA1 Axis Links to Unfavorable Progression in Liver Cancer. UALCAN analysis revealed https://www.frontiersin.org/files/Articles/1492531/fimmu-15-1492531-HTML/image_m/fimmu-15-1492531-g004.jpg Figure 4. RPA1-ETAA1 Axis Correlates with PD-L1 Nuclear Translocation. (A) The correlations of ETAA1 https://www.frontiersin.org/files/Articles/1492531/fimmu-15-1492531-HTML/image_m/fimmu-15-1492531-t001.jpg Table 1. Correlation Analysis of ETAA1-RPA1 with Regulators of PD-L1 Nuclear Translocation in LIHC v https://www.frontiersin.org/files/Articles/1492531/fimmu-15-1492531-HTML/image_m/fimmu-15-1492531-t002.jpg Table 2. Correlation Analysis of PD-L1, ETAA1, and RPA1 with Nuclear Signaling Regulators using TIME https://www.frontiersin.org/files/Articles/1492531/fimmu-15-1492531-HTML/image_m/fimmu-15-1492531-g005.jpg Figure 5. Schematic of RPA1-ETAA1 Axis Mediated PD-L1 Nuclear Translocation. A model illustrating RP https://www.frontiersin.org/files/Articles/1492531/fimmu-15-1492531-HTML/image_m/fimmu-15-1492531-t003.jpg Table 3. Correlation Analysis of ETAA1 with Gene Markers of Immune Cell Subsets using TIMER2.0 and G https://www.frontiersin.org/files/Articles/1492531/fimmu-15-1492531-HTML/image_m/fimmu-15-1492531-t004.jpg Table 4. Correlation Analysis of RPA1-ETAA1 with Nuclear Factors Linked to Tumor Metastasis using TI https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1718081/full https://www.frontiersin.org/files/Articles/1718081/fenvs-13-1718081-HTML/image_m/fenvs-13-1718081-g001.jpg Figure 1. Flow diagram following PRISMA 2020 showing 29 studies included. https://www.frontiersin.org/files/Articles/1718081/fenvs-13-1718081-HTML/image_m/fenvs-13-1718081-t001.jpg Table 1. Study Characteristics, including study location, water source, adsorbent type and source, s https://www.frontiersin.org/files/Articles/1718081/fenvs-13-1718081-HTML/image_m/fenvs-13-1718081-g002.jpg Figure 2. Yearly distribution of publications on low-cost adsorbents for water treatment. https://www.frontiersin.org/files/Articles/1718081/fenvs-13-1718081-HTML/image_m/fenvs-13-1718081-g003.jpg Figure 3. Percentage distribution of research focus areas on low-cost adsorbents in South Africa (20 https://www.frontiersin.org/files/Articles/1718081/fenvs-13-1718081-HTML/image_m/fenvs-13-1718081-g004.jpg Figure 4. Percentage distribution of water sources treated in the include studies. https://www.frontiersin.org/files/Articles/1718081/fenvs-13-1718081-HTML/image_m/fenvs-13-1718081-t002.jpg Table 2. Experimental conditions and performance outcomes. https://www.frontiersin.org/files/Articles/1718081/fenvs-13-1718081-HTML/image_m/fenvs-13-1718081-g005.jpg Figure 5. Total number of included articles at the range of selected year for the review. https://www.frontiersin.org/files/Articles/1718081/fenvs-13-1718081-HTML/image_m/fenvs-13-1718081-g006.jpg Figure 6. Distribution of water contaminant sources identified in the included studies. https://www.frontiersin.org/files/Articles/1718081/fenvs-13-1718081-HTML/image_m/fenvs-13-1718081-g007.jpg Figure 7. Percentage distribution of adsorbent types for water treatment applications in South Afric https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1599769/full https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g001.jpg Figure 1. Workflow of the study. https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g002.jpg Figure 2. Identification of palmitoylation-related differentially expressed genes (DEGs) in glioma a https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g003.jpg Figure 3. The subgroup analysis in the TCGA-GBMLGG dataset based on 46 palmitoylation-related genes https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g004.jpg Figure 4. Immune infiltration and mutation analysis in different clusters. (A) The comparisons of st https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g005.jpg Figure 5. Construction and validation of the palmitoylation-related risk score (PRRS) model. (A) Uni https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g006.jpg Figure 6. The PRRS was associated with the clinicopathological characteristics of patients with glio https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g007.jpg Figure 7. Development and assessment of the nomogram for overall survival prediction. In the TCGA-GB https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g008.jpg Figure 8. Functional analysis based on the PRRS model. Exhibition of DEGs in (A) GSE16011 and (B) GS https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g009.jpg Figure 9. Differences in immune infiltration among risk groups. The stromal and immune scores of the https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g010.jpg Figure 10. Single-cell transcriptome analysis of PRGs expression distribution in multiple cell types https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g011.jpg Figure 11. Analysis of mutation and methylation levels of PRGs in glioma. (A) Homozygous or heterozy https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g012.jpg Figure 12. The benefit of the PRRS in immunotherapy. (A) Box plot showing that the PRRS of patients https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g013.jpg Figure 13. Drug sensitivity analysis. Antitumor medication predictions based on the expression of 9 https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g014.jpg Figure 14. Molecular docking of the drug small molecules and (A) APOC1, (B) FXYD1, (C) ZCCHC12, (D) https://www.frontiersin.org/files/Articles/1599769/fimmu-16-1599769-HTML/image_m/fimmu-16-1599769-g015.jpg Figure 15. AT-7519, BIX02189, and THZ-2-101–1 are capable of inhibiting glioma cell migration and in https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1770158/full https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-g001.jpg Figure 1. Flowchart. https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-t001.jpg Table 1. Comparison of baseline characteristics between non-clinical and clinical pregnancy groups. https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-t002.jpg Table 2. Comparison of baseline characteristics between miscarriage and live birth groups among clin https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-t003.jpg Table 3. Univariate logistic regression analysis of factors associated with clinical pregnancy in ad https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-t004.jpg Table 4. Multivariable logistic regression analysis of factors associated with clinical pregnancy in https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-g002.jpg Figure 2. Clinical pregnancy prediction model in adenomyosis patients undergoing FET. (A) Nomogram f https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-t005.jpg Table 5. Clinical pregnancy rates according to model-predicted probability strata. https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-t006.jpg Table 6. Univariate logistic regression analysis of factors associated with live birth in adenomyosi https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-t007.jpg Table 7. Multivariable logistic regression analysis of factors associated with live birth in adenomy https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-g003.jpg Figure 3. Live birth prediction model among clinical pregnancy cycles. (A) Nomogram for predicting l https://www.frontiersin.org/files/Articles/1770158/fendo-17-1770158-HTML/image_m/fendo-17-1770158-t008.jpg Table 8. Live birth rates according to model-predicted probability strata. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1681531/full https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-g001.jpg Figure 1. Literature search inclusion process. https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-t001.jpg Table 1. The details of included studies. https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-g002.jpg Figure 2. Risk of bias assessment for all included studies. https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-g003.jpg Figure 3. Forest plot comparing the incidence of postoperative delirium (POD) between the esketamine https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-t002.jpg Table 2. Summary for GRADE assessment. https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-g004.jpg Figure 4. Forest plot comparing the incidence of postoperative nausea and vomiting (PONV) between th https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-g005.jpg Figure 5. Forest plot comparing the postoperative 24-h pain score between the esketamine and control https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-g006.jpg Figure 6. Subgroup analysis of postoperative delirium (POD) incidence by patient age, comparing the https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-g007.jpg Figure 7. Subgroup analysis of postoperative delirium (POD) incidence by type of surgery, comparing https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-g008.jpg Figure 8. Sensitivity analysis for postoperative delirium (POD) incidence. https://www.frontiersin.org/files/Articles/1681531/fphar-16-1681531-HTML/image_m/fphar-16-1681531-g009.jpg Figure 9. Trial sequential analysis for postoperative delirium (POD) incidence. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1681072/full https://www.frontiersin.org/files/Articles/1681072/fimmu-16-1681072-HTML/image_m/fimmu-16-1681072-t001.jpg Table 1. Performance of multiple machine learning models for identifying breast cancer recurrence st https://www.frontiersin.org/files/Articles/1681072/fimmu-16-1681072-HTML/image_m/fimmu-16-1681072-g001.jpg Figure 1. Brief technical flowchart. https://www.frontiersin.org/files/Articles/1681072/fimmu-16-1681072-HTML/image_m/fimmu-16-1681072-g002.jpg Figure 2. (A) and (B) LASSO coefficient convergence paths. https://www.frontiersin.org/files/Articles/1681072/fimmu-16-1681072-HTML/image_m/fimmu-16-1681072-g003.jpg Figure 3. Machine learning-based feature selection (A) SVM-RFE algorithm performance showing accurac https://www.frontiersin.org/files/Articles/1681072/fimmu-16-1681072-HTML/image_m/fimmu-16-1681072-g004.jpg Figure 4. Performance comparison of machine learning models receiver operating characteristic (ROC) https://www.frontiersin.org/files/Articles/1681072/fimmu-16-1681072-HTML/image_m/fimmu-16-1681072-g005.jpg Figure 5. Model interpretability and clinical utility analysis (A, B) Decision curve analysis (DCA) https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1723546/full https://www.frontiersin.org/files/Articles/1723546/fonc-15-1723546-HTML/image_m/fonc-15-1723546-t001.jpg Table 1. Comparison of several common colorectal screening methods. https://www.frontiersin.org/files/Articles/1723546/fonc-15-1723546-HTML/image_m/fonc-15-1723546-t002.jpg Table 2. Summary of biomarker-based colorectal cancer screening methods. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1773599/full https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-g001.jpg Figure 1. A flowchart for screening immune-related adverse events of bevacizumab and temozolomide fr https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t001.jpg Table 1. Basic characteristics of reports grouped according to reported drug use combinations in the https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t002.jpg Table 2. Basic characteristics of the population grouped according to reported drug use combinations https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t003.jpg Table 3. Analysis of immune-related adverse event signals of bevacizumab in the FAERS database. https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t004.jpg Table 4. Analysis of immune-related adverse event signals of bevacizumab in the CVARD database. https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t005.jpg Table 5. Analysis of immune-related adverse event signals of temozolomide in the FAERS database. https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t006.jpg Table 6. Analysis of immune-related adverse event signals of temozolomide in the CVARD database. https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t007.jpg Table 7. Analysis of immune-related adverse event signals for the combination therapy of bevacizumab https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t008.jpg Table 8. Analysis of immune-related adverse event signals for the combination therapy of bevacizumab https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t009.jpg Table 9. Time-to-onset (TTO) analysis of key immune-related adverse events across treatment groups. https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t010.jpg Table 10. Weibull distribution analysis of time dependency for immune-related adverse events across https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t011.jpg Table 11. Potential drug–drug interaction signals (more-than-additive reporting disproportionality p https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t012.jpg Table 12. Potential drug–drug interaction signals (more-than-additive reporting disproportionality p https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t013.jpg Table 13. Potential drug–drug interaction signals (more-than-additive reporting disproportionality p https://www.frontiersin.org/files/Articles/1773599/fmed-13-1773599-HTML/image_m/fmed-13-1773599-t014.jpg Table 14. Multivariable logistic regression analysis of risk factors for immune-related adverse even https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1702414/full https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-t001.jpg Table 1. Operational definitions. https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-t002.jpg Table 2. Descriptive statistics. https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-t003.jpg Table 3. Correlation matrix. https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-t004.jpg Table 4. Model summary. https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-t005.jpg Table 5. ANOVA. https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-t006.jpg Table 6. Linear regression model coefficients. https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-t007.jpg Table 7. Model description for all the banks. https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-t008.jpg Table 8. ARIMA model parameters. https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-t009.jpg Table 9. Exponential smoothing model parameters. https://www.frontiersin.org/files/Articles/1702414/frai-09-1702414-HTML-r1/image_m/frai-09-1702414-g001.jpg Figure 1. Forecasting for Z-score value of the banks. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1757905/full https://www.frontiersin.org/files/Articles/1757905/fendo-17-1757905-HTML/image_m/fendo-17-1757905-g001.jpg Figure 1. Flowchart of the study population selection process. The initial cohort consisted of 2,780 https://www.frontiersin.org/files/Articles/1757905/fendo-17-1757905-HTML/image_m/fendo-17-1757905-t001.jpg Table 1. Comparison of baseline characteristics before and after propensity-score matchinga. https://www.frontiersin.org/files/Articles/1757905/fendo-17-1757905-HTML/image_m/fendo-17-1757905-g002.jpg Figure 2. Longitudinal postnatal growth trajectories of term small-for-gestational-age (SGA) infants https://www.frontiersin.org/files/Articles/1757905/fendo-17-1757905-HTML/image_m/fendo-17-1757905-t002.jpg Table 2. Logistic regression analysis of predictors for catch-up growth success at 4 years of agea. https://www.frontiersin.org/files/Articles/1757905/fendo-17-1757905-HTML/image_m/fendo-17-1757905-t003.jpg Table 3. Predictive performance of early growth indicators for catch-up growth at 4 years in term SG https://www.frontiersin.org/files/Articles/1757905/fendo-17-1757905-HTML/image_m/fendo-17-1757905-g003.jpg Figure 3. Receiver operating characteristic (ROC) curves of early growth indicators for predicting c https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1768111/full https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g001.jpg Figure 1. Design of animal experiment. https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-t001.jpg Table 1. Antagonistic activity of three L. sakei strains and LGG against Aeromonas hydrophila. https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-t002.jpg Table 2. Effect of L. sakei on the growth performance of crucian carp. https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g002.jpg Figure 2. Effect of L. sakei on the intestinal histological structure of Crucian carp (a–f): PBS gro https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g003.jpg Figure 3. (a) Biochemical indexes in intestinal tissues (enzyme activities were normalized to protei https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g004.jpg Figure 4. Effect of L. sakei on the expression of intestinal inflammatory factors of crucian carp (a https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g005.jpg Figure 5. Sequencing depth and diversity analysis based on OTUs. (a) Rarefaction curve, the horizont https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g006.jpg Figure 6. Effect of L. sakei on the intestinal flora of crucian carp. (a) Linear discriminant analys https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g007.jpg Figure 7. T-text analysis of the intestinal flora of crucian carp based on genus level. (a) PBS vs. https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g008.jpg Figure 8. Effect of L. sakei on the transcriptional gene expression in the intestinal contents of cr https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g009.jpg Figure 9. Bubble chart of top 20 significantly enriched KEGG pathways for each pairwise comparison: https://www.frontiersin.org/files/Articles/1768111/fnut-13-1768111-HTML/image_m/fnut-13-1768111-g010.jpg Figure 10. Differentially expressed genes enriched in key immune and metabolic pathways. Green indic https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1651094/full https://www.frontiersin.org/files/Articles/1651094/fendo-16-1651094-HTML/image_m/fendo-16-1651094-g001.jpg Figure 1. Factors influencing negatively bone health status before and after birth. IUGR, intrauteri https://www.frontiersin.org/files/Articles/1651094/fendo-16-1651094-HTML/image_m/fendo-16-1651094-t001.jpg Table 1. Key points of the main technologies to assess bone health in early infancy. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1652738/full https://www.frontiersin.org/files/Articles/1652738/fmed-12-1652738-HTML/image_m/fmed-12-1652738-t001.jpg Table 1. Cytochemical analysis of peritoneal fluid. https://www.frontiersin.org/files/Articles/1652738/fmed-12-1652738-HTML/image_m/fmed-12-1652738-g001.jpg Figure 1. Timeline of patient evolution. https://www.frontiersin.org/files/Articles/1652738/fmed-12-1652738-HTML/image_m/fmed-12-1652738-t002.jpg Table 2. Reported cases of Streptococcus agalactiae peritonitis associated with peritoneal dialysis. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1753034/full https://www.frontiersin.org/files/Articles/1753034/fpls-17-1753034-HTML-r1/image_m/fpls-17-1753034-g001.jpg Figure 1. (A) Numbers of publications dealing with combined effects of AMF and bacteria in phytoreme https://www.frontiersin.org/files/Articles/1753034/fpls-17-1753034-HTML-r1/image_m/fpls-17-1753034-t001.jpg Table 1. Studies on combined effects of AMF and bacteria in phytoremediation and restoration of meta https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1774128/full https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-g001.jpg Figure 1. Data integration and heterogeneous graph construction. (a–c) Entity/edge types and the het https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-g002.jpg Figure 2. The MSAT representation learning module. (a,b) Inputs and embedding initialization. (c) Mu https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-g003.jpg Figure 3. Link prediction and clinically aligned interpretation. (a) Link prediction for CMM–ADR ass https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-t001.jpg Table 1. Dataset statistics for the constructed heterogeneous graph. https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-g004.jpg Figure 4. Temporal trend of CMM-related adverse event reports in FAERS (annualized, 2004–2024). The https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-t007.jpg Algorithm 1. https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-t002.jpg Table 2. Performance of MSAT and baseline models for CMM–ADR prediction. https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-t003.jpg Table 3. Ablation study of MSAT components for CMM–ADR prediction. https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-g005.jpg Figure 5. Multi-dimensional evaluation of MSAT. (a) Cold-start generalization on a literature-derive https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-g006.jpg Figure 6. Effect of class imbalance on model performance. (a–f) AUC, AUPRC, precision, recall, F1-sc https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-t004.jpg Table 4. End-to-end performance comparison of all models under 1:10 negative sampling. https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-t005.jpg Table 5. Predicted CMM–ADR results: external evidence check for the Top 15 highest-confidence predic https://www.frontiersin.org/files/Articles/1774128/fphar-17-1774128-HTML-r1/image_m/fphar-17-1774128-t006.jpg Table 6. TCM functional system mapping validation results. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1792072/full https://www.frontiersin.org/files/Articles/1792072/fendo-17-1792072-HTML/image_m/fendo-17-1792072-t001.jpg Table 1. Clinical and biochemical characteristics of patients with PA and EH. https://www.frontiersin.org/files/Articles/1792072/fendo-17-1792072-HTML/image_m/fendo-17-1792072-g001.jpg Figure 1. Differences in 24-h Uald levels and UARR between PA and EH patients. (A) Comparison of 24- https://www.frontiersin.org/files/Articles/1792072/fendo-17-1792072-HTML/image_m/fendo-17-1792072-t002.jpg Table 2. Clinical and biochemical characteristics in relation to PA. https://www.frontiersin.org/files/Articles/1792072/fendo-17-1792072-HTML/image_m/fendo-17-1792072-g002.jpg Figure 2. ROC curves of 24-h Uald, UARR, and ARR for the diagnosis of PA. (A) ROC curves without str https://www.frontiersin.org/files/Articles/1792072/fendo-17-1792072-HTML/image_m/fendo-17-1792072-t003.jpg Table 3. Diagnostic performance of 24-h Uald, UARR, and ARR for diagnosing PA. https://www.frontiersin.org/files/Articles/1792072/fendo-17-1792072-HTML/image_m/fendo-17-1792072-t004.jpg Table 4. Clinical and biochemical characteristics of Patients with PA and EH stratified by drug. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1738629/full https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g001.jpg Figure 1. Literature screening process (PRISMA framework). https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-t002.jpg Table 2. Acupuncture regimens were included in the literature. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g002.jpg Figure 2. Network diagram of the meridian-acupoint usage frequency. SP6, Sanyinjiao; ST36, Foot Sanl https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g003.jpg Figure 3. Risk of bias in the included studies. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g004.jpg Figure 4. Risk bias summary plot of the included studies. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g005.jpg Figure 5. Forest plot of the effects of acupuncture on anxiety scores (subgroup analysis: EA vs. MA) https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g006.jpg Figure 6. Funnel chart of anxiety states. Asymmetry was observed but Egger’s test was not significan https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g007.jpg Figure 7. Forest plot of the effects of acupuncture on depression scores (subgroup analysis: EA vs. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g008.jpg Figure 8. Funnel chart of depressive state. Asymmetry was noted; Egger’s test, however, was non-sign https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g009.jpg Figure 9. Effects of acupuncture on testosterone. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g010.jpg Figure 10. Effect of acupuncture on the insulin resistance index. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g011.jpg Figure 11. Effect of acupuncture on body mass index. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g012.jpg Figure 12. Effect of acupuncture on the waist-to-hip ratio. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-t003.jpg Table 3. Coverage of adverse reactions. https://www.frontiersin.org/files/Articles/1738629/fmed-13-1738629-HTML/image_m/fmed-13-1738629-g013.jpg Figure 13. Effect of acupuncture on adverse reactions. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1753894/full https://www.frontiersin.org/files/Articles/1753894/fphar-16-1753894-HTML/image_m/fphar-16-1753894-g001.jpg Figure 1. Workflow summarizing treatments, cell models, and mechanistic analyses. Melanoma cells (SK https://www.frontiersin.org/files/Articles/1753894/fphar-16-1753894-HTML/image_m/fphar-16-1753894-g002.jpg Figure 2. Cytotoxic effects and selectivity of 2-AEH2P and MβCD. Heatmap showing the viability of SK https://www.frontiersin.org/files/Articles/1753894/fphar-16-1753894-HTML/image_m/fphar-16-1753894-t001.jpg Table 1. Half-maximal inhibitory concentration (IC50 ± SD), 95% confidence intervals, and Selectivit https://www.frontiersin.org/files/Articles/1753894/fphar-16-1753894-HTML/image_m/fphar-16-1753894-g003.jpg Figure 3. Morphological changes and cell-cycle alterations induced by 2-AEH2P and MβCD. Representati https://www.frontiersin.org/files/Articles/1753894/fphar-16-1753894-HTML/image_m/fphar-16-1753894-g004.jpg Figure 4. Effects of 2-AEH2P and MβCD on proliferation, mitochondrial function, and apoptotic marker https://www.frontiersin.org/files/Articles/1753894/fphar-16-1753894-HTML/image_m/fphar-16-1753894-g005.jpg Figure 5. Interaction profile of 2-AEH2P and MβCD in melanoma cells. Bliss independence–based intera https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1741714/full https://www.frontiersin.org/files/Articles/1741714/fnut-13-1741714-HTML/image_m/fnut-13-1741714-g001.jpg Figure 1. The metabolic characteristics of the three energy-producing nutrients in pregnant women an https://www.frontiersin.org/files/Articles/1741714/fnut-13-1741714-HTML/image_m/fnut-13-1741714-t001.jpg Table 1. Effects of exercise in women with metabolic diseases during pregnancy. https://www.frontiersin.org/files/Articles/1741714/fnut-13-1741714-HTML/image_m/fnut-13-1741714-t002.jpg Table 2. Prenatal exercise intervention based on FITT principle. https://www.frontiersin.org/files/Articles/1741714/fnut-13-1741714-HTML/image_m/fnut-13-1741714-g002.jpg Figure 2. Benefits of exercise during pregnancy for pregnant women and their offspring. Engaging in https://www.frontiersin.org/files/Articles/1741714/fnut-13-1741714-HTML/image_m/fnut-13-1741714-g003.jpg Figure 3. The molecular and metabolic pathways involved in maternal exercise, and their effects on o https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1691895/full https://www.frontiersin.org/files/Articles/1691895/fbioe-13-1691895-HTML-r1/image_m/fbioe-13-1691895-g001.jpg Figure 1. Typical acetabular posterior column and posterior wall fracture model. https://www.frontiersin.org/files/Articles/1691895/fbioe-13-1691895-HTML-r1/image_m/fbioe-13-1691895-g002.jpg Figure 2. Photographs and radiographic imaging showing the three configurations used for APCWF fixat https://www.frontiersin.org/files/Articles/1691895/fbioe-13-1691895-HTML-r1/image_m/fbioe-13-1691895-g003.jpg Figure 3. Experimental setup. (A) Specimen positioned to simulate standing posture for biomechanical https://www.frontiersin.org/files/Articles/1691895/fbioe-13-1691895-HTML-r1/image_m/fbioe-13-1691895-g004.jpg Figure 4. Violin plot showing overall displacement under 0–1400 N load (*p < 0.05). https://www.frontiersin.org/files/Articles/1691895/fbioe-13-1691895-HTML-r1/image_m/fbioe-13-1691895-g005.jpg Figure 5. Average axial stiffness of fixation constructs under a 1400 N compressive force (*p < 0.05 https://www.frontiersin.org/files/Articles/1691895/fbioe-13-1691895-HTML-r1/image_m/fbioe-13-1691895-g006.jpg Figure 6. Violin plot showing average displacement of posterior wall under 0–1400 N load (*p < 0.05) https://www.frontiersin.org/files/Articles/1691895/fbioe-13-1691895-HTML-r1/image_m/fbioe-13-1691895-g007.jpg Figure 7. Violin plot showing average displacement of posterior column under 0–1400 N load test (*p https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1624969/full https://www.frontiersin.org/files/Articles/1624969/fnhum-19-1624969-HTML/image_m/fnhum-19-1624969-t001.jpg Table 1. Descriptive statistics. https://www.frontiersin.org/files/Articles/1624969/fnhum-19-1624969-HTML/image_m/fnhum-19-1624969-g001.jpg Figure 1. Sway velocity across vision, surface and fatigue conditions. https://www.frontiersin.org/files/Articles/1624969/fnhum-19-1624969-HTML/image_m/fnhum-19-1624969-g002.jpg Figure 2. Sway RMS across vision, surface and fatigue conditions. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1794628/full https://www.frontiersin.org/files/Articles/1794628/fimmu-17-1794628-HTML/image_m/fimmu-17-1794628-g001.jpg Figure 1. Pathophysiological network of diabetic immunometabolic disorders. This diagram illustrates https://www.frontiersin.org/files/Articles/1794628/fimmu-17-1794628-HTML/image_m/fimmu-17-1794628-g002.jpg Figure 2. Impact of diabetes as an immunometabolic disorder on the entire clinical cycle of infectio https://www.frontiersin.org/files/Articles/1794628/fimmu-17-1794628-HTML/image_m/fimmu-17-1794628-t001.jpg Table 1. Clinical characteristics of common infections in the context of diabetes as an immunometabo https://www.frontiersin.org/files/Articles/1794628/fimmu-17-1794628-HTML/image_m/fimmu-17-1794628-t002.jpg Table 2. Immunometabolic characteristics and potential infection risks of common glucose-lowering me https://www.frontiersin.org/files/Articles/1794628/fimmu-17-1794628-HTML/image_m/fimmu-17-1794628-t003.jpg Table 3. Key challenges and future directions in the field of diabetes immunometabolism and infectio https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1769562/full https://www.frontiersin.org/files/Articles/1769562/fonc-16-1769562-HTML/image_m/fonc-16-1769562-t001.jpg Table 1. Correlation between seed activity, implantation parameters, and symptom relief outcomes in https://www.frontiersin.org/files/Articles/1769562/fonc-16-1769562-HTML/image_m/fonc-16-1769562-t002.jpg Table 2. Comparison of VAS scores between the two groups of patients at different time points before https://www.frontiersin.org/files/Articles/1769562/fonc-16-1769562-HTML/image_m/fonc-16-1769562-g001.jpg Figure 1. A 65-year-old male patient with lung cancer and L4 vertebrae bone metastasis. (A) The CT s https://www.frontiersin.org/files/Articles/1769562/fonc-16-1769562-HTML/image_m/fonc-16-1769562-g002.jpg Figure 2. Finite element model of the L4–L5 spinal segment with 125-I seed implantation. (A, B) Ante https://www.frontiersin.org/files/Articles/1769562/fonc-16-1769562-HTML/image_m/fonc-16-1769562-t003.jpg Table 3. Comparison of L4-L5 range of motion (ROM) in the intact finite element model of this study https://www.frontiersin.org/files/Articles/1769562/fonc-16-1769562-HTML/image_m/fonc-16-1769562-t004.jpg Table 4. Material properties for finite element analysis of the lumbar L4-L5 metastasis model. https://www.frontiersin.org/files/Articles/1769562/fonc-16-1769562-HTML/image_m/fonc-16-1769562-g003.jpg Figure 3. von Mises stress distribution in vertebral models across healthy, tumor, and 125-I implant https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1763692/full https://www.frontiersin.org/files/Articles/1763692/fmed-13-1763692-HTML/image_m/fmed-13-1763692-t001.jpg Table 1. Sequences of the forward and reverse primers of the tested genes. https://www.frontiersin.org/files/Articles/1763692/fmed-13-1763692-HTML/image_m/fmed-13-1763692-g001.jpg Figure 1. Characterization of GO and GO-PEG. (A) FTIR spectra of GO and GO-PEG. (B) TEM images of GO https://www.frontiersin.org/files/Articles/1763692/fmed-13-1763692-HTML/image_m/fmed-13-1763692-g002.jpg Figure 2. Transfection, cytocompatibility of GO-PEG/pBMP-2, and characterization of SPEEK-(GO-PEG/pB https://www.frontiersin.org/files/Articles/1763692/fmed-13-1763692-HTML/image_m/fmed-13-1763692-g003.jpg Figure 3. Biocompatibility of SPEEK-(GO-PEG/pBMP-2). (A) Representative SEM images of rBMSC adhesion https://www.frontiersin.org/files/Articles/1763692/fmed-13-1763692-HTML/image_m/fmed-13-1763692-g004.jpg Figure 4. Osteogenic properties of SPEEK-(GO-PEG/pBMP-2). (A) ALP staining of rBMSCs cultured on dif https://www.frontiersin.org/files/Articles/1763692/fmed-13-1763692-HTML/image_m/fmed-13-1763692-g005.jpg Figure 5. Antibacterial activity of SPEEK-(GO-PEG/pBMP-2). (A–D) Colony counts of S. aureus cultured https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2025.1670709/full https://www.frontiersin.org/files/Articles/1670709/fragi-06-1670709-HTML/image_m/fragi-06-1670709-g001.jpg Figure 1. An overview of this study’s design. Created with Biorender.com. https://www.frontiersin.org/files/Articles/1670709/fragi-06-1670709-HTML/image_m/fragi-06-1670709-g002.jpg Figure 2. A CONSORT flow diagram showing the enrollment, group allocation, and analysis of the study https://www.frontiersin.org/files/Articles/1670709/fragi-06-1670709-HTML/image_m/fragi-06-1670709-g003.jpg Figure 3. Representative pretest (left) and posttest (right) B-mode ultrasound images of the vastus https://www.frontiersin.org/files/Articles/1670709/fragi-06-1670709-HTML/image_m/fragi-06-1670709-t001.jpg Table 1. Test-retest statistics for each of the dependent variables in this study. The SEM and MD ar https://www.frontiersin.org/files/Articles/1670709/fragi-06-1670709-HTML/image_m/fragi-06-1670709-t002.jpg Table 2. Table depicting overall and subgroup KOOS scores for all completed participants. https://www.frontiersin.org/files/Articles/1670709/fragi-06-1670709-HTML/image_m/fragi-06-1670709-g004.jpg Figure 4. JASP raincloud plots showing group × time differences for (a) vastus lateralis (VL) and (b https://www.frontiersin.org/files/Articles/1670709/fragi-06-1670709-HTML/image_m/fragi-06-1670709-g005.jpg Figure 5. JASP raincloud plots showing group × time differences for (a) vastus lateralis (VL) and (b https://www.frontiersin.org/files/Articles/1670709/fragi-06-1670709-HTML/image_m/fragi-06-1670709-g006.jpg Figure 6. JASP raincloud plots showing group × time differences for (a) one-repetition maximum (1RM) https://www.frontiersin.org/files/Articles/1670709/fragi-06-1670709-HTML/image_m/fragi-06-1670709-g007.jpg Figure 7. JASP raincloud plots showing group × time differences for (a) isometric peak torque and (b https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1707646/full https://www.frontiersin.org/files/Articles/1707646/fpls-16-1707646-HTML/image_m/fpls-16-1707646-g001.jpg Figure 1. Schematic representation and setup of a benchtop hyperspectral imaging system for cassava https://www.frontiersin.org/files/Articles/1707646/fpls-16-1707646-HTML/image_m/fpls-16-1707646-g002.jpg Figure 2. Workflow of the image classification process, including data collection, processing, hyper https://www.frontiersin.org/files/Articles/1707646/fpls-16-1707646-HTML/image_m/fpls-16-1707646-g003.jpg Figure 3. Accuracy results of classification methods differentiating healthy cassava leaves from tho https://www.frontiersin.org/files/Articles/1707646/fpls-16-1707646-HTML/image_m/fpls-16-1707646-t001.jpg Table 1. Result of the performance evaluation metrics of the models on the test set. https://www.frontiersin.org/files/Articles/1707646/fpls-16-1707646-HTML/image_m/fpls-16-1707646-g004.jpg Figure 4. Cassava leaves spectral reflectance characterization: normalized spectral reflectance curv https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1704550/full https://www.frontiersin.org/files/Articles/1704550/fsufs-09-1704550-HTML/image_m/fsufs-09-1704550-g001.jpg Figure 1. ICTs used by smallholder farmers. Source Authors. https://www.frontiersin.org/files/Articles/1704550/fsufs-09-1704550-HTML/image_m/fsufs-09-1704550-g002.jpg Figure 2. Map showing the study area. Source Authors. https://www.frontiersin.org/files/Articles/1704550/fsufs-09-1704550-HTML/image_m/fsufs-09-1704550-t001.jpg Table 1. Distribution of the age of respondents. https://www.frontiersin.org/files/Articles/1704550/fsufs-09-1704550-HTML/image_m/fsufs-09-1704550-t002.jpg Table 2. Farming experience of respondents in years. https://www.frontiersin.org/files/Articles/1704550/fsufs-09-1704550-HTML/image_m/fsufs-09-1704550-t003.jpg Table 3. The frequency of respondents seeking agricultural information. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1741074/full https://www.frontiersin.org/files/Articles/1741074/fcvm-13-1741074-HTML-r1/image_m/fcvm-13-1741074-i001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1741074/fcvm-13-1741074-HTML-r1/image_m/fcvm-13-1741074-g001.jpg Figure 1. Chest x-Ray of patients with leadless pacemaker vs conventional single-chamber transvenous https://www.frontiersin.org/files/Articles/1741074/fcvm-13-1741074-HTML-r1/image_m/fcvm-13-1741074-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1741074/fcvm-13-1741074-HTML-r1/image_m/fcvm-13-1741074-g002.jpg Figure 2. Kaplan–Meier curves of survival free from the primary composite clinical efficacy endpoint https://www.frontiersin.org/files/Articles/1741074/fcvm-13-1741074-HTML-r1/image_m/fcvm-13-1741074-g003.jpg Figure 3. Kaplan–meier curves of survival free from the primary composite clinical efficacy endpoint https://www.frontiersin.org/files/Articles/1741074/fcvm-13-1741074-HTML-r1/image_m/fcvm-13-1741074-t002.jpg Table 2. Cox univariate and multivariate analysis for predictors of the primary composite clinical e https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1618476/full https://www.frontiersin.org/files/Articles/1618476/fonc-15-1618476-HTML-r1/image_m/fonc-15-1618476-g001.jpg Figure 1. Study design. https://www.frontiersin.org/files/Articles/1618476/fonc-15-1618476-HTML-r1/image_m/fonc-15-1618476-t001.jpg Table 1. COMET inclusion/exclusion criteria (18). https://www.frontiersin.org/files/Articles/1618476/fonc-15-1618476-HTML-r1/image_m/fonc-15-1618476-t002.jpg Table 2. Descriptive and comparative analysis of continuous variables (VAB, EVAB and OVAB). https://www.frontiersin.org/files/Articles/1618476/fonc-15-1618476-HTML-r1/image_m/fonc-15-1618476-t003.jpg Table 3. Descriptive and comparative analysis of categorical variables (VAB, EVAB, and OVAB). https://www.frontiersin.org/files/Articles/1618476/fonc-15-1618476-HTML-r1/image_m/fonc-15-1618476-t004.jpg Table 4. VAB LR-DCIS comparison to surgical pathology. https://www.frontiersin.org/files/Articles/1618476/fonc-15-1618476-HTML-r1/image_m/fonc-15-1618476-t005.jpg Table 5. Contingency table comparing results from VAB, OVAB, EVAB vs. surgery (Gold Standard). https://www.frontiersin.org/files/Articles/1618476/fonc-15-1618476-HTML-r1/image_m/fonc-15-1618476-t006.jpg Table 6. VAB, OVAB, EVAB results. https://www.frontiersin.org/files/Articles/1618476/fonc-15-1618476-HTML-r1/image_m/fonc-15-1618476-t007.jpg Table 7. VAB LR-DCIS cases matched surgical outcome and COMET criteria. https://www.frontiersin.org/files/Articles/1618476/fonc-15-1618476-HTML-r1/image_m/fonc-15-1618476-g002.jpg Figure 2. VAB real world data for LR-DCIS active monitoring. VAB, Vacuum assisted biopsy; HR-DCIS, D https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1658961/full https://www.frontiersin.org/files/Articles/1658961/fnut-12-1658961-HTML/image_m/fnut-12-1658961-t001.jpg Table 1. Specific formula ingredients of whole grains. https://www.frontiersin.org/files/Articles/1658961/fnut-12-1658961-HTML/image_m/fnut-12-1658961-t002.jpg Table 2. Setting parameters of the mass spectrometer instrument. https://www.frontiersin.org/files/Articles/1658961/fnut-12-1658961-HTML/image_m/fnut-12-1658961-t003.jpg Table 3. Baseline, interim, and final vitamin D levels (ng/mL). https://www.frontiersin.org/files/Articles/1658961/fnut-12-1658961-HTML/image_m/fnut-12-1658961-g001.jpg Figure 1. Vitamin D levels and physical activity levels in study groups across different time period https://www.frontiersin.org/files/Articles/1658961/fnut-12-1658961-HTML/image_m/fnut-12-1658961-t004.jpg Table 4. Distribution of participants with normal levels of 25-OH VD in each group at different time https://www.frontiersin.org/files/Articles/1658961/fnut-12-1658961-HTML/image_m/fnut-12-1658961-g002.jpg Figure 2. Changes in the vitamin D level within study groups across different time periods: (a) 25-O https://www.frontiersin.org/files/Articles/1658961/fnut-12-1658961-HTML/image_m/fnut-12-1658961-t005.jpg Table 5. Comparison of vitamin D levels among participants within groups at different time periods. https://www.frontiersin.org/files/Articles/1658961/fnut-12-1658961-HTML/image_m/fnut-12-1658961-t006.jpg Table 6. Comparison among different groups in the same period. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1774209/full https://www.frontiersin.org/files/Articles/1774209/fnut-13-1774209-HTML/image_m/fnut-13-1774209-g001.jpg Figure 1. Study design and flow chart. https://www.frontiersin.org/files/Articles/1774209/fnut-13-1774209-HTML/image_m/fnut-13-1774209-t001.jpg Table 1. Characteristics of the participants at baseline. https://www.frontiersin.org/files/Articles/1774209/fnut-13-1774209-HTML/image_m/fnut-13-1774209-g002.jpg Figure 2. Weight change from baseline to 12 weeks. https://www.frontiersin.org/files/Articles/1774209/fnut-13-1774209-HTML/image_m/fnut-13-1774209-t002.jpg Table 2. Effects of whole grain on weight loss and body composition. https://www.frontiersin.org/files/Articles/1774209/fnut-13-1774209-HTML/image_m/fnut-13-1774209-t003.jpg Table 3. Effects of whole grain on cardiovascular risk factors during 12-week trial period. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1718718/full https://www.frontiersin.org/files/Articles/1718718/fpsyg-16-1718718-HTML/image_m/fpsyg-16-1718718-t001.jpg Table 1. Breakdown of each test item and score in the KA screening test, distribution of each segmen https://www.frontiersin.org/files/Articles/1718718/fpsyg-16-1718718-HTML/image_m/fpsyg-16-1718718-g001.jpg Figure 1. Inclusion and exclusion criteria. KA, KOJI AWARENESS. https://www.frontiersin.org/files/Articles/1718718/fpsyg-16-1718718-HTML/image_m/fpsyg-16-1718718-t002.jpg Table 2. Demographic characteristics of participants. https://www.frontiersin.org/files/Articles/1718718/fpsyg-16-1718718-HTML/image_m/fpsyg-16-1718718-t003.jpg Table 3. Differences between groups in scores and KA change rate. https://www.frontiersin.org/files/Articles/1718718/fpsyg-16-1718718-HTML/image_m/fpsyg-16-1718718-g002.jpg Figure 2. Differences in scores between groups in the total score and each segment score. *p = 0.009 https://www.frontiersin.org/files/Articles/1718718/fpsyg-16-1718718-HTML/image_m/fpsyg-16-1718718-g003.jpg Figure 3. Differences between groups in seasonal change in the total score and each segment score *p https://www.frontiersin.org/files/Articles/1718718/fpsyg-16-1718718-HTML/image_m/fpsyg-16-1718718-g004.jpg Figure 4. Differences between groups in the seasonal rate of change in the total score and each segm https://www.frontiersin.org/files/Articles/1718718/fpsyg-16-1718718-HTML/image_m/fpsyg-16-1718718-t004.jpg Table 4. Rate of injury in each group. https://www.frontiersin.org/files/Articles/1718718/fpsyg-16-1718718-HTML/image_m/fpsyg-16-1718718-t005.jpg Table 5. Types of running-related injuries and competition and training time lost. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1792551/full https://www.frontiersin.org/files/Articles/1792551/fmed-13-1792551-HTML/image_m/fmed-13-1792551-g001.jpg Figure 1. Abdominal CT scan of the patient post admission. (A) Baseline (3 months before admission): https://www.frontiersin.org/files/Articles/1792551/fmed-13-1792551-HTML/image_m/fmed-13-1792551-t001.jpg Table 1. Results of antimicrobial susceptibility testing for S. putrefaciens. https://www.frontiersin.org/files/Articles/1792551/fmed-13-1792551-HTML/image_m/fmed-13-1792551-t002.jpg Table 2. Results of antimicrobial susceptibility testing for E. faecium. https://www.frontiersin.org/files/Articles/1792551/fmed-13-1792551-HTML/image_m/fmed-13-1792551-t003.jpg Table 3. Laboratory results of the patient. https://www.frontiersin.org/files/Articles/1792551/fmed-13-1792551-HTML/image_m/fmed-13-1792551-g002.jpg Figure 2. Clinical management flowchart of the case presentation. https://www.frontiersin.org/files/Articles/1792551/fmed-13-1792551-HTML/image_m/fmed-13-1792551-t004.jpg Table 4. Summary of reported cases of biliary tract/cholecystitis caused by S. putrefaciens. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1731122/full https://www.frontiersin.org/files/Articles/1731122/fendo-17-1731122-HTML/image_m/fendo-17-1731122-g001.jpg Figure 1. Flow diagram for the selection of participants included in the present analysis. https://www.frontiersin.org/files/Articles/1731122/fendo-17-1731122-HTML/image_m/fendo-17-1731122-t001.jpg Table 1. Comparison of BMD across demographic and clinical variables (Mean ± SD). https://www.frontiersin.org/files/Articles/1731122/fendo-17-1731122-HTML/image_m/fendo-17-1731122-g002.jpg Figure 2. Comparison of BMD by categorical variables with statistically significant differences. Box https://www.frontiersin.org/files/Articles/1731122/fendo-17-1731122-HTML/image_m/fendo-17-1731122-t002.jpg Table 2. Spearman’s correlation between BMD and related variables. https://www.frontiersin.org/files/Articles/1731122/fendo-17-1731122-HTML/image_m/fendo-17-1731122-g003.jpg Figure 3. Scatterplot showing the correlation between BMD and kinesiophobia (TSK). https://www.frontiersin.org/files/Articles/1731122/fendo-17-1731122-HTML/image_m/fendo-17-1731122-t003.jpg Table 3. Regression coefficients for BMD predictors. https://www.frontiersin.org/files/Articles/1731122/fendo-17-1731122-HTML/image_m/fendo-17-1731122-g004.jpg Figure 4. Forest plot of factors associated with BMD from multivariable linear regression analysis. https://www.frontiersin.org/files/Articles/1731122/fendo-17-1731122-HTML/image_m/fendo-17-1731122-t004.jpg Table 4. Model summary. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1752395/full https://www.frontiersin.org/files/Articles/1752395/fmolb-13-1752395-HTML/image_m/fmolb-13-1752395-t001.jpg Table 1. Primer sequences for RT-qPCR analysis of AFI axis genes. https://www.frontiersin.org/files/Articles/1752395/fmolb-13-1752395-HTML/image_m/fmolb-13-1752395-t002.jpg Table 2. Antibody list used in the study. https://www.frontiersin.org/files/Articles/1752395/fmolb-13-1752395-HTML/image_m/fmolb-13-1752395-g001.jpg Figure 1. Functional and Morphological Validation of the Spinal Cord Injury Model. (A) Representativ https://www.frontiersin.org/files/Articles/1752395/fmolb-13-1752395-HTML/image_m/fmolb-13-1752395-g002.jpg Figure 2. Expression Profile of AFI Axis Genes in Bladder and Kidney from Sham and SCI Groups. Quant https://www.frontiersin.org/files/Articles/1752395/fmolb-13-1752395-HTML/image_m/fmolb-13-1752395-g003.jpg Figure 3. Protein Expression of AFI Axis Members in Bladder from Sham and SCI Groups. (A–C) Western https://www.frontiersin.org/files/Articles/1752395/fmolb-13-1752395-HTML/image_m/fmolb-13-1752395-g004.jpg Figure 4. Protein Expression of AFI Axis Members in Kidney from Sham and SCI Groups. (A–D) Western b https://www.frontiersin.org/files/Articles/1752395/fmolb-13-1752395-HTML/image_m/fmolb-13-1752395-g005.jpg Figure 5. Immunofluorescence analysis of AFI-axis proteins in the bladder. Representative immunofluo https://www.frontiersin.org/files/Articles/1752395/fmolb-13-1752395-HTML/image_m/fmolb-13-1752395-g006.jpg Figure 6. Immunofluorescence analysis of AFI-axis proteins in the kidney. Representative immunofluor https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1633987/full https://www.frontiersin.org/files/Articles/1633987/fdgth-07-1633987-HTML/image_m/fdgth-07-1633987-g001.jpg Figure 1. Screens from the pathway prototype. (a) Home screen. (b) WHO-5 wellbeing survey (Q1). (c) https://www.frontiersin.org/files/Articles/1633987/fdgth-07-1633987-HTML/image_m/fdgth-07-1633987-g002.jpg Figure 2. Screens from the pathway prototype (contd.). (a) Preferences screen. (b) Recommendations. https://www.frontiersin.org/files/Articles/1633987/fdgth-07-1633987-HTML/image_m/fdgth-07-1633987-t001.jpg Table 1. Changes following pretest sessions. https://www.frontiersin.org/files/Articles/1633987/fdgth-07-1633987-HTML/image_m/fdgth-07-1633987-t002.jpg Table 2. Demographic characteristics of participants. https://www.frontiersin.org/files/Articles/1633987/fdgth-07-1633987-HTML/image_m/fdgth-07-1633987-t003.jpg Table 3. Changes following each cycle of usability tests. https://www.frontiersin.org/files/Articles/1633987/fdgth-07-1633987-HTML/image_m/fdgth-07-1633987-t004.jpg Table 4. Responses to single ease question (Cycle 3). https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1665975/full https://www.frontiersin.org/files/Articles/1665975/fdgth-07-1665975-HTML/image_m/fdgth-07-1665975-g001.jpg Figure 1. PRISMA flowchart. https://www.frontiersin.org/files/Articles/1665975/fdgth-07-1665975-HTML/image_m/fdgth-07-1665975-t001.jpg Table 1. Key features of target populations. https://www.frontiersin.org/files/Articles/1665975/fdgth-07-1665975-HTML/image_m/fdgth-07-1665975-t002.jpg Table 2. Key features of target objectives and outcomes. https://www.frontiersin.org/files/Articles/1665975/fdgth-07-1665975-HTML/image_m/fdgth-07-1665975-t003.jpg Table 3. Key features of intervention parameters. https://www.frontiersin.org/files/Articles/1665975/fdgth-07-1665975-HTML/image_m/fdgth-07-1665975-t004.jpg Table 4. Key features of intervention examined. https://www.frontiersin.org/files/Articles/1665975/fdgth-07-1665975-HTML/image_m/fdgth-07-1665975-t005.jpg Table 5. Key features of intervention delivery. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1783902/full https://www.frontiersin.org/files/Articles/1783902/fimmu-17-1783902-HTML/image_m/fimmu-17-1783902-g001.jpg Figure 1. Sequence of Tpr chimera constructs generated by GenScript. DNA encoding TprC/D or TprK chi https://www.frontiersin.org/files/Articles/1783902/fimmu-17-1783902-HTML/image_m/fimmu-17-1783902-g002.jpg Figure 2. Detection of surface expression of TprD and TprK in B. burgdorferi B314 strain. IFA was pe https://www.frontiersin.org/files/Articles/1783902/fimmu-17-1783902-HTML/image_m/fimmu-17-1783902-g003.jpg Figure 3. Immunofluorescence analysis using convalescent patient and rabbit sera confirm the express https://www.frontiersin.org/files/Articles/1783902/fimmu-17-1783902-HTML/image_m/fimmu-17-1783902-g004.jpg Figure 4. Detection of TprD and TprK on the surface of T. pallidum co-cultured with Sf1Ep cells in v https://www.frontiersin.org/files/Articles/1783902/fimmu-17-1783902-HTML/image_m/fimmu-17-1783902-g005.jpg Figure 5. IFA-based detection of surface proteins of T. pallidum Nichols and SS14 with secondary syp https://www.frontiersin.org/files/Articles/1783902/fimmu-17-1783902-HTML/image_m/fimmu-17-1783902-g006.jpg Figure 6. Opsonophagocytosis of B314 strains labeled with anti-TprD and anti-TprK antibodies with pH https://www.frontiersin.org/files/Articles/1783902/fimmu-17-1783902-HTML/image_m/fimmu-17-1783902-g007.jpg Figure 7. Opsonophagocytosis of T. pallidum Nichols and SS14 strains labeled with anti-TprD and anti https://www.frontiersin.org/files/Articles/1783902/fimmu-17-1783902-HTML/image_m/fimmu-17-1783902-g008.jpg Figure 8. B314 acquires ability to bind to mammalian cells after TprD and TprK expression. Radiolabe https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1695321/full https://www.frontiersin.org/files/Articles/1695321/fimmu-16-1695321-HTML/image_m/fimmu-16-1695321-g001.jpg Figure 1. Pathogenesis of irritable bowel syndrome. This figure illustrates various factors contribu https://www.frontiersin.org/files/Articles/1695321/fimmu-16-1695321-HTML/image_m/fimmu-16-1695321-t001.jpg Table 1. Summary of studies on IBS and gut microbiota dysbiosis. https://www.frontiersin.org/files/Articles/1695321/fimmu-16-1695321-HTML/image_m/fimmu-16-1695321-t002.jpg Table 2. Structured taxonomy & functional roles in IBS. https://www.frontiersin.org/files/Articles/1695321/fimmu-16-1695321-HTML/image_m/fimmu-16-1695321-t003.jpg Table 3. Subtype-specific features and potential microbiome-targeted strategies. https://www.frontiersin.org/files/Articles/1695321/fimmu-16-1695321-HTML/image_m/fimmu-16-1695321-g002.jpg Figure 2. Microbiome-targeted interventions for irritable bowel syndrome. This figure provides a com https://www.frontiersin.org/files/Articles/1695321/fimmu-16-1695321-HTML/image_m/fimmu-16-1695321-t004.jpg Table 4. Summary of microbiota-targeted treatments for IBS. https://www.frontiersin.org/files/Articles/1695321/fimmu-16-1695321-HTML/image_m/fimmu-16-1695321-t005.jpg Table 5. Evidence map of microbiome-related interventions in IBS. https://www.frontiersin.org/files/Articles/1695321/fimmu-16-1695321-HTML/image_m/fimmu-16-1695321-t006.jpg Table 6. Summary of novel treatment strategies for IBS. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1665510/full https://www.frontiersin.org/files/Articles/1665510/fnagi-17-1665510-HTML-r1/image_m/fnagi-17-1665510-g001.jpg Figure 1. Impact of misdiagnosis on functional cognitive disorder outcomes. https://www.frontiersin.org/files/Articles/1665510/fnagi-17-1665510-HTML-r1/image_m/fnagi-17-1665510-t001.jpg Table 1. Psychological features and metacognitive distortions in functional cognitive disorder. https://www.frontiersin.org/files/Articles/1665510/fnagi-17-1665510-HTML-r1/image_m/fnagi-17-1665510-g002.jpg Figure 2. Key concepts in understanding functional cognitive disorder. https://www.frontiersin.org/files/Articles/1665510/fnagi-17-1665510-HTML-r1/image_m/fnagi-17-1665510-t002.jpg Table 2. Assessment tools and diagnostic aids in functional cognitive disorder. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1735735/full https://www.frontiersin.org/files/Articles/1735735/fimmu-17-1735735-HTML/image_m/fimmu-17-1735735-g001.jpg Figure 1. Overview of the PhIP-Seq workflow. The schematic illustrates the major steps of Phage-Immu https://www.frontiersin.org/files/Articles/1735735/fimmu-17-1735735-HTML/image_m/fimmu-17-1735735-g002.jpg Figure 2. Challenges and optimization strategies in PhIP-Seq library design. A central Venn diagram https://www.frontiersin.org/files/Articles/1735735/fimmu-17-1735735-HTML/image_m/fimmu-17-1735735-t001.jpg Table 1. Representative applications of PhIP-Seq and clinical implications. https://www.frontiersin.org/files/Articles/1735735/fimmu-17-1735735-HTML/image_m/fimmu-17-1735735-g003.jpg Figure 3. Clinical applications of PhIP-Seq across immunological domains. The landscape diagram cate https://www.frontiersin.org/files/Articles/1735735/fimmu-17-1735735-HTML/image_m/fimmu-17-1735735-g004.jpg Figure 4. Future directions and integration of PhIP-Seq into multi-omics and precision medicine fram https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1748832/full https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t001.jpg Table 1. Definitions and construction of variables. https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t002.jpg Table 2. Descriptive statistics of key variables. https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t003.jpg Table 3. Baseline regression results: parental involvement and children’s abilities. https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t004.jpg Table 4. Effects of parental involvement on different dimensions of non-cognitive abilities. https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t005.jpg Table 5. Robustness test: quantile regressions (bootstrap SEs). https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t006.jpg Table 6. A quantile regression analysis for non-cognitive abilities. https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t007.jpg Table 7. Mechanism analysis: mediating channels of parental involvement. https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t008.jpg Table 8. Heterogeneity analysis for cognitive ability. https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t009.jpg Table 9. Heterogeneity analysis for non-cognitive abilities. https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t010.jpg Table 10. Moderating effects of parental educational aspirations and academic pressure on the relati https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-g001.jpg Figure 1. Marginal effect of emotional closeness on cognitive ability across levels of parental educ https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-t011.jpg Table 11. Moderating effects of parental educational aspirations and academic pressure on non-cognit https://www.frontiersin.org/files/Articles/1748832/fpsyg-17-1748832-HTML/image_m/fpsyg-17-1748832-g002.jpg Figure 2. Marginal effect of parental activity frequency on emotional stability by parental academic https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1748408/full https://www.frontiersin.org/files/Articles/1748408/fpls-17-1748408-HTML/image_m/fpls-17-1748408-g001.jpg Figure 1. Effects of different microbial application methods on agronomic traits of tobacco. (a) Pla https://www.frontiersin.org/files/Articles/1748408/fpls-17-1748408-HTML/image_m/fpls-17-1748408-g002.jpg Figure 2. Responses of flue-cured tobacco yield and grade to microbial application using different m https://www.frontiersin.org/files/Articles/1748408/fpls-17-1748408-HTML/image_m/fpls-17-1748408-t001.jpg Table 1. Soil physicochemical properties affected by different microbial application methods. https://www.frontiersin.org/files/Articles/1748408/fpls-17-1748408-HTML/image_m/fpls-17-1748408-g003.jpg Figure 3. Diversity and abundance patterns of rhizosphere microbial communities under microbial appl https://www.frontiersin.org/files/Articles/1748408/fpls-17-1748408-HTML/image_m/fpls-17-1748408-g004.jpg Figure 4. Rhizosphere microbial networks shaped by different microbial application methods. (a) Bact https://www.frontiersin.org/files/Articles/1748408/fpls-17-1748408-HTML/image_m/fpls-17-1748408-t002.jpg Table 2. Topological properties of rhizosphere microbial networks under microbial application using https://www.frontiersin.org/files/Articles/1748408/fpls-17-1748408-HTML/image_m/fpls-17-1748408-g005.jpg Figure 5. Relationships of rhizosphere microbial communities, soil physicochemical properties, tobac https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1722245/full https://www.frontiersin.org/files/Articles/1722245/fendo-16-1722245-HTML-r1/image_m/fendo-16-1722245-g001.jpg Figure 1. The purine metabolism process of hyperuricemia and the mechanism of action of various inte https://www.frontiersin.org/files/Articles/1722245/fendo-16-1722245-HTML-r1/image_m/fendo-16-1722245-g002.jpg Figure 2. Schematic diagram of purine metabolism pathway and uric acid generation mechanism. https://www.frontiersin.org/files/Articles/1722245/fendo-16-1722245-HTML-r1/image_m/fendo-16-1722245-t001.jpg Table 1. Comparison of commonly used western medicines for lowering uric. https://www.frontiersin.org/files/Articles/1722245/fendo-16-1722245-HTML-r1/image_m/fendo-16-1722245-t002.jpg Table 2. Representative Traditional Chinese Medicines/monomers with uric acid-lowering activity. https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2026.1776174/full https://www.frontiersin.org/files/Articles/1776174/frph-08-1776174-HTML/image_m/frph-08-1776174-g001.jpg Figure 1. Study flow and availability of follow-up windows. A schematic summary of the clinical coho https://www.frontiersin.org/files/Articles/1776174/frph-08-1776174-HTML/image_m/frph-08-1776174-t001.jpg Table 1. Symptom improvement from baseline (median and responder rate). https://www.frontiersin.org/files/Articles/1776174/frph-08-1776174-HTML/image_m/frph-08-1776174-t002.jpg Table 2A. Baseline characteristics (overall). https://www.frontiersin.org/files/Articles/1776174/frph-08-1776174-HTML/image_m/frph-08-1776174-t003.jpg Table 2B. Age distribution at first treatment. https://www.frontiersin.org/files/Articles/1776174/frph-08-1776174-HTML/image_m/frph-08-1776174-t004.jpg Table 2C. Distribution of total laser sessions per patient. https://www.frontiersin.org/files/Articles/1776174/frph-08-1776174-HTML/image_m/frph-08-1776174-t005.jpg Table 2D. Baseline symptom burden (VAS). https://www.frontiersin.org/files/Articles/1776174/frph-08-1776174-HTML/image_m/frph-08-1776174-g002.jpg Figure 2. Symptom trajectories after fractional CO2 laser therapy. Mean VAS scores for six symptoms https://www.frontiersin.org/files/Articles/1776174/frph-08-1776174-HTML/image_m/frph-08-1776174-t006.jpg Table 3. Dose–response (short-term): responder rate by total sessions for key symptoms. https://www.frontiersin.org/files/Articles/1776174/frph-08-1776174-HTML/image_m/frph-08-1776174-g003.jpg Figure 3. Patient satisfaction after fractional CO2 laser therapy. Patient-reported satisfaction was https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1786997/full https://www.frontiersin.org/files/Articles/1786997/fendo-17-1786997-HTML/image_m/fendo-17-1786997-g001.jpg Figure 1. Flow chart. AF, atrial fibrillation; MIMIC, Medical Information Mart for Intensive Care; U https://www.frontiersin.org/files/Articles/1786997/fendo-17-1786997-HTML/image_m/fendo-17-1786997-t001.jpg Table 1. Baseline characteristics for primary cohort. https://www.frontiersin.org/files/Articles/1786997/fendo-17-1786997-HTML/image_m/fendo-17-1786997-g002.jpg Figure 2. (a) Kaplan–Meier curves for 1-year all-cause mortality according to quartiles of the UAR. https://www.frontiersin.org/files/Articles/1786997/fendo-17-1786997-HTML/image_m/fendo-17-1786997-t002.jpg Table 2. Association between UAR and 1-year all-cause mortality in Cox proportional hazards analyses https://www.frontiersin.org/files/Articles/1786997/fendo-17-1786997-HTML/image_m/fendo-17-1786997-t003.jpg Table 3. Association between UAR and 1-year all-cause mortality in Cox proportional hazards analyses https://www.frontiersin.org/files/Articles/1786997/fendo-17-1786997-HTML/image_m/fendo-17-1786997-g003.jpg Figure 3. (a) Time-dependent AUC comparison of CHA2DS2-VASc with and without UAR in primary cohort. https://www.frontiersin.org/files/Articles/1786997/fendo-17-1786997-HTML/image_m/fendo-17-1786997-g004.jpg Figure 4. (a) RSF-SHAP variable importance in primary cohort. (b) RSF-SHAP beeswarm plot showing fea https://www.frontiersin.org/files/Articles/1786997/fendo-17-1786997-HTML/image_m/fendo-17-1786997-g005.jpg Figure 5. Subgroup analyses of the association between UAR and 1-year all-cause mortality in primary https://www.frontiersin.org/files/Articles/1786997/fendo-17-1786997-HTML/image_m/fendo-17-1786997-g006.jpg Figure 6. (a) Nonlinear association between the UAR and 1-year cardiac mortality assessed using rest https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1801577/full https://www.frontiersin.org/files/Articles/1801577/fnagi-18-1801577-HTML/image_m/fnagi-18-1801577-t001.jpg Table 1. Characteristics of the study population stratified by PDFF or cT1. https://www.frontiersin.org/files/Articles/1801577/fnagi-18-1801577-HTML/image_m/fnagi-18-1801577-t002.jpg Table 2. Association of PDFF and cT1 with brain-PAD. https://www.frontiersin.org/files/Articles/1801577/fnagi-18-1801577-HTML/image_m/fnagi-18-1801577-g001.jpg Figure 1. Multiple adjusted restricted cubic splines showing Brain-PAD associated with log transform https://www.frontiersin.org/files/Articles/1801577/fnagi-18-1801577-HTML/image_m/fnagi-18-1801577-g002.jpg Figure 2. Joint effects of PDFF, cT1, APOEε4, and PRSAD on brain-PAD. SE, standard error; PDFF, prot https://www.frontiersin.org/files/Articles/1801577/fnagi-18-1801577-HTML/image_m/fnagi-18-1801577-g003.jpg Figure 3. Joint effects of PDFF, cT1, and lifestyle on brain-PAD. SE, standard error; PDFF, proton d https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1672117/full https://www.frontiersin.org/files/Articles/1672117/fimmu-16-1672117-HTML/image_m/fimmu-16-1672117-g001.jpg Figure 1. C. rodentium induced acute colitis within two weeks. C. rodentium (1.0×109 colony forming https://www.frontiersin.org/files/Articles/1672117/fimmu-16-1672117-HTML/image_m/fimmu-16-1672117-g002.jpg Figure 2. Histological analysis and cytokine profiles of C. rodentium-infected mice in acute phase. https://www.frontiersin.org/files/Articles/1672117/fimmu-16-1672117-HTML/image_m/fimmu-16-1672117-g003.jpg Figure 3. C. rodentium induced visceral sensitivity in TLR9 KO mice. C. rodentium or PBS was adminis https://www.frontiersin.org/files/Articles/1672117/fimmu-16-1672117-HTML/image_m/fimmu-16-1672117-g004.jpg Figure 4. Histological analysis and cytokine profiles of C. rodentium-infected mice in recovered pha https://www.frontiersin.org/files/Articles/1672117/fimmu-16-1672117-HTML/image_m/fimmu-16-1672117-t001.jpg Table 1. Pathway analysis. https://www.frontiersin.org/files/Articles/1672117/fimmu-16-1672117-HTML/image_m/fimmu-16-1672117-g005.jpg Figure 5. Bradykinin receptors upregulated in C. rodentium-infected TLR9 KO mice. Expression levels https://www.frontiersin.org/files/Articles/1672117/fimmu-16-1672117-HTML/image_m/fimmu-16-1672117-g006.jpg Figure 6. Bdkrb1 and Bdkrb2 expressed in intestinal mucosal epithelium but not enteric nervous syste https://www.frontiersin.org/files/Articles/1672117/fimmu-16-1672117-HTML/image_m/fimmu-16-1672117-g007.jpg Figure 7. Neutralizing Bdkrb1 and Bdkrb2 ameliorate visceral hypersensitivity. R715 (1 mg/kg), HOE 1 https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1602666/full https://www.frontiersin.org/files/Articles/1602666/fcvm-12-1602666-HTML/image_m/fcvm-12-1602666-g001.jpg Figure 1. The flow chart of the trial. https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2026.1779949/full https://www.frontiersin.org/files/Articles/1779949/frhs-06-1779949-HTML/image_m/frhs-06-1779949-t001.jpg Table 1. Demographic data of participants. https://www.frontiersin.org/files/Articles/1779949/frhs-06-1779949-HTML/image_m/frhs-06-1779949-t002.jpg Table 2. Association of demographic data and radiation protection knowledge scores. https://www.frontiersin.org/files/Articles/1779949/frhs-06-1779949-HTML/image_m/frhs-06-1779949-t003.jpg Table 3. Multivariate analysis of CT requests and demographic confounders. https://www.frontiersin.org/files/Articles/1779949/frhs-06-1779949-HTML/image_m/frhs-06-1779949-g001.jpg Figure 1. Reasons for ordering CT scans without clear clinical indications. Respondents were asked: https://www.frontiersin.org/files/Articles/1779949/frhs-06-1779949-HTML/image_m/frhs-06-1779949-t004.jpg Table 4. Ad hoc analysis of participants’ response. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1750441/full https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g001.jpg Figure 1. Flowchart of study selection. https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-t001.jpg Table 1. Baseline characteristics of the included studies. https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g002.jpg Figure 2. Quality evaluation of the included studies. https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g003.jpg Figure 3. Quality evaluation of the included studies. https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g004.jpg Figure 4. Comparison of NRS scores in PDN patients treated with pregabalin combined with duloxetine. https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g005.jpg Figure 5. Comparison of VAS scores in PDN patients treated with pregabalin combined with duloxetine. https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g006.jpg Figure 6. Comparison of BPI-MSF scores in PDN patients treated with pregabalin combined with duloxet https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g007.jpg Figure 7. Comparison of PDQ scores in PDN patients treated with pregabalin combined with duloxetine. https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g008.jpg Figure 8. Comparison of the number of patients achieving≥50% pain relief in PDN patients treated wit https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g009.jpg Figure 9. Comparison of the number of patients achieving≥30% pain relief in PDN patients treated wit https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g010.jpg Figure 10. Comparison of adverse event incidence with pregabalin combined with duloxetine. https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g011.jpg Figure 11. Comparison of somnolence incidence with pregabalin combined with duloxetine. https://www.frontiersin.org/files/Articles/1750441/fendo-17-1750441-HTML/image_m/fendo-17-1750441-g012.jpg Figure 12. Comparison of nausea/vomiting incidence with pregabalin combined with duloxetine. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1751808/full https://www.frontiersin.org/files/Articles/1751808/fphys-17-1751808-HTML/image_m/fphys-17-1751808-g001.jpg Figure 1. (a) static stretching and (b) PNF stretching. https://www.frontiersin.org/files/Articles/1751808/fphys-17-1751808-HTML/image_m/fphys-17-1751808-g002.jpg Figure 2. Sit and reach test (For hamstring flexibility evaluated). https://www.frontiersin.org/files/Articles/1751808/fphys-17-1751808-HTML/image_m/fphys-17-1751808-g003.jpg Figure 3. Vertical jump height evaluated. https://www.frontiersin.org/files/Articles/1751808/fphys-17-1751808-HTML/image_m/fphys-17-1751808-g004.jpg Figure 4. Broad jump distance evaluated. https://www.frontiersin.org/files/Articles/1751808/fphys-17-1751808-HTML/image_m/fphys-17-1751808-g005.jpg Figure 5. Static postural stability evaluated. https://www.frontiersin.org/files/Articles/1751808/fphys-17-1751808-HTML/image_m/fphys-17-1751808-g006.jpg Figure 6. Dynamic balance evaluated (Y-Balance test). https://www.frontiersin.org/files/Articles/1751808/fphys-17-1751808-HTML/image_m/fphys-17-1751808-t001.jpg Table 1. Summary of two-way repeated measures ANOVA for all dependent variables. https://www.frontiersin.org/files/Articles/1751808/fphys-17-1751808-HTML/image_m/fphys-17-1751808-t002.jpg Table 2. Pre- and post-intervention performance measures and simple main effect comparisons across c https://www.frontiersin.org/files/Articles/1751808/fphys-17-1751808-HTML/image_m/fphys-17-1751808-t003.jpg Table 3. Pre- and post-intervention static balance performance (COP) and simple main effect comparis https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1769853/full https://www.frontiersin.org/files/Articles/1769853/fmars-13-1769853-HTML-r1/image_m/fmars-13-1769853-g001.jpg Figure 1. Bathymetry (left) and map of the MSFD broad habitat types (right) present in the GSA20 [da https://www.frontiersin.org/files/Articles/1769853/fmars-13-1769853-HTML-r1/image_m/fmars-13-1769853-g002.jpg Figure 2. (A) Distribution of the MPAs and other protected areas in the study area. MPAs within GSA2 https://www.frontiersin.org/files/Articles/1769853/fmars-13-1769853-HTML-r1/image_m/fmars-13-1769853-g003.jpg Figure 3. (A) Swept area ratio (SAR) and (B) baseline benthic conditions as reflected by the RBS ind https://www.frontiersin.org/files/Articles/1769853/fmars-13-1769853-HTML-r1/image_m/fmars-13-1769853-t001.jpg Table 1. Area, SAR, proportion of trawled cells and RBS estimated per MSFD broad habitat type in the https://www.frontiersin.org/files/Articles/1769853/fmars-13-1769853-HTML-r1/image_m/fmars-13-1769853-t002.jpg Table 2. Wilcoxon signed-rank test results comparing RBS indicators between baseline conditions and https://www.frontiersin.org/files/Articles/1769853/fmars-13-1769853-HTML-r1/image_m/fmars-13-1769853-g004.jpg Figure 4. Predicted benthic conditions in the study area as reflected by the RBS indicator under the https://www.frontiersin.org/files/Articles/1769853/fmars-13-1769853-HTML-r1/image_m/fmars-13-1769853-g005.jpg Figure 5. Predicted benthic conditions as reflected in RBS indicator under the dynamic Scenario 5 in https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1745613/full https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g001.jpg Figure 1. Study site and layout of geophysical survey profiles. (a) Geographic location of the study https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g002.jpg Figure 2. (a) Field data acquisition of ERT method, (b) Schematic diagram of data acquisition mode. https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g003.jpg Figure 3. Field data acquisition using the EMI method. https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g004.jpg Figure 4. Ground temperature profiles during two data acquisition campaigns. https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g005.jpg Figure 5. (a) Surface characteristics and the location of the ERT survey line (P1, orange line) and https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g006.jpg Figure 6. (a) Surface conditions, and (b) ERT inversion results at survey line P2. https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g007.jpg Figure 7. (a) Surface characteristics, and (b) EMI inversion results at survey line P3. https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g008.jpg Figure 8. (a) Surface characteristics, (b) ERT inversion results, and (c) EMI inversion results at s https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g009.jpg Figure 9. (a) Surface characteristics at survey line P5 in November 2024; (b) Surface characteristic https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g010.jpg Figure 10. (a) Surface characteristics, and (b) EMI inversion results at survey line P6. https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g011.jpg Figure 11. TDS derived from the inversion of ERT data along survey lines P2 (a) and P5 (b). https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g012.jpg Figure 12. Change in TDS and the desalination rate along survey line P1 (P4). (a) before salt leachi https://www.frontiersin.org/files/Articles/1745613/fenvs-13-1745613-HTML/image_m/fenvs-13-1745613-g013.jpg Figure 13. Change in TDS and the desalination rate along survey line P3 (P6). (a) before salt leachi https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1770012/full https://www.frontiersin.org/files/Articles/1770012/fonc-16-1770012-HTML-r2/image_m/fonc-16-1770012-g001.jpg Figure 1. PRISMA flow diagram for the included studies that investigated clinical outcomes based on https://www.frontiersin.org/files/Articles/1770012/fonc-16-1770012-HTML-r2/image_m/fonc-16-1770012-g002.jpg Figure 2. Overall survival. (A) Forest Plot of hazard ratios for OS comparing patients with CH versu https://www.frontiersin.org/files/Articles/1770012/fonc-16-1770012-HTML-r2/image_m/fonc-16-1770012-g003.jpg Figure 3. Progression-free survival. (A) Forest Plot of hazard ratios for PFS comparing patients wit https://www.frontiersin.org/files/Articles/1770012/fonc-16-1770012-HTML-r2/image_m/fonc-16-1770012-g004.jpg Figure 4. Cardiovascular events. (A) Forest plot of odds ratios for cardiovascular events comparing https://www.frontiersin.org/files/Articles/1770012/fonc-16-1770012-HTML-r2/image_m/fonc-16-1770012-g005.jpg Figure 5. Risk of mortality. (A) Forest plot of odds ratios for risk of mortality comparing patients https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1734451/full https://www.frontiersin.org/files/Articles/1734451/fpsyg-17-1734451-HTML-r1/image_m/fpsyg-17-1734451-g001.jpg Figure 1. FP effects for each age group. Dots represent the condition mean and error bars are the 95 https://www.frontiersin.org/files/Articles/1734451/fpsyg-17-1734451-HTML-r1/image_m/fpsyg-17-1734451-g002.jpg Figure 2. FP effects for each age group. Dots represent the condition mean and error bars are the 95 https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2026.1784522/full https://www.frontiersin.org/files/Articles/1784522/fncel-20-1784522-HTML/image_m/fncel-20-1784522-g001.jpg Figure 1. Lectin-binding and immunohistochemical staining for perineuronal nets (PNNs) and markers o https://www.frontiersin.org/files/Articles/1784522/fncel-20-1784522-HTML/image_m/fncel-20-1784522-g002.jpg Figure 2. Impact of combined neonatal phencyclidine (PCP) and isolation rearing on perineuronal nets https://www.frontiersin.org/files/Articles/1784522/fncel-20-1784522-HTML/image_m/fncel-20-1784522-g003.jpg Figure 3. Impact of combined neonatal phencyclidine (PCP) and isolation rearing on markers of lipid https://www.frontiersin.org/files/Articles/1784522/fncel-20-1784522-HTML/image_m/fncel-20-1784522-t001.jpg Table 1. Summary of frontal cortical changes following combined neonatal phencyclidine (PCP) and iso https://www.frontiersin.org/files/Articles/1784522/fncel-20-1784522-HTML/image_m/fncel-20-1784522-g004.jpg Figure 4. Proposed framework linking the frontal cortical impacts of combined neonatal phencyclidine https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1644808/full https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-t001.jpg Table 1. List of human cadaveric specimens used in the study for the scanning process, highlighting https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g001.jpg Figure 1. Cerebrum and spinal cord. (A) Lateral view: right lateral sulcus (1), right precentral gyr https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g002.jpg Figure 2. Intracranial structures: (A) general view (right): falx cerebri (1), right half of the ten https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g003.jpg Figure 3. Region of the face. (A) Right side, highlighting facial artery (1), right angular artery ( https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g004.jpg Figure 4. Neck and axillary fossa dissection. (A) General view, highlighting right sternocleidomasto https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g005.jpg Figure 5. Sternocostal surface of the heart in the thoracic cavity. Lifted pericardial sac (1) with https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g006.jpg Figure 6. Posterior mediastinum. (A) General view highlighting trachea (1), right main bronchus (2) https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g007.jpg Figure 7. Heart—four chamber view (A) General view: right atrium (1), left atrium (2), right ventric https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g008.jpg Figure 8. Abdominal cavity, anterior view. (A) General view with liver (1), falciform ligament of th https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g009.jpg Figure 9. Left kidney in retroperitoneal space, anterior view. (A) General view: left kidney (1), bo https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g010.jpg Figure 10. Male pelvic cavity dissection, femoral triangle region. (A) General view: abdominal aorta https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-g011.jpg Figure 11. Left lower limb, posterior side with popliteal fossa. (A) General view: left sciatic nerv https://www.frontiersin.org/files/Articles/1644808/fmed-12-1644808-HTML/image_m/fmed-12-1644808-t002.jpg Table 2. List of 3D models, highlighting the key dissection details, with corresponding figures (Fig https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1772840/full https://www.frontiersin.org/files/Articles/1772840/fimmu-17-1772840-HTML-r1/image_m/fimmu-17-1772840-t001.jpg Table 1A. Characteristics of patients with IgE lower and higher than 25 IU/mL and the population for https://www.frontiersin.org/files/Articles/1772840/fimmu-17-1772840-HTML-r1/image_m/fimmu-17-1772840-g001.jpg Figure 1. Cumulative risk of chronic lymphocytic leukemia (CLL) according to baseline serum IgE leve https://www.frontiersin.org/files/Articles/1772840/fimmu-17-1772840-HTML-r1/image_m/fimmu-17-1772840-t002.jpg Table 1B. Median of continuous variables of patients with IgE lower and higher than 25 IU/mL and the https://www.frontiersin.org/files/Articles/1772840/fimmu-17-1772840-HTML-r1/image_m/fimmu-17-1772840-t003.jpg Table 2. Cox regression analysis of 7 and 10 year incidence of CLL according to baseline IgE levels. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1634187/full https://www.frontiersin.org/files/Articles/1634187/fimmu-16-1634187-HTML/image_m/fimmu-16-1634187-g001.jpg Figure 1. The long-term local humoral immune response in common marmosets after intramuscular (IM) o https://www.frontiersin.org/files/Articles/1634187/fimmu-16-1634187-HTML/image_m/fimmu-16-1634187-g002.jpg Figure 2. The long-term systemic humoral immune response in common marmosets after intramuscular (IM https://www.frontiersin.org/files/Articles/1634187/fimmu-16-1634187-HTML/image_m/fimmu-16-1634187-g003.jpg Figure 3. Time-dependent correlations between different humoral immune response parameters measured https://www.frontiersin.org/files/Articles/1634187/fimmu-16-1634187-HTML/image_m/fimmu-16-1634187-g004.jpg Figure 4. Time-dependent changes in cross-reactivity of serum IgGs and NtAbs in common marmosets tha https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1666360/full https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g001.jpg Figure 1. Fluorescence microscopy study demonstrating the inhibitory effect of Adansonia digitata ba https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g002.jpg Figure 2. Dose-dependent cytotoxic effect of ADEE on MDA-MB-231 cells as determined by the MTT assay https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g003.jpg Figure 3. Apoptosis-inducing effect of ADEE in MDA-MB-231 breast cancer cells envisioned by Hoechst https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g004.jpg Figure 4. Binding interaction of Tetracycline (Reference inhibitor) with the binding pocket of pqsA https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g005.jpg Figure 5. A comparative analysis of GNINA regarding pqsA binding with compounds 559495 and 22217550, https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-t001.jpg Table 1. Docking results for pqsA complexes utilizing GNINA. https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g006.jpg Figure 6. A comparative analysis of GNINA validation 2OXX binding with compounds 559495 and 22217550 https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-t002.jpg Table 2. Results from deep learning docking studies of CK2 complexes using GNINA. https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g007.jpg Figure 7. The analysis of molecular dynamics (MD) simulations for pqsA complexes investigates the in https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-t003.jpg Table 3. MMPBSA analysis of the pqsA complexes. https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g008.jpg Figure 8. Binding free energy analysis of pqsA complexes with the phytocompounds (CID_559495 and CID https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g009.jpg Figure 9. Principal component analysis (PCA) and the analysis of the free energy landscape (FEL) for https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g010.jpg Figure 10. FEL (Left) derived from PCA of pqsA complexes, highlighting key Protein_Ligand interactio https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g011.jpg Figure 11. The analysis of MD simulations of the top CK2 complexes is illustrated, including (a) RMS https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-t004.jpg Table 4. Binding affinity of lead complexes calculated through MMPBSA analysis. https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g012.jpg Figure 12. The analysis of binding free energy using MM/PBSA has been performed on the top lead comp https://www.frontiersin.org/files/Articles/1666360/fmolb-12-1666360-HTML/image_m/fmolb-12-1666360-g013.jpg Figure 13. Principal Component Analysis (PCA) and Free Energy Landscape (FEL) analyses of the leadin https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1777595/full https://www.frontiersin.org/files/Articles/1777595/fmolb-13-1777595-HTML/image_m/fmolb-13-1777595-g001.jpg Figure 1. Schematic overview of experimental and computational approaches used to study protein-prot https://www.frontiersin.org/files/Articles/1777595/fmolb-13-1777595-HTML/image_m/fmolb-13-1777595-t001.jpg Table 1. Comparative overview of classical and emerging PPI technologies in plants. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1680215/full https://www.frontiersin.org/files/Articles/1680215/fneur-16-1680215-HTML/image_m/fneur-16-1680215-g001.jpg Figure 1. Description of the four main phases of the OPERA project, with their objectives and specif https://www.frontiersin.org/files/Articles/1680215/fneur-16-1680215-HTML/image_m/fneur-16-1680215-g002.jpg Figure 2. Schematic representation of the PRoBio platform, integrating VR-based cognitive training, https://www.frontiersin.org/files/Articles/1680215/fneur-16-1680215-HTML/image_m/fneur-16-1680215-t001.jpg Table 1. Brief description of the two rehabilitation protocols (A and B). https://www.frontiersin.org/files/Articles/1680215/fneur-16-1680215-HTML/image_m/fneur-16-1680215-g003.jpg Figure 3. Graphic representation of the Usability Study procedures and the data flow within the OPER https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1798232/full https://www.frontiersin.org/files/Articles/1798232/fvets-13-1798232-HTML-r1/image_m/fvets-13-1798232-t001.jpg Table 1. Overall serological surveillance results for BLV in Kazakhstan, 2025. https://www.frontiersin.org/files/Articles/1798232/fvets-13-1798232-HTML-r1/image_m/fvets-13-1798232-t002.jpg Table 2. Comparison of AGID and ELISA results for BLV detection in cattle, Kazakhstan, 2025. https://www.frontiersin.org/files/Articles/1798232/fvets-13-1798232-HTML-r1/image_m/fvets-13-1798232-g001.jpg Figure 1. Regional comparison of disease prevalence estimated by AGID and ELISA: (A) Systematic sens https://www.frontiersin.org/files/Articles/1798232/fvets-13-1798232-HTML-r1/image_m/fvets-13-1798232-t003.jpg Table 3. PCR results in relation to serological status of cattle examined in 2025. https://www.frontiersin.org/files/Articles/1798232/fvets-13-1798232-HTML-r1/image_m/fvets-13-1798232-g002.jpg Figure 2. Genetic confirmation of BLV by partial sequencing. https://www.frontiersin.org/files/Articles/1798232/fvets-13-1798232-HTML-r1/image_m/fvets-13-1798232-t004.jpg Table 4. BLV-positive samples identified by PCR. https://www.frontiersin.org/files/Articles/1798232/fvets-13-1798232-HTML-r1/image_m/fvets-13-1798232-g003.jpg Figure 3. Administrative regions were classified into low-, moderate-, and high-risk zones according https://www.frontiersin.org/files/Articles/1798232/fvets-13-1798232-HTML-r1/image_m/fvets-13-1798232-g004.jpg Figure 4. Integrated spatial risk zoning of BLV in Kazakhstan, 2025. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1726905/full https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-t001.jpg Table 1. Analysis of baseline data for training and validation sets. https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-t002.jpg Table 2. Univariate logistic regression analysis in the training set. https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-g001.jpg Figure 1. (A) Lasso log lambda. (B) Lasso regression with cross-validation. https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-g002.jpg Figure 2. Boruta screening. https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-t003.jpg Table 3. Predictive performance comparison of the seven types of machine learning algorithms. https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-g003.jpg Figure 3. (A) AUCs of 7 machine learning models on the training set. (B) AUCs of 7 machine learning https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-g004.jpg Figure 4. SHAP diagram. https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-t004.jpg Table 4. Multivariate logistic regression analysis in the training set. https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-g005.jpg Figure 5. (A) Training set of 500 bootstrap ROC curves. (B) Internal validation set of 500 bootstrap https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-g006.jpg Figure 6. (A) Training set of 500 bootstrap calibration curves. (B) Internal validation set of 500 b https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-t005.jpg Table 5. Performance metrics of logistic regression models in the external validation cohort and dia https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-g007.jpg Figure 7. Nomogram for predicting schizophrenia. https://www.frontiersin.org/files/Articles/1726905/fmed-12-1726905-HTML/image_m/fmed-12-1726905-g008.jpg Figure 8. (A) DCA for the training set. (B) DCA for the internal validation. (C) DCA for the externa https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1708850/full https://www.frontiersin.org/files/Articles/1708850/fimmu-16-1708850-HTML/image_m/fimmu-16-1708850-g001.jpg Figure 1. Mechanisms of ferroptosis in cells. The graphic was created by Figdraw (www.figdraw.com). https://www.frontiersin.org/files/Articles/1708850/fimmu-16-1708850-HTML/image_m/fimmu-16-1708850-g002.jpg Figure 2. The biological functions of CSCs in cancer. CSCs plays crucial roles in tumor growth, meta https://www.frontiersin.org/files/Articles/1708850/fimmu-16-1708850-HTML/image_m/fimmu-16-1708850-t001.jpg Table 1. NcRNAs mediate the relationship between ferroptosis and stemness in cancers. https://www.frontiersin.org/files/Articles/1708850/fimmu-16-1708850-HTML/image_m/fimmu-16-1708850-g003.jpg Figure 3. Metabolic reprogramming modulates ferroptosis in CSCs. Cancer metabolic reprogramming, inc https://www.frontiersin.org/files/Articles/1708850/fimmu-16-1708850-HTML/image_m/fimmu-16-1708850-t002.jpg Table 2. The significance of stemness and ferroptosis-related biomarkers in cancer. https://www.frontiersin.org/files/Articles/1708850/fimmu-16-1708850-HTML/image_m/fimmu-16-1708850-t003.jpg Table 3. The drugs traget ferroptosis and CSCs in cancer. https://www.frontiersin.org/files/Articles/1708850/fimmu-16-1708850-HTML/image_m/fimmu-16-1708850-g004.jpg Figure 4. The schematic summary of the interplay between ferroptosis and CSCs. The graphic was creat https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1774432/full https://www.frontiersin.org/files/Articles/1774432/fpubh-14-1774432-HTML/image_m/fpubh-14-1774432-t001.jpg Table 1. Patient characteristics by perceived HIV knowledge. https://www.frontiersin.org/files/Articles/1774432/fpubh-14-1774432-HTML/image_m/fpubh-14-1774432-t002.jpg Table 2. Factors associated with perceived HIV knowledge: univariable and multivariable ordinal regr https://www.frontiersin.org/files/Articles/1774432/fpubh-14-1774432-HTML/image_m/fpubh-14-1774432-t003.jpg Table 3. Factors associated with perceived HIV knowledge: univariable and multivariable ordinal regr https://www.frontiersin.org/files/Articles/1774432/fpubh-14-1774432-HTML/image_m/fpubh-14-1774432-t004.jpg Table 4. Factors associated with perceived HIV knowledge: univariable and multivariable ordinal regr https://www.frontiersin.org/files/Articles/1774432/fpubh-14-1774432-HTML/image_m/fpubh-14-1774432-g001.jpg Figure 1. Forest plot of adjusted associations with perceived HIV knowledge. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1616641/full https://www.frontiersin.org/files/Articles/1616641/fpsyt-16-1616641-HTML/image_m/fpsyt-16-1616641-g001.jpg Figure 1. CONSORT flow diagram. HAMD, Hamilton Depression Rating Scale; HAMA, Hamilton Anxiety Ratin https://www.frontiersin.org/files/Articles/1616641/fpsyt-16-1616641-HTML/image_m/fpsyt-16-1616641-t001.jpg Table 1. Baseline demographic and clinical characteristics (Mean ± SD or n(%)). https://www.frontiersin.org/files/Articles/1616641/fpsyt-16-1616641-HTML/image_m/fpsyt-16-1616641-t002.jpg Table 2. Reduction rates of HAMD, HAMA, and PSQI scores across groups. https://www.frontiersin.org/files/Articles/1616641/fpsyt-16-1616641-HTML/image_m/fpsyt-16-1616641-g002.jpg Figure 2. Reduction rates of HAMD, HAMA, and PSQI scores across groups and between-group comparisons https://www.frontiersin.org/files/Articles/1616641/fpsyt-16-1616641-HTML/image_m/fpsyt-16-1616641-t003.jpg Table 3. Logistic regression analysis of factors influencing efficacy (HAMD reduction rate ≥25%). https://www.frontiersin.org/files/Articles/1616641/fpsyt-16-1616641-HTML/image_m/fpsyt-16-1616641-t004.jpg Table 4. Adverse events among patients receiving treatment. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1685902/full https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g001.jpg Figure 1. The workflow of the study. WGCNA, weighted gene co-expression network analysis; DEGs, diff https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g002.jpg Figure 2. Integrative analysis of DEGs. (A, B) The box plots of the merged database. (C) Volcano map https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g003.jpg Figure 3. Gene set enrichment analysis (GSEA) for the DEGs and merged dataset. (A) Bubble plot for B https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g004.jpg Figure 4. The blue module exhibited the strongest correlation with ESCC. (A) The scale-free fit inde https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g005.jpg Figure 5. Integrative analysis of the blue module. (A) Venn diagram plotting the intersection of WGC https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g006.jpg Figure 6. Diagnostic model construction and evaluation. (A, B) Construction of the diagnostic model https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-t001.jpg Table 1. Multivariate logistic regression analysis of screening diagnostic genes. https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-t002.jpg Table 2. Performance of the diagnostic model. https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g007.jpg Figure 7. Experimental verification of signature gene expression in mouse ESCC model. (A) The modeli https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g008.jpg Figure 8. Immune infiltration analysis. (A) Expression of different immune cells in ESCC and normal https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g009.jpg Figure 9. GSEA for SORBS2. (A) BP. (B) CC. (C) MF. (D) KEGG. https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g010.jpg Figure 10. Analysis of single-cell RNA sequencing data of six esophageal samples. (A) The correlatio https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g011.jpg Figure 11. Inferences of cell–cell communication by cellchat. (A) CellChat network depicting interac https://www.frontiersin.org/files/Articles/1685902/fimmu-17-1685902-HTML/image_m/fimmu-17-1685902-g012.jpg Figure 12. Human ESCC Tissue Immunofluorescence. (A) The expression of DAPI (blue, nucleus), TAGLN ( https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1781499/full https://www.frontiersin.org/files/Articles/1781499/fmed-13-1781499-HTML-r2/image_m/fmed-13-1781499-t001.jpg Table 1. Comparison of previous works on uterine diseases. https://www.frontiersin.org/files/Articles/1781499/fmed-13-1781499-HTML-r2/image_m/fmed-13-1781499-g001.jpg Figure 1. Overall architecture of the proposed DenseNet121-transformer hybrid model for uterine clas https://www.frontiersin.org/files/Articles/1781499/fmed-13-1781499-HTML-r2/image_m/fmed-13-1781499-t002.jpg Table 2. The Number and distribution of images in each KAUH-UCCTD category. https://www.frontiersin.org/files/Articles/1781499/fmed-13-1781499-HTML-r2/image_m/fmed-13-1781499-g002.jpg Figure 2. An example from the image dataset (KAUH-UCCTD). https://www.frontiersin.org/files/Articles/1781499/fmed-13-1781499-HTML-r2/image_m/fmed-13-1781499-t003.jpg Table 3. Performance of the evaluated models for uterine CT image classification. https://www.frontiersin.org/files/Articles/1781499/fmed-13-1781499-HTML-r2/image_m/fmed-13-1781499-g003.jpg Figure 3. Performance comparison of different models. https://www.frontiersin.org/files/Articles/1781499/fmed-13-1781499-HTML-r2/image_m/fmed-13-1781499-g004.jpg Figure 4. Comparison of training and validation performance between the proposed model and DenseNet1 https://www.frontiersin.org/files/Articles/1781499/fmed-13-1781499-HTML-r2/image_m/fmed-13-1781499-g005.jpg Figure 5. Confusion matrices of all compared models on the KAUH-UCCTD dataset. https://www.frontiersin.org/files/Articles/1781499/fmed-13-1781499-HTML-r2/image_m/fmed-13-1781499-g006.jpg Figure 6. Confusion matrix of the proposed model on the KAUH-UCM Dataset. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1751535/full https://www.frontiersin.org/files/Articles/1751535/fnins-20-1751535-HTML-r1/image_m/fnins-20-1751535-g001.jpg Figure 1. Distinctive facial and skeletal features of the patient. (A) Microcephaly, wide eye distan https://www.frontiersin.org/files/Articles/1751535/fnins-20-1751535-HTML-r1/image_m/fnins-20-1751535-t001.jpg Table 1. Clinical characteristics of patients with WABS presenting with epilepsy. https://www.frontiersin.org/files/Articles/1751535/fnins-20-1751535-HTML-r1/image_m/fnins-20-1751535-t002.jpg Table 2. Clinical information of WABS patients with epilepsy. https://www.frontiersin.org/files/Articles/1751535/fnins-20-1751535-HTML-r1/image_m/fnins-20-1751535-g002.jpg Figure 2. Axial MRI demonstrates widening of the right temporal lobe gyrus. (a) 7 months of age; (b) https://www.frontiersin.org/files/Articles/1751535/fnins-20-1751535-HTML-r1/image_m/fnins-20-1751535-g003.jpg Figure 3. MRI demonstrates bilateral periventricular gray matter heterotopia. (A) DWI MRI; (B) T2 Fl https://www.frontiersin.org/files/Articles/1751535/fnins-20-1751535-HTML-r1/image_m/fnins-20-1751535-t003.jpg Table 3. Genetic information of children with WABS presenting with epilepsy. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1731234/full https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-t001.jpg Table 1. Cervical human vagus nerve morphology compared between studies. https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g001.jpg Figure 1. Human vagus nerve preparation, scanning setup and segmentation at intervals. The vagus ner https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g002.jpg Figure 2. Segmentation through multiple cross-sections in a scan. An example of scan with multiple s https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g003.jpg Figure 3. Anastomoses observed in human vagus nerve. Two examples of the frequent anastomosing (merg https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g004.jpg Figure 4. Schematic representation of fascicle organization along the cranio-caudal axis of the righ https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g005.jpg Figure 5. IHC of a human vagus nerve example 1. An example of strong double staining of neurofilamen https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g006.jpg Figure 6. IHC of a human vagus nerve example 2. Another example of double staining of neurofilament https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g007.jpg Figure 7. MicroCT with IHC 1. An example from one nerve of IHC compared to microCT 14 cm from mid-ce https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g008.jpg Figure 8. MicroCT with IHC 2. An example from one nerve of IHC compared to microCT 13 cm from mid-ce https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g009.jpg Figure 9. MicroCT with IHC 3. An example from one nerve of IHC compared to microCT 7 cm from mid-cer https://www.frontiersin.org/files/Articles/1731234/fnins-20-1731234-HTML/image_m/fnins-20-1731234-g010.jpg Figure 10. Didactic illustration of organization in the cervical vagus nerve of pigs and humans. (A) https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1710237/full https://www.frontiersin.org/files/Articles/1710237/fsurg-12-1710237-HTML/image_m/fsurg-12-1710237-g001.jpg Figure 1. Preoperative lumbar magnetic resonance imaging (MRI) showing L5/S1 disc herniation with as https://www.frontiersin.org/files/Articles/1710237/fsurg-12-1710237-HTML/image_m/fsurg-12-1710237-g002.jpg Figure 2. Postoperative head CT scan 2 h after surgery, showing gas in the right frontal brain sulcu https://www.frontiersin.org/files/Articles/1710237/fsurg-12-1710237-HTML/image_m/fsurg-12-1710237-g003.jpg Figure 3. Schematic diagram of gas entering the dura mater through a dural tear. Under water pressur https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1651951/full https://www.frontiersin.org/files/Articles/1651951/fpsyg-16-1651951-HTML/image_m/fpsyg-16-1651951-g001.jpg Figure 1. Impact of OCSA - Themes and Subthemes. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1602449/full https://www.frontiersin.org/files/Articles/1602449/fpsyg-16-1602449-HTML/image_m/fpsyg-16-1602449-t001.jpg Table 1. List of thematic statements. https://www.frontiersin.org/files/Articles/1602449/fpsyg-16-1602449-HTML/image_m/fpsyg-16-1602449-t002.jpg Table 2. Summary of the ideal service design recommendations. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1701721/full https://www.frontiersin.org/files/Articles/1701721/fsufs-10-1701721-HTML-r1/image_m/fsufs-10-1701721-t001.jpg Table 1. Inclusion and exclusion criteria for the systematic review. https://www.frontiersin.org/files/Articles/1701721/fsufs-10-1701721-HTML-r1/image_m/fsufs-10-1701721-g001.jpg Figure 1. Comparative bibliometric network visualization of keyword co-occurrences in sugarcane resi https://www.frontiersin.org/files/Articles/1701721/fsufs-10-1701721-HTML-r1/image_m/fsufs-10-1701721-g002.jpg Figure 2. PRISMA-adapted research framework guiding the synthesis of literature on climate change im https://www.frontiersin.org/files/Articles/1701721/fsufs-10-1701721-HTML-r1/image_m/fsufs-10-1701721-g003.jpg Figure 3. Socio-ecological cascade of risk in SIDS sugarcane systems. https://www.frontiersin.org/files/Articles/1701721/fsufs-10-1701721-HTML-r1/image_m/fsufs-10-1701721-t002.jpg Table 2. Biophysical trigger: global evidence of sugarcane system degradation from chronic climate s https://www.frontiersin.org/files/Articles/1701721/fsufs-10-1701721-HTML-r1/image_m/fsufs-10-1701721-t003.jpg Table 3. Force of acute shocks: documented catastrophic losses from extreme weather events. https://www.frontiersin.org/files/Articles/1701721/fsufs-10-1701721-HTML-r1/image_m/fsufs-10-1701721-t004.jpg Table 4. Socio-ecological cascade in action: a sourced synthesis of how biophysical triggers ignite https://www.frontiersin.org/files/Articles/1701721/fsufs-10-1701721-HTML-r1/image_m/fsufs-10-1701721-t005.jpg Table 5. Critique of the fragmented paradigm: diagnosing systemic failures in the current adaptation https://www.frontiersin.org/files/Articles/1701721/fsufs-10-1701721-HTML-r1/image_m/fsufs-10-1701721-g004.jpg Figure 4. The integrated resilience framework. This conceptual model illustrates the system architec https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1758366/full https://www.frontiersin.org/files/Articles/1758366/fcvm-13-1758366-HTML/image_m/fcvm-13-1758366-g001.jpg Figure 1. Flow chart for the identification, inclusion and exclusion of studies. https://www.frontiersin.org/files/Articles/1758366/fcvm-13-1758366-HTML/image_m/fcvm-13-1758366-g002.jpg Figure 2. Forest plot for percentage reduction in Lp(a). https://www.frontiersin.org/files/Articles/1758366/fcvm-13-1758366-HTML/image_m/fcvm-13-1758366-g003.jpg Figure 3. Forest plot for absolute reduction in Lp(a). https://www.frontiersin.org/files/Articles/1758366/fcvm-13-1758366-HTML/image_m/fcvm-13-1758366-g004.jpg Figure 4. Forest plot for percentage reduction in LDL-C. https://www.frontiersin.org/files/Articles/1758366/fcvm-13-1758366-HTML/image_m/fcvm-13-1758366-g005.jpg Figure 5. Forest plot for percentage reduction in apoB. https://www.frontiersin.org/files/Articles/1758366/fcvm-13-1758366-HTML/image_m/fcvm-13-1758366-g006.jpg Figure 6. Forest plot for AEs. https://www.frontiersin.org/files/Articles/1758366/fcvm-13-1758366-HTML/image_m/fcvm-13-1758366-g007.jpg Figure 7. Forest plot for injection site reactions. https://www.frontiersin.org/files/Articles/1758366/fcvm-13-1758366-HTML/image_m/fcvm-13-1758366-g008.jpg Figure 8. Forest plot for percentage reduction in different classes of Lp(a)-targeted therapies. https://www.frontiersin.org/files/Articles/1758366/fcvm-13-1758366-HTML/image_m/fcvm-13-1758366-g009.jpg Figure 9. Forest plot for absolute reduction in different classes of Lp(a)-targeted therapies. https://www.frontiersin.org/journals/research-metrics-and-analytics/articles/10.3389/frma.2026.1762083/full https://www.frontiersin.org/files/Articles/1762083/frma-11-1762083-HTML/image_m/frma-11-1762083-t001.jpg Table 1. Literature search strategy, logic, and screening results. https://www.frontiersin.org/files/Articles/1762083/frma-11-1762083-HTML/image_m/frma-11-1762083-g001.jpg Figure 1. PRISMA 2020 flow diagram. https://www.frontiersin.org/files/Articles/1762083/frma-11-1762083-HTML/image_m/frma-11-1762083-t002.jpg Table 2. Characteristics of Included Studies (N = 104). https://www.frontiersin.org/files/Articles/1762083/frma-11-1762083-HTML/image_m/frma-11-1762083-t003.jpg Table 3. Summary and comparison of the state's macro-strategic roles in the literature. https://www.frontiersin.org/files/Articles/1762083/frma-11-1762083-HTML/image_m/frma-11-1762083-t004.jpg Table 4. Functional categorization of semiconductor industry policy instruments in the literature. https://www.frontiersin.org/files/Articles/1762083/frma-11-1762083-HTML/image_m/frma-11-1762083-t005.jpg Table 5. Dimensional categorization of the regional innovation ecosystem (micro-context) in the lite https://www.frontiersin.org/files/Articles/1762083/frma-11-1762083-HTML/image_m/frma-11-1762083-t006.jpg Table 6. Synthesis of key themes and representative literature across multi-level dimensions. https://www.frontiersin.org/files/Articles/1762083/frma-11-1762083-HTML/image_m/frma-11-1762083-g002.jpg Figure 2. The “dual fit” governance mechanism. https://www.frontiersin.org/files/Articles/1762083/frma-11-1762083-HTML/image_m/frma-11-1762083-t007.jpg Table 7. Cross-case diagnostic analysis of policy-ecosystem fit. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1722500/full https://www.frontiersin.org/files/Articles/1722500/fmicb-17-1722500-HTML/image_m/fmicb-17-1722500-t001.jpg Table 1. Main features of human microbiome molecular data sources. Database type, content, data, and https://www.frontiersin.org/files/Articles/1722500/fmicb-17-1722500-HTML/image_m/fmicb-17-1722500-g001.jpg Figure 1. Conceptual workflow on microbiome data and metadata FAIRfication and enrichment. The propo https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1674876/full https://www.frontiersin.org/files/Articles/1674876/fspor-07-1674876-HTML/image_m/fspor-07-1674876-i001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1674876/fspor-07-1674876-HTML/image_m/fspor-07-1674876-g001.jpg Figure 1. Experimental protocol for the EG and CG. T0 represents the baseline assessment. The orange https://www.frontiersin.org/files/Articles/1674876/fspor-07-1674876-HTML/image_m/fspor-07-1674876-g002.jpg Figure 2. Measurement setup. https://www.frontiersin.org/files/Articles/1674876/fspor-07-1674876-HTML/image_m/fspor-07-1674876-g003.jpg Figure 3. Algorithm diagram used to compute tapping movement parameters. https://www.frontiersin.org/files/Articles/1674876/fspor-07-1674876-HTML/image_m/fspor-07-1674876-t001.jpg Table 1. Results of the mixed ANOVA (within-subject effects) showing the influence of test phase on https://www.frontiersin.org/files/Articles/1674876/fspor-07-1674876-HTML/image_m/fspor-07-1674876-t002.jpg Table 2. Results of the mixed ANOVA evaluating the effect of group (between-subject effects) on para https://www.frontiersin.org/files/Articles/1674876/fspor-07-1674876-HTML/image_m/fspor-07-1674876-g004.jpg Figure 4. Comparison between the CG and EG for ΔTest-Npeaks (top) and ΔTest-Time (bottom). Horizonta https://www.frontiersin.org/files/Articles/1674876/fspor-07-1674876-HTML/image_m/fspor-07-1674876-g005.jpg Figure 5. Comparison between the CG and EG for ΔIntTime (top) and ΔAccpeak (bottom). https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2025.1743125/full https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g001.jpg Figure 1. Geographical location and overview of the Yihe River Basin (YRB). https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g002.jpg Figure 2. Current land use types in the Yihe River Basin (YRB). https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-t001.jpg Table 1. Categories and definitions of the correlations between net primary productivity (NPP) and c https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g003.jpg Figure 3. Temporal evolution of annual total net primary productivity (NPP) in the Yihe River Basin https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g004.jpg Figure 4. Temporal evolution of annual mean net primary productivity (NPP) in the Yihe River Basin ( https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g005.jpg Figure 5. Spatial distribution patterns of multi-year average annual mean net primary productivity ( https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g006.jpg Figure 6. Spatial distribution patterns of annual mean net primary productivity (NPP) at five repres https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g007.jpg Figure 7. Spatial distribution patterns of annual mean net primary productivity (NPP) trends across https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g008.jpg Figure 8. Interannual variation and anomalies in annual mean temperature (Ta) and annual precipitati https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g009.jpg Figure 9. Spatial distribution patterns of the two primary climatic factors across the Yihe River Ba https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g010.jpg Figure 10. Spatial distribution patterns of the correlation and significance between annual mean tem https://www.frontiersin.org/files/Articles/1743125/ffgc-08-1743125-HTML/image_m/ffgc-08-1743125-g011.jpg Figure 11. Spatial distribution patterns of the correlation and significance between Precipitation a https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1706895/full https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g001.jpg Figure 1. Imaging markers on different sequences. (A) Pituitary adenoma invading the left cavernous https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g002.jpg Figure 2. Delineation of the pituitary adenoma ROI using 3D slicer. (A) On T2-weighted imaging, a la https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g003.jpg Figure 3. Intraoperative findings of dural invasion. (A) During microscopic transsphenoidal resectio https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-t001.jpg Table 1. Baseline characteristics of variables associated with cavernous sinus invasion following tr https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g004.jpg Figure 4. Forest plot of univariate analysis. This figure presents the effects and statistical signi https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g005.jpg Figure 5. Nomogram for multivariate analysis of cavernous sinus dural invasion. A nomogram was devel https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g006.jpg Figure 6. ROC curves of different features. ROC curves were generated for the selected clinical feat https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g007.jpg Figure 7. Training set ROC, testing set ROC, and DCA curves for machine learning models based on cli https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-t002.jpg Table 2. Performance comparison and consistency testing of ten machine learning models based on clin https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g008.jpg Figure 8. Selection of radiomic features. (A, B). Lasso regression was used to select radiomic featu https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g009.jpg Figure 9. ROC, test set ROC, and DCA curves for the machine learning model based on radiomic feature https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-t003.jpg Table 3. Performance comparison and consistency test results of ten machine learning models built on https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g010.jpg Figure 10. Correlation heatmap of clinical features and radiomic features. This heatmap illustrates https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g011.jpg Figure 11. ROC curves, test set ROC curves, and DCA curves for the machine learning models construct https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-t004.jpg Table 4. Performance and consistency test results of machine learning models built using clinical an https://www.frontiersin.org/files/Articles/1706895/fonc-15-1706895-HTML/image_m/fonc-15-1706895-g012.jpg Figure 12. SHAP score analysis - bar and scatter plots. This figure presents the SHAP (SHapley Addit https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1786295/full https://www.frontiersin.org/files/Articles/1786295/fpsyt-17-1786295-HTML/image_m/fpsyt-17-1786295-t001.jpg Table 1. Summary of patient demographic information. https://www.frontiersin.org/files/Articles/1786295/fpsyt-17-1786295-HTML/image_m/fpsyt-17-1786295-t002.jpg Table 2. Summary statistics for each patient reported outcome measure by timepoint. https://www.frontiersin.org/files/Articles/1786295/fpsyt-17-1786295-HTML/image_m/fpsyt-17-1786295-t003.jpg Table 3. A summary of the twelve mixed effects models. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1721711/full https://www.frontiersin.org/files/Articles/1721711/fimmu-17-1721711-HTML/image_m/fimmu-17-1721711-g001.jpg Figure 1. Clinical timeline illustrating the oncologic course and treatment response prior to the f https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1694587/full https://www.frontiersin.org/files/Articles/1694587/fonc-15-1694587-HTML/image_m/fonc-15-1694587-g001.jpg Figure 1. Study flowchart and determination of optimal cutoffs for LMR and PINI. (A) Flow diagram of https://www.frontiersin.org/files/Articles/1694587/fonc-15-1694587-HTML/image_m/fonc-15-1694587-t001.jpg Table 1. Patient demographics and baseline characteristics. https://www.frontiersin.org/files/Articles/1694587/fonc-15-1694587-HTML/image_m/fonc-15-1694587-t002.jpg Table 2. Association of preoperative CIPI and PAR with clinicopathological features in the training https://www.frontiersin.org/files/Articles/1694587/fonc-15-1694587-HTML/image_m/fonc-15-1694587-g002.jpg Figure 2. Kaplan–Meier survival analyses and predictive performance of INPR. (A) Overall survival (O https://www.frontiersin.org/files/Articles/1694587/fonc-15-1694587-HTML/image_m/fonc-15-1694587-t003.jpg Table 3. Scoring criteria of the Immune-Nutritional Prognostic Ratio (INPR) based on PINI and LMR le https://www.frontiersin.org/files/Articles/1694587/fonc-15-1694587-HTML/image_m/fonc-15-1694587-t004.jpg Table 4. Univariate and multivariate analysis of influencing factors (Cox regression). https://www.frontiersin.org/files/Articles/1694587/fonc-15-1694587-HTML/image_m/fonc-15-1694587-g003.jpg Figure 3. Construction and performance of the INPR-based nomogram. (A) Variable importance ranking f https://www.frontiersin.org/files/Articles/1694587/fonc-15-1694587-HTML/image_m/fonc-15-1694587-g004.jpg Figure 4. Calibration and decision curve analysis (DCA) for predicting 1-, 3-, and 5-year overall su https://www.frontiersin.org/files/Articles/1694587/fonc-15-1694587-HTML/image_m/fonc-15-1694587-g005.jpg Figure 5. Calibration and decision curve analysis (DCA) for predicting 1-, 3-, and 5-year overall su https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1652525/full https://www.frontiersin.org/files/Articles/1652525/fenvs-13-1652525-HTML/image_m/fenvs-13-1652525-g001.jpg Figure 1. Geographic Distribution of Trait-Based ecological publications in selected African countri https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1743085/full https://www.frontiersin.org/files/Articles/1743085/fpubh-13-1743085-HTML/image_m/fpubh-13-1743085-t001.jpg Table 1. Interview results based on the dimension of public service quality at Ngudi Waluyo Wlingi H https://www.frontiersin.org/files/Articles/1743085/fpubh-13-1743085-HTML/image_m/fpubh-13-1743085-t002.jpg Table 2. Interview results based on the dimension of health service quality at Ngudi Waluyo Wlingi H https://www.frontiersin.org/files/Articles/1743085/fpubh-13-1743085-HTML/image_m/fpubh-13-1743085-t003.jpg Table 3. Results of the four-dimensional digital governance interview at Ngudi Waluyo Wlingi Hospita https://www.frontiersin.org/files/Articles/1743085/fpubh-13-1743085-HTML/image_m/fpubh-13-1743085-t004.jpg Table 4. Interview results based on the digital governance dimension at Ngudi Waluyo Wlingi Hospital https://www.frontiersin.org/files/Articles/1743085/fpubh-13-1743085-HTML/image_m/fpubh-13-1743085-t005.jpg Table 5. Interview results based on the principles of good governance in public service of Ngudi Wal https://www.frontiersin.org/files/Articles/1743085/fpubh-13-1743085-HTML/image_m/fpubh-13-1743085-g001.jpg Figure 1. Model of the framework for the impact of digital governance implementation on the quality https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1637835/full https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-g001.jpg Figure 1. Timeline of the observational study demonstrating which variables were retrospectively col https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-g002.jpg Figure 2. Selection procedure of final MS cohort used for data-analyses. The icobrain ms software wa https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-t001.jpg Table 1. Cut-offs used to categorize MS patients as “disability worsening.” https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-t002.jpg Table 2. Differences in BVL between different DMT modalities. https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-t003.jpg Table 3. Demographics of MS and HC cohorts. https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-t004.jpg Table 4. Comparison of brain volume measures between “disability worsening” and “stable disability” https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-t005.jpg Table 5. Linear regression models with clinical scores as dependent variables and MRI measures as in https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-g003.jpg Figure 3. Scatterplots representing the relation between the change in MSFCSDMT and (A) percentage w https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-t006.jpg Table 6. Demographics of MS cohorts used for logistic regression analyses, using a cut-off of −0.4% https://www.frontiersin.org/files/Articles/1637835/fneur-16-1637835-HTML/image_m/fneur-16-1637835-t007.jpg Table 7. Logistic regression models for annualized whole brain volume loss with a cut-off of −0.40% https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1737436/full https://www.frontiersin.org/files/Articles/1737436/fspor-08-1737436-HTML/image_m/fspor-08-1737436-g001.jpg Figure 1. Overall distribution of spike techniques (left panel) and within different countries. https://www.frontiersin.org/files/Articles/1737436/fspor-08-1737436-HTML/image_m/fspor-08-1737436-g002.jpg Figure 2. Frequency of shoulder injuries related to spike technique for all participants (left panel https://www.frontiersin.org/files/Articles/1737436/fspor-08-1737436-HTML/image_m/fspor-08-1737436-g003.jpg Figure 3. Frequencies of shoulder symptoms related to spike technique for all participants (left pan https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1715491/full https://www.frontiersin.org/files/Articles/1715491/fpubh-14-1715491-HTML/image_m/fpubh-14-1715491-t001.jpg Table 1. Case studies overview. https://www.frontiersin.org/files/Articles/1715491/fpubh-14-1715491-HTML/image_m/fpubh-14-1715491-t002.jpg Table 2. COVID-19 impact map: quantity of change for outcomes by stakeholders. https://www.frontiersin.org/files/Articles/1715491/fpubh-14-1715491-HTML/image_m/fpubh-14-1715491-t003.jpg Table 3. COVID-19: estimated inputs allocated to PCR and professional Ag-RDTs use. https://www.frontiersin.org/files/Articles/1715491/fpubh-14-1715491-HTML/image_m/fpubh-14-1715491-t004.jpg Table 4. Diabetes impact map: quantity of change for outcomes by stakeholders. https://www.frontiersin.org/files/Articles/1715491/fpubh-14-1715491-HTML/image_m/fpubh-14-1715491-t005.jpg Table 5. Diabetes: estimated inputs allocated to the use of HbA1c and SMBG. https://www.frontiersin.org/files/Articles/1715491/fpubh-14-1715491-HTML/image_m/fpubh-14-1715491-t006.jpg Table 6. HF impact map: quantity of change for outcomes by stakeholders. https://www.frontiersin.org/files/Articles/1715491/fpubh-14-1715491-HTML/image_m/fpubh-14-1715491-t007.jpg Table 7. HF: estimated inputs allocated for the possible use of B-type natriuretic peptide test. https://www.frontiersin.org/files/Articles/1715491/fpubh-14-1715491-HTML/image_m/fpubh-14-1715491-t008.jpg Table 8. Lung cancer impact map: quantity of change for outcomes by stakeholders. https://www.frontiersin.org/files/Articles/1715491/fpubh-14-1715491-HTML/image_m/fpubh-14-1715491-t009.jpg Table 9. Lung cancer: estimated inputs allocated to molecular testing. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1785800/full https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g001.jpg Figure 1. Comparison of mAP@0.5, model parameters and model size for different algorithms. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g002.jpg Figure 2. The overall framework of the model training process, including data acquisition, data prep https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g003.jpg Figure 3. Information on the location of data collection and Example image of data characteristics: https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g004.jpg Figure 4. Display of images after data enhancement. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g005.jpg Figure 5. Structure of HMA-YOLO. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g006.jpg Figure 6. Hybrid structure of cnn and transformer: (a) Structure of HCT; (b) Structure of MHA; (c) S https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g007.jpg Figure 7. Structure of EUCB. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g008.jpg Figure 8. Structure of NHKSM. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g009.jpg Figure 9. Structure of EMSCB. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g010.jpg Figure 10. Structure of DCMSCK. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g011.jpg Figure 11. Structure of AMCCDH. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-t001.jpg Table 1. Ablation experiment results. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g012.jpg Figure 12. Visualization of AMCCDH application effect: (a1) YOLOv12n detection results; (a2) AMCCDH https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-t002.jpg Table 2. Training hyperparameters. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-t003.jpg Table 3. Ablation experiment results of HCT structural channel allocation. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-t004.jpg Table 4. Comparison experiment results of MBMS-FPN fixed channel dimension. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-t005.jpg Table 5. Comparision experimental results of the sizes of neck heterogeneous kernel. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-t006.jpg Table 6. Comparison experiments results of different object detection models. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-t007.jpg Table 7. Experimental results of model performance evaluation under strict IoU thresholds. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g013.jpg Figure 13. Visualization of detection: (a) Three types of original images representative of the char https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g014.jpg Figure 14. Fault case display: (a) Detection results under rainy weather conditions; (b) Detection r https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-t008.jpg Table 8. Hardware and environment information for edge device deployments. https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g015.jpg Figure 15. Differences in precision and speed indicators of HMA-YOLO on edge devices under different https://www.frontiersin.org/files/Articles/1785800/fpls-17-1785800-HTML/image_m/fpls-17-1785800-g016.jpg Figure 16. Detection performance of HMA-YOLO on edge devices. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1737134/full https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-t001.jpg Table 1. Detailed properties of identified TaMRS2/CorA/NIPA genes. https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g001.jpg Figure 1. Phylogenetic tree of magnesium transporter genes in wheat, rice, maize and Arabidopsis. Th https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g002.jpg Figure 2. Phylogenetic tree of MRS2 genes in five monocotyledons (O. sativa, Z. mays, T.aestivum, S. https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g003.jpg Figure 3. Chromosomal localization of the magnesium transporter genes. The light green column repres https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g004.jpg Figure 4. Synteny analysis of magnesium transporter genes. (A) Synteny analysis of magnesium transpo https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g005.jpg Figure 5. Comparative analysis of the phylogenetics, conserved motifs, exon-intron structure, and pr https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g006.jpg Figure 6. Predicted cis-elements in the promoter regions of the wheat MGT genes. (A) Comparative ana https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g007.jpg Figure 7. Protein association analyses of TaMGT proteins. (A) Predicted protein association networks https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g008.jpg Figure 8. Transcriptome analyses of TaMGTs in different tissues. (A) Heat map of expression profiles https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g009.jpg Figure 9. Relative expression levels of 24 TaMRS2 genes in different tissues. Ordinate coordinates a https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g010.jpg Figure 10. (A-D) SNP analysis and global distribution analysis of major variant loci of TaMRS2-13, T https://www.frontiersin.org/files/Articles/1737134/fpls-17-1737134-HTML/image_m/fpls-17-1737134-g011.jpg Figure 11. The subcellular location of TaMRS2-1, TaMRS2-2, TaMRS2-3, TaMRS2-16, TaMRS2-17, TaMRS2-18 https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1757972/full https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-t001.jpg Table 1. Primers used for the qRT-PCR analysis. https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-t002.jpg Table 2. Effect of coconut meat on the growth performance of Chinese mitten crab. https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-g001.jpg Figure 1. Effect of coconut meat on oxidase activity of Chinese mitten crab among control and experi https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-t003.jpg Table 3. Fatty acid composition and content of muscle and gonadal tissue of Chinese mitten crab cont https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-t004.jpg Table 4. Amino acid composition and content of muscle and gonadal tissue of Chinese mitten crab amon https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-t005.jpg Table 5. Summary of RNA-seq data. https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-g002.jpg Figure 2. Sample relationship analysis in muscle tissue of Chinese mitten crab among control and exp https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-g003.jpg Figure 3. Number of significantly up-regulated and down-regulated genes in muscle of Chinese mitten https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-g004.jpg Figure 4. GO enrichment analysis for the DEGs in muscle of Chinese mitten crab among control and exp https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-g005.jpg Figure 5. KEGG enrichment analysis for the DEGs in muscle of E. sinensis among control and experimen https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-t006.jpg Table 6. The genes with a few significantly changed KEGG pathways after fed with coconut meat. https://www.frontiersin.org/files/Articles/1757972/fnut-13-1757972-HTML/image_m/fnut-13-1757972-g006.jpg Figure 6. Result comparison of the qPCR and RNA-seq. The qPCR and RNA-seq results are shown in red a https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1677374/full https://www.frontiersin.org/files/Articles/1677374/fpsyg-17-1677374-HTML/image_m/fpsyg-17-1677374-g001.jpg Figure 1. A theoretical model of the effect of physical education teacher identity on athletic sport https://www.frontiersin.org/files/Articles/1677374/fpsyg-17-1677374-HTML/image_m/fpsyg-17-1677374-t001.jpg Table 1. Variable correlations and descriptive statistics for study variables. https://www.frontiersin.org/files/Articles/1677374/fpsyg-17-1677374-HTML/image_m/fpsyg-17-1677374-g002.jpg Figure 2. Path diagram of the mediation mode. *p < 0.001, **p < 0.01. https://www.frontiersin.org/files/Articles/1677374/fpsyg-17-1677374-HTML/image_m/fpsyg-17-1677374-t002.jpg Table 2. Path testing of direct effects of each variable. https://www.frontiersin.org/files/Articles/1677374/fpsyg-17-1677374-HTML/image_m/fpsyg-17-1677374-t003.jpg Table 3. Testing the mediating effect of learning motivation. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1660284/full https://www.frontiersin.org/files/Articles/1660284/fphar-16-1660284-HTML/image_m/fphar-16-1660284-g001.jpg Figure 1. Component content of WSP. (A) HPLC chromatogram depicting echinacoside, acteoside, and sch https://www.frontiersin.org/files/Articles/1660284/fphar-16-1660284-HTML/image_m/fphar-16-1660284-g002.jpg Figure 2. WSP improves the perimenopausal symptoms in diet-induced PMOP mice. (A) Number of voluntar https://www.frontiersin.org/files/Articles/1660284/fphar-16-1660284-HTML/image_m/fphar-16-1660284-g003.jpg Figure 3. WSP improves bone microstructure in diet-induced PMOP mice. (A,B) Bone CT scans of mouse f https://www.frontiersin.org/files/Articles/1660284/fphar-16-1660284-HTML/image_m/fphar-16-1660284-g004.jpg Figure 4. WSP improves in vivo calcium and phosphorus levels in diet-induced PMOP mice. (A) Ca; (B) https://www.frontiersin.org/files/Articles/1660284/fphar-16-1660284-HTML/image_m/fphar-16-1660284-g005.jpg Figure 5. WSP increases intestinal calcium absorption in diet-induced PMOP mice. (A) H&E staining of https://www.frontiersin.org/files/Articles/1660284/fphar-16-1660284-HTML/image_m/fphar-16-1660284-g006.jpg Figure 6. WSP increases renal calcium uptake in diet-induced PMOP mice. (A) H&E staining of mouse ki https://www.frontiersin.org/files/Articles/1660284/fphar-16-1660284-HTML/image_m/fphar-16-1660284-g007.jpg Figure 7. WSP promotes bone calcium deposition in diet-induced PMOP mice. (A-D) Expression of femora https://www.frontiersin.org/files/Articles/1660284/fphar-16-1660284-HTML/image_m/fphar-16-1660284-g008.jpg Figure 8. WSP’s Calcium Absorption Mechanism in diet-induced PMOP mice. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1716720/full https://www.frontiersin.org/files/Articles/1716720/fpsyt-17-1716720-HTML-r1/image_m/fpsyt-17-1716720-t001.jpg Table 1. Demographic characteristics of the participants (N = 687). https://www.frontiersin.org/files/Articles/1716720/fpsyt-17-1716720-HTML-r1/image_m/fpsyt-17-1716720-t002.jpg Table 2. Obstetric history and pregnancy-related factors among participants (N = 687). https://www.frontiersin.org/files/Articles/1716720/fpsyt-17-1716720-HTML-r1/image_m/fpsyt-17-1716720-t003.jpg Table 3. Pregnancy anxiety questionnaire-revised 2 (PRAQ-R2). https://www.frontiersin.org/files/Articles/1716720/fpsyt-17-1716720-HTML-r1/image_m/fpsyt-17-1716720-t004.jpg Table 4. Comparison of the prevalence of pregnancy-related anxiety across the different gestational https://www.frontiersin.org/files/Articles/1716720/fpsyt-17-1716720-HTML-r1/image_m/fpsyt-17-1716720-g001.jpg Figure 1. forest plot of odds ratios of PRA. https://www.frontiersin.org/files/Articles/1716720/fpsyt-17-1716720-HTML-r1/image_m/fpsyt-17-1716720-t005.jpg Table 5. Logistic regression analysis of pregnancy-related anxiety in Saudi Arabia. https://www.frontiersin.org/files/Articles/1716720/fpsyt-17-1716720-HTML-r1/image_m/fpsyt-17-1716720-g002.jpg Figure 2. Correlation heatmap of the risk factors and PRA. https://www.frontiersin.org/files/Articles/1716720/fpsyt-17-1716720-HTML-r1/image_m/fpsyt-17-1716720-g003.jpg Figure 3. Conceptual framework: from evidence to action for reducing pregnancy-related anxiety in Sa https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1748131/full https://www.frontiersin.org/files/Articles/1748131/fpubh-14-1748131-HTML/image_m/fpubh-14-1748131-g001.jpg Figure 1. Participant flow diagram. https://www.frontiersin.org/files/Articles/1748131/fpubh-14-1748131-HTML/image_m/fpubh-14-1748131-t001.jpg Table 1. Association between willingness to register as an organ/tissue donor and sociodemographic v https://www.frontiersin.org/files/Articles/1748131/fpubh-14-1748131-HTML/image_m/fpubh-14-1748131-t002.jpg Table 2. Knowledge about organ donation. https://www.frontiersin.org/files/Articles/1748131/fpubh-14-1748131-HTML/image_m/fpubh-14-1748131-t003.jpg Table 3. Attitudes toward organ donation. https://www.frontiersin.org/files/Articles/1748131/fpubh-14-1748131-HTML/image_m/fpubh-14-1748131-t004.jpg Table 4. Beliefs toward organ donation. https://www.frontiersin.org/files/Articles/1748131/fpubh-14-1748131-HTML/image_m/fpubh-14-1748131-t005.jpg Table 5. Association between knowledge, attitude, and belief items and willingness to register as an https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1751074/full https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-t001.jpg Table 1. Cohort characteristic (N = 398). https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-t002.jpg Table 2. Most common organisms identified by urine culture. https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-g001.jpg Figure 1. Study enrollment, sample processing workflow, and analytical framework. https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-g002.jpg Figure 2. Fraction of samples and targets identified by culture and by BIOTIA-DX. (A) Fraction of cu https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-t003.jpg Table 3. Closely related species identified by culture vs. BIOTIA-DX. https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-t004.jpg Table 4. Summary of organisms identified by culture but not identified by BIOTIA-DX. “Borderline” ca https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-t005.jpg Table 5. Most commonly identified organisms by culture and/or BIOTIA-DX (Top 20 of 63 species). https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-t006.jpg Table 6. Resistance rates observed for beta-lactam, sulfonamide/trimethoprim, aminoglycoside, fluoro https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-g003.jpg Figure 3. Beta-lactamase antimicrobial resistance gene profiles in phenotypically resistant bacteria https://www.frontiersin.org/files/Articles/1751074/fcimb-16-1751074-HTML-r1/image_m/fcimb-16-1751074-t007.jpg Table 7. Fluoroquinolone resistance-associated mutations in E. coli isolates (N = 128). https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2025.1644177/full https://www.frontiersin.org/files/Articles/1644177/fpain-06-1644177-HTML/image_m/fpain-06-1644177-g001.jpg Figure 1. Changes in the pressure pain threshold (PPT) of the right knee joint over the 56-day study https://www.frontiersin.org/files/Articles/1644177/fpain-06-1644177-HTML/image_m/fpain-06-1644177-g002.jpg Figure 2. Changes in the paw withdrawal threshold (PWR) of the right knee joint over the 56-day stud https://www.frontiersin.org/files/Articles/1644177/fpain-06-1644177-HTML/image_m/fpain-06-1644177-g003.jpg Figure 3. Histological findings with toluidine blue staining and degeneration scores in cartilage su https://www.frontiersin.org/files/Articles/1644177/fpain-06-1644177-HTML/image_m/fpain-06-1644177-g004.jpg Figure 4. Immunohistochemical findings and quantification of immunoreactive cells for macrophage mar https://www.frontiersin.org/files/Articles/1644177/fpain-06-1644177-HTML/image_m/fpain-06-1644177-g005.jpg Figure 5. Histochemical, immunohistochemical, and immunofluorescence findings and quantification of https://www.frontiersin.org/files/Articles/1644177/fpain-06-1644177-HTML/image_m/fpain-06-1644177-g006.jpg Figure 6. Immunofluorescence findings for phosphorylated NR1 (pNR1)-positive cells in the spinal dor https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1707912/full https://www.frontiersin.org/files/Articles/1707912/fimmu-16-1707912-HTML/image_m/fimmu-16-1707912-g001.jpg Figure 1. Article analysis flow chart. https://www.frontiersin.org/files/Articles/1707912/fimmu-16-1707912-HTML/image_m/fimmu-16-1707912-g002.jpg Figure 2. WGCNA analysis of dataset GSE141512. (A) GSE141512 volcano map; (B, C) Selection of WGCNA https://www.frontiersin.org/files/Articles/1707912/fimmu-16-1707912-HTML/image_m/fimmu-16-1707912-g003.jpg Figure 3. GOKEGG enrichment analysis and PPI construction of turquoise module. (A, B) Bar chart and https://www.frontiersin.org/files/Articles/1707912/fimmu-16-1707912-HTML/image_m/fimmu-16-1707912-g004.jpg Figure 4. Identification and validation of genes related to exosomes and ferroptosis phenotype. (A) https://www.frontiersin.org/files/Articles/1707912/fimmu-16-1707912-HTML/image_m/fimmu-16-1707912-g005.jpg Figure 5. Immune infiltration analysis and validation. (A) Immune infiltration analysis of HP in GSE https://www.frontiersin.org/files/Articles/1707912/fimmu-16-1707912-HTML/image_m/fimmu-16-1707912-g006.jpg Figure 6. Pseudo-temporal trajectory and intercellular communication analysis. (A, B) Trajectory of https://www.frontiersin.org/files/Articles/1707912/fimmu-16-1707912-HTML/image_m/fimmu-16-1707912-g007.jpg Figure 7. CeRNA analysis of HP. (A) HP’s miRNA intersection in three databases; (B) CeRNA analysis o https://www.frontiersin.org/files/Articles/1707912/fimmu-16-1707912-HTML/image_m/fimmu-16-1707912-g008.jpg Figure 8. Validation with four independent datasets. (A-D) Volcanic maps of four independent dataset https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1680395/full https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-t001.jpg Table 1. Per-fold distribution of patients, seizures (Sz.), non-seizures (NSz.) and number of iEEG r https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g001.jpg Figure 1. The NeuroPace® RNS® System and an example iEEG recording. (a) The NeuroPace® RNS® System i https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g002.jpg Figure 2. Visualization of the dataset used for model development and evaluation in the focal epilep https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-t002.jpg Table 2. Total number of seizure and non-seizure iEEG recordings across the four categories (long ep https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g003.jpg Figure 3. (A) Example iEEG channel spectrogram labeled as “seizure” by a human expert. (B) Example i https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g004.jpg Figure 4. (A) Example iEEG channel spectrogram containing stimulation-related artifacts. (B) Spectro https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g005.jpg Figure 5. (A) Example color iEEG channel spectrogram visualized using the jet colormap. (B) Spectrog https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-t003.jpg Table 3. Performance of the electrographic seizure classification models on the 23-patient test sets https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g006.jpg Figure 6. Performance comparison of electrographic seizure classification models using color vs. gra https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g007.jpg Figure 7. Performance comparison of electrographic seizure classification models before vs. after st https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-t004.jpg Table 4. Performance of the electrographic seizure classification models on the clinical validation https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g008.jpg Figure 8. Model performance on iEEG recordings from the clinical validation dataset with unanimous e https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-t005.jpg Table 5. Accuracy and F1 scores of the ViT (86M) model by lead location, on a subset of the clinical https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g009.jpg Figure 9. Confusion matrices showing the performance of the ViT model across four categories of iEEG https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g010.jpg Figure 10. Distribution of iEEG recording durations in the subset of the clinical validation dataset https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-t006.jpg Table 6. ViT (86M) model accuracy on iEEG recordings stratified by recording duration in the clinica https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g011.jpg Figure 11. Performance of the ViT model on an out-of-distribution dataset consisting of iEEG recordi https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g012.jpg Figure 12. Explainability analysis showing attention maps for three high-confidence seizure and non- https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-t007.jpg Table 7. Percentage of false positive and false negative iEEG channels in the test sets of the 5 cro https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g013.jpg Figure 13. Cluster centroids from iEEG channels identified as false positives by the model in the te https://www.frontiersin.org/files/Articles/1680395/fnhum-19-1680395-HTML/image_m/fnhum-19-1680395-g014.jpg Figure 14. Cluster centroids from iEEG channels identified as false negatives by the model in the te https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1748244/full https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-g001.jpg Figure 1. Flow-chart of the randomized trial. https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-t001.jpg Table 1. Participant baseline characteristics (n = 47). https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-g002.jpg Figure 2. Intervention protocol. https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-t002.jpg Table 2. Comparison of load parameters across three PAPE training protocols. https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-t003.jpg Table 3. Changes in swim to 15 m time (seconds). https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-g003.jpg Figure 3. Time course of 15-m swim start performance across groups. Data are presented as mean ± SD. https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-t004.jpg Table 4. Changes in peak horizontal force and average propulsive force (N). https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-g004.jpg Figure 4. The changing trends of Peak Horizontal Force and Average Propulsive Force in different gro https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-t005.jpg Table 5. Changes in Propulsive impulse (N·s) and Take-off velocity (m/s). https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-g005.jpg Figure 5. The changing trends of Propulsive Impulse and Take-off Velocity in different groups. Data https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-t006.jpg Table 6. Changes in CMJ height (cm) and peak power (W/kg). https://www.frontiersin.org/files/Articles/1748244/fphys-17-1748244-HTML/image_m/fphys-17-1748244-g006.jpg Figure 6. The changing trends of CMJ Height and Peak Power in different groups. Data are presented a https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1775281/full https://www.frontiersin.org/files/Articles/1775281/fpsyg-17-1775281-HTML/image_m/fpsyg-17-1775281-t001.jpg Table 1. Demographic information of participants. https://www.frontiersin.org/files/Articles/1775281/fpsyg-17-1775281-HTML/image_m/fpsyg-17-1775281-t002.jpg Table 2. Comparison of perceived effort data across three groups. https://www.frontiersin.org/files/Articles/1775281/fpsyg-17-1775281-HTML/image_m/fpsyg-17-1775281-g001.jpg Figure 1. Scatter plot of normalized actual pressure and normalized perceived effort across skill le https://www.frontiersin.org/files/Articles/1775281/fpsyg-17-1775281-HTML/image_m/fpsyg-17-1775281-t003.jpg Table 3. Comparison of muscle activation patterns across three groups. https://www.frontiersin.org/files/Articles/1775281/fpsyg-17-1775281-HTML/image_m/fpsyg-17-1775281-t004.jpg Table 4. Hierarchical multiple regression analysis of aesthetic ratings (character level, N = 900). https://www.frontiersin.org/files/Articles/1775281/fpsyg-17-1775281-HTML/image_m/fpsyg-17-1775281-t005.jpg Table 5. Correlation between recalibration index and aesthetic rating by character type. https://www.frontiersin.org/files/Articles/1775281/fpsyg-17-1775281-HTML/image_m/fpsyg-17-1775281-t006.jpg Table 6. Mediation analysis of perceptual recalibration. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1729086/full https://www.frontiersin.org/files/Articles/1729086/fimmu-17-1729086-HTML/image_m/fimmu-17-1729086-g001.jpg Figure 1. In vitro coinfection of PR8 and Spn follows bacterial transcriptional pattern and increase https://www.frontiersin.org/files/Articles/1729086/fimmu-17-1729086-HTML/image_m/fimmu-17-1729086-g002.jpg Figure 2. Transcription regulation of in vitro superinfection with Influenza virus and Streptococcus https://www.frontiersin.org/files/Articles/1729086/fimmu-17-1729086-HTML/image_m/fimmu-17-1729086-g003.jpg Figure 3. Gene expression in BMDMs during IAV–bacterial coinfection can be shaped by Streptococcus s https://www.frontiersin.org/files/Articles/1729086/fimmu-17-1729086-HTML/image_m/fimmu-17-1729086-g004.jpg Figure 4. BMDM differentiated from mice of different ages exhibit different responses to coinfection https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1648972/full https://www.frontiersin.org/files/Articles/1648972/feduc-10-1648972-HTML-r2/image_m/feduc-10-1648972-g001.jpg Figure 1. The three research cycles of the Concept-based Learning in Immunology and Microbiology (CL https://www.frontiersin.org/files/Articles/1648972/feduc-10-1648972-HTML-r2/image_m/feduc-10-1648972-g002.jpg Figure 2. Modifications made to Concept-based Learning in Immunology and Microbiology (CLIMb) framew https://www.frontiersin.org/files/Articles/1648972/feduc-10-1648972-HTML-r2/image_m/feduc-10-1648972-g003.jpg Figure 3. Concept-based Learning in Immunology and Microbiology (CLIMb) framework. This framework em https://www.frontiersin.org/files/Articles/1648972/feduc-10-1648972-HTML-r2/image_m/feduc-10-1648972-t001.jpg Table 1. Performance expectation and example responses to CLIMb question prompts provided to partici https://www.frontiersin.org/files/Articles/1648972/feduc-10-1648972-HTML-r2/image_m/feduc-10-1648972-t002.jpg Table 2. Example responses organized based on CLIMb level scoring. https://www.frontiersin.org/files/Articles/1648972/feduc-10-1648972-HTML-r2/image_m/feduc-10-1648972-t003.jpg Table 3. Example responses with distinct scores for each progress variable using the CLIMb framework https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1652289/full https://www.frontiersin.org/files/Articles/1652289/fpls-16-1652289-HTML/image_m/fpls-16-1652289-g001.jpg Figure 1. Implementation plan for intelligent voice service of agricultural machinery. https://www.frontiersin.org/files/Articles/1652289/fpls-16-1652289-HTML/image_m/fpls-16-1652289-g002.jpg Figure 2. Typical scheme of intelligent voice control system for agricultural machinery. https://www.frontiersin.org/files/Articles/1652289/fpls-16-1652289-HTML/image_m/fpls-16-1652289-g003.jpg Figure 3. Architecture of intelligent voice warning technology for agricultural machinery fault diag https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1763646/full https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-g001.jpg Figure 1. The flowchart of patients’ enrollment. TDs, tumor deposits. https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-g002.jpg Figure 2. The schematic workflow of this study. ROI, region of interest. https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-t001.jpg Table 1. Baseline demographic and clinical characteristics of patients. https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-t002.jpg Table 2. Univariable and multivariable analyses of the clinical characteristics in the training coho https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-t003.jpg Table 3. Predictive efficacy of different models for TDs status in advanced gastric cancer across th https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-g003.jpg Figure 3. Receiver operating characteristic (ROC) curves of different models for predicting TDs stat https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-t004.jpg Table 4. Statistical p-values from DeLong tests for pairwise comparisons of all models across the tr https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-g004.jpg Figure 4. Decision curves analysis of different models for predicting TDs status in advanced gastric https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-g005.jpg Figure 5. Calibration curves of different models for predicting TDs status in advanced gastric cance https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-g006.jpg Figure 6. SHAP summary plots corresponding to the intratumoral (A), peritumoral (B), and combined in https://www.frontiersin.org/files/Articles/1763646/fonc-16-1763646-HTML/image_m/fonc-16-1763646-g007.jpg Figure 7. Model output interpretability analysis targeting a 56-year-old patient with positive TDs. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1688002/full https://www.frontiersin.org/files/Articles/1688002/fpubh-13-1688002-HTML-r3/image_m/fpubh-13-1688002-g001.jpg Figure 1. Community capitals leveraged for the initial start-up of the Glen Rock Neighborhood Networ https://www.frontiersin.org/files/Articles/1688002/fpubh-13-1688002-HTML-r3/image_m/fpubh-13-1688002-t001.jpg Table 1. Overview of GRNN development phases and benchmarks pre-launch. https://www.frontiersin.org/files/Articles/1688002/fpubh-13-1688002-HTML-r3/image_m/fpubh-13-1688002-t002.jpg Table 2. Overview of GRNN committees. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1771752/full https://www.frontiersin.org/files/Articles/1771752/fspor-08-1771752-HTML/image_m/fspor-08-1771752-t001.jpg Table 1. Teams and the number of matches included from each male EHF EURO championship. https://www.frontiersin.org/files/Articles/1771752/fspor-08-1771752-HTML/image_m/fspor-08-1771752-t002.jpg Table 2. Teams and the number of matches included from each female EHF EURO championship . https://www.frontiersin.org/files/Articles/1771752/fspor-08-1771752-HTML/image_m/fspor-08-1771752-t003.jpg Table 3. Descriptive statistics (mean, SD) for type of goals scored per match across male European c https://www.frontiersin.org/files/Articles/1771752/fspor-08-1771752-HTML/image_m/fspor-08-1771752-t004.jpg Table 4. Descriptive statistics (mean, SD) for type of goals scored per match across female European https://www.frontiersin.org/files/Articles/1771752/fspor-08-1771752-HTML/image_m/fspor-08-1771752-t005.jpg Table 5. Overview of results from generalised linear mixed models. https://www.frontiersin.org/files/Articles/1771752/fspor-08-1771752-HTML/image_m/fspor-08-1771752-g001.jpg Figure 1. Frequency distribution of the attacking goals for men and women in the 2024 European champ https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1775638/full https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g001.jpg Figure 1. Spot assay (A) and plaque morphology of UHKP against KP-03 (B) and KP-8890 (C) after 24 h https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-t001.jpg Table 1. Clinical origin, lytic activity and EOP of UHKP phage against selected Klebsiella strains. https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g002.jpg Figure 2. Growth reduction potential of UHKP against K. pneumoniae strain KP-03 at MOI 1 and 0.1 com https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g003.jpg Figure 3. Biofilm formation and removal by UHKP. Panels A and B represent K. pneumoniae KP-03 biofil https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g004.jpg Figure 4. Biofilm removal (CFU/mL reduction) following UHKP treatment for 6, 12, and 24 h against 1– https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g005.jpg Figure 5. Stability of UHKP under different conditions. (A) Effect of pH treatments (1 and 2 h), (B) https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g006.jpg Figure 6. One-step growth curve of Klebsiella phage UHKP. A latent period of 30 min was observed, wi https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g007.jpg Figure 7. Transmission electron micrograph of UHKP, revealing icosahedral capsid with a short non-co https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g008.jpg Figure 8. Linear Genome map of UHKP drawn with SnapGene 6.0. https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g009.jpg Figure 9. Phylogenetic tree of the large terminase subunit (A) and whole-genome sequences (B) of UHK https://www.frontiersin.org/files/Articles/1775638/fmicb-17-1775638-HTML/image_m/fmicb-17-1775638-g010.jpg Figure 10. Proteomic tree constructed using VIPTree of phage UHKP (A) Circular tree of related phage https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1723978/full https://www.frontiersin.org/files/Articles/1723978/fimmu-17-1723978-HTML/image_m/fimmu-17-1723978-t001.jpg Table 1. Classification of our subset of patients (n=5) diagnosed with intimal sarcoma. https://www.frontiersin.org/files/Articles/1723978/fimmu-17-1723978-HTML/image_m/fimmu-17-1723978-g001.jpg Figure 1. Histological characteristics of L328, L329, L331 and L332. (A) L328 presenting epithelioid https://www.frontiersin.org/files/Articles/1723978/fimmu-17-1723978-HTML/image_m/fimmu-17-1723978-t002.jpg Table 2. Classification of the UPS and LMS included in the DGE analysis (GEO: GSE71120). https://www.frontiersin.org/files/Articles/1723978/fimmu-17-1723978-HTML/image_m/fimmu-17-1723978-g002.jpg Figure 2. Chromosome 12 amplifications in our intimal sarcomas. Scatter plots representing chr12 cop https://www.frontiersin.org/files/Articles/1723978/fimmu-17-1723978-HTML/image_m/fimmu-17-1723978-g003.jpg Figure 3. Integrated analysis of immune cell composition and TIS score. (A) Hierarchical clustering https://www.frontiersin.org/files/Articles/1723978/fimmu-17-1723978-HTML/image_m/fimmu-17-1723978-g004.jpg Figure 4. Molecular and immune characterization of our intimal sarcomas (n=5), leiomyosarcomas (n=5) https://www.frontiersin.org/files/Articles/1723978/fimmu-17-1723978-HTML/image_m/fimmu-17-1723978-t003.jpg Table 3. Most enriched immune-related pathways in intimal sarcomas versus LMS in red (GSEA Hallmark) https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1732913/full https://www.frontiersin.org/files/Articles/1732913/fmed-13-1732913-HTML/image_m/fmed-13-1732913-t001.jpg Table 1. Student groups and the details of the procedure. https://www.frontiersin.org/files/Articles/1732913/fmed-13-1732913-HTML/image_m/fmed-13-1732913-g001.jpg Figure 1. Pictorial representation of the jigsaw model. https://www.frontiersin.org/files/Articles/1732913/fmed-13-1732913-HTML/image_m/fmed-13-1732913-t002.jpg Table 2. The Jigsaw student presentation rubric evaluated cognitive understanding and affective skil https://www.frontiersin.org/files/Articles/1732913/fmed-13-1732913-HTML/image_m/fmed-13-1732913-t003.jpg Table 3. Scores of groups performance across different categories. https://www.frontiersin.org/files/Articles/1732913/fmed-13-1732913-HTML/image_m/fmed-13-1732913-t004.jpg Table 4. One-way ANOVA and post-hoc interpretation showing differences in expert group, assignment, https://www.frontiersin.org/files/Articles/1732913/fmed-13-1732913-HTML/image_m/fmed-13-1732913-t005.jpg Table 5. Showing the correlation of various activities scores across groups. https://www.frontiersin.org/files/Articles/1732913/fmed-13-1732913-HTML/image_m/fmed-13-1732913-t006.jpg Table 6. Correlation of total scores across groups. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1742972/full https://www.frontiersin.org/files/Articles/1742972/fnut-13-1742972-HTML-r1/image_m/fnut-13-1742972-t001.jpg Table 1. Baseline clinical characteristics of the analyzed participants (n = 89)1. https://www.frontiersin.org/files/Articles/1742972/fnut-13-1742972-HTML-r1/image_m/fnut-13-1742972-t002.jpg Table 2. Correlation between ALA intake and serum n-3 fatty acid concentrations. https://www.frontiersin.org/files/Articles/1742972/fnut-13-1742972-HTML-r1/image_m/fnut-13-1742972-t003.jpg Table 3. The relationship between baPWV and n-3/n-6 fatty acid intake and serum concentrations. https://www.frontiersin.org/files/Articles/1742972/fnut-13-1742972-HTML-r1/image_m/fnut-13-1742972-g001.jpg Figure 1. (A) The relationship between ALA intake and baPWV; (B) The relationship between serum ALA https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1686224/full https://www.frontiersin.org/files/Articles/1686224/fimmu-17-1686224-HTML/image_m/fimmu-17-1686224-g001.jpg Figure 1. Schematic overview of the development and validation of an off-the-shelf (OTS) neoantigen https://www.frontiersin.org/files/Articles/1686224/fimmu-17-1686224-HTML/image_m/fimmu-17-1686224-g002.jpg Figure 2. Characterization of a recurrent mutation panel from the TCGA-COAD dataset. (A) Workflow fo https://www.frontiersin.org/files/Articles/1686224/fimmu-17-1686224-HTML/image_m/fimmu-17-1686224-g003.jpg Figure 3. Mutation frequency and neoantigen presentation across HLA-I loci in Asian and Caucasian po https://www.frontiersin.org/files/Articles/1686224/fimmu-17-1686224-HTML/image_m/fimmu-17-1686224-g004.jpg Figure 4. Validation of the off-the-shelf neoantigen panel in a Vietnamese CRC cohort (n = 67). (A) https://www.frontiersin.org/files/Articles/1686224/fimmu-17-1686224-HTML/image_m/fimmu-17-1686224-g005.jpg Figure 5. Functional validation and single-cell immune profiling of neoantigen-specific T cell respo https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1747710/full https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g001.jpg Figure 1. Phylogenetic analysis of WOX proteins: 18 PeuWOX proteins, 18 PtWOX proteins, and 15 AtWOX https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g002.jpg Figure 2. Phylogenetic relationship, gene structure, and motif composition of WOX genes in P. euphra https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g003.jpg Figure 3. Cis-acting elements in the promoter region 1,500-bp upstream of the PeuWOX genes were inve https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g004.jpg Figure 4. The chromosomal positions of WOX genes in P. euphratica species were investigated. In tota https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-t001.jpg Table 1. Ka/Ks analysis and predictable divergence time (MYA). https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g005.jpg Figure 5. Synteny study of PeuWOX and other species. WOX gene collinearity among P. euphratica and t https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g006.jpg Figure 6. Relative expression analysis of the PeuWOX genes in response to stem tissues under salt st https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g007.jpg Figure 7. The relative expression analysis of the PeuWOX genes in response to root tissues under sal https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g008.jpg Figure 8. Expression pattern analysis of PeuWOX genes in leaf tissues under salt stress. To validate https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g009.jpg Figure 9. Expression pattern analysis of PeuWOX genes in stem tissues under drought stress. The expr https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g010.jpg Figure 10. Expression pattern analysis of PeuWOX genes in root tissues under drought stress. The exp https://www.frontiersin.org/files/Articles/1747710/fpls-16-1747710-HTML/image_m/fpls-16-1747710-g011.jpg Figure 11. Expression pattern analysis of PeuWOX genes in leaf tissues under drought stress. The exp https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1713588/full https://www.frontiersin.org/files/Articles/1713588/fphys-16-1713588-HTML/image_m/fphys-16-1713588-t001.jpg Table 1. Participant characteristics, anxiety scores, and 2000 m rowing test outcomes (mean ± SD). https://www.frontiersin.org/files/Articles/1713588/fphys-16-1713588-HTML/image_m/fphys-16-1713588-g001.jpg Figure 1. Study design. https://www.frontiersin.org/files/Articles/1713588/fphys-16-1713588-HTML/image_m/fphys-16-1713588-g002.jpg Figure 2. Cortisol, testosterone, and T/C ratio in low- and high-anxiety athletes. Note: Symbols den https://www.frontiersin.org/files/Articles/1713588/fphys-16-1713588-HTML/image_m/fphys-16-1713588-t002.jpg Table 2. Cohen’s d effect sizes for hormonal and neurotransmitter markers. https://www.frontiersin.org/files/Articles/1713588/fphys-16-1713588-HTML/image_m/fphys-16-1713588-g003.jpg Figure 3. Serotonin, dopamine, and S/D ratio in low- and high-anxiety athletes. Note: Symbols denote https://www.frontiersin.org/files/Articles/1713588/fphys-16-1713588-HTML/image_m/fphys-16-1713588-g004.jpg Figure 4. β-endorphin, anandamide, and 2-arachidonoylglycerol in low- and high-anxiety athletes. Not https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1644925/full https://www.frontiersin.org/files/Articles/1644925/fonc-15-1644925-HTML/image_m/fonc-15-1644925-t001.jpg Table 1. Interview outline. https://www.frontiersin.org/files/Articles/1644925/fonc-15-1644925-HTML/image_m/fonc-15-1644925-t002.jpg Table 2. Participant Characteristics(N = 14). https://www.frontiersin.org/files/Articles/1644925/fonc-15-1644925-HTML/image_m/fonc-15-1644925-t003.jpg Table 3. Themes and Sub-Themes. https://www.frontiersin.org/files/Articles/1644925/fonc-15-1644925-HTML/image_m/fonc-15-1644925-t004.jpg Table 4. Culturally-adapted shared decision-making: key differences between 2estern and chinese cont https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1742379/full https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-t001.jpg Table 1. Reliability and CFA results (N = 320). https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-t002.jpg Table 2. Descriptive statistics and correlations (N = 320). https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-t003.jpg Table 3. SEM path analysis (N = 320). https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-g001.jpg Figure 1. Structural equation model (main paths). Note: Statistical significance is indicated as fol https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-t004.jpg Table 4. Bootstrap mediation results. https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-t005.jpg Table 5. Moderating effect of perfectionism. https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-g002.jpg Figure 2. Moderating effect of perfectionism. https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-t006.jpg Table 6. Moderating effect of self compassion. https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-g003.jpg Figure 3. Moderating effect of self compassion. https://www.frontiersin.org/files/Articles/1742379/fpsyg-17-1742379-HTML-r2/image_m/fpsyg-17-1742379-t007.jpg Table 7. Multi group path comparison. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1774371/full https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-g001.jpg Figure 1. Example ultrasound images and corresponding binary masks for the three dataset classes: (a https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-g002.jpg Figure 2. Overview of the proposed two-stage framework. https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-g003.jpg Figure 3. Training and validation accuracy and loss curves for the U-Net model. https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-g004.jpg Figure 4. Visual comparison of ground truth and predicted tumor masks for two benign and two maligna https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-g005.jpg Figure 5. Visual illustration of extracted shape features for (a) a benign tumor and (b) a malignant https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-g006.jpg Figure 6. Box plots showing the distribution of the four morphological features. https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-t001.jpg Table 1. Sample distribution per class in training and testing sets for segmentation and classificat https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-g007.jpg Figure 7. Confusion matrices of SVM classification for training (a) and testing (b) datasets. https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-g008.jpg Figure 8. Representative misclassified cases from the test set demonstrating the model’s limitations https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-g009.jpg Figure 9. Classification accuracy across 5 folds in stratified cross-validation. https://www.frontiersin.org/files/Articles/1774371/fbioe-14-1774371-HTML-r1/image_m/fbioe-14-1774371-t002.jpg Table 2. Comparison of the proposed method with recent studies in breast tumor segmentation, feature https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1642829/full https://www.frontiersin.org/files/Articles/1642829/fonc-15-1642829-HTML/image_m/fonc-15-1642829-t001.jpg Table 1. Baseline characteristics of enrolled patients. https://www.frontiersin.org/files/Articles/1642829/fonc-15-1642829-HTML/image_m/fonc-15-1642829-g001.jpg Figure 1. Predictive value of baseline lymphocyte subsets for treatment efficacy in enrolled patient https://www.frontiersin.org/files/Articles/1642829/fonc-15-1642829-HTML/image_m/fonc-15-1642829-g002.jpg Figure 2. Kaplan-Meier survival curve analysis showing the relationship between values of CD3-CD16+C https://www.frontiersin.org/files/Articles/1642829/fonc-15-1642829-HTML/image_m/fonc-15-1642829-g003.jpg Figure 3. Predictive value of baseline lymphocyte subsets for therapeutic outcomes in patients recei https://www.frontiersin.org/files/Articles/1642829/fonc-15-1642829-HTML/image_m/fonc-15-1642829-g004.jpg Figure 4. Predictive value of baseline lymphocyte subsets on the efficacy of chemotherapy in patient https://www.frontiersin.org/files/Articles/1642829/fonc-15-1642829-HTML/image_m/fonc-15-1642829-g005.jpg Figure 5. Predictive value of second cycle lymphocyte subsets on the therapeutic efficacy. (A-F) The https://www.frontiersin.org/files/Articles/1642829/fonc-15-1642829-HTML/image_m/fonc-15-1642829-g006.jpg Figure 6. Nomogram model predicts treatment efficacy of lymphocyte subsets between the response and https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2026.1717183/full https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g001.jpg Figure 1. Schematic representation of P-MEX technique with piezoelectric nozzle (discharge unit) (Si https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g002.jpg Figure 2. Diagram of a single material dispensing unit - 1 workpiece holder, 2 nozzles, 3 shutter, 4 https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g003.jpg Figure 3. Effect of DAR on droplet deposition. (a) Wr > Wid: droplets overlap beyond the nominal tra https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-t001.jpg Table 1. Selected study parameters for ABS. https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-t002.jpg Table 2. Process parameter. https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g004.jpg Figure 4. Overhang test specimen: (a) Inclined surfaces with incremental angles (b) Bridging structu https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g005.jpg Figure 5. Key dimensions of the overhang test specimen. https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g006.jpg Figure 6. Arrangement of specimens in the fourth build plate. https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g007.jpg Figure 7. Examples of printed specimens. https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-t003.jpg Table 3. Technical specifications of the Artec Spider 3D scanner, as provided by (Artec Space Spider https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g008.jpg Figure 8. Reference surfaces used for dimensional deviation analysis. https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g009.jpg Figure 9. Global average results achieved with the standard slicing strategy. Mean data is computed https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g010.jpg Figure 10. Global average results achieved with the reverse slicing strategy. Mean data is computed https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-t004.jpg Table 4. Standard deviation values computed across five repetitions of the test for the standard dev https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-t005.jpg Table 5. Standard deviation values computed across five repetitions of the test for the RMS values c https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g011.jpg Figure 11. Standard Deviation and RMS errors of the deviation maps calculated for the 21°, 24° and 2 https://www.frontiersin.org/files/Articles/1717183/fmech-12-1717183-HTML/image_m/fmech-12-1717183-g012.jpg Figure 12. Control X analysis of the third build plate, with a deviation map expressed in mm. Values https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2025.1725159/full https://www.frontiersin.org/files/Articles/1725159/falgy-06-1725159-HTML/image_m/falgy-06-1725159-g001.jpg Figure 1. Percentages of the different treatments (biologicals, primary ESS, revision ESS) throughou https://www.frontiersin.org/files/Articles/1725159/falgy-06-1725159-HTML/image_m/falgy-06-1725159-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1713120/full https://www.frontiersin.org/files/Articles/1713120/fimmu-17-1713120-HTML/image_m/fimmu-17-1713120-g001.jpg Figure 1. CD4+ T-cell analysis of splenocytes from SF mice. (A) Gating strategy for the identificati https://www.frontiersin.org/files/Articles/1713120/fimmu-17-1713120-HTML/image_m/fimmu-17-1713120-g002.jpg Figure 2. Liver histological evaluation of RAG1KO mice after adoptive transfer of SF-CD4+ T cells is https://www.frontiersin.org/files/Articles/1713120/fimmu-17-1713120-HTML/image_m/fimmu-17-1713120-g003.jpg Figure 3. Liver macrophage (MΦ) evaluation from RAG1KO mice after adoptive transfer of CD4+ T cells, https://www.frontiersin.org/files/Articles/1713120/fimmu-17-1713120-HTML/image_m/fimmu-17-1713120-g004.jpg Figure 4. Liver genes altered by SF-CD4+ T-cell transfer to RAG1KO mice. Biological pathways were co https://www.frontiersin.org/files/Articles/1713120/fimmu-17-1713120-HTML/image_m/fimmu-17-1713120-g005.jpg Figure 5. Liver genes altered by Prob-SF-CD4+ T-cell transfer to RAG1KO mice compared with SF-CD4+ T https://www.frontiersin.org/files/Articles/1713120/fimmu-17-1713120-HTML/image_m/fimmu-17-1713120-g006.jpg Figure 6. RNA alternative splicing events in DEGs. (A) The volcano plot displays DEGs that are also https://www.frontiersin.org/files/Articles/1713120/fimmu-17-1713120-HTML/image_m/fimmu-17-1713120-g007.jpg Figure 7. Gut microbiota analysis in RAG1KO mice after adoptive transfer of CD4+ T cells. (A) Bray–C https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2026.1726804/full https://www.frontiersin.org/files/Articles/1726804/frhs-06-1726804-HTML/image_m/frhs-06-1726804-t001.jpg Table 1. Characteristics of the interviewees. https://www.frontiersin.org/files/Articles/1726804/frhs-06-1726804-HTML/image_m/frhs-06-1726804-g001.jpg Figure 1. Summary of findings. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1752176/full https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g001.jpg Figure 1. Flowchart of the proposed methodology, illustrating the sequential steps of data collectio https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g002.jpg Figure 2. EEG electrode configuration on the 10–10 system. Reproduced from Asanza et al. (2023), lic https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g003.jpg Figure 3. Setup of the experimental environment for EEG recording. Reproduced from Asanza et al. (20 https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g004.jpg Figure 4. Visual timeline of motor tasks and rest intervals. Reproduced from Asanza et al. (2023), l https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g005.jpg Figure 5. Sample EEG data trace during motor task execution. https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g006.jpg Figure 6. Results of 5-fold cross-validation, showing classification accuracy for each fold. https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g007.jpg Figure 7. Confusion matrices for training and testing data. https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-t001.jpg Table 1. Classification performance metrics for training and testing datasets. https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-t002.jpg Table 2. Per-class classification performance on the test dataset. https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g008.jpg Figure 8. Impact of the number of hidden layers on classification accuracy in the GMDH network. https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g009.jpg Figure 9. Ablation study illustrating the contribution of different EEG feature subsets to motor-tas https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-t003.jpg Table 3. Comparison of EEG classification methods based on accuracy, computational load, and feature https://www.frontiersin.org/files/Articles/1752176/fnins-20-1752176-HTML-r1/image_m/fnins-20-1752176-g010.jpg Figure 10. Classification accuracy comparison between the proposed GMDH model and standard lightweig https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1699656/full https://www.frontiersin.org/files/Articles/1699656/fendo-16-1699656-HTML/image_m/fendo-16-1699656-t001.jpg Table 1. Baseline characteristics of participants in the nested case–control study of SOALS. https://www.frontiersin.org/files/Articles/1699656/fendo-16-1699656-HTML/image_m/fendo-16-1699656-t002.jpg Table 2. Associations of omic signatures of insulin resistance with incident T2D in SOALS. https://www.frontiersin.org/files/Articles/1699656/fendo-16-1699656-HTML/image_m/fendo-16-1699656-g001.jpg Figure 1. Plasma, saliva, multi-fluid metabolomics, proteomics, and multi-omics profile for insulin https://www.frontiersin.org/files/Articles/1699656/fendo-16-1699656-HTML/image_m/fendo-16-1699656-g002.jpg Figure 2. ROC comparison for T2D across omics signatures. (A) Comparison of ROC curves for plasma si https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1766731/full https://www.frontiersin.org/files/Articles/1766731/fped-14-1766731-HTML/image_m/fped-14-1766731-t001.jpg Table 1. Baseline characteristics of Fontan patients. https://www.frontiersin.org/files/Articles/1766731/fped-14-1766731-HTML/image_m/fped-14-1766731-t002.jpg Table 2. Clinical, laboratory and cardiopulmonary exercise test characteristics of Fontan patients a https://www.frontiersin.org/files/Articles/1766731/fped-14-1766731-HTML/image_m/fped-14-1766731-t003.jpg Table 3. Clinical, laboratory and cardiopulmonary exercise test characteristics in Fontan patients w https://www.frontiersin.org/files/Articles/1766731/fped-14-1766731-HTML/image_m/fped-14-1766731-t004.jpg Table 4. Correlations of VO2, PETCO2, VE/VCO2, TSAT, RDW. https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2026.1768435/full https://www.frontiersin.org/files/Articles/1768435/fcomp-08-1768435-HTML-r1/image_m/fcomp-08-1768435-g001.jpg Figure 1. Offer from the proposer in study 2. https://www.frontiersin.org/files/Articles/1768435/fcomp-08-1768435-HTML-r1/image_m/fcomp-08-1768435-g002.jpg Figure 2. Mediation analysis performed in study 2. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1648838/full https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-g001.jpg Figure 1. Flowchart for patient screening. https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-t001.jpg Table 1. Baseline characteristics in the training cohort and validation cohort. https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-t002.jpg Table 2. Univariate analysis in the training cohort. https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-g002.jpg Figure 2. Characteristic factors were screened by the LASSO binary logistic regression model. (A) Lo https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-t003.jpg Table 3. Multivariate logistic regression analysis in the training cohort. https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-g003.jpg Figure 3. Nomogram for predicting the occurrence of ANEs after CAS for symptomatic carotid stenosis. https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-g004.jpg Figure 4. Calibration curves of the nomogram in the training cohort (A) and validation cohort (B). https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-g005.jpg Figure 5. ROC curves for the ANE nomogram in the training cohort (A) and validation cohort (B). https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-g006.jpg Figure 6. DCA for the ANE nomogram in the training cohort and validation cohort. https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-t004.jpg Table 4. Univariate and multivariate Firth penalized logistic regression analyses for ischemic ANEs https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-t005.jpg Table 5. Univariate and multivariate Firth penalized logistic regression analyses for hemorrhagic AN https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-g007.jpg Figure 7. SHAP summary plots of the composite prediction model stratified by outcome subtype. (A) Is https://www.frontiersin.org/files/Articles/1648838/fneur-16-1648838-HTML/image_m/fneur-16-1648838-g008.jpg Figure 8. ROC curves of the composite model stratified by ANE subtype in the training cohort (A) and https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2026.1765957/full https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-g001.jpg Figure 1. Geographic, ecosystem, and altitudinal location map of the study area and sampling unit in https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-g002.jpg Figure 2. Species–area curves fitted to Clench’s model in SDTF and montane forests in the Sonche dis https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-g003.jpg Figure 3. Richness of tree flora in the forests of the Sonche district along different altitudinal g https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-t001.jpg Table 1. Tree species with the highest Importance Value Index (IVI) by altitudinal range in the fore https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-g004.jpg Figure 4. Rank–abundance curves of tree species across different altitudinal gradients in the forest https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-g005.jpg Figure 5. Relative abundance of the most important tree species in the forests of the Sonche distric https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-t002.jpg Table 2. Alpha diversity of tree communities in seasonally dry tropical forests (SDTF) and montane f https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-g006.jpg Figure 6. Analysis of floristic similarity between plots in the forests of the Sonche district, base https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-g007.jpg Figure 7. Distribution of tree frequency according to diameter in the forests of the Sonche district https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-g008.jpg Figure 8. Distribution of tree height classes in the forests of the Sonche district: (a) height clas https://www.frontiersin.org/files/Articles/1765957/ffgc-09-1765957-HTML/image_m/ffgc-09-1765957-g009.jpg Figure 9. Multiple correspondence analysis (MCA) showing the floristic-structural differentiation of https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1758476/full https://www.frontiersin.org/files/Articles/1758476/fimmu-17-1758476-HTML/image_m/fimmu-17-1758476-g001.jpg Figure 1. Transcriptomic analysis of the lymph nodes of mice immunized with rBCG-LTAK63. Groups of m https://www.frontiersin.org/files/Articles/1758476/fimmu-17-1758476-HTML/image_m/fimmu-17-1758476-g002.jpg Figure 2. Schematic representation of the experimental design. (A) Inflammasome activation assay: BM https://www.frontiersin.org/files/Articles/1758476/fimmu-17-1758476-HTML/image_m/fimmu-17-1758476-g003.jpg Figure 3. rBCG-LTAK63 induces BMDMs IL-1β production even without priming. BMDMs from C57Bl/6 mice w https://www.frontiersin.org/files/Articles/1758476/fimmu-17-1758476-HTML/image_m/fimmu-17-1758476-g004.jpg Figure 4. rBCG-LTAK63 induces IL-1β secretion via inflammasome-associated pathways. BMDMs from Casp- https://www.frontiersin.org/files/Articles/1758476/fimmu-17-1758476-HTML/image_m/fimmu-17-1758476-g005.jpg Figure 5. rBCG-LTAK63 enhances CD4+ T cell activation and polarization. Splenocytes from naïve C57Bl https://www.frontiersin.org/files/Articles/1758476/fimmu-17-1758476-HTML/image_m/fimmu-17-1758476-g006.jpg Figure 6. Host inflammatory response determines the cytokine secretion in rBCG-LTAK63-infected BMDMs https://www.frontiersin.org/files/Articles/1758476/fimmu-17-1758476-HTML/image_m/fimmu-17-1758476-g007.jpg Figure 7. Protection against Mtb challenge induced by BCG or rBCG-LTAK63 in AIRmax and AIRminTT mice https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1765782/full https://www.frontiersin.org/files/Articles/1765782/fpubh-14-1765782-HTML-r1/image_m/fpubh-14-1765782-g001.jpg Figure 1. PRISMA 2020 flow diagram for the systematic review of early-childhood safety education stu https://www.frontiersin.org/files/Articles/1765782/fpubh-14-1765782-HTML-r1/image_m/fpubh-14-1765782-t001.jpg Table 1. Multidimensional domains of early-childhood safety education. https://www.frontiersin.org/files/Articles/1765782/fpubh-14-1765782-HTML-r1/image_m/fpubh-14-1765782-t002.jpg Table 2. A quantitative count of category appearances across episodes shows the distribution of safe https://www.frontiersin.org/files/Articles/1765782/fpubh-14-1765782-HTML-r1/image_m/fpubh-14-1765782-g002.jpg Figure 2. Multidimensional pedagogical safety integration model (MPSIM). https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1735296/full https://www.frontiersin.org/files/Articles/1735296/fmicb-17-1735296-HTML/image_m/fmicb-17-1735296-g001.jpg Figure 1. Changes in rumen microbial α-diversity at different dietary stages (Data Source: Pinto et https://www.frontiersin.org/files/Articles/1735296/fmicb-17-1735296-HTML/image_m/fmicb-17-1735296-g002.jpg Figure 2. Conceptual representation of ruminal cross-feeding networks under forage-rich and concentr https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2025.1653315/full https://www.frontiersin.org/files/Articles/1653315/fdmed-06-1653315-HTML/image_m/fdmed-06-1653315-g001.jpg Figure 1. Clinical features (BOP, bleeding on probing; PPD, probing pocket depth) of disease in male https://www.frontiersin.org/files/Articles/1653315/fdmed-06-1653315-HTML/image_m/fdmed-06-1653315-g002.jpg Figure 2. Tabulation of individual genes that were upregulated (positive) or down-regulated (negativ https://www.frontiersin.org/files/Articles/1653315/fdmed-06-1653315-HTML/image_m/fdmed-06-1653315-g003.jpg Figure 3. Venn diagrams of specific gene relationships during disease in female and male samples. Nu https://www.frontiersin.org/files/Articles/1653315/fdmed-06-1653315-HTML/image_m/fdmed-06-1653315-g004.jpg Figure 4. Top 20 biological pathways with overexpressed genes in disease initiation, early progressi https://www.frontiersin.org/files/Articles/1653315/fdmed-06-1653315-HTML/image_m/fdmed-06-1653315-t001.jpg Table 1. Gene ontology analysis of overexpressed biological pathways in female vs. male samples. Gen https://www.frontiersin.org/files/Articles/1653315/fdmed-06-1653315-HTML/image_m/fdmed-06-1653315-g005.jpg Figure 5. (A) UMAP visualization of the clustering of individual gingival tissue samples based upon https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1734550/full https://www.frontiersin.org/files/Articles/1734550/fneur-17-1734550-HTML/image_m/fneur-17-1734550-g001.jpg Figure 1. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram. https://www.frontiersin.org/files/Articles/1734550/fneur-17-1734550-HTML/image_m/fneur-17-1734550-g002.jpg Figure 2. Rate of publication of studies that use DTI to study moderate–severe TBI from 2012 to 2022 https://www.frontiersin.org/files/Articles/1734550/fneur-17-1734550-HTML/image_m/fneur-17-1734550-g003.jpg Figure 3. Geographic distribution of moderate–severe TBI studies. Country of origin, determined by w https://www.frontiersin.org/files/Articles/1734550/fneur-17-1734550-HTML/image_m/fneur-17-1734550-t001.jpg Table 1. Overview of demographic data for included msTBI studies. https://www.frontiersin.org/files/Articles/1734550/fneur-17-1734550-HTML/image_m/fneur-17-1734550-g004.jpg Figure 4. Post-injury DTI acquisition. This bar graph denotes when DTI was acquired in the included https://www.frontiersin.org/files/Articles/1734550/fneur-17-1734550-HTML/image_m/fneur-17-1734550-g005.jpg Figure 5. Longitudinal studies. A total of 33 studies reported longitudinal data. “N=” represent the https://www.frontiersin.org/files/Articles/1734550/fneur-17-1734550-HTML/image_m/fneur-17-1734550-g006.jpg Figure 6. Slice thickness. The boxplots show the difference in acquisition slice thickness from the https://www.frontiersin.org/files/Articles/1734550/fneur-17-1734550-HTML/image_m/fneur-17-1734550-t002.jpg Table 2. DTI associations with clinical measures. https://www.frontiersin.org/files/Articles/1734550/fneur-17-1734550-HTML/image_m/fneur-17-1734550-t003.jpg Table 3. DTI associations with cognitive measures higher scores in each of the domains is associated https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1670638/full https://www.frontiersin.org/files/Articles/1670638/fsufs-09-1670638-HTML-r1/image_m/fsufs-09-1670638-t001.jpg Table 1. City population and number of vendors surveyed per city by neighbourhood and store type. https://www.frontiersin.org/files/Articles/1670638/fsufs-09-1670638-HTML-r1/image_m/fsufs-09-1670638-t002.jpg Table 2. Count and proportion of vendors with at least one organic sentinel product and median (inte https://www.frontiersin.org/files/Articles/1670638/fsufs-09-1670638-HTML-r1/image_m/fsufs-09-1670638-g001.jpg Figure 1. Proportion of organic vendors out of total vendors by neighbourhood (lower-, middle-, and https://www.frontiersin.org/files/Articles/1670638/fsufs-09-1670638-HTML-r1/image_m/fsufs-09-1670638-g002.jpg Figure 2. Boxplot of organic and non-organic rice prices (USD/kg) by country (n = 51 for organic ric https://www.frontiersin.org/files/Articles/1670638/fsufs-09-1670638-HTML-r1/image_m/fsufs-09-1670638-g003.jpg Figure 3. Proportion of organic products using each terminology overall and by country (n = 691 orga https://www.frontiersin.org/files/Articles/1670638/fsufs-09-1670638-HTML-r1/image_m/fsufs-09-1670638-g004.jpg Figure 4. Proportion of organic products displaying each theme overall and by country (n = 691 organ https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1713652/full https://www.frontiersin.org/files/Articles/1713652/fpsyg-17-1713652-HTML/image_m/fpsyg-17-1713652-g001.jpg Figure 1. PRISMA 2020 flow diagram illustrating the study selection process. Eighty-five records wer https://www.frontiersin.org/files/Articles/1713652/fpsyg-17-1713652-HTML/image_m/fpsyg-17-1713652-t001.jpg Table 1. Executive function assessment approaches: instruments, targeted components, age range, and https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1772293/full https://www.frontiersin.org/files/Articles/1772293/fpubh-14-1772293-HTML/image_m/fpubh-14-1772293-g001.jpg Figure 1. PRISMA diagram representing the process for identification of studies enabling extraction https://www.frontiersin.org/files/Articles/1772293/fpubh-14-1772293-HTML/image_m/fpubh-14-1772293-g002.jpg Figure 2. Flow diagram to demonstrate the development of a taxonomy from an initial list of pharmaci https://www.frontiersin.org/files/Articles/1772293/fpubh-14-1772293-HTML/image_m/fpubh-14-1772293-g003.jpg Figure 3. Proportional representation of pharmacist-led interventions identified in the reviewed lit https://www.frontiersin.org/files/Articles/1772293/fpubh-14-1772293-HTML/image_m/fpubh-14-1772293-t001.jpg Table 1. Healthcare professionals’ characteristics in relation to their professional experience, rol https://www.frontiersin.org/files/Articles/1772293/fpubh-14-1772293-HTML/image_m/fpubh-14-1772293-t002.jpg Table 2. Taxonomy of cancer-focused pharmacist-led interventions based on the CMO model. https://www.frontiersin.org/files/Articles/1772293/fpubh-14-1772293-HTML/image_m/fpubh-14-1772293-g004.jpg Figure 4. Results of the second Delphi round: Importance-Feasibility matrix of Pharmacist-led interv https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1744452/full https://www.frontiersin.org/files/Articles/1744452/fmicb-17-1744452-HTML/image_m/fmicb-17-1744452-g001.jpg Figure 1. Schematic representation of the Anthropogenic pressures and its impact on environmental an https://www.frontiersin.org/files/Articles/1744452/fmicb-17-1744452-HTML/image_m/fmicb-17-1744452-g002.jpg Figure 2. Microbiome-based solutions aim to preserve and restore microbial diversity. This figure wa https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1782357/full https://www.frontiersin.org/files/Articles/1782357/fmed-13-1782357-HTML/image_m/fmed-13-1782357-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1782357/fmed-13-1782357-HTML/image_m/fmed-13-1782357-g002.jpg Figure 2. Network graphs of primary and secondary outcomes. Panels show network evidence diagrams fo https://www.frontiersin.org/files/Articles/1782357/fmed-13-1782357-HTML/image_m/fmed-13-1782357-g003.jpg Figure 3. Forest plots for all outcomes from the network meta-analysis. Mean difference (MD) or odds https://www.frontiersin.org/files/Articles/1782357/fmed-13-1782357-HTML/image_m/fmed-13-1782357-g004.jpg Figure 4. P-score ranking for primary and secondary outcomes. Color intensity corresponds to P-Score https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1748443/full https://www.frontiersin.org/files/Articles/1748443/fbioe-14-1748443-HTML/image_m/fbioe-14-1748443-g001.jpg Figure 1. Effects of selected bioactive compounds on cell morphology. (a) Analysis of cell area for https://www.frontiersin.org/files/Articles/1748443/fbioe-14-1748443-HTML/image_m/fbioe-14-1748443-g002.jpg Figure 2. Fluorescent images of samples treated with bioactive compounds, stained with DAPI (nuclei https://www.frontiersin.org/files/Articles/1748443/fbioe-14-1748443-HTML/image_m/fbioe-14-1748443-g003.jpg Figure 3. Effects of selected bioactive compounds on cell proliferation and cell migration. (a) Anal https://www.frontiersin.org/files/Articles/1748443/fbioe-14-1748443-HTML/image_m/fbioe-14-1748443-g004.jpg Figure 4. Representative fluorescence 3D images of cells after imaging the DAPI channel across 102 z https://www.frontiersin.org/files/Articles/1748443/fbioe-14-1748443-HTML/image_m/fbioe-14-1748443-g005.jpg Figure 5. Fluorescent images of samples treated with bioactive compounds, stained with DAPI (nuclei) https://www.frontiersin.org/files/Articles/1748443/fbioe-14-1748443-HTML/image_m/fbioe-14-1748443-g006.jpg Figure 6. Effects of selected bioactive compounds on actin texture analysis through the SER spot ind https://www.frontiersin.org/files/Articles/1748443/fbioe-14-1748443-HTML/image_m/fbioe-14-1748443-g007.jpg Figure 7. (a) Fluorescent images of samples treated with Trametinib, stained with DAPI (blue), SOX9 https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1687204/full https://www.frontiersin.org/files/Articles/1687204/fonc-15-1687204-HTML/image_m/fonc-15-1687204-g001.jpg Figure 1. Trends in cases of COVID-19 during the pre-Omicron and Omicron eras in Japan. (A) Colored https://www.frontiersin.org/files/Articles/1687204/fonc-15-1687204-HTML/image_m/fonc-15-1687204-t001.jpg Table 1. Characteristics of the study patients. https://www.frontiersin.org/files/Articles/1687204/fonc-15-1687204-HTML/image_m/fonc-15-1687204-t002.jpg Table 2. Univariate and multivariate analysis of factors associated with severe and/or prolonged dis https://www.frontiersin.org/files/Articles/1687204/fonc-15-1687204-HTML/image_m/fonc-15-1687204-g002.jpg Figure 2. Cumulative incidence of severe and/or prolonged COVID-19 in patients with hematologic dise https://www.frontiersin.org/files/Articles/1687204/fonc-15-1687204-HTML/image_m/fonc-15-1687204-t003.jpg Table 3. Univariate and multivariate analysis for overall survival. https://www.frontiersin.org/files/Articles/1687204/fonc-15-1687204-HTML/image_m/fonc-15-1687204-g003.jpg Figure 3. Overall survival of COVID-19 patients with hematologic diseases. Kaplan–Meier curves of ov https://www.frontiersin.org/files/Articles/1687204/fonc-15-1687204-HTML/image_m/fonc-15-1687204-t004.jpg Table 4. Univariate and multivariate analysis for COVID-19-related mortality. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1747341/full https://www.frontiersin.org/files/Articles/1747341/fphys-17-1747341-HTML/image_m/fphys-17-1747341-t001.jpg Table 1. The regulatory role of TGR5 in different immune cells. https://www.frontiersin.org/files/Articles/1747341/fphys-17-1747341-HTML/image_m/fphys-17-1747341-g001.jpg Figure 1. TGR5-mediated anti-inflammatory mechanisms and their implications in chronic liver disease https://www.frontiersin.org/files/Articles/1747341/fphys-17-1747341-HTML/image_m/fphys-17-1747341-t002.jpg Table 2. The regulatory role of TGR5 across different cell types in the iver. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1561693/full https://www.frontiersin.org/files/Articles/1561693/fnut-12-1561693-HTML/image_m/fnut-12-1561693-g001.jpg Figure 1. Experimental schedule of experimental procedures, IDPN and vitamin A, D treatment, testing https://www.frontiersin.org/files/Articles/1561693/fnut-12-1561693-HTML/image_m/fnut-12-1561693-g002.jpg Figure 2. Body weight change (A), HTR (B), and stereotypic behavior change (C) from model creation t https://www.frontiersin.org/files/Articles/1561693/fnut-12-1561693-HTML/image_m/fnut-12-1561693-g003.jpg Figure 3. Vitamins A and D significantly regulate the striatum metabolic profile of TS rats. OPLS-DA https://www.frontiersin.org/files/Articles/1561693/fnut-12-1561693-HTML/image_m/fnut-12-1561693-t001.jpg Table 1. The identification of differential metabolites between Model and Con group. https://www.frontiersin.org/files/Articles/1561693/fnut-12-1561693-HTML/image_m/fnut-12-1561693-g004.jpg Figure 4. Vitamins A and D affect the striatal metabolic pathway in TS rats. (A) Metabolic pathways https://www.frontiersin.org/files/Articles/1561693/fnut-12-1561693-HTML/image_m/fnut-12-1561693-g005.jpg Figure 5. Changes in fecal bacterial composition in TS rats at 10 weeks. Bacterial α diversity (A–C) https://www.frontiersin.org/files/Articles/1561693/fnut-12-1561693-HTML/image_m/fnut-12-1561693-g006.jpg Figure 6. Correlation of the fecal microbiome with striatal metabolites. For the metabolites and the https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1477283/full https://www.frontiersin.org/files/Articles/1477283/fpsyg-16-1477283-HTML/image_m/fpsyg-16-1477283-t001.jpg Table 1. Descriptive scores of our sample and the validation studies scores. https://www.frontiersin.org/files/Articles/1477283/fpsyg-16-1477283-HTML/image_m/fpsyg-16-1477283-t002.jpg Table 2. Bivariate correlations between forgiveness, strengths, and emotional symptomatology. https://www.frontiersin.org/files/Articles/1477283/fpsyg-16-1477283-HTML/image_m/fpsyg-16-1477283-t003.jpg Table 3. One-way ANOVA test and significance levels for all groups (forgiveness). https://www.frontiersin.org/files/Articles/1477283/fpsyg-16-1477283-HTML/image_m/fpsyg-16-1477283-g001.jpg Figure 1. Mean score of positive variables for forgiveness score profiles. https://www.frontiersin.org/files/Articles/1477283/fpsyg-16-1477283-HTML/image_m/fpsyg-16-1477283-g002.jpg Figure 2. Mean score of emotional symptoms for forgiveness score profiles. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1692681/full https://www.frontiersin.org/files/Articles/1692681/fpsyg-17-1692681-HTML/image_m/fpsyg-17-1692681-g001.jpg Figure 1. Research framework. https://www.frontiersin.org/files/Articles/1692681/fpsyg-17-1692681-HTML/image_m/fpsyg-17-1692681-g002.jpg Figure 2. Distribution of happiness. Histogram with equal-width bins of 1/6; cases with missing, ref https://www.frontiersin.org/files/Articles/1692681/fpsyg-17-1692681-HTML/image_m/fpsyg-17-1692681-t001.jpg Table 1. Control variables. https://www.frontiersin.org/files/Articles/1692681/fpsyg-17-1692681-HTML/image_m/fpsyg-17-1692681-t002.jpg Table 2. Mediators and moderators. https://www.frontiersin.org/files/Articles/1692681/fpsyg-17-1692681-HTML/image_m/fpsyg-17-1692681-t003.jpg Table 3. Ordered-logit model approach to digital economy's influence. https://www.frontiersin.org/files/Articles/1692681/fpsyg-17-1692681-HTML/image_m/fpsyg-17-1692681-t004.jpg Table 4. Instrumental variable adoption. https://www.frontiersin.org/files/Articles/1692681/fpsyg-17-1692681-HTML/image_m/fpsyg-17-1692681-t005.jpg Table 5. Robustness tests. https://www.frontiersin.org/files/Articles/1692681/fpsyg-17-1692681-HTML/image_m/fpsyg-17-1692681-t006.jpg Table 6. Moderating effect of the multidimensional digital divide. https://www.frontiersin.org/files/Articles/1692681/fpsyg-17-1692681-HTML/image_m/fpsyg-17-1692681-t007.jpg Table 7. Mediation test. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1624770/full https://www.frontiersin.org/files/Articles/1624770/fimmu-16-1624770-HTML/image_m/fimmu-16-1624770-g001.jpg Figure 1. Molecular Regulation of NLRP3. The activation of NLRP3 is tightly controlled by multiple i https://www.frontiersin.org/files/Articles/1624770/fimmu-16-1624770-HTML/image_m/fimmu-16-1624770-g002.jpg Figure 2. Emerging Mechanisms of Inflammasome Activation. (A) Endocytic trafficking dysfunction: Und https://www.frontiersin.org/files/Articles/1624770/fimmu-16-1624770-HTML/image_m/fimmu-16-1624770-t001.jpg Table 1. Comprehensive overview of current NLRP3 inflammasome inhibitors across human diseases. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1741860/full https://www.frontiersin.org/files/Articles/1741860/fpsyt-17-1741860-HTML/image_m/fpsyt-17-1741860-t001.jpg Table 1. Sample demographics and clinical characteristics with descriptive statistics. https://www.frontiersin.org/files/Articles/1741860/fpsyt-17-1741860-HTML/image_m/fpsyt-17-1741860-g001.jpg Figure 1. Knowledge Discovery in Databases (KDD) framework used to identify depression complexity pr https://www.frontiersin.org/files/Articles/1741860/fpsyt-17-1741860-HTML/image_m/fpsyt-17-1741860-g002.jpg Figure 2. 3D PCA projection of participants colored by risk clusters. https://www.frontiersin.org/files/Articles/1741860/fpsyt-17-1741860-HTML/image_m/fpsyt-17-1741860-g003.jpg Figure 3. PCA loadings illustrating variable weights in cluster separation space. https://www.frontiersin.org/files/Articles/1741860/fpsyt-17-1741860-HTML/image_m/fpsyt-17-1741860-g004.jpg Figure 4. Global SHAP importance ranking. https://www.frontiersin.org/files/Articles/1741860/fpsyt-17-1741860-HTML/image_m/fpsyt-17-1741860-g005.jpg Figure 5. Cluster-specific SHAP importance across risk profiles. https://www.frontiersin.org/files/Articles/1741860/fpsyt-17-1741860-HTML/image_m/fpsyt-17-1741860-t002.jpg Table 2. Descriptive characteristics and group comparisons among the three profiles. https://www.frontiersin.org/files/Articles/1741860/fpsyt-17-1741860-HTML/image_m/fpsyt-17-1741860-t003.jpg Table 3. Global and pairwise effect sizes for categorical and continuous variables across complexity https://www.frontiersin.org/files/Articles/1741860/fpsyt-17-1741860-HTML/image_m/fpsyt-17-1741860-t004.jpg Table 4. Recommended management strategies by the patient’s complexity profile. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1757647/full https://www.frontiersin.org/files/Articles/1757647/fpsyt-17-1757647-HTML-r1/image_m/fpsyt-17-1757647-g001.jpg Figure 1. Diagram of the n-back task, displaying the two conditions (A) 1-back and (B) 2-back, alter https://www.frontiersin.org/files/Articles/1757647/fpsyt-17-1757647-HTML-r1/image_m/fpsyt-17-1757647-g002.jpg Figure 2. Representative scheme of the location of the core sites of frontoparietal– dorsolateral pr https://www.frontiersin.org/files/Articles/1757647/fpsyt-17-1757647-HTML-r1/image_m/fpsyt-17-1757647-t001.jpg Table 1. Demographic and clinical data of study groups: schizophrenia (SCZ), autism spectrum disorde https://www.frontiersin.org/files/Articles/1757647/fpsyt-17-1757647-HTML-r1/image_m/fpsyt-17-1757647-g003.jpg Figure 3. fMRI activation during the n-back contrast. For each group, schizophrenia (SCZ), autism sp https://www.frontiersin.org/files/Articles/1757647/fpsyt-17-1757647-HTML-r1/image_m/fpsyt-17-1757647-g004.jpg Figure 4. Functional connectivity (z scores) between key hubs of the (A) frontoparietal network – do https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1749543/full https://www.frontiersin.org/files/Articles/1749543/fpsyt-17-1749543-HTML/image_m/fpsyt-17-1749543-t001.jpg Table 1. The predefined rules used for revising behavioral labels. https://www.frontiersin.org/files/Articles/1749543/fpsyt-17-1749543-HTML/image_m/fpsyt-17-1749543-g001.jpg Figure 1. Traditional behavioral assessments in HD mice models. (A) Body weight (g). (B) Rotarod lat https://www.frontiersin.org/files/Articles/1749543/fpsyt-17-1749543-HTML/image_m/fpsyt-17-1749543-t002.jpg Table 2. Cluster, Movement definition, and action classification. https://www.frontiersin.org/files/Articles/1749543/fpsyt-17-1749543-HTML/image_m/fpsyt-17-1749543-g002.jpg Figure 2. Spontaneous behavioral characteristics of HD mice. (A) Behavioral atlas of mice. (B) LDA c https://www.frontiersin.org/files/Articles/1749543/fpsyt-17-1749543-HTML/image_m/fpsyt-17-1749543-g003.jpg Figure 3. Kinetic parameters of movement in HD mice. (A) Distribution of locomotion speeds (Running, https://www.frontiersin.org/files/Articles/1749543/fpsyt-17-1749543-HTML/image_m/fpsyt-17-1749543-g004.jpg Figure 4. The alterations of motor behavior and movement patterns in HD mice. (A) Comparison of move https://www.frontiersin.org/files/Articles/1749543/fpsyt-17-1749543-HTML/image_m/fpsyt-17-1749543-g005.jpg Figure 5. Alterations in movement time observed in HD model mice. (A) Movement fraction index in mic https://www.frontiersin.org/files/Articles/1749543/fpsyt-17-1749543-HTML/image_m/fpsyt-17-1749543-g006.jpg Figure 6. Changes in locomotor activity patterns of HD mice. (A) Representative movement trajectorie https://www.frontiersin.org/files/Articles/1749543/fpsyt-17-1749543-HTML/image_m/fpsyt-17-1749543-g007.jpg Figure 7. Characteristics of movement transition patterns in HD mice. (A) Probability network of mov https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1764343/full https://www.frontiersin.org/files/Articles/1764343/fimmu-17-1764343-HTML/image_m/fimmu-17-1764343-g001.jpg Figure 1. Siglec-7 agonism reduced TLR3-induced pro-inflammatory cytokines and induced rapid interna https://www.frontiersin.org/files/Articles/1764343/fimmu-17-1764343-HTML/image_m/fimmu-17-1764343-g002.jpg Figure 2. Agonism of Siglec-7 decreased TLR3-mediated TNFα in primary monocytes and macrophages. (A) https://www.frontiersin.org/files/Articles/1764343/fimmu-17-1764343-HTML/image_m/fimmu-17-1764343-g003.jpg Figure 3. Siglec-7 is trafficked to the endolysosome after rapid internalization. (A) Representative https://www.frontiersin.org/files/Articles/1764343/fimmu-17-1764343-HTML/image_m/fimmu-17-1764343-g004.jpg Figure 4. Siglec-7 agonism reduces TLR3-mediated NF-κB phosphorylation. (A) Representative western b https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1691053/full https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-g001.jpg Figure 1. Study site map where accelerometer data loggers were deployed on nesting females. Abbrevia https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-g002.jpg Figure 2. Satellite tag (anterior, with antenna) and accelerometer (ADL), which is posterior to sate https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-g003.jpg Figure 3. A complete nesting event from loggerhead sea turtle Sally (A), which was outfitted with a https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-t001.jpg Table 1. Typical data signatures associated with nesting behaviors in four species of sea turtle. https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-g004.jpg Figure 4. A non-nesting emergence (NNE) from Ali, a female green turtle in U.S. Virgin Islands (A). https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-t002.jpg Table 2. The number of nesting, egg-laying, and non-nesting emergence (NNE) events for each species https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-t003.jpg Table 3. The number of individuals tagged, the number of recovered accelerometers (ADL), and the ave https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-g005.jpg Figure 5. Abacus plot showing the recording duration and observed nesting and non-nesting emergence https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-t004.jpg Table 4. Inter-nesting intervals (days) calculated from accelerometer (ADL) data. https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-g006.jpg Figure 6. Boxplot showing the duration for each phase of nesting emergence. The horizontal histogram https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-g007.jpg Figure 7. Frequency of non-nesting emergences in relation to the subsequent nesting period. Color-co https://www.frontiersin.org/files/Articles/1691053/fmars-12-1691053-HTML-r1/image_m/fmars-12-1691053-g008.jpg Figure 8. Frequency histograms showing time of day for nesting events and non-nesting emergence (NNE https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1685988/full https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-g001.jpg Figure 1. Location of rookeries (A) and foraging grounds (B) from which 740 bp sequences for the haw https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-g002.jpg Figure 2. Distribution of hawksbill turtle (Eretmochelys imbricata) haplotypes among West Atlantic r https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-t001.jpg Table 1. Distribution of 41 hawksbill turtle (Eretmochelys imbricata) haplotypes at 19 West Atlantic https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-t002.jpg Table 2. Known distribution of Panama endemic, Southwest Caribbean (SWC) endemic, and SWC near-endem https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-t003.jpg Table 3. Distribution of hawksbill turtle (Eretmochelys imbricata) haplotypes among 15 FGs in the We https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-g003.jpg Figure 3. Minimal spanning network of 60 haplotypes known from 19 West Atlantic hawksbill turtle (Er https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-g004.jpg Figure 4. Rarefaction curves for hawksbill turtle (Eretmochelys imbricata) haplotypes at West Atlant https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-t004.jpg Table 4. Genetic diversity (h, π, and number of haplotypes) at 23 hawksbill turtle (Eretmochelys imb https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-t005.jpg Table 5. Haplotype diversity (h), nucleotide diversity (π), number of rookeries contributing at Q02. https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-g005.jpg Figure 5. UPGMA tree of 19 West Atlantic rookeries and four Caribbean Panama nesting beach populatio https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-g006.jpg Figure 6. Estimated mean contributions from 20 different West Atlantic rookeries to 15 hawksbill (Er https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-g007.jpg Figure 7. Comparison of results for many-to-many, foraging ground-centric MSAs with uniform priors ( https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-t006.jpg Table 6.. Estimated proportional contributions to hawksbill turtle (Eretmochelys imbricata) foraging https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-g008.jpg Figure 8. Measures of the “groups to soups” continuum for hawksbill turtle (Eretmochelys imbricata) https://www.frontiersin.org/files/Articles/1685988/fmars-12-1685988-HTML-r2/image_m/fmars-12-1685988-g009.jpg Figure 9. Major currents of the West Atlantic. Map modified from Gaspar et al. (2022). The major “cu https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1670650/full https://www.frontiersin.org/files/Articles/1670650/fimmu-16-1670650-HTML/image_m/fimmu-16-1670650-g001.jpg Figure 1. Synaptic surveillance in healthy brain vs. synaptic stripping after injury and degeneratio https://www.frontiersin.org/files/Articles/1670650/fimmu-16-1670650-HTML/image_m/fimmu-16-1670650-g002.jpg Figure 2. Neuroinflammatory responses following spinal cord injury: Traumatic SCI disrupts the blood https://www.frontiersin.org/files/Articles/1670650/fimmu-16-1670650-HTML/image_m/fimmu-16-1670650-t001.jpg Table 1. Crosstalk between microglia and surrounding CNS cells after SCI. https://www.frontiersin.org/files/Articles/1670650/fimmu-16-1670650-HTML/image_m/fimmu-16-1670650-t002.jpg Table 2. Cytokine expression by microglia and macrophages after SCI categorized by injury phases. https://www.frontiersin.org/files/Articles/1670650/fimmu-16-1670650-HTML/image_m/fimmu-16-1670650-g003.jpg Figure 3. Blood brain barrier disruption and immune cell infiltration mediated by the CCL2–CCR2 axis https://www.frontiersin.org/files/Articles/1670650/fimmu-16-1670650-HTML/image_m/fimmu-16-1670650-t003.jpg Table 3. Pre-clinical and clinical trials for SCI anti-inflammatory treatments. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1743598/full https://www.frontiersin.org/files/Articles/1743598/fpubh-14-1743598-HTML-r2/image_m/fpubh-14-1743598-t001.jpg Table 1. Study participants. https://www.frontiersin.org/files/Articles/1743598/fpubh-14-1743598-HTML-r2/image_m/fpubh-14-1743598-t002.jpg Table 2. Themes and subthemes emerging from qualitative analysis. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1691693/full https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-t001.jpg Table 1. Clinicopathological characteristics of patients in development cohort. https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-g001.jpg Figure 1. The receiver operating characteristics (ROC) curves for serum albumin level (A) and derive https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-g002.jpg Figure 2. Kaplan-Meier survival curves for cancer-specific survival stratified by Alb-dNLR score gro https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-g003.jpg Figure 3. Kaplan-Meier survival curves for overall survival stratified by Alb-dNLR score groups in t https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-t002.jpg Table 2. Univariate and multivariate analyses of parameters related with CSS in development cohort. https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-g004.jpg Figure 4. Selection of prognostic factors in the development cohort using LASSO regression. (A) the https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-g005.jpg Figure 5. Constructed nomograms for prognostic prediction of 1,3,5-year cancer-specific survival of https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-g006.jpg Figure 6. Validation of constructed nomograms for 1, 3, and 5-year CSS in development cohort. Calibr https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-g007.jpg Figure 7. Kaplan-Meier survival curves for cancer-specific survival stratified by Alb-dNLR score gro https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-g008.jpg Figure 8. Kaplan-Meier survival curves for overall survival stratified by Alb-dNLR score groups in t https://www.frontiersin.org/files/Articles/1691693/fonc-15-1691693-HTML/image_m/fonc-15-1691693-g009.jpg Figure 9. Validation of constructed nomograms for 1, 3, and 5-year CSS in the validation cohort. Cal https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1780066/full https://www.frontiersin.org/files/Articles/1780066/fpsyg-17-1780066-HTML/image_m/fpsyg-17-1780066-t001.jpg Table 1. Means, standard deviations, and correlation coefficients of the study variables. https://www.frontiersin.org/files/Articles/1780066/fpsyg-17-1780066-HTML/image_m/fpsyg-17-1780066-t002.jpg Table 2. Results of confirmatory factor analysis. https://www.frontiersin.org/files/Articles/1780066/fpsyg-17-1780066-HTML/image_m/fpsyg-17-1780066-t003.jpg Table 3. Hierarchical regression results. https://www.frontiersin.org/files/Articles/1780066/fpsyg-17-1780066-HTML/image_m/fpsyg-17-1780066-t004.jpg Table 4. Bootstrap mediation analysis results. https://www.frontiersin.org/files/Articles/1780066/fpsyg-17-1780066-HTML/image_m/fpsyg-17-1780066-g001.jpg Figure 1. Theoretical model of the relationship between physical exercise and job performance. *p <  https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1754193/full https://www.frontiersin.org/files/Articles/1754193/fpubh-14-1754193-HTML-r1/image_m/fpubh-14-1754193-t001.jpg Table 1. Demographic characteristics of participants (n = 1,137). https://www.frontiersin.org/files/Articles/1754193/fpubh-14-1754193-HTML-r1/image_m/fpubh-14-1754193-t002.jpg Table 2. Item analysis, reliability, and validity test of the 49 items. https://www.frontiersin.org/files/Articles/1754193/fpubh-14-1754193-HTML-r1/image_m/fpubh-14-1754193-t003.jpg Table 3. Exploratory factor analysis of the total scale. https://www.frontiersin.org/files/Articles/1754193/fpubh-14-1754193-HTML-r1/image_m/fpubh-14-1754193-t004.jpg Table 4. Model fit indices and reliability of community residents’ knowledge-attitude-practice towar https://www.frontiersin.org/files/Articles/1754193/fpubh-14-1754193-HTML-r1/image_m/fpubh-14-1754193-g001.jpg Figure 1. Confirmatory factor analysis model of community residents’ knowledge-attitude-practice tow https://www.frontiersin.org/files/Articles/1754193/fpubh-14-1754193-HTML-r1/image_m/fpubh-14-1754193-t005.jpg Table 5. The discriminant validity of each factor. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1699626/full https://www.frontiersin.org/files/Articles/1699626/fdgth-08-1699626-HTML/image_m/fdgth-08-1699626-g001.jpg Figure 1. Study selection. https://www.frontiersin.org/files/Articles/1699626/fdgth-08-1699626-HTML/image_m/fdgth-08-1699626-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1699626/fdgth-08-1699626-HTML/image_m/fdgth-08-1699626-g002.jpg Figure 2. Risk of bias 2 assessment for sleep quality of the included studies. https://www.frontiersin.org/files/Articles/1699626/fdgth-08-1699626-HTML/image_m/fdgth-08-1699626-t002.jpg Table 2. GRADE assessment for each outcome. https://www.frontiersin.org/files/Articles/1699626/fdgth-08-1699626-HTML/image_m/fdgth-08-1699626-t003.jpg Table 3. Questionnaires summary. https://www.frontiersin.org/files/Articles/1699626/fdgth-08-1699626-HTML/image_m/fdgth-08-1699626-g003.jpg Figure 3. Comparison of exergaming and no intervention on overall sleep quality assessment. https://www.frontiersin.org/files/Articles/1699626/fdgth-08-1699626-HTML/image_m/fdgth-08-1699626-g004.jpg Figure 4. Comparison of exergaming and traditional exercise on overall sleep quality assessment. https://www.frontiersin.org/files/Articles/1699626/fdgth-08-1699626-HTML/image_m/fdgth-08-1699626-t004.jpg Table 4. Effects of exergaming intervention on sleep. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1739782/full https://www.frontiersin.org/files/Articles/1739782/fpls-17-1739782-HTML/image_m/fpls-17-1739782-g001.jpg Figure 1. Daily dynamics of photosynthetic photon flux density (PPFD) under litter thickness levels https://www.frontiersin.org/files/Articles/1739782/fpls-17-1739782-HTML/image_m/fpls-17-1739782-g002.jpg Figure 2. Daily dynamics of the R:Fr under different litter thickness levels of C lanceolata (A), S. https://www.frontiersin.org/files/Articles/1739782/fpls-17-1739782-HTML/image_m/fpls-17-1739782-g003.jpg Figure 3. Daily dynamics of soil surface temperature under different litter thickness levels of C la https://www.frontiersin.org/files/Articles/1739782/fpls-17-1739782-HTML/image_m/fpls-17-1739782-g004.jpg Figure 4. Soil water content (% of soil dry weight) under different litter types and thickness level https://www.frontiersin.org/files/Articles/1739782/fpls-17-1739782-HTML/image_m/fpls-17-1739782-g005.jpg Figure 5. Effects of litter type and thickness on (A) seedling emergence, and (B) seedling survival https://www.frontiersin.org/files/Articles/1739782/fpls-17-1739782-HTML/image_m/fpls-17-1739782-g006.jpg Figure 6. Effect of litter type and thickness on (A) seedling height, (B) stem diameter, (C) root le https://www.frontiersin.org/files/Articles/1739782/fpls-17-1739782-HTML/image_m/fpls-17-1739782-g007.jpg Figure 7. Effects of litter type and thickness on (A) root biomass, (B) stem biomass, (C) leaf bioma https://www.frontiersin.org/files/Articles/1739782/fpls-17-1739782-HTML/image_m/fpls-17-1739782-g008.jpg Figure 8. Effects of litter type and thickness on seedling biomass allocation: (A) root mass ratio, https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2026.1786937/full https://www.frontiersin.org/files/Articles/1786937/fphy-14-1786937-HTML/image_m/fphy-14-1786937-g001.jpg Figure 1. Normal users and anomalous users. https://www.frontiersin.org/files/Articles/1786937/fphy-14-1786937-HTML/image_m/fphy-14-1786937-g002.jpg Figure 2. Architecture of the ST-MVAN model. https://www.frontiersin.org/files/Articles/1786937/fphy-14-1786937-HTML/image_m/fphy-14-1786937-t001.jpg Table 1. Comparison of the AUC performance of different models on the Digg and Yelp datasets (Mean ± https://www.frontiersin.org/files/Articles/1786937/fphy-14-1786937-HTML/image_m/fphy-14-1786937-g003.jpg Figure 3. Comparison of ROC curves between ST-MVAN and baseline models on the Digg dataset. https://www.frontiersin.org/files/Articles/1786937/fphy-14-1786937-HTML/image_m/fphy-14-1786937-g004.jpg Figure 4. Comparison of ROC curves between ST-MVAN and baseline models on the Yelp dataset. https://www.frontiersin.org/files/Articles/1786937/fphy-14-1786937-HTML/image_m/fphy-14-1786937-t002.jpg Table 2. Ablation study results on Digg and Yelp datasets. https://www.frontiersin.org/files/Articles/1786937/fphy-14-1786937-HTML/image_m/fphy-14-1786937-g005.jpg Figure 5. Visualization of ablation study results. https://www.frontiersin.org/files/Articles/1786937/fphy-14-1786937-HTML/image_m/fphy-14-1786937-g006.jpg Figure 6. Boxplot of AUC results on the Digg dataset under different training ratios. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1724038/full https://www.frontiersin.org/files/Articles/1724038/fmed-13-1724038-HTML/image_m/fmed-13-1724038-t001.jpg Table 1. Participant timeline. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1715026/full https://www.frontiersin.org/files/Articles/1715026/fsurg-12-1715026-HTML/image_m/fsurg-12-1715026-g001.jpg Figure 1. 3D reconstruction of the preoperative CT scan. (A) Type IIIB endoleak due to a disconnecti https://www.frontiersin.org/files/Articles/1715026/fsurg-12-1715026-HTML/image_m/fsurg-12-1715026-g002.jpg Figure 2. 3D reconstructions of the 12-month CTA showing correct positioning and patency of the sten https://www.frontiersin.org/files/Articles/1715026/fsurg-12-1715026-HTML/image_m/fsurg-12-1715026-t001.jpg Table 1. Outcomes of endovascular treatment of aortic disease in patients with connective tissue dis https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1781942/full https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-t001.jpg Table 1. Sample demographic characteristics (N = 550). https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-t002.jpg Table 2. Reliability and convergent validity of latent variables. https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-t003.jpg Table 3. Discriminant validity test (square root of AVE and correlations). https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-t004.jpg Table 4. t-test of variables by nationality. https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-g001.jpg Figure 1. Boxplot comparison of core variables between Chinese and Kazakhstani teachers. Boxes repre https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-t005.jpg Table 5. Correlation matrix of variables. https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-g002.jpg Figure 2. Path model of nationality-moderated mediation effect. Solid arrows represent direct paths; https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-t006.jpg Table 6. Hierarchical regression analysis for job satisfaction (N = 550). https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-t007.jpg Table 7. Bootstrap test for mediation effect. https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-t008.jpg Table 8. Moderation analysis of nationality. https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-g003.jpg Figure 3. Moderating effect of nationality on the relationship between perceived incentives and job https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-t009.jpg Table 9. Two-way ANOVA for job satisfaction by academic rank and teaching tenure. https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-g004.jpg Figure 4. Heatmap of job satisfaction by academic rank and teaching tenure. Data derived from two-wa https://www.frontiersin.org/files/Articles/1781942/fpsyg-17-1781942-HTML/image_m/fpsyg-17-1781942-g005.jpg Figure 5. U-shaped trajectory of job satisfaction over teaching tenure by academic rank. Error bars https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1626286/full https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-g001.jpg Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram d https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-t001.jpg Table 1. Study and patient characteristics of the included studies. https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-t002.jpg Table 2. Technical aspects of included studies. https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-g002.jpg Figure 2. Risk of bias and applicability concerns of the included studies using the revised Quality https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-g003.jpg Figure 3. Forest plots displaying the sensitivity and specificity of the internal validation sets. S https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-g004.jpg Figure 4. Summary receiver operating characteristic (SROC) curves of ultrasound-based artificial int https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-g005.jpg Figure 5. Fagan’s nomogram for artificial intelligence on the internal validation set (a) and sonogr https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-g006.jpg Figure 6. Forest plots displaying the sensitivity and specificity of the sonographers. Squares denot https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-t003.jpg Table 3. Sensitivity analysis of ultrasound-based artificial intelligence performance. https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-t004.jpg Table 4. Subgroup analysis of artificial intelligence performance in internal validation sets for ov https://www.frontiersin.org/files/Articles/1626286/fonc-15-1626286-HTML/image_m/fonc-15-1626286-g007.jpg Figure 7. Deek’s funnel plot was used to evaluate internal validation set (a) and sonographers (b) t https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1763720/full https://www.frontiersin.org/files/Articles/1763720/fnut-13-1763720-HTML/image_m/fnut-13-1763720-t001.jpg Table 1. Characteristics of participants. https://www.frontiersin.org/files/Articles/1763720/fnut-13-1763720-HTML/image_m/fnut-13-1763720-t002.jpg Table 2. Comparison of the positive rates of allergens in different genders [n(%)]. https://www.frontiersin.org/files/Articles/1763720/fnut-13-1763720-HTML/image_m/fnut-13-1763720-t003.jpg Table 3. Comparison of allergen positive rates in different ages [n(%)]. https://www.frontiersin.org/files/Articles/1763720/fnut-13-1763720-HTML/image_m/fnut-13-1763720-g001.jpg Figure 1. Sensitization rates to inhaled and food allergens across age groups. The bars show the per https://www.frontiersin.org/files/Articles/1763720/fnut-13-1763720-HTML/image_m/fnut-13-1763720-t004.jpg Table 4. Comparison of positive rates of allergens in children with eczema and urticaria [n(%)]. https://www.frontiersin.org/files/Articles/1763720/fnut-13-1763720-HTML/image_m/fnut-13-1763720-g002.jpg Figure 2. Comparison of sensitization rates between eczema and urticaria children. Prevalence of sen https://www.frontiersin.org/files/Articles/1763720/fnut-13-1763720-HTML/image_m/fnut-13-1763720-g003.jpg Figure 3. Sensitization patterns and clinical correlations in pediatric eczema and urticaria. (A) Ba https://www.frontiersin.org/files/Articles/1763720/fnut-13-1763720-HTML/image_m/fnut-13-1763720-t005.jpg Table 5. Factors associated with sensitization to food and inhaled allergens: results of multivariab https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1678396/full https://www.frontiersin.org/files/Articles/1678396/fmicb-16-1678396-HTML/image_m/fmicb-16-1678396-t001.jpg Table 1. The specific primers and probes designed for H1N1 and RSV detection. https://www.frontiersin.org/files/Articles/1678396/fmicb-16-1678396-HTML/image_m/fmicb-16-1678396-g001.jpg Figure 1. Conserved sequences (partial) of HA gene of influenza A virus H1N1 subtype and F gene of r https://www.frontiersin.org/files/Articles/1678396/fmicb-16-1678396-HTML/image_m/fmicb-16-1678396-g002.jpg Figure 2. Simultaneous detection of H1N1 and RSV by LAMP-LFD. (A) Schematic diagram of detection pro https://www.frontiersin.org/files/Articles/1678396/fmicb-16-1678396-HTML/image_m/fmicb-16-1678396-g003.jpg Figure 3. Optimization of reaction temperature and primer ratio for real-time fluorescent LAMP detec https://www.frontiersin.org/files/Articles/1678396/fmicb-16-1678396-HTML/image_m/fmicb-16-1678396-g004.jpg Figure 4. Feasibility of LAMP-LFD detection and optimization of reaction time. (A) Feasibility analy https://www.frontiersin.org/files/Articles/1678396/fmicb-16-1678396-HTML/image_m/fmicb-16-1678396-g005.jpg Figure 5. Sensitivity and specificity of the LAMP-LFD assay for H1N1 and RSV detection. (A) LAMP-LFD https://www.frontiersin.org/files/Articles/1678396/fmicb-16-1678396-HTML/image_m/fmicb-16-1678396-g006.jpg Figure 6. Validation of dual LAMP-LFD assay using clinical throat swab samples. (A) Negative results https://www.frontiersin.org/files/Articles/1678396/fmicb-16-1678396-HTML/image_m/fmicb-16-1678396-t002.jpg Table 2. Comparison of LAMP-LFD detection and RT-qPCR detection of actual sample results. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1787049/full https://www.frontiersin.org/files/Articles/1787049/fmicb-17-1787049-HTML/image_m/fmicb-17-1787049-t001.jpg Table 1. The specific primers designed for NoV GII and HAdV-F41 detection. https://www.frontiersin.org/files/Articles/1787049/fmicb-17-1787049-HTML/image_m/fmicb-17-1787049-g001.jpg Figure 1. Schematic of the duplex LAMP-LFIA method. (a) Workflow from nucleic acid extraction to dua https://www.frontiersin.org/files/Articles/1787049/fmicb-17-1787049-HTML/image_m/fmicb-17-1787049-g002.jpg Figure 2. Optimization of duplex RT LAMP conditions for detection of NoV GII and HAdV F41. (a) Real- https://www.frontiersin.org/files/Articles/1787049/fmicb-17-1787049-HTML/image_m/fmicb-17-1787049-g003.jpg Figure 3. Synthesis and characterization of AuNPs, AuNFs, and their conjugates. (a) Photographs of s https://www.frontiersin.org/files/Articles/1787049/fmicb-17-1787049-HTML/image_m/fmicb-17-1787049-g004.jpg Figure 4. Optimization of the LAMP-LFIA. (a) Optimization of Volume Ratio between SA AuNPs and IgY-A https://www.frontiersin.org/files/Articles/1787049/fmicb-17-1787049-HTML/image_m/fmicb-17-1787049-g005.jpg Figure 5. Analytical performance evaluation of the duplex LAMP-LFIA assay. (a,b) Real-time fluoresce https://www.frontiersin.org/files/Articles/1787049/fmicb-17-1787049-HTML/image_m/fmicb-17-1787049-g006.jpg Figure 6. Applicability evaluation of dual LAMP-LFIA detection method in actual positive clinical sa https://www.frontiersin.org/files/Articles/1787049/fmicb-17-1787049-HTML/image_m/fmicb-17-1787049-t002.jpg Table 2. Comparison of clinical sample validation results between LAMP-LFIA and RT-qPCR. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1700807/full https://www.frontiersin.org/files/Articles/1700807/fpsyt-16-1700807-HTML/image_m/fpsyt-16-1700807-t001.jpg Table 1. Sociodemographic and Obstetrics Characteristics of the Participants (n=290). https://www.frontiersin.org/files/Articles/1700807/fpsyt-16-1700807-HTML/image_m/fpsyt-16-1700807-t002.jpg Table 2. Fear of childbirth and intolerance of uncertainty scores of pregnant women (n = 290). https://www.frontiersin.org/files/Articles/1700807/fpsyt-16-1700807-HTML/image_m/fpsyt-16-1700807-t003.jpg Table 3. Characteristics of pregnant women stratified by preference for mode of delivery. https://www.frontiersin.org/files/Articles/1700807/fpsyt-16-1700807-HTML/image_m/fpsyt-16-1700807-t004.jpg Table 4. Using the product distribution method to test the mediating effect. https://www.frontiersin.org/files/Articles/1700807/fpsyt-16-1700807-HTML/image_m/fpsyt-16-1700807-g001.jpg Figure 1. Mediation model diagram of fear of childbirth between pregnant women's intolerance of unce https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2026.1733275/full https://www.frontiersin.org/files/Articles/1733275/frhs-06-1733275-HTML/image_m/frhs-06-1733275-t001.jpg Table 1. Characteristics of the study participants. https://www.frontiersin.org/files/Articles/1733275/frhs-06-1733275-HTML/image_m/frhs-06-1733275-t002.jpg Table 2. Differences in performance regarding IPC related to personal characteristics. https://www.frontiersin.org/files/Articles/1733275/frhs-06-1733275-HTML/image_m/frhs-06-1733275-t003.jpg Table 3. Changes in the knowledge and practice of IPC between the two groups. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1584004/full https://www.frontiersin.org/files/Articles/1584004/fpsyt-16-1584004-HTML-r1/image_m/fpsyt-16-1584004-t001.jpg Table 1. Demographic and mental health information of the surveyed healthcare workers. https://www.frontiersin.org/files/Articles/1584004/fpsyt-16-1584004-HTML-r1/image_m/fpsyt-16-1584004-t002.jpg Table 2. Linear regression analysis of each variable with workplace ostracism, work-family conflict, https://www.frontiersin.org/files/Articles/1584004/fpsyt-16-1584004-HTML-r1/image_m/fpsyt-16-1584004-t003.jpg Table 3. Correlation analysis of work-family conflict, sense of coherence and workplace ostracism. https://www.frontiersin.org/files/Articles/1584004/fpsyt-16-1584004-HTML-r1/image_m/fpsyt-16-1584004-t004.jpg Table 4. Mediation effect analysis of sense of coherence between work-family conflict and workplace https://www.frontiersin.org/files/Articles/1584004/fpsyt-16-1584004-HTML-r1/image_m/fpsyt-16-1584004-g001.jpg Figure 1. Model for evaluating the moderated mediation effect of sense of coherence between work-fam https://www.frontiersin.org/files/Articles/1584004/fpsyt-16-1584004-HTML-r1/image_m/fpsyt-16-1584004-g002.jpg Figure 2. The moderating role of sense of coherence in the relationship between work-family conflict https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1731089/full https://www.frontiersin.org/files/Articles/1731089/fnagi-18-1731089-HTML/image_m/fnagi-18-1731089-g001.jpg Figure 1. Distinct metabolic characteristics of neurons and astrocytes. Neurons do not store glycoge https://www.frontiersin.org/files/Articles/1731089/fnagi-18-1731089-HTML/image_m/fnagi-18-1731089-g002.jpg Figure 2. Metabolic profiles of oligodendrocytes at different differentiation stages. OPCs exhibit h https://www.frontiersin.org/files/Articles/1731089/fnagi-18-1731089-HTML/image_m/fnagi-18-1731089-g003.jpg Figure 3. Metabolic shift toward glycolysis in activated microglia. Under homeostatic conditions or https://www.frontiersin.org/files/Articles/1731089/fnagi-18-1731089-HTML/image_m/fnagi-18-1731089-g004.jpg Figure 4. Lactate shuttling among different glial cells and neurons. (a) Astrocyte-neuron lactate sh https://www.frontiersin.org/files/Articles/1731089/fnagi-18-1731089-HTML/image_m/fnagi-18-1731089-t001.jpg Table 1. Comparison of ANLS/AMLS/ONLS and GSG models. https://www.frontiersin.org/files/Articles/1731089/fnagi-18-1731089-HTML/image_m/fnagi-18-1731089-t002.jpg Table 2. Lactate production and transport in AD. https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2026.1798866/full https://www.frontiersin.org/files/Articles/1798866/fcomm-11-1798866-HTML/image_m/fcomm-11-1798866-g001.jpg Figure 1. Digital marketing framework for sustainable medical tourism development. The four strategi https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1793846/full https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g001.jpg Figure 1. Effects of different treatments on biomass and root system characteristics of M9T337 roots https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g002.jpg Figure 2. Effects of different treatments on the Leaves antioxidant enzyme activities and ROS conte https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g003.jpg Figure 3. Effects of different treatments on melatonin and copper content in M9T337 rootstocks. Mela https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g004.jpg Figure 4. Effects of different treatments on sugar metabolism in M9T337 rootstocks. Sugar metabolism https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g005.jpg Figure 5. Effects of different treatments on the 13C accumulation and distribution rate of M9T337 ro https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g006.jpg Figure 6. Effects of different treatments on NO3− flux in the root tips of M9T337 seedlings. Noninva https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g007.jpg Figure 7. Effects of different treatments on nitrogen metabolism in M9T337 rootstocks. Leaves nitrog https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g008.jpg Figure 8. Effects of different treatments on 15N uptake, utilization, and distribution in M9T337 roo https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g009.jpg Figure 9. Expression of genes related to melatonin, copper absorption, transport, detoxification, an https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g010.jpg Figure 10. Correlation analysis between antioxidant enzymes, total 15N accumulation, total 13C accum https://www.frontiersin.org/files/Articles/1793846/fpls-17-1793846-HTML/image_m/fpls-17-1793846-g011.jpg Figure 11. A model of the effect of exogenous melatonin on the antioxidant system and carbon-nitroge https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1750736/full https://www.frontiersin.org/files/Articles/1750736/fneur-17-1750736-HTML/image_m/fneur-17-1750736-g001.jpg Figure 1. Flowchart illustrating the selection process of study participants. https://www.frontiersin.org/files/Articles/1750736/fneur-17-1750736-HTML/image_m/fneur-17-1750736-t001.jpg Table 1. Baseline characteristics of study participants by dementia status. https://www.frontiersin.org/files/Articles/1750736/fneur-17-1750736-HTML/image_m/fneur-17-1750736-t002.jpg Table 2. The HR (95% CI) of dementia according to modified TyG indices in the three models. https://www.frontiersin.org/files/Articles/1750736/fneur-17-1750736-HTML/image_m/fneur-17-1750736-g002.jpg Figure 2. Association of modified TyG indices and dementia risk using a multivariable-adjusted restr https://www.frontiersin.org/files/Articles/1750736/fneur-17-1750736-HTML/image_m/fneur-17-1750736-t003.jpg Table 3. Associations between brain structures and modified TyG indices. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1727989/full https://www.frontiersin.org/files/Articles/1727989/fmed-13-1727989-HTML/image_m/fmed-13-1727989-g001.jpg Figure 1. The through-the-scope twin clip. The clips are controlled independently by two handles. Th https://www.frontiersin.org/files/Articles/1727989/fmed-13-1727989-HTML/image_m/fmed-13-1727989-g002.jpg Figure 2. Operation steps using TTS-TC for defect closure. (A) A defect was located in the gastric a https://www.frontiersin.org/files/Articles/1727989/fmed-13-1727989-HTML/image_m/fmed-13-1727989-t001.jpg Table 1. Demographic and clinical characteristics of patients in the study. https://www.frontiersin.org/files/Articles/1727989/fmed-13-1727989-HTML/image_m/fmed-13-1727989-t002.jpg Table 2. Clinical outcomes. https://www.frontiersin.org/files/Articles/1727989/fmed-13-1727989-HTML/image_m/fmed-13-1727989-g003.jpg Figure 3. Comparison of operating time using TTS-TCs between different cases. https://www.frontiersin.org/files/Articles/1727989/fmed-13-1727989-HTML/image_m/fmed-13-1727989-t003.jpg Table 3. Comparisons between different subgroups. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1745986/full https://www.frontiersin.org/files/Articles/1745986/fmed-13-1745986-HTML/image_m/fmed-13-1745986-t001.jpg Table 1. Baseline characteristics of kidney transplant recipients by outcome (mean ± SD; ranges show https://www.frontiersin.org/files/Articles/1745986/fmed-13-1745986-HTML/image_m/fmed-13-1745986-t002.jpg Table 2. Donor and procedural characteristics by outcome group. https://www.frontiersin.org/files/Articles/1745986/fmed-13-1745986-HTML/image_m/fmed-13-1745986-t003.jpg Table 3. Number of compatible donor-recipient pairs in the HLA system in the ULS and EGF groups. https://www.frontiersin.org/files/Articles/1745986/fmed-13-1745986-HTML/image_m/fmed-13-1745986-t004.jpg Table 4. Selected preoperative and postoperative complications or diseases in the study groups. https://www.frontiersin.org/files/Articles/1745986/fmed-13-1745986-HTML/image_m/fmed-13-1745986-t005.jpg Table 5. Rejection-related outcomes within 1 year. https://www.frontiersin.org/files/Articles/1745986/fmed-13-1745986-HTML/image_m/fmed-13-1745986-t006.jpg Table 6. Selected blood tests in specified time periods after transplantation (Mean±SD; ranges shown https://www.frontiersin.org/files/Articles/1745986/fmed-13-1745986-HTML/image_m/fmed-13-1745986-t007.jpg Table 7. Univariate analysis of binary and non-binary nominal factors in the analyzed groups. https://www.frontiersin.org/files/Articles/1745986/fmed-13-1745986-HTML/image_m/fmed-13-1745986-g001.jpg Figure 1. Exploratory univariable logistic regression: model-estimated probability of recognized acu https://www.frontiersin.org/files/Articles/1745986/fmed-13-1745986-HTML/image_m/fmed-13-1745986-t008.jpg Table 8. Multivariable logistic regression—odds of EGF (≤10 years) vs ULS (≥25 years). https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2025.1599405/full https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-t001.jpg Table 1. A list of the selected ETCCDI mean and extreme temperature and precipitation indices analys https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g001.jpg Figure 1. Africa’s domain showing the IPCC AR6 climate reference regions (red line) for Africa used https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g002.jpg Figure 2. Mean annual spatial distribution of (a–x and A–X) mean and extreme temperature indices ove https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g003.jpg Figure 3. Spatial distribution of (a-r) mean efficacy of all SAI experiments (GLENS, GLENS_eq, and G https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g004.jpg Figure 4. Mean annual spatial distribution of (a-x and A-X) mean and extreme precipitation indices o https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g005.jpg Figure 5. Spatial distribution of (a-r) mean efficacy of all SAI experiments (GLENS, GLENS_eq, and G https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g006.jpg Figure 6. Mean annual of mean and extreme temperature indices over Africa’s climatic (AR6) zones for https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g007.jpg Figure 7. Mean annual of mean and extreme temperature indices across African countries for the basel https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g008.jpg Figure 8. Mean efficacy of SAI experiments to compensate for the influence of global warming relativ https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g009.jpg Figure 9. Mean annual of mean and extreme precipiation indices over Africa’s climatic (AR6) zones fo https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g010.jpg Figure 10. Mean annual of mean and extreme precipitation indices across African countries for the ba https://www.frontiersin.org/files/Articles/1599405/fclim-07-1599405-HTML/image_m/fclim-07-1599405-g011.jpg Figure 11. Mean efficacy of SAI experiments to compensate for the influence of global warming relati https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2026.1751165/full https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-t001.jpg Table 1. Weighted distribution of participant characteristics (N = 1,183). https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-t002.jpg Table 2. Weighted percentage distribution of background characteristics among women by screening out https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-g001.jpg Figure 1. Spatial distribution of sexual autonomy rates among young women in Ghana. https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-g002.jpg Figure 2. Spatial distribution of HIV testing rates among young women in Ghana. https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-g003.jpg Figure 3. Spatial distribution of cervical cancer screening rates among young women in Ghana. https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-g004.jpg Figure 4. Spatial distribution of breast cancer screening rates among young women in Ghana. https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-t003.jpg Table 3. Multivariable multilevel mixed effects model of predictors of screening practices among you https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-g005.jpg Figure 5. Predictive ability of HIV testing, breast cancer and cervical cancer screening models. https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-t004.jpg Table 4. Inequality measures across different wealth quintiles for young women's screening behaviour https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-g006.jpg Figure 6. Lorenz curve with concentration index of socioeconomic inequality across various screening https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-t005.jpg Table 5. Subgroup analysis for socioeconomic inequalities in young women's screening behaviour outco https://www.frontiersin.org/files/Articles/1751165/frph-08-1751165-HTML/image_m/frph-08-1751165-t006.jpg Table 6. Equality measures in screening behaviours across education, region, residence (theil index) https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1657741/full https://www.frontiersin.org/files/Articles/1657741/fpls-16-1657741-HTML-r3/image_m/fpls-16-1657741-g001.jpg Figure 1. Comparison of expression level among BBI genes observed in seeds (A) and leaves (B) collec https://www.frontiersin.org/files/Articles/1657741/fpls-16-1657741-HTML-r3/image_m/fpls-16-1657741-g002.jpg Figure 2. Maximum clade credibility tree computed by BEAST 2.6.7 based on BBI1 (BBI genes part of th https://www.frontiersin.org/files/Articles/1657741/fpls-16-1657741-HTML-r3/image_m/fpls-16-1657741-g003.jpg Figure 3. Maximum clade credibility tree computed by BEAST 2.6.7 based on BBI2 (BBI genes part of th https://www.frontiersin.org/files/Articles/1657741/fpls-16-1657741-HTML-r3/image_m/fpls-16-1657741-g004.jpg Figure 4. The ML tree, produced by IQ-TREE and based on the BBI1 (BBI genes part of the BBI1 orthogr https://www.frontiersin.org/files/Articles/1657741/fpls-16-1657741-HTML-r3/image_m/fpls-16-1657741-g005.jpg Figure 5. The ML tree, produced by IQ-TREE and based on the BBI2 (BBI genes part of the BBI2 orthogr https://www.frontiersin.org/files/Articles/1657741/fpls-16-1657741-HTML-r3/image_m/fpls-16-1657741-g006.jpg Figure 6. Box-plot representing the distribution values of ΔGbinding calculated for each system. BBI https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1690480/full https://www.frontiersin.org/files/Articles/1690480/fphar-16-1690480-HTML/image_m/fphar-16-1690480-t001.jpg Table 1. Review of protocols, including predefined clinical outcomes and prescription authority. https://www.frontiersin.org/files/Articles/1690480/fphar-16-1690480-HTML/image_m/fphar-16-1690480-g001.jpg Figure 1. Flowchart. https://www.frontiersin.org/files/Articles/1690480/fphar-16-1690480-HTML/image_m/fphar-16-1690480-g002.jpg Figure 2. Quality of life during the study using EQ-5D VAS score. https://www.frontiersin.org/files/Articles/1690480/fphar-16-1690480-HTML/image_m/fphar-16-1690480-g003.jpg Figure 3. Medication Appropriateness Index (MAI) score during the study. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1712595/full https://www.frontiersin.org/files/Articles/1712595/fphar-16-1712595-HTML-r1/image_m/fphar-16-1712595-t001.jpg Table 1. Participants characteristics. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1615297/full https://www.frontiersin.org/files/Articles/1615297/fmicb-16-1615297-HTML/image_m/fmicb-16-1615297-g001.jpg Figure 1. Effects of GRG on loperamide-induced constipation mice. (A) Experimental design. (B) The s https://www.frontiersin.org/files/Articles/1615297/fmicb-16-1615297-HTML/image_m/fmicb-16-1615297-g002.jpg Figure 2. Effects of GRG on histology of colonic tissue. https://www.frontiersin.org/files/Articles/1615297/fmicb-16-1615297-HTML/image_m/fmicb-16-1615297-g003.jpg Figure 3. The effect of GRG treatment on the motility-related hormone in mice. (A) Hormone concentra https://www.frontiersin.org/files/Articles/1615297/fmicb-16-1615297-HTML/image_m/fmicb-16-1615297-g004.jpg Figure 4. Effects of GRG treatment on fecal gut microbiota in mice. Alpha diversity measured by the https://www.frontiersin.org/files/Articles/1615297/fmicb-16-1615297-HTML/image_m/fmicb-16-1615297-g005.jpg Figure 5. GRG therapy improved the metabolic function of gut microbiota. Picrust 2 predicted signifi https://www.frontiersin.org/files/Articles/1615297/fmicb-16-1615297-HTML/image_m/fmicb-16-1615297-g006.jpg Figure 6. GRG treatment affects fecal SCFA contents. (A) Fecal SCFA contents in different groups. To https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1706611/full https://www.frontiersin.org/files/Articles/1706611/fped-14-1706611-HTML-r1/image_m/fped-14-1706611-t001.jpg Table 1. Clinical information of variants in enrolled children with Alport syndrome. https://www.frontiersin.org/files/Articles/1706611/fped-14-1706611-HTML-r1/image_m/fped-14-1706611-t002.jpg Table 2. Gene variants and pedigree verification of enrolled children with Alport syndrome. https://www.frontiersin.org/files/Articles/1706611/fped-14-1706611-HTML-r1/image_m/fped-14-1706611-g001.jpg Figure 1. Structure of plasmid COL4A3-smBiT and COL4A5-lgBiT. https://www.frontiersin.org/files/Articles/1706611/fped-14-1706611-HTML-r1/image_m/fped-14-1706611-g002.jpg Figure 2. Sequencing results of recombinant plasmids carrying COL4A3, COL4A4, and COL4A5 variants. F https://www.frontiersin.org/files/Articles/1706611/fped-14-1706611-HTML-r1/image_m/fped-14-1706611-t003.jpg Table 3. Luminescence intensities of WT, VUS-associated, and pathogenic variant plasmids. https://www.frontiersin.org/files/Articles/1706611/fped-14-1706611-HTML-r1/image_m/fped-14-1706611-g003.jpg Figure 3. The luminescence intensity of VUS-associated plasmids and the WT plasmid (*P < 0.01, n = 3 https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2025.1658632/full https://www.frontiersin.org/files/Articles/1658632/fspas-12-1658632-HTML/image_m/fspas-12-1658632-g001.jpg Figure 1. Photographs of water-released minerals obtained from the Martian regolith simulant JSC Mar https://www.frontiersin.org/files/Articles/1658632/fspas-12-1658632-HTML/image_m/fspas-12-1658632-t001.jpg Table 1. Chroococcidiopsis strains used in this study. https://www.frontiersin.org/files/Articles/1658632/fspas-12-1658632-HTML/image_m/fspas-12-1658632-g002.jpg Figure 2. Workflow of bacterial feedstock preparation from cyanobacterial biomass. (A) cyanobacteria https://www.frontiersin.org/files/Articles/1658632/fspas-12-1658632-HTML/image_m/fspas-12-1658632-g003.jpg Figure 3. Chroococcidiopsis strains after 21 days of growth under VL. (A) Cultures of the FaRLiP str https://www.frontiersin.org/files/Articles/1658632/fspas-12-1658632-HTML/image_m/fspas-12-1658632-g004.jpg Figure 4. CSLM λscan of Chroococcidiopsis strains grown for 21 days (A) FaRLiP strain CCMEE 010 grow https://www.frontiersin.org/files/Articles/1658632/fspas-12-1658632-HTML/image_m/fspas-12-1658632-g005.jpg Figure 5. Biomass production from Chroococcidiopsis strains grown for 21 days under VL condition. Fa https://www.frontiersin.org/files/Articles/1658632/fspas-12-1658632-HTML/image_m/fspas-12-1658632-g006.jpg Figure 6. Growth of E. coli with lysates from Chroococcidiopsis strains grown for 21 days under VL c https://www.frontiersin.org/files/Articles/1658632/fspas-12-1658632-HTML/image_m/fspas-12-1658632-t002.jpg Table 2. Urea transport and catabolism genes in Chroococcidiopsis sp. CCMEE 010 and CCMEE 029. https://www.frontiersin.org/files/Articles/1658632/fspas-12-1658632-HTML/image_m/fspas-12-1658632-g007.jpg Figure 7. Genomic region associated with urea acquisition and catabolism in Chroococcidiopsis sp. CC https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1738625/full https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-g001.jpg Figure 1. GeneCytNet workflow integrating VAE-GAT architecture for predictive classification and mec https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-t001.jpg Table 1. Distribution of genes and sample composition. https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-t002.jpg Table 2. Gene module distribution from louvain clustering. https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-t003.jpg Table 3. Performance comparison of GeneCytNet and baseline models. https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-t004.jpg Table 4. Ablation study results demonstrating the contribution of each architectural component. https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-g002.jpg Figure 2. Performance benchmarking of GeneCytNet against baseline models. (A) Comparative bar chart https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-g003.jpg Figure 3. Interpretability analysis of GeneCytNet. (A) SHAP value plot of the top N biomarkers. IL6R https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-t005.jpg Table 5. Cytokine perturbation effects on RA risk prediction. https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-g004.jpg Figure 4. Bar chart showing the change in the model’s predicted RA probability (Δp) upon upregulatio https://www.frontiersin.org/files/Articles/1738625/fimmu-17-1738625-HTML/image_m/fimmu-17-1738625-g005.jpg Figure 5. Simulated treatment response based on cytokine perturbation profiles. This projection illu https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1672587/full https://www.frontiersin.org/files/Articles/1672587/fimmu-16-1672587-HTML-r1/image_m/fimmu-16-1672587-g001.jpg Figure 1. Single-cell transcriptomic landscape of esophageal tissues across histological subtypes. ( https://www.frontiersin.org/files/Articles/1672587/fimmu-16-1672587-HTML-r1/image_m/fimmu-16-1672587-g002.jpg Figure 2. Identification and functional characterization of malignant epithelial cells across esopha https://www.frontiersin.org/files/Articles/1672587/fimmu-16-1672587-HTML-r1/image_m/fimmu-16-1672587-g003.jpg Figure 3. Molecular heterogeneity and transcriptional subtypes of malignant epithelial cells in SCCE https://www.frontiersin.org/files/Articles/1672587/fimmu-16-1672587-HTML-r1/image_m/fimmu-16-1672587-g004.jpg Figure 4. Differentiation trajectories and evolutionary dynamics of SCCE epithelial cells. (A) Pseud https://www.frontiersin.org/files/Articles/1672587/fimmu-16-1672587-HTML-r1/image_m/fimmu-16-1672587-g005.jpg Figure 5. Comprehensive analysis of tumor-infiltrating lymphoid and myeloid cells across esophageal https://www.frontiersin.org/files/Articles/1672587/fimmu-16-1672587-HTML-r1/image_m/fimmu-16-1672587-g006.jpg Figure 6. SCCE-specific enrichment of eCAFs and associated regulatory activity of ELF3. (A) UMAP plo https://www.frontiersin.org/files/Articles/1672587/fimmu-16-1672587-HTML-r1/image_m/fimmu-16-1672587-g007.jpg Figure 7. Intercellular communication landscape and collagen signaling features in SCCE. (A–C) Heatm https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1791509/full https://www.frontiersin.org/files/Articles/1791509/fphar-17-1791509-HTML-r1/image_m/fphar-17-1791509-g001.jpg Figure 1. MG53 deficiency exacerbates DSS-induced colitis. Age-matched male WT and MG53 knockout (MG https://www.frontiersin.org/files/Articles/1791509/fphar-17-1791509-HTML-r1/image_m/fphar-17-1791509-g002.jpg Figure 2. rhMG53 protein treatment mitigates DSS-induced IBD in MG53−/− mice. MG53−/− mice were admi https://www.frontiersin.org/files/Articles/1791509/fphar-17-1791509-HTML-r1/image_m/fphar-17-1791509-g003.jpg Figure 3. MG53 inhibits DSS-induced inflammation. (A) Representative images of immunofluorescence CD https://www.frontiersin.org/files/Articles/1791509/fphar-17-1791509-HTML-r1/image_m/fphar-17-1791509-g004.jpg Figure 4. rhMG53 inhibits the activation of NLRP3 inflammasome. (A) Immunoblot of IL-1β p17 and casp https://www.frontiersin.org/files/Articles/1791509/fphar-17-1791509-HTML-r1/image_m/fphar-17-1791509-g005.jpg Figure 5. rhMG53 inhibits the formation of ASC oligomers mediated by NLRP3 inflammasome. (A) Immunof https://www.frontiersin.org/files/Articles/1791509/fphar-17-1791509-HTML-r1/image_m/fphar-17-1791509-g006.jpg Figure 6. MG53 inhibits the formation of NLRP3 puncta by interacting with NLRP3. (A) HEK293T cells w https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2025.1647171/full https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-g001.jpg Figure 1. Distribution of solid waste final disposal sites in the North of Guayas province. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-g002.jpg Figure 2. Study phases applied to case study. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-t001.jpg Table 1. Selection criteria for FDS location. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-t002.jpg Table 2. Criteria and subcriteria for FDS selection. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-t003.jpg Table 3. Characteristics of the waste collection service. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-t004.jpg Table 4. Characteristics of final waste disposal sites. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-g003.jpg Figure 3. (A) Location of the gravity center of waste production, (B) sensitivity analysis of cluste https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-t005.jpg Table 5. Projection of waste production until 2040. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-t006.jpg Table 6. Shift of the GCWP compared to the year 2023. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-g004.jpg Figure 4. Territorial suitability according to FDS selection criteria (EA, excluded areas; WB, water https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-t007.jpg Table 7. Weighting of criteria obtained from the focus group. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-g005.jpg Figure 5. Territorial suitability zoning of northern Guayas for the location of FDS. (A–C) correspon https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-g006.jpg Figure 6. Final disposal sites arranged for cantonal clusters. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-t008.jpg Table 8. Reordering of selection criteria. https://www.frontiersin.org/files/Articles/1647171/frsus-06-1647171-HTML/image_m/frsus-06-1647171-g007.jpg Figure 7. Configuration of territorial suitability map for scenarios 1, 2, 3, and 4. For each of the https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2025.1631938/full https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-g001.jpg Figure 1. Case study location. (a) Geographic location of Ecuador, (b) guayaquil canton, (c) ESPOL p https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-g002.jpg Figure 2. Stages applied to water availability assessment. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-t001.jpg Table 1. Information on the lake uses for green areas irrigation and activities at the experimental https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-t002.jpg Table 2. Evaluation criteria for site selection. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-g003.jpg Figure 3. (a) Lake watershed delimitation; (b) engineering lake geomorphology. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-g004.jpg Figure 4. (a) Historical rainfall record; (b) precipitation and runoff flow for a 2-year return peri https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-g005.jpg Figure 5. (a) Annual evapotranspiration from monthly historical temperature records; (b) evapotransp https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-g006.jpg Figure 6. ESPOL population project. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-g007.jpg Figure 7. Water demand projection of the ESPOL population. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-t003.jpg Table 3. Water samples analysis from different points of the engineering lake. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-t004.jpg Table 4. Lake water availability under current conditions and consumption scenarios. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-g008.jpg Figure 8. Protected areas and drinking water distribution network in ESPOL. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-t005.jpg Table 5. Comparative criteria matrix: weights obtained from the AHP. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-t006.jpg Table 6. Likert evaluation results. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-t007.jpg Table 7. SWOT analysis. https://www.frontiersin.org/files/Articles/1631938/frwa-07-1631938-HTML-r1/image_m/frwa-07-1631938-t008.jpg Table 8. Strategies to the sustainable water resources management. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1694170/full https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-g001.jpg Figure 1. Flow diagram of systematic literature search selection. RCT randomized controlled trial. https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-t001.jpg Table 1. Study inclusion criteria based on PICOS strategy. https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-g002.jpg Figure 2. Risk of bias assessment in included studies. https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-g003.jpg Figure 3. Forest plots of exercise intervention on executive function. A positive effect value indic https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-t002.jpg Table 2. Subgroup analysis of the effects of different moderating variables on executive function. https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-g004.jpg Figure 4. Forest plots of exercise intervention on attention function. A positive effect value indic https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-t003.jpg Table 3. Subgroup analysis of the effects of different moderating variables on attention function. https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-g005.jpg Figure 5. Forest plots of exercise intervention on memory function. A negative effect value indicate https://www.frontiersin.org/files/Articles/1694170/fpubh-13-1694170-HTML/image_m/fpubh-13-1694170-t004.jpg Table 4. Characteristics of the included studies. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1717290/full https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-g001.jpg Figure 1. Traditional Chinese acupuncture-related therapies for treating cancer-related depression. https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-g002.jpg Figure 2. PRISMA flow chart for literature screening. https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-t001.jpg Table 1. Characteristics of the included randomized controlled trials for CRD. https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-g003.jpg Figure 3. Forest plot of the overall effectiveness rate of combined traditional Chinese acupuncture- https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-t002.jpg Table 2. Subgroup analysis of the total effective rate after treatment based on the type of traditio https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-g004.jpg Figure 4. Forest plot of the Self-rating Depression Scale for combined traditional Chinese acupunctu https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-t003.jpg Table 3. Subgroup analysis of SDS after treatment based on the type of traditional Chinese acupunctu https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-g005.jpg Figure 5. Forest plot of the Hamilton Depression Scale for combined traditional Chinese acupuncture- https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-t004.jpg Table 4. Subgroup analysis of HAM-D after treatment based on the type of traditional Chinese acupunc https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-g006.jpg Figure 6. Forest plot of the Self-rating Anxiety Scale for combined traditional Chinese acupuncture- https://www.frontiersin.org/files/Articles/1717290/fpsyt-16-1717290-HTML/image_m/fpsyt-16-1717290-t005.jpg Table 5. Analysis of quality of life assessment scale and other outcome measures. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1579924/full https://www.frontiersin.org/files/Articles/1579924/fimmu-16-1579924-HTML/image_m/fimmu-16-1579924-g001.jpg Figure 1. Flow chart of the study. https://www.frontiersin.org/files/Articles/1579924/fimmu-16-1579924-HTML/image_m/fimmu-16-1579924-g002.jpg Figure 2. Number of publications per year and the cumulative number. https://www.frontiersin.org/files/Articles/1579924/fimmu-16-1579924-HTML/image_m/fimmu-16-1579924-g003.jpg Figure 3. Research keywords on CD38 in aging and age-related diseases. (A) Keyword clusters color-co https://www.frontiersin.org/files/Articles/1579924/fimmu-16-1579924-HTML/image_m/fimmu-16-1579924-g004.jpg Figure 4. CiteSpace visualization map of timeline viewer related to CD38 in aging and age-related di https://www.frontiersin.org/files/Articles/1579924/fimmu-16-1579924-HTML/image_m/fimmu-16-1579924-g005.jpg Figure 5. The systemic outcomes of increased CD38 activity across various organs. This figure illust https://www.frontiersin.org/files/Articles/1579924/fimmu-16-1579924-HTML/image_m/fimmu-16-1579924-g006.jpg Figure 6. Mechanisms of CD38 activity changes and aging. This figure illustrates the biological mech https://www.frontiersin.org/files/Articles/1579924/fimmu-16-1579924-HTML/image_m/fimmu-16-1579924-t001.jpg Table 1. Summary of CD38 detection methods. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1728742/full https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-g001.jpg Figure 1. Schematic diagram illustrating the localization of ST36 and CV12 in the rat. The illustrat https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-g002.jpg Figure 2. Flow diagram for the selection of articles. Adapted with permission from Page et al. (73), https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-g003.jpg Figure 3. Results of data mining. (A) Risk of bias graph. (B) Risk of bias summary. (C) Cooccurrence https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-t001.jpg Table 1. Frequency of acupoints in acupuncture for FD. https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-t002.jpg Table 2. Association rules of acupoints for FD treatment. https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-t003.jpg Table 3. Division of network associations in acupuncture for FD. https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-t004.jpg Table 4. Analysis of core acupoints for FD treated with acupuncture. https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-g004.jpg Figure 4. Effects of electroacupuncture intervention on body weight, 3-hour food intake, and gastric https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-g005.jpg Figure 5. Staining observations of eosinophils (Eos) and mast cells (MCs) in the duodenum of functio https://www.frontiersin.org/files/Articles/1728742/fmed-13-1728742-HTML-r2/image_m/fmed-13-1728742-g006.jpg Figure 6. Pathological observation and immunofluorescence (IF) analysis of tight junction proteins ( https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1572342/full https://www.frontiersin.org/files/Articles/1572342/fimmu-16-1572342-HTML/image_m/fimmu-16-1572342-g001.jpg Figure 1. Y320E mutation enhances human T cell priming in vitro. (A) Surface expression of HLA A*020 https://www.frontiersin.org/files/Articles/1572342/fimmu-16-1572342-HTML/image_m/fimmu-16-1572342-g002.jpg Figure 2. Transcriptomic analysis of human primed antigen-specific CD8+ T cells reveals effects of H https://www.frontiersin.org/files/Articles/1572342/fimmu-16-1572342-HTML/image_m/fimmu-16-1572342-g003.jpg Figure 3. Y320E mutation enhances CD8+ T cell priming in vivo. (A) Schematic representation of the O https://www.frontiersin.org/files/Articles/1572342/fimmu-16-1572342-HTML/image_m/fimmu-16-1572342-g004.jpg Figure 4. Y320E mutation in DC2.4/SCT vaccination improves memory T cell response to rechallenge. (A https://www.frontiersin.org/files/Articles/1572342/fimmu-16-1572342-HTML/image_m/fimmu-16-1572342-g005.jpg Figure 5. Y320E mutation in DC2.4/SCT vaccination enhances tumor control. (A) Schematic representati https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1736931/full https://www.frontiersin.org/files/Articles/1736931/fimmu-16-1736931-HTML/image_m/fimmu-16-1736931-g001.jpg Figure 1. IL-7 acts at multiple sites to enhance CAR-T, TIL, and endogenous T cell therapies. https://www.frontiersin.org/files/Articles/1736931/fimmu-16-1736931-HTML/image_m/fimmu-16-1736931-t001.jpg Table 1. Comparison of IL-2, IL-7, and IL-15 effects on cell therapies. https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2026.1762225/full https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g001.jpg Figure 1. The amount of MPs in different regions of the Danube River from its source to the Black Se https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g002.jpg Figure 2. Basin-scale overview of the Danube River from the source to the Black Sea, focusing on the https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g003.jpg Figure 3. Multilevel manta net device. https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g004.jpg Figure 4. MP monitoring during two floods in november-december 2023. https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g005.jpg Figure 5. The MP delivery rate and concentration in each sampling point in cross-sections. 1 in 2022 https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g006.jpg Figure 6. The MP delivery rate and concentration in each sampling point in cross-sections. 2 in 2022 https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g007.jpg Figure 7. The MP delivery rate and concentration in each sampling point in the second cross-sections https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-t001.jpg Table 1. Descriptive statistics for CMP and qMP (Flux) in each cross-section (n: Number of sampling https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g008.jpg Figure 8. Distribution of MP mass concentration [CMP, (A)], and MP transport rate or flux [qMP, (B)] https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-t002.jpg Table 2. Inferential statistics (significance tests). https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-t003.jpg Table 3. MP polymer type and its relative abundances were determined through FTIR analysis related t https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g009.jpg Figure 9. MP particles with different sizes, colors, and polymer types under a digital microscope. https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g010.jpg Figure 10. Prevailing MP shapes in the collected samples. https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g011.jpg Figure 11. Occurrence of different colored MPs in the collected samples. https://www.frontiersin.org/files/Articles/1762225/frwa-08-1762225-HTML-r1/image_m/frwa-08-1762225-g012.jpg Figure 12. Correlation curve based on the mean daily CMP and Q values. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1771381/full https://www.frontiersin.org/files/Articles/1771381/fpls-17-1771381-HTML-r1/image_m/fpls-17-1771381-g001.jpg Figure 1. Strategy for variant selection and final number of SNPs and InDels tiled in the Axiom®Viti https://www.frontiersin.org/files/Articles/1771381/fpls-17-1771381-HTML-r1/image_m/fpls-17-1771381-g002.jpg Figure 2. Distribution of the 21,610 variants of the Axiom®Vitis22K SNParray that have a unique posi https://www.frontiersin.org/files/Articles/1771381/fpls-17-1771381-HTML-r1/image_m/fpls-17-1771381-t001.jpg Table 1. Observed heterozygosity metrics across Vitis species. https://www.frontiersin.org/files/Articles/1771381/fpls-17-1771381-HTML-r1/image_m/fpls-17-1771381-g003.jpg Figure 3. Principal component analysis (PCA) based on SNP data illustrating the genetic diversity of https://www.frontiersin.org/files/Articles/1771381/fpls-17-1771381-HTML-r1/image_m/fpls-17-1771381-g004.jpg Figure 4. Validation of 9 SNPs associated with flower sex in the breeding parental and flower sex pa https://www.frontiersin.org/files/Articles/1771381/fpls-17-1771381-HTML-r1/image_m/fpls-17-1771381-g005.jpg Figure 5. Validation of polymorphisms associated with seed-to-berry ratio (A), berry color (B) and m https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1780738/full https://www.frontiersin.org/files/Articles/1780738/fped-14-1780738-HTML/image_m/fped-14-1780738-g001.jpg Figure 1. Force-sensing robotic bone drilling system. (A) UR10 collaborative robotic arm; (B) six-ax https://www.frontiersin.org/files/Articles/1780738/fped-14-1780738-HTML/image_m/fped-14-1780738-g002.jpg Figure 2. Time history of axial drilling force (Fx) with force-drop–based penetration detection. https://www.frontiersin.org/files/Articles/1780738/fped-14-1780738-HTML/image_m/fped-14-1780738-g003.jpg Figure 3. Effect of feed rate on post-penetration overdrill depth. https://www.frontiersin.org/files/Articles/1780738/fped-14-1780738-HTML/image_m/fped-14-1780738-g004.jpg Figure 4. Effect of spindle speed on post-penetration overdrill depth. https://www.frontiersin.org/files/Articles/1780738/fped-14-1780738-HTML/image_m/fped-14-1780738-t001.jpg Table 1. Descriptive statistics of post-penetration overdrill depth (mm). https://www.frontiersin.org/files/Articles/1780738/fped-14-1780738-HTML/image_m/fped-14-1780738-t002.jpg Table 2. Paired comparison of post-penetration overdrill depth (manual−robot; mm). https://www.frontiersin.org/files/Articles/1780738/fped-14-1780738-HTML/image_m/fped-14-1780738-g005.jpg Figure 5. Comparison of overdrilling with manual drilling vs. force-sensing robotic drill-through st https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1757222/full https://www.frontiersin.org/files/Articles/1757222/fmed-13-1757222-HTML-r1/image_m/fmed-13-1757222-g001.jpg Figure 1. Angle of needle insertion in the medial gastrocnemius. (A) Medial gastrocnemius. (B) Soleu https://www.frontiersin.org/files/Articles/1757222/fmed-13-1757222-HTML-r1/image_m/fmed-13-1757222-g002.jpg Figure 2. CONSORT flow diagram of participants. https://www.frontiersin.org/files/Articles/1757222/fmed-13-1757222-HTML-r1/image_m/fmed-13-1757222-t001.jpg Table 1. Baseline demographic and outcome measures. https://www.frontiersin.org/files/Articles/1757222/fmed-13-1757222-HTML-r1/image_m/fmed-13-1757222-t002.jpg Table 2. Intra and between groups differences in outcome measures. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1775093/full https://www.frontiersin.org/files/Articles/1775093/fimmu-17-1775093-HTML/image_m/fimmu-17-1775093-g001.jpg Figure 1. An overview of the principal biologic strategies discussed in this review and their intera https://www.frontiersin.org/files/Articles/1775093/fimmu-17-1775093-HTML/image_m/fimmu-17-1775093-t001.jpg Table 1. Results from phase II and III clinical trials of immunotherapy in patients with glioblastom https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1799218/full https://www.frontiersin.org/files/Articles/1799218/fmed-13-1799218-HTML/image_m/fmed-13-1799218-t001.jpg Table 1. Demographic characteristics of the study population and steroid use in circumcised patients https://www.frontiersin.org/files/Articles/1799218/fmed-13-1799218-HTML/image_m/fmed-13-1799218-t002.jpg Table 2. DLQI in different groups of patients. https://www.frontiersin.org/files/Articles/1799218/fmed-13-1799218-HTML/image_m/fmed-13-1799218-t003.jpg Table 3. Associations of phimosis and circumcision with circumcision status and onset of penile carc https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1769978/full https://www.frontiersin.org/files/Articles/1769978/fvets-13-1769978-HTML/image_m/fvets-13-1769978-t001.jpg Table 1. Composition (% DM) of the silages used in the experimental diets. https://www.frontiersin.org/files/Articles/1769978/fvets-13-1769978-HTML/image_m/fvets-13-1769978-t002.jpg Table 2. Ingredients and chemical analysis of the rations fed in the experimental diets. https://www.frontiersin.org/files/Articles/1769978/fvets-13-1769978-HTML/image_m/fvets-13-1769978-t003.jpg Table 3. Cheese yield and physicochemical composition. https://www.frontiersin.org/files/Articles/1769978/fvets-13-1769978-HTML/image_m/fvets-13-1769978-t004.jpg Table 4. Fatty acid (%FAME) composition of cheese. https://www.frontiersin.org/files/Articles/1769978/fvets-13-1769978-HTML/image_m/fvets-13-1769978-t005.jpg Table 5. Volatile compounds of cheese. https://www.frontiersin.org/files/Articles/1769978/fvets-13-1769978-HTML/image_m/fvets-13-1769978-g001.jpg Figure 1. Relative frequency distribution of aromatic classes (acids, alcohols, ketones, esters, and https://www.frontiersin.org/files/Articles/1769978/fvets-13-1769978-HTML/image_m/fvets-13-1769978-g002.jpg Figure 2. Principal component analysis (PCA) biplot of volatile compounds of the experimental cheese https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1751030/full https://www.frontiersin.org/files/Articles/1751030/fped-14-1751030-HTML-r1/image_m/fped-14-1751030-g001.jpg Figure 1. Study flow diagram and analytic cohorts. The figure illustrates the construction of the tw https://www.frontiersin.org/files/Articles/1751030/fped-14-1751030-HTML-r1/image_m/fped-14-1751030-t001.jpg Table 1. Baseline characteristics of the sex- and birth-year-matched Kaplan–Meier cohort. https://www.frontiersin.org/files/Articles/1751030/fped-14-1751030-HTML-r1/image_m/fped-14-1751030-g002.jpg Figure 2. Kaplan–Meier curves of celiac disease-free survival among females with and without ventric https://www.frontiersin.org/files/Articles/1751030/fped-14-1751030-HTML-r1/image_m/fped-14-1751030-g003.jpg Figure 3. Kaplan–Meier curves of celiac disease-free survival among males with and without ventricul https://www.frontiersin.org/files/Articles/1751030/fped-14-1751030-HTML-r1/image_m/fped-14-1751030-t002.jpg Table 2. Baseline characteristics of the Cox regression cohort (n = 493,382). https://www.frontiersin.org/files/Articles/1751030/fped-14-1751030-HTML-r1/image_m/fped-14-1751030-t003.jpg Table 3. Multivariable Cox proportional hazards model for incident celiac disease in the CHS cohort https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1745687/full https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g001.jpg Figure 1. Samples analyzed per year, species, and results. The number of samples tested is reported https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g002.jpg Figure 2. Tested farms per year. The number of tested farms is reported on the Y-axis. https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g003.jpg Figure 3. Total number of positives obtained by region and province. Fisher’s method was used to plo https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g004.jpg Figure 4. Types of analyzed matrices per species and results. G: goats, S: sheep. The number of test https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-t001.jpg Table 1. Distribution of genotypes identified in this study divided per region. https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g005.jpg Figure 5. Genotyped samples between 2019–2024 and their geographic distribution. https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-t002.jpg Table 2. Number of SRLV genotyped per year. https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g006.jpg Figure 6. Focus on Umbria and Marche regions, sequenced samples divided per year. https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g007.jpg Figure 7. Focus on Basilicata region, sequenced samples divided per year. https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g008.jpg Figure 8. Geographical distribution of SRLV sub-genotypes over the Italian territory from 2019 to 20 https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g009.jpg Figure 9. Phylogenetic tree associated with genotype A, based on the sequencing of the 800 bp gag-po https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g010.jpg Figure 10. Phylogenetic tree associated with genotype B, based on the sequencing of the 800 bp gag-p https://www.frontiersin.org/files/Articles/1745687/fmicb-16-1745687-HTML/image_m/fmicb-16-1745687-g011.jpg Figure 11. Violin plots representing the p-distances distribution of genotypes A and B. https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2025.1767630/full https://www.frontiersin.org/files/Articles/1767630/fnmol-18-1767630-HTML/image_m/fnmol-18-1767630-g001.jpg Figure 1. Multi-omics approaches in AD research. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1550052/full https://www.frontiersin.org/files/Articles/1550052/fpsyg-16-1550052-HTML/image_m/fpsyg-16-1550052-t001.jpg Table 1. Characteristics of study participants. https://www.frontiersin.org/files/Articles/1550052/fpsyg-16-1550052-HTML/image_m/fpsyg-16-1550052-t002.jpg Table 2. Correlations for the main variables. https://www.frontiersin.org/files/Articles/1550052/fpsyg-16-1550052-HTML/image_m/fpsyg-16-1550052-t003.jpg Table 3. Mediation model test. https://www.frontiersin.org/files/Articles/1550052/fpsyg-16-1550052-HTML/image_m/fpsyg-16-1550052-g001.jpg Figure 1. Pathway of social isolation score on anxiety symptoms in MHD patients. https://www.frontiersin.org/files/Articles/1550052/fpsyg-16-1550052-HTML/image_m/fpsyg-16-1550052-t004.jpg Table 4. Significance test for mediating effects of social isolation score, anxiety symptoms, and ps https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1766008/full https://www.frontiersin.org/files/Articles/1766008/fphys-17-1766008-HTML-r1/image_m/fphys-17-1766008-g001.jpg Figure 1. Schematic of the experimental intervention protocol. https://www.frontiersin.org/files/Articles/1766008/fphys-17-1766008-HTML-r1/image_m/fphys-17-1766008-g002.jpg Figure 2. Schematic of the respiratory muscle strength test procedure. https://www.frontiersin.org/files/Articles/1766008/fphys-17-1766008-HTML-r1/image_m/fphys-17-1766008-t001.jpg Table 1. Baseline measurements of the experimental and control groups. https://www.frontiersin.org/files/Articles/1766008/fphys-17-1766008-HTML-r1/image_m/fphys-17-1766008-g003.jpg Figure 3. Combined training of inspiratory muscle resistance and lower limb resistance. https://www.frontiersin.org/files/Articles/1766008/fphys-17-1766008-HTML-r1/image_m/fphys-17-1766008-g004.jpg Figure 4. Changes in (MIP) and (MEP) after 12-week intervention. Data are presented as mean ± SEM. * https://www.frontiersin.org/files/Articles/1766008/fphys-17-1766008-HTML-r1/image_m/fphys-17-1766008-g005.jpg Figure 5. Changes after 12-week intervention (AT) and (VO2max). Data are presented as mean ± SEM. *P https://www.frontiersin.org/files/Articles/1766008/fphys-17-1766008-HTML-r1/image_m/fphys-17-1766008-g006.jpg Figure 6. Changes after 12 weeks of intervention (SVC, FVC, FEV1, MVV) Data are presented as mean ± https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1619959/full https://www.frontiersin.org/files/Articles/1619959/fmed-12-1619959-HTML/image_m/fmed-12-1619959-g001.jpg Figure 1. PRISMA flow diagram of the search process for studies. https://www.frontiersin.org/files/Articles/1619959/fmed-12-1619959-HTML/image_m/fmed-12-1619959-g002.jpg Figure 2. Summary of network meta-analysis results on postoperative complications for each type of p https://www.frontiersin.org/files/Articles/1619959/fmed-12-1619959-HTML/image_m/fmed-12-1619959-g003.jpg Figure 3. Summary of network meta-analysis results on the length of hospital stay for each type of p https://www.frontiersin.org/files/Articles/1619959/fmed-12-1619959-HTML/image_m/fmed-12-1619959-t001.jpg Table 1. Secondary outcomes of prehabilitation to postoperative outcomes in colorectal cancer patien https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1782840/full https://www.frontiersin.org/files/Articles/1782840/fcimb-16-1782840-HTML/image_m/fcimb-16-1782840-g001.jpg Figure 1. Cohort screening and selection flowchart. https://www.frontiersin.org/files/Articles/1782840/fcimb-16-1782840-HTML/image_m/fcimb-16-1782840-t001.jpg Table 1. Baseline before and after PSM. https://www.frontiersin.org/files/Articles/1782840/fcimb-16-1782840-HTML/image_m/fcimb-16-1782840-t002.jpg Table 2. Primary outcome before and after PSM. https://www.frontiersin.org/files/Articles/1782840/fcimb-16-1782840-HTML/image_m/fcimb-16-1782840-t003.jpg Table 3. Age-stratified composite clinical efficacy and treatment × age interaction test in the PSM- https://www.frontiersin.org/files/Articles/1782840/fcimb-16-1782840-HTML/image_m/fcimb-16-1782840-t004.jpg Table 4. Secondary outcomes before and after PSM. https://www.frontiersin.org/files/Articles/1782840/fcimb-16-1782840-HTML/image_m/fcimb-16-1782840-t005.jpg Table 5. Safety outcomes in the full, unmatched cohort: Comparison between AZI and ERY. https://www.frontiersin.org/files/Articles/1782840/fcimb-16-1782840-HTML/image_m/fcimb-16-1782840-t006.jpg Table 6. Economic outcomes in the full cohort: Macrolide drug costs and wastage comparison between A https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1759636/full https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-g001.jpg Figure 1. Patient selection flowchart and distribution of treatment groups before and after IPTW. IP https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-t001.jpg Table 1. Baseline characteristics of the study population before IPTW. https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-t002.jpg Table 2. Weighted baseline characteristics after IPTW adjustment. https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-g002.jpg Figure 2. Standardized mean differences for baseline covariates before and after IPTW. IPTW, inverse https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-t003.jpg Table 3. Primary and secondary outcomes across the four treatment groups before IPTW adjustment. https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-t004.jpg Table 4. IPTW-adjusted risk differences and mean differences for primary and secondary outcomes acro https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-g003.jpg Figure 3. IPTW-adjusted risk differences for clinical outcomes. IPTW, inverse probability of treatme https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-g004.jpg Figure 4. IPTW-adjusted mean differences for secondary outcomes. IPTW, inverse probability of treatm https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-t005.jpg Table 5. IPTW-adjusted clinical and safety outcomes in patients aged 65–70 years treated with ertape https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-t006.jpg Table 6. IPTW-adjusted clinical and safety outcomes in patients aged 71–80 years treated with ertape https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-t007.jpg Table 7. IPTW-adjusted clinical and safety outcomes in patients aged ≥81 years treated with ertapene https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-t008.jpg Table 8. IPTW-adjusted clinical and secondary outcomes in patients with gastrointestinal source cIAI https://www.frontiersin.org/files/Articles/1759636/fcimb-15-1759636-HTML/image_m/fcimb-15-1759636-t009.jpg Table 9. IPTW-adjusted clinical and secondary outcomes in patients with non-gastrointestinal source https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1726074/full https://www.frontiersin.org/files/Articles/1726074/fcimb-15-1726074-HTML/image_m/fcimb-15-1726074-t001.jpg Table 1. Demographic and clinical characteristics of the study population. https://www.frontiersin.org/files/Articles/1726074/fcimb-15-1726074-HTML/image_m/fcimb-15-1726074-t002.jpg Table 2. Phenotypic antimicrobial susceptibility testing MIC ranges for the included strains. https://www.frontiersin.org/files/Articles/1726074/fcimb-15-1726074-HTML/image_m/fcimb-15-1726074-g001.jpg Figure 1. Phylogenetic relatedness and genomic features of Enterococcus faecium. https://www.frontiersin.org/files/Articles/1726074/fcimb-15-1726074-HTML/image_m/fcimb-15-1726074-g002.jpg Figure 2. Phylogenetic relatedness and genomic features of Enterococcus faecalis. https://www.frontiersin.org/files/Articles/1726074/fcimb-15-1726074-HTML/image_m/fcimb-15-1726074-g003.jpg Figure 3. Adhesion capacity of reference isolates to epithelial cells. Each point represents an inde https://www.frontiersin.org/files/Articles/1726074/fcimb-15-1726074-HTML/image_m/fcimb-15-1726074-g004.jpg Figure 4. Effects of bacterial cell-free supernatants (CS) on Caco-2 cell viability at 6 h. Each poi https://www.frontiersin.org/files/Articles/1726074/fcimb-15-1726074-HTML/image_m/fcimb-15-1726074-g005.jpg Figure 5. Effects of bacterial cell-free supernatants (CS) on Caco-2 cells after 24 h of exposure. E https://www.frontiersin.org/files/Articles/1726074/fcimb-15-1726074-HTML/image_m/fcimb-15-1726074-g006.jpg Figure 6. Induction of oxidative stress in Caco-2 cells following exposure to bacterial cell-free su https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1687243/full https://www.frontiersin.org/files/Articles/1687243/fmicb-16-1687243-HTML/image_m/fmicb-16-1687243-g001.jpg Figure 1. Schematic diagram illustrating the clinical spectrum of Staphylococcus aureus and the path https://www.frontiersin.org/files/Articles/1687243/fmicb-16-1687243-HTML/image_m/fmicb-16-1687243-g002.jpg Figure 2. Schematic illustration depicting the structural organization of Staphylococcus aureus and https://www.frontiersin.org/files/Articles/1687243/fmicb-16-1687243-HTML/image_m/fmicb-16-1687243-t001.jpg Table 1. Key virulence molecules expressed by Staphylococcus aureus. https://www.frontiersin.org/files/Articles/1687243/fmicb-16-1687243-HTML/image_m/fmicb-16-1687243-g003.jpg Figure 3. The immune mechanisms activated during Staphylococcus aureus septic arthritis, focusing on https://www.frontiersin.org/files/Articles/1687243/fmicb-16-1687243-HTML/image_m/fmicb-16-1687243-g004.jpg Figure 4. Signaling functions of Toll-like receptors. The signaling pathways activated by Toll-like https://www.frontiersin.org/files/Articles/1687243/fmicb-16-1687243-HTML/image_m/fmicb-16-1687243-g005.jpg Figure 5. Activation and consequences of formyl-peptide receptor (FPR) in bacterial infections. FPR1 https://www.frontiersin.org/files/Articles/1687243/fmicb-16-1687243-HTML/image_m/fmicb-16-1687243-g006.jpg Figure 6. Schematic illustration of bone osteoclastogenesis. Monocytes from capillaries differentiat https://www.frontiersin.org/files/Articles/1687243/fmicb-16-1687243-HTML/image_m/fmicb-16-1687243-g007.jpg Figure 7. Summary of key findings in this article. This figure illustrates the complex interplay bet https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1672889/full https://www.frontiersin.org/files/Articles/1672889/fcimb-15-1672889-HTML/image_m/fcimb-15-1672889-g001.jpg Figure 1. Heatmap of pairwise average nucleotide identity (ANI) values for 218 genome assemblies of https://www.frontiersin.org/files/Articles/1672889/fcimb-15-1672889-HTML/image_m/fcimb-15-1672889-g002.jpg Figure 2. Nocardia genome phylogenetic relationships based on the concatenation of thenucleotide seq https://www.frontiersin.org/files/Articles/1672889/fcimb-15-1672889-HTML/image_m/fcimb-15-1672889-g003.jpg Figure 3. The MIC Minimum Inhibitory Concentrations (MICs) of 15 antimicrobials for Nocardia species https://www.frontiersin.org/files/Articles/1672889/fcimb-15-1672889-HTML/image_m/fcimb-15-1672889-g004.jpg Figure 4. Distribution of Antibiotics Resistance Genes (ARGs) in the genomes of 148 multiple Nocardi https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1670516/full https://www.frontiersin.org/files/Articles/1670516/fnins-19-1670516-HTML/image_m/fnins-19-1670516-g001.jpg Figure 1. Alcohol and its metabolite ethanol directly damage neuronal cells, leading to mitochondria https://www.frontiersin.org/files/Articles/1670516/fnins-19-1670516-HTML/image_m/fnins-19-1670516-g002.jpg Figure 2. The mechanism by which alcohol induces the activation of the NLRP3 inflammasome, resulting https://www.frontiersin.org/files/Articles/1670516/fnins-19-1670516-HTML/image_m/fnins-19-1670516-g003.jpg Figure 3. Inhibition of neuronal damage by active components of eleutherococcus senticosus. [Arrows https://www.frontiersin.org/files/Articles/1670516/fnins-19-1670516-HTML/image_m/fnins-19-1670516-t001.jpg Table 1. The specific mechanism by which eleutherococcus inhibits neuronal apoptosis. https://www.frontiersin.org/files/Articles/1670516/fnins-19-1670516-HTML/image_m/fnins-19-1670516-g004.jpg Figure 4. Inhibition of microglial activation by active components of eleutherococcus senticosus. Th https://www.frontiersin.org/files/Articles/1670516/fnins-19-1670516-HTML/image_m/fnins-19-1670516-g005.jpg Figure 5. Construction of animal models and cell models related to KS. The cognitive impairment mode https://www.frontiersin.org/files/Articles/1670516/fnins-19-1670516-HTML/image_m/fnins-19-1670516-t002.jpg Table 2. The targets and specific mechanisms of ES active ingredients in treating KS. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1721987/full https://www.frontiersin.org/files/Articles/1721987/fmicb-16-1721987-HTML/image_m/fmicb-16-1721987-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1721987/fmicb-16-1721987-HTML/image_m/fmicb-16-1721987-t001.jpg Table 1. Primer sequence used in the real-time PCR analysis. https://www.frontiersin.org/files/Articles/1721987/fmicb-16-1721987-HTML/image_m/fmicb-16-1721987-g001.jpg Figure 1. Screening and identification of bacteriocin-producing Lactiplantibacillus pentosus C82 iso https://www.frontiersin.org/files/Articles/1721987/fmicb-16-1721987-HTML/image_m/fmicb-16-1721987-g002.jpg Figure 2. Optimization of growth conditions for bacteriocin production by Lactiplantibacillus pentos https://www.frontiersin.org/files/Articles/1721987/fmicb-16-1721987-HTML/image_m/fmicb-16-1721987-g003.jpg Figure 3. Stability and functional characterization of bacteriocin produced by Lactiplantibacillus p https://www.frontiersin.org/files/Articles/1721987/fmicb-16-1721987-HTML/image_m/fmicb-16-1721987-t002.jpg Table 2. Purification of bacteriocin from L. pentosus C82. https://www.frontiersin.org/files/Articles/1721987/fmicb-16-1721987-HTML/image_m/fmicb-16-1721987-g004.jpg Figure 4. In vivo developmental toxicity. (A) Bacteriocin in zebrafish embryos and larvae. (B) Perce https://www.frontiersin.org/files/Articles/1721987/fmicb-16-1721987-HTML/image_m/fmicb-16-1721987-g005.jpg Figure 5. Effect of C82 bacteriocin treatment on oxidative stress biomarkers, cytokine expression, a https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1705965/full https://www.frontiersin.org/files/Articles/1705965/fmicb-16-1705965-HTML-r2/image_m/fmicb-16-1705965-t001.jpg Table 1. Baseline characteristics of the study population. https://www.frontiersin.org/files/Articles/1705965/fmicb-16-1705965-HTML-r2/image_m/fmicb-16-1705965-g001.jpg Figure 1. Targeted bile acid metabolomics in preterm infants with different BPD severities. (A) OPLS https://www.frontiersin.org/files/Articles/1705965/fmicb-16-1705965-HTML-r2/image_m/fmicb-16-1705965-g002.jpg Figure 2. Gut microbiota composition on postnatal day 7 across BPD severities. (A) α-diversity indic https://www.frontiersin.org/files/Articles/1705965/fmicb-16-1705965-HTML-r2/image_m/fmicb-16-1705965-g003.jpg Figure 3. Correlation analysis of bile acids and microbial genera. (A) Spearman correlation heatmap https://www.frontiersin.org/files/Articles/1705965/fmicb-16-1705965-HTML-r2/image_m/fmicb-16-1705965-g004.jpg Figure 4. Machine learning models for BPD severity classification. (A) Global feature importance and https://www.frontiersin.org/files/Articles/1705965/fmicb-16-1705965-HTML-r2/image_m/fmicb-16-1705965-g005.jpg Figure 5. Schematic of the gut bile acid-microbiota axis and BPD risk in preterm infants. At day 7 o https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1653505/full https://www.frontiersin.org/files/Articles/1653505/fmicb-16-1653505-HTML-r2/image_m/fmicb-16-1653505-t001.jpg Table 1. Fecal sample collection and donor information. https://www.frontiersin.org/files/Articles/1653505/fmicb-16-1653505-HTML-r2/image_m/fmicb-16-1653505-g001.jpg Figure 1. Isolation and genomic characterization of a B. breve strain from feces of healthy Chinese https://www.frontiersin.org/files/Articles/1653505/fmicb-16-1653505-HTML-r2/image_m/fmicb-16-1653505-g002.jpg Figure 2. Evaluation of genetic manipulation potential in Bifidobacterium strains. (A) Phylogenomic https://www.frontiersin.org/files/Articles/1653505/fmicb-16-1653505-HTML-r2/image_m/fmicb-16-1653505-g003.jpg Figure 3. Targeted deletion of pyrE in B. breve GZX43 via homologous recombination using a shuttle v https://www.frontiersin.org/files/Articles/1653505/fmicb-16-1653505-HTML-r2/image_m/fmicb-16-1653505-t002.jpg Table 2. Differential 5-FOA resistance profiles between ΔpyrE and WT strains. https://www.frontiersin.org/files/Articles/1653505/fmicb-16-1653505-HTML-r2/image_m/fmicb-16-1653505-g004.jpg Figure 4. Dual-plasmid strategy for targeted gene knockout in B. breve GZX43. (A) Schematic of the d https://www.frontiersin.org/files/Articles/1653505/fmicb-16-1653505-HTML-r2/image_m/fmicb-16-1653505-g005.jpg Figure 5. Targeted deletion of GE001229 in B. breve GZX43 using a dual-plasmid knockout system. (A) https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1766850/full https://www.frontiersin.org/files/Articles/1766850/fmicb-17-1766850-HTML/image_m/fmicb-17-1766850-g001.jpg Figure 1. Schematic diagram of integrated multi-omics data analysis workflow for children with ASD. https://www.frontiersin.org/files/Articles/1766850/fmicb-17-1766850-HTML/image_m/fmicb-17-1766850-t001.jpg Table 1. Clinical manifestations of 51 children with ASD. https://www.frontiersin.org/files/Articles/1766850/fmicb-17-1766850-HTML/image_m/fmicb-17-1766850-g002.jpg Figure 2. Clinical association analysis and identification of MUC pathway variants as key drivers in https://www.frontiersin.org/files/Articles/1766850/fmicb-17-1766850-HTML/image_m/fmicb-17-1766850-t002.jpg Table 2. Clinically interpretable pathogenic variants identified by WES in 51 children with ASD. https://www.frontiersin.org/files/Articles/1766850/fmicb-17-1766850-HTML/image_m/fmicb-17-1766850-t003.jpg Table 3. Details of significant rare deleterious variants identified in MUC pathway genes in childre https://www.frontiersin.org/files/Articles/1766850/fmicb-17-1766850-HTML/image_m/fmicb-17-1766850-g003.jpg Figure 3. Structural dysbiosis, functional alterations, and impaired ecological stability of the gut https://www.frontiersin.org/files/Articles/1766850/fmicb-17-1766850-HTML/image_m/fmicb-17-1766850-g004.jpg Figure 4. System-wide metabolic disturbances and identification of clinically relevant metabolite mo https://www.frontiersin.org/files/Articles/1766850/fmicb-17-1766850-HTML/image_m/fmicb-17-1766850-g005.jpg Figure 5. Multi-omics integration reveals complex interactions among host genetics, gut microbiota, https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1664708/full https://www.frontiersin.org/files/Articles/1664708/fmicb-16-1664708-HTML/image_m/fmicb-16-1664708-gr0001.jpg Graphical Abstract. Pharmacological mechanisms of GE and its active ingredients. https://www.frontiersin.org/files/Articles/1664708/fmicb-16-1664708-HTML/image_m/fmicb-16-1664708-g001.jpg Figure 1. Timepoints of fecal sampling and asthma outcome assessment. Blue dots (●) indicate fecal s https://www.frontiersin.org/files/Articles/1664708/fmicb-16-1664708-HTML/image_m/fmicb-16-1664708-t001.jpg Table 1. Summary of epidemiological studies on prenatal exposures, gut microbiota, and childhood ast https://www.frontiersin.org/files/Articles/1664708/fmicb-16-1664708-HTML/image_m/fmicb-16-1664708-t002.jpg Table 2. Covariates considered in studies on prenatal exposure and childhood asthma. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1681214/full https://www.frontiersin.org/files/Articles/1681214/fmicb-16-1681214-HTML/image_m/fmicb-16-1681214-t001.jpg Table 1. EDCs exposure levels in pregnant women across various countries and time periods. https://www.frontiersin.org/files/Articles/1681214/fmicb-16-1681214-HTML/image_m/fmicb-16-1681214-g001.jpg Figure 1. Global levels of endocrine-disrupting chemicals (EDCs) in pregnant women. https://www.frontiersin.org/files/Articles/1681214/fmicb-16-1681214-HTML/image_m/fmicb-16-1681214-g002.jpg Figure 2. Prenatal exposure to endocrine-disrupting chemicals (EDCs) and risk of atopic dermatitis i https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1632529/full https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-t001.jpg Table 1. Nutrient levels of experimental diets for pigeons. https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-t002.jpg Table 2. Effects of different metabolizable energy levels on the apparent digestion and metabolism o https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-g001.jpg Figure 1. Effects of varying dietary metabolizable energy levels on serum biochemical indices of exe https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-g002.jpg Figure 2. Effects of varying dietary metabolizable energy levels on the serum antioxidant capacity o https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-t003.jpg Table 3. Effects of different dietary metabolic energy levels on blood gas of exercise–trained pigeo https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-t004.jpg Table 4. Effects of different metabolizable energy levels in the diet on the alpha diversity of feca https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-g003.jpg Figure 3. Effects of varying dietary metabolizable energy levels on the β-diversity of fecal flora i https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-g004.jpg Figure 4. Effects of varying dietary metabolizable energy levels on the relative abundance of fecal https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-g005.jpg Figure 5. Effects of varying dietary metabolizable energy levels on the relative abundance of fecal https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-g006.jpg Figure 6. Effects of varying dietary metabolizable energy levels on the relative abundance of fecal https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-g007.jpg Figure 7. Effects of varying dietary metabolizable energy levels on LEfSe analysis of fecal flora in https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-g008.jpg Figure 8. Effects of varying dietary metabolizable energy levels on functional prediction of fecal f https://www.frontiersin.org/files/Articles/1632529/fmicb-16-1632529-HTML-r2/image_m/fmicb-16-1632529-g009.jpg Figure 9. Heatmaps of correlation analysis. Asterisk “*” denotes a significant difference between gr https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1723364/full https://www.frontiersin.org/files/Articles/1723364/fcimb-16-1723364-HTML/image_m/fcimb-16-1723364-g001.jpg Figure 1. Fabrication and morphological characterization of AKK-MC. (A) Phylogenetic tree was constr https://www.frontiersin.org/files/Articles/1723364/fcimb-16-1723364-HTML/image_m/fcimb-16-1723364-g002.jpg Figure 2. AKK-MC enhances colonization efficiency in juvenile mice intestine. (A) Experimental desig https://www.frontiersin.org/files/Articles/1723364/fcimb-16-1723364-HTML/image_m/fcimb-16-1723364-g003.jpg Figure 3. AKK and AKK-MC formulation alleviate phenotype of LPS-induced ALI. (A) Protein concentrati https://www.frontiersin.org/files/Articles/1723364/fcimb-16-1723364-HTML/image_m/fcimb-16-1723364-g004.jpg Figure 4. AKK and AKK-MC formulation alleviate LPS-induced intestinal histological damage. (A) H&E s https://www.frontiersin.org/files/Articles/1723364/fcimb-16-1723364-HTML/image_m/fcimb-16-1723364-g005.jpg Figure 5. AKK and AKK-MC formulation restore LPS-induced intestinal goblet cells. (A) AB-PAS stainin https://www.frontiersin.org/files/Articles/1723364/fcimb-16-1723364-HTML/image_m/fcimb-16-1723364-g006.jpg Figure 6. AKK and AKK-MC formulation reduce LPS-induced intestinal mast cell infiltration. (A) TBO s https://www.frontiersin.org/files/Articles/1723364/fcimb-16-1723364-HTML/image_m/fcimb-16-1723364-g007.jpg Figure 7. AKK and AKK-MC formulation alleviate LPS-induced ALI through the gut-lung axis in juvenile https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1685526/full https://www.frontiersin.org/files/Articles/1685526/fphys-16-1685526-HTML/image_m/fphys-16-1685526-g001.jpg Figure 1. BPD and Mitochondrial Dysfunction. This figure illustrates the central role of mitochondri https://www.frontiersin.org/files/Articles/1685526/fphys-16-1685526-HTML/image_m/fphys-16-1685526-g002.jpg Figure 2. Mitochondria-Dependent Cell Death in BPD. This figure integrates six major cell death moda https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1662922/full https://www.frontiersin.org/files/Articles/1662922/fped-13-1662922-HTML-r1/image_m/fped-13-1662922-g001.jpg Figure 1. H2 ameliorates BPD induced by hyperoxia. (A) Hematoxylin–eosin staining was performed to a https://www.frontiersin.org/files/Articles/1662922/fped-13-1662922-HTML-r1/image_m/fped-13-1662922-g002.jpg Figure 2. H2 activates AHR and its downstream molecule CPEB4, thereby reducing endoplasmic reticulum https://www.frontiersin.org/files/Articles/1662922/fped-13-1662922-HTML-r1/image_m/fped-13-1662922-g003.jpg Figure 3. Effect of H2 on RLE-6TN cells in a hyperoxic environment was investigated. (A) CCK-8 assay https://www.frontiersin.org/files/Articles/1662922/fped-13-1662922-HTML-r1/image_m/fped-13-1662922-g004.jpg Figure 4. Effect of AHR on RLE-6TN cells in a hyperoxic environment was investigated. (A) Cell viabi https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1654502/full https://www.frontiersin.org/files/Articles/1654502/fcimb-15-1654502-HTML/image_m/fcimb-15-1654502-t001.jpg Table 1. The clinical characteristics of the BPD and non-BPD infants. https://www.frontiersin.org/files/Articles/1654502/fcimb-15-1654502-HTML/image_m/fcimb-15-1654502-t002.jpg Table 2. The treatment and prognosis of the BPD and non-BPD infants. https://www.frontiersin.org/files/Articles/1654502/fcimb-15-1654502-HTML/image_m/fcimb-15-1654502-g001.jpg Figure 1. Microbiome features at intubation differ between infants with and without BPD. (A) Box plo https://www.frontiersin.org/files/Articles/1654502/fcimb-15-1654502-HTML/image_m/fcimb-15-1654502-g002.jpg Figure 2. Microbiome features at day 7 post-intubation differ between infants with and without BPD. https://www.frontiersin.org/files/Articles/1654502/fcimb-15-1654502-HTML/image_m/fcimb-15-1654502-g003.jpg Figure 3. Microbiome features at day 14 post-intubation differ between infants with and without BPD. https://www.frontiersin.org/files/Articles/1654502/fcimb-15-1654502-HTML/image_m/fcimb-15-1654502-g004.jpg Figure 4. Microbiome dynamics in infants with and without BPD. (A) Box plots of simpson index of the https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1657411/full https://www.frontiersin.org/files/Articles/1657411/fped-13-1657411-HTML/image_m/fped-13-1657411-g001.jpg Figure 1. The treatment plan for CTB in premature infants. CTB, congenital tuberculosis. https://www.frontiersin.org/files/Articles/1657411/fped-13-1657411-HTML/image_m/fped-13-1657411-g002.jpg Figure 2. The flowchart of our research. Demographic information and clinical data were obtained fro https://www.frontiersin.org/files/Articles/1657411/fped-13-1657411-HTML/image_m/fped-13-1657411-t001.jpg Table 1. Characteristics of preterm infants positive for CTB. https://www.frontiersin.org/files/Articles/1657411/fped-13-1657411-HTML/image_m/fped-13-1657411-t002.jpg Table 2. Imaging and clinical features of CTB positives. https://www.frontiersin.org/files/Articles/1657411/fped-13-1657411-HTML/image_m/fped-13-1657411-g003.jpg Figure 3. The lung imaging changes of a very premature child of 27+5 weeks from birth to diagnosis o https://www.frontiersin.org/files/Articles/1657411/fped-13-1657411-HTML/image_m/fped-13-1657411-g004.jpg Figure 4. Hypothesis-generating diagnostic flowchart for CTB in preterm infants from a high-burden T https://www.frontiersin.org/journals/animal-science/articles/10.3389/fanim.2026.1751875/full https://www.frontiersin.org/files/Articles/1751875/fanim-07-1751875-HTML/image_m/fanim-07-1751875-g001.jpg Figure 1. Representative Gaddi livestock guardian dogs and seasonal camps across an altitudinal grad https://www.frontiersin.org/files/Articles/1751875/fanim-07-1751875-HTML/image_m/fanim-07-1751875-g002.jpg Figure 2. Workflow of the variant calling pipeline implemented using GATK4 following the Broad Insti https://www.frontiersin.org/files/Articles/1751875/fanim-07-1751875-HTML/image_m/fanim-07-1751875-t001.jpg Table 1. Raw data and alignment statistics for individual GM and GF. https://www.frontiersin.org/files/Articles/1751875/fanim-07-1751875-HTML/image_m/fanim-07-1751875-g003.jpg Figure 3. Genome Scope profile of the Gaddi dog (GM) based on k-mer frequency distribution derived f https://www.frontiersin.org/files/Articles/1751875/fanim-07-1751875-HTML/image_m/fanim-07-1751875-t002.jpg Table 2. High-impact SNPs detected in Gaddi dog Genome (ANNOVAR). https://www.frontiersin.org/files/Articles/1751875/fanim-07-1751875-HTML/image_m/fanim-07-1751875-t003.jpg Table 3. Pathways affected by high-impact SNPs and Indels in Gaddi dog genome. https://www.frontiersin.org/files/Articles/1751875/fanim-07-1751875-HTML/image_m/fanim-07-1751875-g004.jpg Figure 4. Gene Ontology (GO) enrichment analysis of genes harboring high-impact variants identified https://www.frontiersin.org/files/Articles/1751875/fanim-07-1751875-HTML/image_m/fanim-07-1751875-g005.jpg Figure 5. Gene Ontology (GO) enrichment analysis of genes harboring high-impact variants identified https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1674456/full https://www.frontiersin.org/files/Articles/1674456/fmicb-16-1674456-HTML-r1/image_m/fmicb-16-1674456-g001.jpg Figure 1. Flowchart of the mechanism by which mesenchymal stem cells inhibit Pseudomonas aeruginosa https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1774582/full https://www.frontiersin.org/files/Articles/1774582/fgene-17-1774582-HTML/image_m/fgene-17-1774582-g001.jpg Figure 1. PRISMA flow diagram depicting the study selection process. https://www.frontiersin.org/files/Articles/1774582/fgene-17-1774582-HTML/image_m/fgene-17-1774582-g002.jpg Figure 2. Descriptive characteristics of the included Mendelian randomization studies. (a) Annual pu https://www.frontiersin.org/files/Articles/1774582/fgene-17-1774582-HTML/image_m/fgene-17-1774582-g003.jpg Figure 3. Distribution of evidence category for MR analyses by exposure categories. https://www.frontiersin.org/files/Articles/1774582/fgene-17-1774582-HTML/image_m/fgene-17-1774582-g004.jpg Figure 4. Descriptive synthesis of the evidence landscape from MR studies on exposures associated wi https://www.frontiersin.org/files/Articles/1774582/fgene-17-1774582-HTML/image_m/fgene-17-1774582-g005.jpg Figure 5. Causal associations and evidence strength grading of the six exposures associated with RA. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1768076/full https://www.frontiersin.org/files/Articles/1768076/fphys-17-1768076-HTML/image_m/fphys-17-1768076-t001.jpg Table 1. Physical performance variables assessed from laboratory tests (i.e., maximal incremental te https://www.frontiersin.org/files/Articles/1768076/fphys-17-1768076-HTML/image_m/fphys-17-1768076-g001.jpg Figure 1. Absolute (A–C) and relative (D–F) power records and critical power (CP) data (mean ± SD) i https://www.frontiersin.org/files/Articles/1768076/fphys-17-1768076-HTML/image_m/fphys-17-1768076-g002.jpg Figure 2. General classification (A) and stage-by-stage results (B) for elite and amateur cyclists i https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1716975/full https://www.frontiersin.org/files/Articles/1716975/fbioe-13-1716975-HTML/image_m/fbioe-13-1716975-t001.jpg Table 1. Suggestions for CQAs for NK cell products, based on release criteria from phase 1 and 2 can https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1660202/full https://www.frontiersin.org/files/Articles/1660202/fneur-16-1660202-HTML/image_m/fneur-16-1660202-t001.jpg Table 1. Basic characteristics and performance of stent types. https://www.frontiersin.org/files/Articles/1660202/fneur-16-1660202-HTML/image_m/fneur-16-1660202-t002.jpg Table 2. Clinical performance and biocompatibility of stent types. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1695646/full https://www.frontiersin.org/files/Articles/1695646/fonc-15-1695646-HTML/image_m/fonc-15-1695646-g001.jpg Figure 1. Preoperative clinical and imaging findings. (A) A giant cystic mass in the right upper lab https://www.frontiersin.org/files/Articles/1695646/fonc-15-1695646-HTML/image_m/fonc-15-1695646-g002.jpg Figure 2. Postoperative outcome and H&E staining. (A) The cystic mass was completely excised. (B) Al https://www.frontiersin.org/files/Articles/1695646/fonc-15-1695646-HTML/image_m/fonc-15-1695646-t001.jpg Table 1. Summarizing of labial minora epidermal cyst. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1671251/full https://www.frontiersin.org/files/Articles/1671251/fimmu-16-1671251-HTML/image_m/fimmu-16-1671251-g001.jpg Figure 1. Flowchart of IMN patients receiving three treatment regimens. https://www.frontiersin.org/files/Articles/1671251/fimmu-16-1671251-HTML/image_m/fimmu-16-1671251-g002.jpg Figure 2. Trends in clinical remission rates during the 24-month follow-up period. https://www.frontiersin.org/files/Articles/1671251/fimmu-16-1671251-HTML/image_m/fimmu-16-1671251-t001.jpg Table 1. Baseline characteristics of IMN patients included in this study. https://www.frontiersin.org/files/Articles/1671251/fimmu-16-1671251-HTML/image_m/fimmu-16-1671251-t002.jpg Table 2. Pathological stage and clinical response rate in three treatment groups. https://www.frontiersin.org/files/Articles/1671251/fimmu-16-1671251-HTML/image_m/fimmu-16-1671251-g003.jpg Figure 3. Serial levels of albumin and proteinuria after different treatments in patients who had be https://www.frontiersin.org/files/Articles/1671251/fimmu-16-1671251-HTML/image_m/fimmu-16-1671251-t003.jpg Table 3. Clinical characteristics of IMN patients after 12 months of rituximab treatment. https://www.frontiersin.org/files/Articles/1671251/fimmu-16-1671251-HTML/image_m/fimmu-16-1671251-t004.jpg Table 4. Complete remission or composite (complete or partial remission) from 3 to 24 months based o https://www.frontiersin.org/files/Articles/1671251/fimmu-16-1671251-HTML/image_m/fimmu-16-1671251-t005.jpg Table 5. Risk factors for no-remission of IMN patients (logistic regression). https://www.frontiersin.org/files/Articles/1671251/fimmu-16-1671251-HTML/image_m/fimmu-16-1671251-t006.jpg Table 6. Adverse events. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1782074/full https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-t001.jpg Table 1. Composition of the sample. https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-t002.jpg Table 2. Structure and content of training modules. https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-t003.jpg Table 3. ANOVA results for the SCCI scale. https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-g001.jpg Figure 1. Mean SCCI scores at pre-test, post-test and follow up for both groups. Error bars represen https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-t004.jpg Table 4. Non-Parametric test results for planning and implementation scores. https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-g002.jpg Figure 2. Mean scores for lesson plans and video lessons in the pre-test and post-test. Error bars r https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-t005.jpg Table 5. Chi-square results for the evolution of co-teaching modalities. https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-g003.jpg Figure 3. Percentage frequency of co-teaching models, comparison between pre-test and post-test. Obs https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-t006.jpg Table 6. Two-way mixed ANOVA results for the SAED scale. https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-g004.jpg Figure 4. Mean scores on the overall SAED scale at pre-test, post-test, and follow up for both group https://www.frontiersin.org/files/Articles/1782074/feduc-11-1782074-HTML/image_m/feduc-11-1782074-t007.jpg Table 7. Friedman test results for self-efficacy across three time points. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1699056/full https://www.frontiersin.org/files/Articles/1699056/fmicb-17-1699056-HTML/image_m/fmicb-17-1699056-t001.jpg Table 1. Distribution of plasmids of transconjugants (n = 40). https://www.frontiersin.org/files/Articles/1699056/fmicb-17-1699056-HTML/image_m/fmicb-17-1699056-t002.jpg Table 2. Overall phenotypic antibiotic resistance profile of transconjugants captured from air and w https://www.frontiersin.org/files/Articles/1699056/fmicb-17-1699056-HTML/image_m/fmicb-17-1699056-t003.jpg Table 3. Comparison of antibiotic resistance in wastewater and air samples. https://www.frontiersin.org/files/Articles/1699056/fmicb-17-1699056-HTML/image_m/fmicb-17-1699056-t004.jpg Table 4. Distrubtion of ARGs (n = 33). https://www.frontiersin.org/files/Articles/1699056/fmicb-17-1699056-HTML/image_m/fmicb-17-1699056-g001.jpg Figure 1. Virulence genes of plasmids and heat map of resistance genes. (A) Sankey diagram of virule https://www.frontiersin.org/files/Articles/1699056/fmicb-17-1699056-HTML/image_m/fmicb-17-1699056-g002.jpg Figure 2. peccDNA113 genome alignment circle diagram and Collinear Comparison between peccDNA113 and https://www.frontiersin.org/files/Articles/1699056/fmicb-17-1699056-HTML/image_m/fmicb-17-1699056-g003.jpg Figure 3. Phylogenetic tree of plasmid peccDNA113. A phylogenetic tree was constructed based on SNP https://www.frontiersin.org/files/Articles/1699056/fmicb-17-1699056-HTML/image_m/fmicb-17-1699056-g004.jpg Figure 4. SNP analysis of plasmid ps15D023_8. A phylogenetic tree was constructed based on SNP analy https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1704591/full https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g001.jpg Figure 1. Research workflow: human multi-omics discovery, single-cell validation, and mouse model ve https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g002.jpg Figure 2. IL-18 receptor axis expression increases with clinical severity in OVA-induced allergic co https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g003.jpg Figure 3. Validation of the ovalbumin (OVA)-induced allergic conjunctivitis (AC) mouse model. (A–F) https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g004.jpg Figure 4. In vivo validation confirms upregulation of IL-18/IL-18 receptor axis and activation of th https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-t001.jpg Table 1. Sequences of mouse primers used for qRT-PCR analysis. https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g005.jpg Figure 5. Multi-omics integration identifies a core set of nine potential risk genes for allergic co https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g006.jpg Figure 6. Functional enrichment analysis of the nine consensus risk genes highlights their roles in https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-t002.jpg Table 2. Differential expression of Il18r1 and Il18rap in the allergic conjunctivitis mouse model co https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g007.jpg Figure 7. Il18r1 and Il18rap are selectively upregulated in NK and T cells during allergic conjuncti https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g008.jpg Figure 8. Expression of IL-18 receptor components correlates with interferon-γ (Ifng) expression in https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g009.jpg Figure 9. Cell–cell communication analysis suggests a remodeled immune network in allergic conjuncti https://www.frontiersin.org/files/Articles/1704591/fimmu-16-1704591-HTML/image_m/fimmu-16-1704591-g010.jpg Figure 10. Pseudotime analysis reveals distinct activation dynamics of NF-κB and JAK–STAT pathways i https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1711398/full https://www.frontiersin.org/files/Articles/1711398/fonc-15-1711398-HTML/image_m/fonc-15-1711398-g001.jpg Figure 1. Study flowchart (n = number of patients). https://www.frontiersin.org/files/Articles/1711398/fonc-15-1711398-HTML/image_m/fonc-15-1711398-t001.jpg Table 1. Characteristics of the included HCC patients. https://www.frontiersin.org/files/Articles/1711398/fonc-15-1711398-HTML/image_m/fonc-15-1711398-t002.jpg Table 2. Sensitivities of 18F-FDG and 18F-PSMA-1007 PET/CT in patient-based analysis. https://www.frontiersin.org/files/Articles/1711398/fonc-15-1711398-HTML/image_m/fonc-15-1711398-g002.jpg Figure 2. PET/CT images in a 54-year-old male patient with well-differentiated HCC. (A) 18F-PSMA-100 https://www.frontiersin.org/files/Articles/1711398/fonc-15-1711398-HTML/image_m/fonc-15-1711398-t003.jpg Table 3. Sensitivities of 18F-PSMA-1007 and 18F-FDG PET/CT in lesion-based analysis. https://www.frontiersin.org/files/Articles/1711398/fonc-15-1711398-HTML/image_m/fonc-15-1711398-t004.jpg Table 4. Uptake Intensities of 18F-PSMA-1007 and 18F-FDG in HCC. https://www.frontiersin.org/files/Articles/1711398/fonc-15-1711398-HTML/image_m/fonc-15-1711398-g003.jpg Figure 3. Comparison of 18F-PSMA-1007 and 18F-FDG PET/CT imaging in a 63-year-old man with HCC (arro https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1786423/full https://www.frontiersin.org/files/Articles/1786423/fnagi-18-1786423-HTML/image_m/fnagi-18-1786423-g001.jpg Figure 1. The three main contributions of computational models in combination with MRI data: Diagnos https://www.frontiersin.org/files/Articles/1786423/fnagi-18-1786423-HTML/image_m/fnagi-18-1786423-t001.jpg Table 1. Summary of the main studies using machine learning algorithms trained on MRI features to di https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1775012/full https://www.frontiersin.org/files/Articles/1775012/fpsyg-17-1775012-HTML/image_m/fpsyg-17-1775012-g001.jpg Figure 1. An example trial sequence. Participants were instructed to memorize the shape of a sample https://www.frontiersin.org/files/Articles/1775012/fpsyg-17-1775012-HTML/image_m/fpsyg-17-1775012-t001.jpg Table 1. Mean accuracy rates (AR, %) and standard errors as a function of group, test phase, and mat https://www.frontiersin.org/files/Articles/1775012/fpsyg-17-1775012-HTML/image_m/fpsyg-17-1775012-g002.jpg Figure 2. Search reaction times (RTs) are shown as a function of group, test phase, and match condit https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1783609/full https://www.frontiersin.org/files/Articles/1783609/fpls-17-1783609-HTML/image_m/fpls-17-1783609-g001.jpg Figure 1. Overview of the secretome prediction pipeline and secretome statistics across 26 powdery m https://www.frontiersin.org/files/Articles/1783609/fpls-17-1783609-HTML/image_m/fpls-17-1783609-g002.jpg Figure 2. Orthogroup-based comparative analysis of secretome candidates from 26 powdery mildew isola https://www.frontiersin.org/files/Articles/1783609/fpls-17-1783609-HTML/image_m/fpls-17-1783609-g003.jpg Figure 3. Length distribution and cysteine composition of secretome candidates from powdery mildew f https://www.frontiersin.org/files/Articles/1783609/fpls-17-1783609-HTML/image_m/fpls-17-1783609-g004.jpg Figure 4. Distribution and positional occurrence of N-terminal Y/F/WxC motifs in the powdery mildew https://www.frontiersin.org/files/Articles/1783609/fpls-17-1783609-HTML/image_m/fpls-17-1783609-g005.jpg Figure 5. Statistical analysis of subcellular targeting and functional characteristics of powdery mi https://www.frontiersin.org/files/Articles/1783609/fpls-17-1783609-HTML/image_m/fpls-17-1783609-g006.jpg Figure 6. Conservation patterns and lineage specificity of known powdery mildew effectors across sec https://www.frontiersin.org/files/Articles/1783609/fpls-17-1783609-HTML/image_m/fpls-17-1783609-g007.jpg Figure 7. Blumeria-specific effector families within predicted secretome candidates. Bubble plots su https://www.frontiersin.org/files/Articles/1783609/fpls-17-1783609-HTML/image_m/fpls-17-1783609-t001.jpg Table 1. Summary of BLASTP homologous hits for six non-canonical powdery mildew effectors across pre https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1791407/full https://www.frontiersin.org/files/Articles/1791407/fgene-17-1791407-HTML/image_m/fgene-17-1791407-g001.jpg Figure 1. Workflow of the main technical approaches for genetic diversity studies in Cyatheaceae pla https://www.frontiersin.org/files/Articles/1791407/fgene-17-1791407-HTML/image_m/fgene-17-1791407-t001.jpg Table 1. Taxonomic abbreviations used in this review. https://www.frontiersin.org/files/Articles/1791407/fgene-17-1791407-HTML/image_m/fgene-17-1791407-t002.jpg Table 2. Comparison of major technical approaches used in genetic-diversity studies of the family Cy https://www.frontiersin.org/files/Articles/1791407/fgene-17-1791407-HTML/image_m/fgene-17-1791407-t003.jpg Table 3. Genetic diversity aspects related to biotic and abiotic stress in Cyatheaceae. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1726620/full https://www.frontiersin.org/files/Articles/1726620/fmolb-12-1726620-HTML-r1/image_m/fmolb-12-1726620-g001.jpg Figure 1. Summary of the study population, sex-specific salivary microbiome differences, and microbi https://www.frontiersin.org/files/Articles/1726620/fmolb-12-1726620-HTML-r1/image_m/fmolb-12-1726620-t001.jpg Table 1. Demographic and clinical characteristics. https://www.frontiersin.org/files/Articles/1726620/fmolb-12-1726620-HTML-r1/image_m/fmolb-12-1726620-g002.jpg Figure 2. Overview of study design and analytical workflow. The schematic summarizes the sequential https://www.frontiersin.org/files/Articles/1726620/fmolb-12-1726620-HTML-r1/image_m/fmolb-12-1726620-g003.jpg Figure 3. Sex-specific differences in clinical scores among PD patients. Violin plots compare PD_Fem https://www.frontiersin.org/files/Articles/1726620/fmolb-12-1726620-HTML-r1/image_m/fmolb-12-1726620-g004.jpg Figure 4. Alpha-diversity of the salivary microbiome by disease status and sex. Panels show (A) obse https://www.frontiersin.org/files/Articles/1726620/fmolb-12-1726620-HTML-r1/image_m/fmolb-12-1726620-g005.jpg Figure 5. Beta-diversity of the salivary microbiome across PD and HC groups stratified by sex. Panel https://www.frontiersin.org/files/Articles/1726620/fmolb-12-1726620-HTML-r1/image_m/fmolb-12-1726620-g006.jpg Figure 6. Sex-specific taxonomic composition and indicator taxa of the salivary microbiome in PD and https://www.frontiersin.org/files/Articles/1726620/fmolb-12-1726620-HTML-r1/image_m/fmolb-12-1726620-g007.jpg Figure 7. Sex-stratified associations between salivary microbiota and clinical scales in PD. (A) Man https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2026.1776101/full https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g001.jpg Figure 1. Research design flowchart. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g002.jpg Figure 2. The smart city wheel: six dimensions and key indicators (adapted from Cohen, 2012). https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-t001.jpg Table 1. Profile of expert panel participating in the AHP–DEMATEL evaluation. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g003.jpg Figure 3. Integrated AHP-DEMATEL framework for evaluating and prioritizing smart city themes. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-t002.jpg https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g004.jpg Figure 4. Keyword frequency cloud of turkey-based smart city literature (2010–2024). https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g005.jpg Figure 5. Keyword frequency cloud of global smart city literature (2010–2024). https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g006.jpg Figure 6. Conceptual co-occurrence network of Turkey-based smart city literature. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g007.jpg Figure 7. Conceptual co-occurrence network of global smart city literature. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g008.jpg Figure 8. Visibility of thematic clusters in smart city literature. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g009.jpg Figure 9. Comparative keyword frequency distribution in smart city literature. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g010.jpg Figure 10. Keyword distribution matrix across smart city dimensions and thematic clusters. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-t003.jpg Table 2. Smart city dimensions and associated sub-criteria with key characteristics. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-t004.jpg Table 3. AHP weighting matrices and consistency ratios for smart city dimensions and sub-criteria. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-t005.jpg Table 4. Causal analysis of smart city evaluation criteria based on DEMATEL results. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g011.jpg Figure 11. Cause-effect diagram. https://www.frontiersin.org/files/Articles/1776101/frsc-08-1776101-HTML/image_m/frsc-08-1776101-g012.jpg Figure 12. Strategic framework for smart city priorities in Turkey. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1721291/full https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-g001.jpg Figure 1. Flowchart demonstrate the proposed IVIFS-RSA enhancement framework. https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-t009.jpg Algorithm 1. RSA-IVIFI image enhancement. https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-g002.jpg Figure 2. Original low-light images. Images reproduced from Chen et al. 2018 Deep Retinex Decomposit https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-g003.jpg Figure 3. Enhanced images with optimized parameter values (a*,hM*) obtained through RSA-based tuning https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-t001.jpg Table 1. Parameter employed in RSA optimization. https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-t002.jpg Table 2. Comparative analysis demonstrating the optimization efficiency and parameter tuning precisi https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-t003.jpg Table 3. Visual comparison of various enhancement methods applied to all 14 test images in the LOL d https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-g004.jpg Figure 4. Visual comparison of various enhancement results for Image 11: (a) Original, (b) CLAHE, (c https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-g005.jpg Figure 5. Histogram analysis of the Image 11 under various enhancement methods: (a) Original, (b) CL https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-t004.jpg Table 4. Average quantitative metrics for different image enhancement methods. https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-t005.jpg Table 5. Entropy-based performance analysis. https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-t006.jpg Table 6. AMBE-based performance analysis. https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-t007.jpg Table 7. CII-based performance analysis. https://www.frontiersin.org/files/Articles/1721291/frai-08-1721291-HTML/image_m/frai-08-1721291-t008.jpg Table 8. PSNR-based performance analysis. https://www.frontiersin.org/journals/fungal-biology/articles/10.3389/ffunb.2025.1712444/full https://www.frontiersin.org/files/Articles/1712444/ffunb-06-1712444-HTML/image_m/ffunb-06-1712444-t001.jpg Table 1. Origin of isolates subjected to WGS, with total available isolates in parentheses. https://www.frontiersin.org/files/Articles/1712444/ffunb-06-1712444-HTML/image_m/ffunb-06-1712444-g001.jpg Figure 1. Phylogenetic placement of bakery isolates within Penicillium section Fasciculata, series C https://www.frontiersin.org/files/Articles/1712444/ffunb-06-1712444-HTML/image_m/ffunb-06-1712444-g002.jpg Figure 2. Maximum Likelihood phylogeny of 64 P. commune isolates, based on 208 SNP positions remaini https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2026.1731228/full https://www.frontiersin.org/files/Articles/1731228/frsc-08-1731228-HTML/image_m/frsc-08-1731228-g001.jpg Figure 1. Spatial distribution of designated trading sites in Bulawayo. https://www.frontiersin.org/files/Articles/1731228/frsc-08-1731228-HTML/image_m/frsc-08-1731228-t001.jpg Table 1. Planned informal trading sites in Bulawayo urban. https://www.frontiersin.org/files/Articles/1731228/frsc-08-1731228-HTML/image_m/frsc-08-1731228-g002.jpg Figure 2. Mapping of trading sites in relation to land use zones in Bulawayo. https://www.frontiersin.org/files/Articles/1731228/frsc-08-1731228-HTML/image_m/frsc-08-1731228-g003.jpg Figure 3. Planned informal trading sites distribution by distance zones and marginalisation. https://www.frontiersin.org/files/Articles/1731228/frsc-08-1731228-HTML/image_m/frsc-08-1731228-g004.jpg Figure 4. Distribution of trading sites in relation to population density in Bulawayo. https://www.frontiersin.org/files/Articles/1731228/frsc-08-1731228-HTML/image_m/frsc-08-1731228-t002.jpg Table 2. Average number of clients per day per distance zone cross-tabulation. https://www.frontiersin.org/files/Articles/1731228/frsc-08-1731228-HTML/image_m/frsc-08-1731228-t003.jpg Table 3. Chi-square tests of enterprise location distance zone and average number of clients per day https://www.frontiersin.org/files/Articles/1731228/frsc-08-1731228-HTML/image_m/frsc-08-1731228-t004.jpg Table 4. Distance zones * informal traders’ willingness to relocate cross-tabulation. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1779799/full https://www.frontiersin.org/files/Articles/1779799/fpsyg-17-1779799-HTML/image_m/fpsyg-17-1779799-g001.jpg Figure 1. Hypothesized conceptual model of the association between physical activity motivation (PAM https://www.frontiersin.org/files/Articles/1779799/fpsyg-17-1779799-HTML/image_m/fpsyg-17-1779799-g002.jpg Figure 2. Theory mapping for the hypothesized model shown in Figure 1. Each path is annotated with t https://www.frontiersin.org/files/Articles/1779799/fpsyg-17-1779799-HTML/image_m/fpsyg-17-1779799-t001.jpg Table 1. Means, SD, and correlations among variables. https://www.frontiersin.org/files/Articles/1779799/fpsyg-17-1779799-HTML/image_m/fpsyg-17-1779799-t002.jpg Table 2. Sex differences. https://www.frontiersin.org/files/Articles/1779799/fpsyg-17-1779799-HTML/image_m/fpsyg-17-1779799-t003.jpg Table 3. Year of study differences. https://www.frontiersin.org/files/Articles/1779799/fpsyg-17-1779799-HTML/image_m/fpsyg-17-1779799-g003.jpg Figure 3. Standardized regression coefficients (β) are shown for the serial mediation model estimate https://www.frontiersin.org/files/Articles/1779799/fpsyg-17-1779799-HTML/image_m/fpsyg-17-1779799-t004.jpg Table 4. Regression analysis of the serial mediation model. https://www.frontiersin.org/files/Articles/1779799/fpsyg-17-1779799-HTML/image_m/fpsyg-17-1779799-t005.jpg Table 5. Mediating effect test and effect size. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1527765/full https://www.frontiersin.org/files/Articles/1527765/fcimb-15-1527765-HTML/image_m/fcimb-15-1527765-g007.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1527765/fcimb-15-1527765-HTML/image_m/fcimb-15-1527765-t001.jpg Table 1. Prevalent cases, age-standardized prevalence rates, incident cases, age-standardized incide https://www.frontiersin.org/files/Articles/1527765/fcimb-15-1527765-HTML/image_m/fcimb-15-1527765-g001.jpg Figure 1. Global distribution of enteric infections disease burden in 2021. (A) ASPR of enteric infe https://www.frontiersin.org/files/Articles/1527765/fcimb-15-1527765-HTML/image_m/fcimb-15-1527765-g002.jpg Figure 2. Sex, age-structured, and subtype analysis of enteric infections disease burden in 2021. (A https://www.frontiersin.org/files/Articles/1527765/fcimb-15-1527765-HTML/image_m/fcimb-15-1527765-g003.jpg Figure 3. The association of the SDI and ASPR, ASIR ASMR, ASR of DALYs of enteric infections, globe https://www.frontiersin.org/files/Articles/1527765/fcimb-15-1527765-HTML/image_m/fcimb-15-1527765-g004.jpg Figure 4. The enteric infections DALYs and deaths attributable to risk factors compared in 2021 and https://www.frontiersin.org/files/Articles/1527765/fcimb-15-1527765-HTML/image_m/fcimb-15-1527765-g005.jpg Figure 5. Etiological analysis of enteric infections for both sexes among all age groups in 1990 and https://www.frontiersin.org/files/Articles/1527765/fcimb-15-1527765-HTML/image_m/fcimb-15-1527765-g006.jpg Figure 6. Changes in prevalence, incidence, deaths, and DALYs for both sexes according to population https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1770533/full https://www.frontiersin.org/files/Articles/1770533/fpsyg-17-1770533-HTML/image_m/fpsyg-17-1770533-g001.jpg Figure 1. Representation of the experimental setup and the sequence of events. https://www.frontiersin.org/files/Articles/1770533/fpsyg-17-1770533-HTML/image_m/fpsyg-17-1770533-g002.jpg Figure 2. Grasp compatibility effects in Experiment 1 and 2. For each participant, GCE was calculate https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1787666/full https://www.frontiersin.org/files/Articles/1787666/fpubh-14-1787666-HTML/image_m/fpubh-14-1787666-g001.jpg Figure 1. A photograph of the sensory circuit in school 2 (used mainly by EYFS but open to all child https://www.frontiersin.org/files/Articles/1787666/fpubh-14-1787666-HTML/image_m/fpubh-14-1787666-g002.jpg Figure 2. A photograph of the start of the daily mile track at School 1. https://www.frontiersin.org/journals/chemical-biology/articles/10.3389/fchbi.2025.1709253/full https://www.frontiersin.org/files/Articles/1709253/fchbi-04-1709253-HTML-r2/image_m/fchbi-04-1709253-g001.jpg Figure 1. Study workflow employing high-field asymmetric-waveform ion mobility spectrometry (FAIMS) https://www.frontiersin.org/files/Articles/1709253/fchbi-04-1709253-HTML-r2/image_m/fchbi-04-1709253-g002.jpg Figure 2. ADP-ribosyl peptide dissociation properties when using FAIMS with a scan range of m/z 400– https://www.frontiersin.org/files/Articles/1709253/fchbi-04-1709253-HTML-r2/image_m/fchbi-04-1709253-g003.jpg Figure 3. Impact of FAIMS on EThcD-dependent ADP-ribosyl PSM yields. (A) Acquisition methods tested. https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1770451/full https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g001.jpg Figure 1. Representative gradient recalled echo image of a sagittal slice of the human brain obtaine https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g002.jpg Figure 2. Representative gradient recalled echo image of an axial slice of the human brain obtained https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g003.jpg Figure 3. Schematic diagram of gait-phase synchronized adaptive DBS (aDBS): By using neural field po https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g004.jpg Figure 4. Activity-dependent adaptive DBS modulates stimulation based on gait-related neural signals https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g005.jpg Figure 5. Multimodal and connectomic approaches for decoding and targeting deep brain circuits. (a) https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g006.jpg Figure 6. (a) An outline of the integrated neuropsychological predictive framework experimental desi https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g007.jpg Figure 7. Advantages and challenges of following the open-source approach. https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g008.jpg Figure 8. The three main groups that are vital for sustaining a technological environment that is co https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g009.jpg Figure 9. The top figure represents 24 h of timeline data with LFP power (upper trace, yellow line) https://www.frontiersin.org/files/Articles/1770451/fnhum-20-1770451-HTML/image_m/fnhum-20-1770451-g010.jpg Figure 10. Distributed votes in response to the question “Where are we in the Gartner Hype Cycle of https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1768454/full https://www.frontiersin.org/files/Articles/1768454/fpsyt-17-1768454-HTML-r1/image_m/fpsyt-17-1768454-t001.jpg Table 1. Sociodemographic characteristics of the study group (n = 1,100). https://www.frontiersin.org/files/Articles/1768454/fpsyt-17-1768454-HTML-r1/image_m/fpsyt-17-1768454-g001.jpg Figure 1. Distribution of GCBS-total scores across four groups. https://www.frontiersin.org/files/Articles/1768454/fpsyt-17-1768454-HTML-r1/image_m/fpsyt-17-1768454-g002.jpg Figure 2. Distribution of GCBS–GM subscale scores across four groups. https://www.frontiersin.org/files/Articles/1768454/fpsyt-17-1768454-HTML-r1/image_m/fpsyt-17-1768454-g003.jpg Figure 3. Distribution of GCBS–MGC subscale scores across four groups. https://www.frontiersin.org/files/Articles/1768454/fpsyt-17-1768454-HTML-r1/image_m/fpsyt-17-1768454-g004.jpg Figure 4. Distribution of GCBS–ECU subscale scores across four groups. https://www.frontiersin.org/files/Articles/1768454/fpsyt-17-1768454-HTML-r1/image_m/fpsyt-17-1768454-g005.jpg Figure 5. Distribution of GCBS–PW subscale scores across four groups. https://www.frontiersin.org/files/Articles/1768454/fpsyt-17-1768454-HTML-r1/image_m/fpsyt-17-1768454-g006.jpg Figure 6. Distribution of GCBS–Col subscale scores across four groups. https://www.frontiersin.org/files/Articles/1768454/fpsyt-17-1768454-HTML-r1/image_m/fpsyt-17-1768454-t002.jpg Table 2. Correlations between spirituality domains (ISS) and General Conspirational Beliefs Scale do https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1573781/full https://www.frontiersin.org/files/Articles/1573781/fonc-15-1573781-HTML/image_m/fonc-15-1573781-g001.jpg Figure 1. (a–d) MR at clinical presentation (upper row) demonstrated a conspicuous expansile lesion https://www.frontiersin.org/files/Articles/1573781/fonc-15-1573781-HTML/image_m/fonc-15-1573781-g002.jpg Figure 2. (a) primary tumors consisted of a solid growth pattern with small cells, high nucleus-to- https://www.frontiersin.org/files/Articles/1573781/fonc-15-1573781-HTML/image_m/fonc-15-1573781-g003.jpg Figure 3. (a–c) CT obtained soon after the clinical deterioration of the patient (upper row) documen https://www.frontiersin.org/files/Articles/1573781/fonc-15-1573781-HTML/image_m/fonc-15-1573781-g004.jpg Figure 4. Copy number variation (CNV) analysis was performed using DNA methylation data from the Ill https://www.frontiersin.org/files/Articles/1573781/fonc-15-1573781-HTML/image_m/fonc-15-1573781-t001.jpg Table 1. Summarizes all reported cases of primary extra-axial medulloblastomas described in the lite https://www.frontiersin.org/journals/language-sciences/articles/10.3389/flang.2025.1721510/full https://www.frontiersin.org/files/Articles/1721510/flang-04-1721510-HTML/image_m/flang-04-1721510-g001.jpg Figure 1. Predicted results for each hypothesis. (a) H1, (b) H2, (c) H3. https://www.frontiersin.org/files/Articles/1721510/flang-04-1721510-HTML/image_m/flang-04-1721510-t001.jpg Table 1. Example stimuli. https://www.frontiersin.org/files/Articles/1721510/flang-04-1721510-HTML/image_m/flang-04-1721510-t002.jpg Table 2. Model output of Experiment 1 for the target and spill-over region. https://www.frontiersin.org/files/Articles/1721510/flang-04-1721510-HTML/image_m/flang-04-1721510-g002.jpg Figure 2. Fitted reading times (with standard error) per condition, relation, and language in Experi https://www.frontiersin.org/files/Articles/1721510/flang-04-1721510-HTML/image_m/flang-04-1721510-t003.jpg Table 3. Example stimuli. https://www.frontiersin.org/files/Articles/1721510/flang-04-1721510-HTML/image_m/flang-04-1721510-g003.jpg Figure 3. Fitted reading times of Experiment 2 per condition. https://www.frontiersin.org/files/Articles/1721510/flang-04-1721510-HTML/image_m/flang-04-1721510-t004.jpg Table 4. Model output for Experiment 2. https://www.frontiersin.org/files/Articles/1721510/flang-04-1721510-HTML/image_m/flang-04-1721510-t005.jpg Table 5. Overview of results. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1717640/full https://www.frontiersin.org/files/Articles/1717640/fmars-12-1717640-HTML-r1/image_m/fmars-12-1717640-g001.jpg Figure 1. A. anguilla sampling localities localized in Corsica, France. All rivers localities were p https://www.frontiersin.org/files/Articles/1717640/fmars-12-1717640-HTML-r1/image_m/fmars-12-1717640-g002.jpg Figure 2. (A) Digitized lateral view of an otolith of A. Anguilla showing the semi-landmarks and lan https://www.frontiersin.org/files/Articles/1717640/fmars-12-1717640-HTML-r1/image_m/fmars-12-1717640-t001.jpg Table 1. Variations of otolith shape indices across the three sites by otolith group. https://www.frontiersin.org/files/Articles/1717640/fmars-12-1717640-HTML-r1/image_m/fmars-12-1717640-g003.jpg Figure 3. Boxplot of shape indices as a function of sites for (A) small otolith and (B) large otolit https://www.frontiersin.org/files/Articles/1717640/fmars-12-1717640-HTML-r1/image_m/fmars-12-1717640-g004.jpg Figure 4. Principal component analysis (PCA) plot for otolith shape indices of the three sites analy https://www.frontiersin.org/files/Articles/1717640/fmars-12-1717640-HTML-r1/image_m/fmars-12-1717640-t002.jpg Table 2. Statistical results of the pairwise comparisons between the six groups (i.e. for the three https://www.frontiersin.org/files/Articles/1717640/fmars-12-1717640-HTML-r1/image_m/fmars-12-1717640-g005.jpg Figure 5. Mean otolith shapes for the (A) small otoliths, (B) large otoliths and (C) all otoliths gr https://www.frontiersin.org/files/Articles/1717640/fmars-12-1717640-HTML-r1/image_m/fmars-12-1717640-g006.jpg Figure 6. Shape variation between the small and large otoliths for samples taken in the rivers (A), https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2026.1760263/full https://www.frontiersin.org/files/Articles/1760263/fcomm-11-1760263-HTML-r1/image_m/fcomm-11-1760263-g001.jpg Figure 1. CADI operational model and workflow. https://www.frontiersin.org/files/Articles/1760263/fcomm-11-1760263-HTML-r1/image_m/fcomm-11-1760263-g002.jpg Figure 2. Presentation of CADI members on the official website. https://www.frontiersin.org/files/Articles/1760263/fcomm-11-1760263-HTML-r1/image_m/fcomm-11-1760263-g003.jpg Figure 3. Profile page for Health growers as CADI member. https://www.frontiersin.org/files/Articles/1760263/fcomm-11-1760263-HTML-r1/image_m/fcomm-11-1760263-g004.jpg Figure 4. Profile page for Agrobac as CADI member. https://www.frontiersin.org/files/Articles/1760263/fcomm-11-1760263-HTML-r1/image_m/fcomm-11-1760263-g005.jpg Figure 5. Strategic readiness repository for health growers and Agrobac—google drive platform used f https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1731068/full https://www.frontiersin.org/files/Articles/1731068/fimmu-17-1731068-HTML/image_m/fimmu-17-1731068-t001.jpg Table 1. Demographic and clinical characteristics of the study cohort, including age, sex, and relev https://www.frontiersin.org/files/Articles/1731068/fimmu-17-1731068-HTML/image_m/fimmu-17-1731068-g001.jpg Figure 1. Longitudinal transcriptomic and immune profiling of TBI patients. (A) Overview of the anal https://www.frontiersin.org/files/Articles/1731068/fimmu-17-1731068-HTML/image_m/fimmu-17-1731068-g002.jpg Figure 2. Weighted Gene Co-expression Network Analysis (WGCNA) identifies transcriptional modules an https://www.frontiersin.org/files/Articles/1731068/fimmu-17-1731068-HTML/image_m/fimmu-17-1731068-g003.jpg Figure 3. Expression and validation of the Yellow module in CD14+ monocytes and comparison with the https://www.frontiersin.org/files/Articles/1731068/fimmu-17-1731068-HTML/image_m/fimmu-17-1731068-g004.jpg Figure 4. Cross-cohort validation of the Yellow module in diverse inflammatory conditions and viral https://www.frontiersin.org/files/Articles/1731068/fimmu-17-1731068-HTML/image_m/fimmu-17-1731068-g005.jpg Figure 5. Epigenomic remodeling of CD14+ monocytes at Yellow module genes in critically ill COVID-19 https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1659374/full https://www.frontiersin.org/files/Articles/1659374/fpsyg-16-1659374-HTML-r1/image_m/fpsyg-16-1659374-g001.jpg Figure 1. Aurora app architecture and therapeutic features. Schematic overview of Aurora, a Spanish- https://www.frontiersin.org/files/Articles/1659374/fpsyg-16-1659374-HTML-r1/image_m/fpsyg-16-1659374-g002.jpg Figure 2. Aurora user interface and digital experience. Representative screenshots of the Aurora app https://www.frontiersin.org/files/Articles/1659374/fpsyg-16-1659374-HTML-r1/image_m/fpsyg-16-1659374-t001.jpg Table 1. Baseline demographic and clinical characteristics of participants. https://www.frontiersin.org/files/Articles/1659374/fpsyg-16-1659374-HTML-r1/image_m/fpsyg-16-1659374-t002.jpg Table 2. Pre and post-changes in anxiety and depression symptoms. https://www.frontiersin.org/files/Articles/1659374/fpsyg-16-1659374-HTML-r1/image_m/fpsyg-16-1659374-g003.jpg Figure 3. Reductions in anxiety symptoms following Aurora use. Distribution of anxiety scores (Goldb https://www.frontiersin.org/files/Articles/1659374/fpsyg-16-1659374-HTML-r1/image_m/fpsyg-16-1659374-g004.jpg Figure 4. Reductions in depression symptoms following Aurora use. Distribution of depression scores https://www.frontiersin.org/files/Articles/1659374/fpsyg-16-1659374-HTML-r1/image_m/fpsyg-16-1659374-g005.jpg Figure 5. Dose–response association between engagement and symptom change. Scatterplots with regress https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1775945/full https://www.frontiersin.org/files/Articles/1775945/fcimb-16-1775945-HTML/image_m/fcimb-16-1775945-g001.jpg Figure 1. Influence of c-di-GMP levels on P. aeruginosa meropenem resistance. Endpoint OD600 reached https://www.frontiersin.org/files/Articles/1775945/fcimb-16-1775945-HTML/image_m/fcimb-16-1775945-g002.jpg Figure 2. Influence of c-di-GMP levels on P. aeruginosa growth in the presence of sub-inhibitory con https://www.frontiersin.org/files/Articles/1775945/fcimb-16-1775945-HTML/image_m/fcimb-16-1775945-g003.jpg Figure 3. Influence of c-di-GMP levels and Hfq on P. aeruginosa growth in the presence of meropenem. https://www.frontiersin.org/files/Articles/1775945/fcimb-16-1775945-HTML/image_m/fcimb-16-1775945-g004.jpg Figure 4. Influence of c-di-GMP levels and CCR on P. aeruginosa meropenem tolerance. (A) Growth curv https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1672671/full https://www.frontiersin.org/files/Articles/1672671/fbinf-06-1672671-HTML/image_m/fbinf-06-1672671-g001.jpg Figure 1. Overview of the single-cell RNA-seq data analysis workflow. The end-to-end process include https://www.frontiersin.org/files/Articles/1672671/fbinf-06-1672671-HTML/image_m/fbinf-06-1672671-g002.jpg Figure 2. Preprocessing of scRNA-seq data. (a) Violin plot depicting the number of detected genes fo https://www.frontiersin.org/files/Articles/1672671/fbinf-06-1672671-HTML/image_m/fbinf-06-1672671-t001.jpg Table 1. Cluster annotations based on canonical marker gene expression. Cluster sizes refer to the n https://www.frontiersin.org/files/Articles/1672671/fbinf-06-1672671-HTML/image_m/fbinf-06-1672671-g003.jpg Figure 3. Cell type annotation based on canonical and dataset-specific marker genes. Dot plot summar https://www.frontiersin.org/files/Articles/1672671/fbinf-06-1672671-HTML/image_m/fbinf-06-1672671-g004.jpg Figure 4. Diffusion pseudotime (DPT) analysis of cell populations. (a) UMAP representation of the se https://www.frontiersin.org/files/Articles/1672671/fbinf-06-1672671-HTML/image_m/fbinf-06-1672671-g005.jpg Figure 5. Classification tree trained on all the cell populations. To enhance readability, a zoomed- https://www.frontiersin.org/files/Articles/1672671/fbinf-06-1672671-HTML/image_m/fbinf-06-1672671-g006.jpg Figure 6. Classification performance of the decision tree model. (a) Confusion Matrix: Visual repres https://www.frontiersin.org/files/Articles/1672671/fbinf-06-1672671-HTML/image_m/fbinf-06-1672671-g007.jpg Figure 7. Decision trees highlighting key gene expression patterns across clusters. The trees illust https://www.frontiersin.org/files/Articles/1672671/fbinf-06-1672671-HTML/image_m/fbinf-06-1672671-g008.jpg Figure 8. Main expression patterns influencing cellular population transitions. Feature importance o https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1749869/full https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-g001.jpg Figure 1. Geographical location of Cagarras Archipelago, Rio de Janeiro — RJ, Brazil, capture and sa https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-g002.jpg Figure 2. Images highlighting different types of blood cells in brown booby, Sula leucogaster (Bodda https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-g003.jpg Figure 3. Images highlighting different types of blood cells in magnificent frigatebirds, Fregata ma https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-g004.jpg Figure 4. Photographs taken during field activities in the Cagarras Archipelago, Rio de Janeiro, Bra https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-t001.jpg Table 1. Hematologic reference intervals (n = 20), according to Gaussian distribution, for brown boo https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-t002.jpg Table 2. Biochemical reference intervals (n = 28), according to Gaussian distribution, for brown boo https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-t003.jpg Table 3. Hematologic reference intervals (n = 20), according to Gaussian distribution, for magnifice https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-t004.jpg Table 4. Biochemical reference intervals (n = 24), according to Gaussian distribution, for magnifice https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-t005.jpg Table 5. One-way ANOVA and Tukey's post-hoc test comparing hematologic parameters among brown booby, https://www.frontiersin.org/files/Articles/1749869/fvets-13-1749869-HTML-r1/image_m/fvets-13-1749869-t006.jpg Table 6. One-way ANOVA and Tukey's post-hoc test comparing biochemistry parameters among brown booby https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1747659/full https://www.frontiersin.org/files/Articles/1747659/fimmu-17-1747659-HTML/image_m/fimmu-17-1747659-t001.jpg Table 1. Demographics, clinical characteristics and CSF findings. https://www.frontiersin.org/files/Articles/1747659/fimmu-17-1747659-HTML/image_m/fimmu-17-1747659-g001.jpg Figure 1. κ-FLC index according to the presence of intrathecal synthesis of different immunoglobulin https://www.frontiersin.org/files/Articles/1747659/fimmu-17-1747659-HTML/image_m/fimmu-17-1747659-t002.jpg Table 2. Multivariable linear regression analyses identifying the contribution of intrathecal IgG an https://www.frontiersin.org/files/Articles/1747659/fimmu-17-1747659-HTML/image_m/fimmu-17-1747659-g002.jpg Figure 2. Contribution of intrathecal IgG and IgM synthesis to κ-FLC index according to multivariabl https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1713452/full https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g001.jpg Figure 1. N. jatamansi rhizomes and whole plant from Himalayan region (A) growing plant (B) dried wh https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g002.jpg Figure 2. Dose-response curves showing percentage inhibition of (a) α-glucosidase and (b) α-amylase https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-t001.jpg Table 1. The IC50 values of N. jatamansi extract for α-glucosidase and α-amylase. https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g003.jpg Figure 3. Dose-response inhibition profiles of N. jatamansi extract and acarbose against carbohydrat https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-t002.jpg Table 2. Binding scores of top five compounds with α-glucosidase (2ZE0) and α-amylase (1B2Y) protein https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g004.jpg Figure 4. 2D Interaction and 3D binding pattern of (a) Virolin (b) Acarbose with α-glucosidase as a https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g005.jpg Figure 5. 2D Interaction and 3D binding pattern with α-glucosidase as a receptor (a) Nardostachysin, https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g006.jpg Figure 6. 2D Interaction and 3D binding pattern of (a) Nardostachysin (b) Acarbose with α-amylase as https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g007.jpg Figure 7. 2D Interaction and 3D binding pattern with α-amylase as a receptor (a) Virolin, (b) Nardos https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g008.jpg Figure 8. RMSD plots of α-amylase with Nardostachysin (a) and α-glucosidase with Virolin (b) during https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g009.jpg Figure 9. RMSF plots during 100 ns simulation: (a) α-amylase by residue, (b) α-glucosidase by residu https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g010.jpg Figure 10. Binding energy profiles per frame during 100 ns simulation: (a) α-amylase-Nardostachysin https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g011.jpg Figure 11. Top 20 residues of the A chain of α-amylase interacting with the ligand Nardostachysin (s https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g012.jpg Figure 12. Top 20 residues of the α-glucosidase A chain interacting with the Virolin ligand (stacked https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g013.jpg Figure 13. Interatomic distance profiles during 100 ns simulation: (a) Virolin with α-glucosidase (C https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-t003.jpg Table 3. Electronic properties and reactivity descriptors of lead compounds. https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g014.jpg Figure 14. HOMO and LUMO molecular orbital surfaces of Virolin calculated at B3LYP/6–311++G(d,p) lev https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g015.jpg Figure 15. HOMO and LUMO molecular orbital surfaces of Nardostachysin calculated at B3LYP/6–311 ++G( https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g016.jpg Figure 16. Molecular electrostatic potential surfaces mapped onto electron density isosurfaces of (a https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-t004.jpg Table 4. Molecular and drug likeliness properties of top 05 compounds evaluated through Lipinski’s R https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g017.jpg Figure 17. (a) BOILED-Egg plot showing predicted gastrointestinal absorption (white region) and bloo https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-t005.jpg Table 5. ADMET-related drug-like parameters of the best selected compounds. https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-t006.jpg Table 6. Acute toxicity predictions across exposure routes of selective Bioactives analyzed by StopT https://www.frontiersin.org/files/Articles/1713452/fphar-16-1713452-HTML-r1/image_m/fphar-16-1713452-g018.jpg Figure 18. Molsoft drug-likeness score visualization: (a) Virolin (score: 0.33), (b) Nardostachysin https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1632406/full https://www.frontiersin.org/files/Articles/1632406/fendo-16-1632406-HTML/image_m/fendo-16-1632406-g001.jpg Figure 1. Flowchart of animal experimental procedures. https://www.frontiersin.org/files/Articles/1632406/fendo-16-1632406-HTML/image_m/fendo-16-1632406-g002.jpg Figure 2. Overview of the causal role of gut microbiota and alopecia areata in MR analysis. Red deno https://www.frontiersin.org/files/Articles/1632406/fendo-16-1632406-HTML/image_m/fendo-16-1632406-g003.jpg Figure 3. Forest plot illustrates the causal relationship between diabetic neuropathy and gut microb https://www.frontiersin.org/files/Articles/1632406/fendo-16-1632406-HTML/image_m/fendo-16-1632406-t001.jpg Table 1. Comparison of baseline and endpoint body weight, blood glucose, MWT and TWL among three gro https://www.frontiersin.org/files/Articles/1632406/fendo-16-1632406-HTML/image_m/fendo-16-1632406-g004.jpg Figure 4. Changes in OTU abundance, alpha diversity, and beta diversity in mice with diabetes and di https://www.frontiersin.org/files/Articles/1632406/fendo-16-1632406-HTML/image_m/fendo-16-1632406-g005.jpg Figure 5. Gut Microbiota Composition at the Phylum Level. (A) Bar plot depicting phylum-level microb https://www.frontiersin.org/files/Articles/1632406/fendo-16-1632406-HTML/image_m/fendo-16-1632406-t002.jpg Table 2. Baseline characteristics of the study population. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1783458/full https://www.frontiersin.org/files/Articles/1783458/fpubh-14-1783458-HTML/image_m/fpubh-14-1783458-g001.jpg Figure 1. Conceptual framework adapted from Andersen’s behavioral model of health services use illus https://www.frontiersin.org/files/Articles/1783458/fpubh-14-1783458-HTML/image_m/fpubh-14-1783458-t001.jpg Table 1. Socio-demographic profile and PM-JAY utilization status of respondents. https://www.frontiersin.org/files/Articles/1783458/fpubh-14-1783458-HTML/image_m/fpubh-14-1783458-t002.jpg Table 2. Descriptive statistics for key barriers on a five-point Likert scale (N = 320). https://www.frontiersin.org/files/Articles/1783458/fpubh-14-1783458-HTML/image_m/fpubh-14-1783458-g002.jpg Figure 2. PM-JAY utilization by district. Source: Primary survey. https://www.frontiersin.org/files/Articles/1783458/fpubh-14-1783458-HTML/image_m/fpubh-14-1783458-t003.jpg Table 3. Chi-square test for association between district and PM-JAY utilization. https://www.frontiersin.org/files/Articles/1783458/fpubh-14-1783458-HTML/image_m/fpubh-14-1783458-t004.jpg Table 4. Pearson correlation matrix among PM-JAY utilization and barrier variables. https://www.frontiersin.org/files/Articles/1783458/fpubh-14-1783458-HTML/image_m/fpubh-14-1783458-t005.jpg Table 5. Chi-square test results for selected variables under PM-JAY. https://www.frontiersin.org/files/Articles/1783458/fpubh-14-1783458-HTML/image_m/fpubh-14-1783458-t006.jpg Table 6. Logistic regression results for PM-JAY utilization (Barrier-based model). https://www.frontiersin.org/files/Articles/1783458/fpubh-14-1783458-HTML/image_m/fpubh-14-1783458-g003.jpg Figure 3. Barriers to PM-JAY utilization: odds ratio by category. Source: Primary survey. https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2026.1685373/full https://www.frontiersin.org/files/Articles/1685373/froh-07-1685373-HTML/image_m/froh-07-1685373-t001.jpg Table 1. Sociodemographic, clinical characteristics and dentition Status of the sample. https://www.frontiersin.org/files/Articles/1685373/froh-07-1685373-HTML/image_m/froh-07-1685373-t002.jpg Table 2. Associations between the number of remaining teeth and cardiometabolic risk factors. https://www.frontiersin.org/files/Articles/1685373/froh-07-1685373-HTML/image_m/froh-07-1685373-t003.jpg Table 3. Associations between functional dentition Status and cardiometabolic risk factors. https://www.frontiersin.org/files/Articles/1685373/froh-07-1685373-HTML/image_m/froh-07-1685373-t004.jpg Table 4. Associations between the number of remaining teeth and cardiometabolic diseasesa. https://www.frontiersin.org/files/Articles/1685373/froh-07-1685373-HTML/image_m/froh-07-1685373-t005.jpg Table 5. Associations between the functional dentition Status and cardiometabolic diseasesa. https://www.frontiersin.org/files/Articles/1685373/froh-07-1685373-HTML/image_m/froh-07-1685373-t006.jpg Table 6. Associations between the number of remaining teeth and cardiometabolic diseases stratified https://www.frontiersin.org/files/Articles/1685373/froh-07-1685373-HTML/image_m/froh-07-1685373-t007.jpg Table 7. Associations between the functional dentition Status and cardiometabolic diseases stratifie https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1654282/full https://www.frontiersin.org/files/Articles/1654282/fped-13-1654282-HTML/image_m/fped-13-1654282-t001.jpg Table 1. Perinatal characteristics of the cohort for children aged between 23 months and 27 months ( https://www.frontiersin.org/files/Articles/1654282/fped-13-1654282-HTML/image_m/fped-13-1654282-t002.jpg Table 2. Developmental outcomes using the parent report of children's abilities-revised (PARCA-R). https://www.frontiersin.org/files/Articles/1654282/fped-13-1654282-HTML/image_m/fped-13-1654282-t003.jpg Table 3. Proportion of children with developmental delay, overall and by sex. https://www.frontiersin.org/files/Articles/1654282/fped-13-1654282-HTML/image_m/fped-13-1654282-t004.jpg Table 4. Pearson correlation. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1716250/full https://www.frontiersin.org/files/Articles/1716250/fonc-16-1716250-HTML/image_m/fonc-16-1716250-g001.jpg Figure 1. Histological examination of the vulvar biopsy (A) Low-power overview (40x): epithelial thi https://www.frontiersin.org/files/Articles/1716250/fonc-16-1716250-HTML/image_m/fonc-16-1716250-g002.jpg Figure 2. Immunohistochemical profile of vulvar biopsy (100X): (A) ER (Estrogen Receptor): strong nu https://www.frontiersin.org/files/Articles/1716250/fonc-16-1716250-HTML/image_m/fonc-16-1716250-g003.jpg Figure 3. Key morphological features of vulvar resection (A) Low-power (40X magnification) panoramic https://www.frontiersin.org/files/Articles/1716250/fonc-16-1716250-HTML/image_m/fonc-16-1716250-t001.jpg Table 1. Summary of main reported cases of mammary-like glands of the vulva since 2013. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1730346/full https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g001.jpg Figure 1. Overview of teriparatide sequence. (A) Sequence of human parathyroid hormone (hPTH). The N https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g002.jpg Figure 2. EpiMatrix analysis of teriparatide API. EpiMatrix detail report. The potential of a 9-mer https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g003.jpg Figure 3. Cytoscape image of the JanusMatrix network for the teriparatide sequence. Given a peptide https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g004.jpg Figure 4. Impurity modifications on teriparatide sequence. Thirty-four teriparatide impurity peptide https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g005.jpg Figure 5. Immunogenicity quadrant plot. (A) Immunogenicity quadrant analysis categorizes peptides an https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-t001.jpg Table 1. Teriparatide impurities selected for in vitro assays. https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-t002.jpg Table 2. Peptide concentrations (µM) evaluated in the IVIP assay. https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-t003.jpg Table 3. Sequence of teriparatide and selected impurities evaluated in the HLA class II binding assa https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g006.jpg Figure 6. Results of the class II HLA binding assay. (A) Results of the HLA binding assay are shown https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g007.jpg Figure 7. Donor PBMC responses to Forteo® and individual impurities. Forteo® and each impurity were https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-t004.jpg Table 4. Overall risk based on in silico and in vitro results. https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g008.jpg Figure 8. Donor PBMC responses to teriparatide API. Teriparatide API (unformulated) from two differe https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g009.jpg Figure 9. Summary of in silico scores and in vitro immunogenicity. Impurities are listed in order of https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g010.jpg Figure 10. Representative flow cytometry histograms in TTBSA. Flow cytometry dot plots show results https://www.frontiersin.org/files/Articles/1730346/fimmu-16-1730346-HTML-r1/image_m/fimmu-16-1730346-g011.jpg Figure 11. TPT2–16 suppresses TT-induced CD4+ memory cell proliferation and activation. (A) Comparis https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1681695/full https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-g001.jpg Figure 1. The flow diagram of case inclusion and exclusion. https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-t001.jpg Table 1. Demographic, clinical, and pathological characteristics associated with treatment plan in t https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-g002.jpg Figure 2. K-M analysis determining the impact of variables on OS (months). Stratified by EOR (A), Tr https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-g003.jpg Figure 3. K-M analysis determining the impact of different treatments on OS(months) in subgroups. Gr https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-g004.jpg Figure 4. K-M analysis determining the impact of different treatments on OS(months) in subgroups. Gr https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-t002.jpg Table 2. Predictive performance comparison between model 1 and model 2. https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-g005.jpg Figure 5. Comparison of time-ROC curve and DCA curve between Model 1 and Model 2. Time-ROC of Model https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-g006.jpg Figure 6. The forest plot shows the hazard ratio of the variable and the 95% confidence interval (A) https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-g007.jpg Figure 7. Construction of prognostic nomogram based on five independent prognostic factors (A), K-M https://www.frontiersin.org/files/Articles/1681695/fonc-15-1681695-HTML/image_m/fonc-15-1681695-t003.jpg Table 3. Overview and statistical efficacy analysis of molecular markers. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1766017/full https://www.frontiersin.org/files/Articles/1766017/fpsyg-17-1766017-HTML/image_m/fpsyg-17-1766017-t001.jpg Table 1. MeSH terms, keywords, and characteristics of articles to be identified, adjusted to the SPI https://www.frontiersin.org/files/Articles/1766017/fpsyg-17-1766017-HTML/image_m/fpsyg-17-1766017-t002.jpg Table 2. Search strategy in databases and in specialized reviews of eating disorders. https://www.frontiersin.org/files/Articles/1766017/fpsyg-17-1766017-HTML/image_m/fpsyg-17-1766017-g001.jpg Figure 1. PRISMA flowchart studies selection. https://www.frontiersin.org/journals/toxicology/articles/10.3389/ftox.2025.1670769/full https://www.frontiersin.org/files/Articles/1670769/ftox-07-1670769-HTML/image_m/ftox-07-1670769-g001.jpg Figure 1. Pathways of Ethanol Metabolism and Its Byproducts. Ethanol metabolism occurs via three pri https://www.frontiersin.org/files/Articles/1670769/ftox-07-1670769-HTML/image_m/ftox-07-1670769-g002.jpg Figure 2. Pathological Consequences of Excessive Alcohol Consumption. Chronic alcohol consumption im https://www.frontiersin.org/files/Articles/1670769/ftox-07-1670769-HTML/image_m/ftox-07-1670769-g003.jpg Figure 3. The Effects of Alcohol on Metabolic and Inflammatory Pathways Associated with Various Diso https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1767523/full https://www.frontiersin.org/files/Articles/1767523/fimmu-17-1767523-HTML/image_m/fimmu-17-1767523-t001.jpg Table 1. Demographic, clinical, and laboratory characteristics among different groups. https://www.frontiersin.org/files/Articles/1767523/fimmu-17-1767523-HTML/image_m/fimmu-17-1767523-g001.jpg Figure 1. Comparisons of LY-X (A), LY-Y (B), LY-Z (C) and LY-H (D) levels among reactive lymphocyte https://www.frontiersin.org/files/Articles/1767523/fimmu-17-1767523-HTML/image_m/fimmu-17-1767523-t002.jpg Table 2. Individual effect of lymphocyte parameters on predicting reactive lymphocytes in peripheral https://www.frontiersin.org/files/Articles/1767523/fimmu-17-1767523-HTML/image_m/fimmu-17-1767523-g002.jpg Figure 2. Smoothing spline analysis of LY-X, LY-Y, LY-Z and LY-H and percentage of reactive lymphocy https://www.frontiersin.org/files/Articles/1767523/fimmu-17-1767523-HTML/image_m/fimmu-17-1767523-g003.jpg Figure 3. Correlation between LY-X, LY-Y, LY-Z and LY-H with WBC, LY#, CD3+, CD4+ and CD8+. *P < 0.0 https://www.frontiersin.org/files/Articles/1767523/fimmu-17-1767523-HTML/image_m/fimmu-17-1767523-g004.jpg Figure 4. ROC curves of LY-X, LY-Y, LY-Z and LY-H for predicting reactive lymphocyte activation in d https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1674383/full https://www.frontiersin.org/files/Articles/1674383/fpls-16-1674383-HTML/image_m/fpls-16-1674383-g001.jpg Figure 1. Genome-wide association studies (GWAS) for the branching trait based on single nucleotide https://www.frontiersin.org/files/Articles/1674383/fpls-16-1674383-HTML/image_m/fpls-16-1674383-g002.jpg Figure 2. Phenotypic comparison of the multi-stemmed MH15 and single-stemmed SH18 sunflower lines at https://www.frontiersin.org/files/Articles/1674383/fpls-16-1674383-HTML/image_m/fpls-16-1674383-g003.jpg Figure 3. Transcriptomic profiling of shoot apical meristems in sunflower lines with contrasting bra https://www.frontiersin.org/files/Articles/1674383/fpls-16-1674383-HTML/image_m/fpls-16-1674383-g004.jpg Figure 4. Tissue-specific expression and co-expression analysis of candidate genes and lncRNAs invol https://www.frontiersin.org/files/Articles/1674383/fpls-16-1674383-HTML/image_m/fpls-16-1674383-g005.jpg Figure 5. Relative expression levels of 13 candidate genes in the two sunflower varieties. Bars show https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1747177/full https://www.frontiersin.org/files/Articles/1747177/fpls-17-1747177-HTML/image_m/fpls-17-1747177-g001.jpg Figure 1. The calcium signaling toolkit: from stress perception to adaptive response. Schematic repr https://www.frontiersin.org/files/Articles/1747177/fpls-17-1747177-HTML/image_m/fpls-17-1747177-g002.jpg Figure 2. Calcium signaling integrates phytohormone pathways and transcriptional networks to orchest https://www.frontiersin.org/files/Articles/1747177/fpls-17-1747177-HTML/image_m/fpls-17-1747177-g003.jpg Figure 3. Spectrum of natural genetic variation in calcium sensor genes. Genetic variation shaping C https://www.frontiersin.org/files/Articles/1747177/fpls-17-1747177-HTML/image_m/fpls-17-1747177-t001.jpg Table 1. Examples of natural allelic variation in calcium sensor genes conferring abiotic stress tol https://www.frontiersin.org/files/Articles/1747177/fpls-17-1747177-HTML/image_m/fpls-17-1747177-t002.jpg Table 2. Toolkit for exploiting natural variation in calcium signaling networks. https://www.frontiersin.org/files/Articles/1747177/fpls-17-1747177-HTML/image_m/fpls-17-1747177-g004.jpg Figure 4. Evolutionary diversification and functional specialization in major crops. Evolutionary di https://www.frontiersin.org/files/Articles/1747177/fpls-17-1747177-HTML/image_m/fpls-17-1747177-g005.jpg Figure 5. An integrated pipeline for harnessing natural variation in calcium signaling to develop cl https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1704282/full https://www.frontiersin.org/files/Articles/1704282/fimmu-16-1704282-HTML/image_m/fimmu-16-1704282-t001.jpg Table 1. Demographic and clinical characteristics of AQP4-ab-positive NMOSD patients and control sub https://www.frontiersin.org/files/Articles/1704282/fimmu-16-1704282-HTML/image_m/fimmu-16-1704282-g001.jpg Figure 1. The proportions of Tfh and B cell subsets were dysregulated during acute attacks, accompan https://www.frontiersin.org/files/Articles/1704282/fimmu-16-1704282-HTML/image_m/fimmu-16-1704282-g002.jpg Figure 2. Tfh cells promoted B cell proliferation, differentiation, and AQP4-ab production. (A–H). S https://www.frontiersin.org/files/Articles/1704282/fimmu-16-1704282-HTML/image_m/fimmu-16-1704282-g003.jpg Figure 3. IL-6 promoted the differentiation and function of Tfh and B cells. (A) The proportion of C https://www.frontiersin.org/files/Articles/1704282/fimmu-16-1704282-HTML/image_m/fimmu-16-1704282-g004.jpg Figure 4. The differential effects of IL-6/21R-Fc on Tfh and B cells. (A) The proportion of CD4+ cel https://www.frontiersin.org/files/Articles/1704282/fimmu-16-1704282-HTML/image_m/fimmu-16-1704282-g005.jpg Figure 5. B cell subsets had counteraction on Tfh cells. (A–G). Sorting T and B cells from healthy c https://www.frontiersin.org/files/Articles/1704282/fimmu-16-1704282-HTML/image_m/fimmu-16-1704282-g006.jpg Figure 6. The IL-6-Tfh-B cell axis in NMOSD. IL-6 secreted by dendritic cells promotes the different https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1652539/full https://www.frontiersin.org/files/Articles/1652539/fimmu-16-1652539-HTML-r1/image_m/fimmu-16-1652539-g001.jpg Figure 1. scRNA-seq analysis identifies nine major cell types in the murine lung microenvironment. ( https://www.frontiersin.org/files/Articles/1652539/fimmu-16-1652539-HTML-r1/image_m/fimmu-16-1652539-g002.jpg Figure 2. The impact of HDM exposure and IL-1β signaling on B cell subclusters. (A) UMAP of the eigh https://www.frontiersin.org/files/Articles/1652539/fimmu-16-1652539-HTML-r1/image_m/fimmu-16-1652539-g003.jpg Figure 3. The impact of HDM exposure and IL-1β signaling on T and NK cell subclusters. (A) UMAP of t https://www.frontiersin.org/files/Articles/1652539/fimmu-16-1652539-HTML-r1/image_m/fimmu-16-1652539-g004.jpg Figure 4. The impact of HDM exposure and IL-1β signaling on neutrophil subclusters. (A) UMAP of the https://www.frontiersin.org/files/Articles/1652539/fimmu-16-1652539-HTML-r1/image_m/fimmu-16-1652539-g005.jpg Figure 5. The impact of HDM exposure and IL-1β signaling on MNPs and stromal cells. (A) UMAP of the https://www.frontiersin.org/files/Articles/1652539/fimmu-16-1652539-HTML-r1/image_m/fimmu-16-1652539-g006.jpg Figure 6. Histopathological analysis of the lungs. (A) Representative photos of four lung lobes (dor https://www.frontiersin.org/files/Articles/1652539/fimmu-16-1652539-HTML-r1/image_m/fimmu-16-1652539-g007.jpg Figure 7. Comparative analysis of lung cytokine expression by qPCR, scRNA-seq, and ELISA. (A) The re https://www.frontiersin.org/files/Articles/1652539/fimmu-16-1652539-HTML-r1/image_m/fimmu-16-1652539-g008.jpg Figure 8. Immunofluorescence (IF) staining analysis of neutrophil recruitment, neutrophil extracellu https://www.frontiersin.org/files/Articles/1652539/fimmu-16-1652539-HTML-r1/image_m/fimmu-16-1652539-g009.jpg Figure 9. Proposed model of the effects of HDM exposure and IL-1β signaling on the lung immune micro https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1781960/full https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-g001.jpg Figure 1. Patients screening process. https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-g002.jpg Figure 2. Surgical steps for unilateral bilateral endoscopic interbody fusion guided by navigation. https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-g003.jpg Figure 3. Radiological measurements of lumbar parameters on standing lateral x-ray images. (A) Disc https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-t001.jpg Table 1. Baseline characteristics and outcome data of patients. https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-t002.jpg Table 2. VAS scores for low back pain in two groups of patients. https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-g004.jpg Figure 4. VAS and ODI scores for two groups of patients. (a) VAS scores for low back pain before and https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-t003.jpg Table 3. VAS scores for leg pain in two groups of patients. https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-t004.jpg Table 4. ODI scores for two groups of patients*. https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-t005.jpg Table 5. Comparison of imaging parameters between two groups of patients. https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-g005.jpg Figure 5. Radiological assessment of lumbar parameters and interbody fusion outcomes in a representa https://www.frontiersin.org/files/Articles/1781960/fsurg-13-1781960-HTML/image_m/fsurg-13-1781960-g006.jpg Figure 6. 68-year-old male, with 1 year of lower back pain and left lower limb numbness, diagnosed w https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1688516/full https://www.frontiersin.org/files/Articles/1688516/fimmu-16-1688516-HTML/image_m/fimmu-16-1688516-g001.jpg Figure 1. The flowchart of the GoutRe cohort and the MIMIC-IV cohort. SMU, Southern Medical Universi https://www.frontiersin.org/files/Articles/1688516/fimmu-16-1688516-HTML/image_m/fimmu-16-1688516-g002.jpg Figure 2. Distribution and predictive value of NLR in determining optimal cutoff and risk of inpatie https://www.frontiersin.org/files/Articles/1688516/fimmu-16-1688516-HTML/image_m/fimmu-16-1688516-g003.jpg Figure 3. Association between NLR and inpatient gout recurrence in the GoutRe and MIMIC-IV cohorts. https://www.frontiersin.org/files/Articles/1688516/fimmu-16-1688516-HTML/image_m/fimmu-16-1688516-t001.jpg Table 1. Multivariate-adjusted hazard ratios (95% CI) of NLR for inpatient gout recurrence. https://www.frontiersin.org/files/Articles/1688516/fimmu-16-1688516-HTML/image_m/fimmu-16-1688516-g004.jpg Figure 4. Incremental predicted value and clinical practicability evaluation analysis. (A) ROC curve https://www.frontiersin.org/files/Articles/1688516/fimmu-16-1688516-HTML/image_m/fimmu-16-1688516-g005.jpg Figure 5. Biological plausibility analyses of the association between NLR and inpatient gout recurre https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1665823/full https://www.frontiersin.org/files/Articles/1665823/fmed-12-1665823-HTML/image_m/fmed-12-1665823-g001.jpg Figure 1. Flow diagram of the literature search and selection process. https://www.frontiersin.org/files/Articles/1665823/fmed-12-1665823-HTML/image_m/fmed-12-1665823-t001.jpg Table 1. Characteristics of included literature (n = 20). https://www.frontiersin.org/files/Articles/1665823/fmed-12-1665823-HTML/image_m/fmed-12-1665823-t002.jpg Table 2. Standardized scores and evaluation results in all areas of the guidelines. https://www.frontiersin.org/files/Articles/1665823/fmed-12-1665823-HTML/image_m/fmed-12-1665823-t003.jpg Table 3. Best evidence summary for management of epidermal growth factor receptor inhibitor-induced https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1648513/full https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g001.jpg Figure 1. Anatomical landmarks placed on foot before scanning; (1) the centre of the heel, (2) the h https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g002.jpg Figure 2. Flowchart of methodology to analyse reliability of foot measurements. https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g003.jpg Figure 3. Scanning positions for reliability analysis of foot measurements, (A) NWB and (B) HWB. https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g004.jpg Figure 4. (A) Foot scan with palpated landmarks visible (indicated with arrows) and (B) Foot scan wi https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g005.jpg Figure 5. (A) Measurement calculation and projected foot trace in Rhino Grasshopper (landmarks displ https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-t001.jpg Table 1. Methodology to calculate measurements in the Rhino Grasshopper software. https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g006.jpg Figure 6. Flowchart of methodology to analyse consistency in orthotic designs (6 scans-landmarks pla https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g007.jpg Figure 7. Scanning positions for orthotic consistency, (A) NWB and (B) PWB, (3D stickers on landmark https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g008.jpg Figure 8. ICC representing intra- and inter-user reliability of foot measurements (N = 24). (A) Intr https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g009.jpg Figure 9. (A) 3D deviation analysis: Intra-designer consistency for palpation-guided vs. scan-derive https://www.frontiersin.org/files/Articles/1648513/fbioe-14-1648513-HTML/image_m/fbioe-14-1648513-g010.jpg Figure 10. 3D deviation analysis: Intra-designer consistency for NWB vs. PWB. Green region indicates https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1677328/full https://www.frontiersin.org/files/Articles/1677328/fenvs-13-1677328-HTML/image_m/fenvs-13-1677328-g001.jpg Figure 1. Indicates the global distribution of sites for the different experimental sites selected f https://www.frontiersin.org/files/Articles/1677328/fenvs-13-1677328-HTML/image_m/fenvs-13-1677328-g002.jpg Figure 2. Weighted effect sizes of nitrogen addition on (a) above-ground biomass, (b) below-ground b https://www.frontiersin.org/files/Articles/1677328/fenvs-13-1677328-HTML/image_m/fenvs-13-1677328-g003.jpg Figure 3. Weighted effect sizes of nitrogen addition on (a) soil organic carbon content. (b) represe https://www.frontiersin.org/files/Articles/1677328/fenvs-13-1677328-HTML/image_m/fenvs-13-1677328-g004.jpg Figure 4. Weighted effect sizes of nitrogen addition on (a) Shannon-Wiener index, (b) species richne https://www.frontiersin.org/files/Articles/1677328/fenvs-13-1677328-HTML/image_m/fenvs-13-1677328-g005.jpg Figure 5. Weighted effect sizes of nitrogen addition on (a) soil ammoniacal nitrogen, (b) soil nitra https://www.frontiersin.org/files/Articles/1677328/fenvs-13-1677328-HTML/image_m/fenvs-13-1677328-g006.jpg Figure 6. Weighted effect sizes of nitrogen addition on (a) fungal to bacterial ratio, (b) soil micr https://www.frontiersin.org/files/Articles/1677328/fenvs-13-1677328-HTML/image_m/fenvs-13-1677328-g007.jpg Figure 7. Analysis of factors affecting carbon dynamics based on a random forest model, with relativ https://www.frontiersin.org/files/Articles/1677328/fenvs-13-1677328-HTML/image_m/fenvs-13-1677328-g008.jpg Figure 8. Analysis of factors affecting carbon dynamics based on the random forest model, with relat https://www.frontiersin.org/files/Articles/1677328/fenvs-13-1677328-HTML/image_m/fenvs-13-1677328-g009.jpg Figure 9. Structural equation modeling (SEM) of the main factors of N addition on soil C dynamics. I https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1771593/full https://www.frontiersin.org/files/Articles/1771593/fimmu-17-1771593-HTML/image_m/fimmu-17-1771593-g001.jpg Figure 1. Post-irradiation blood lymphocyte levels, tumor growth trends, and γ-H2ax and Ki67 immunoh https://www.frontiersin.org/files/Articles/1771593/fimmu-17-1771593-HTML/image_m/fimmu-17-1771593-g002.jpg Figure 2. Immunogenic cell death after various radiotherapy treatments. (A) Immunohistochemistry of https://www.frontiersin.org/files/Articles/1771593/fimmu-17-1771593-HTML/image_m/fimmu-17-1771593-g003.jpg Figure 3. Infiltration of CD8+ T cells, dendritic cells, and natural killer cells at tumor sites aft https://www.frontiersin.org/files/Articles/1771593/fimmu-17-1771593-HTML/image_m/fimmu-17-1771593-g004.jpg Figure 4. The proportion of MDSCs, tumor-associated macrophages, and neutrophils in tumor tissues af https://www.frontiersin.org/files/Articles/1771593/fimmu-17-1771593-HTML/image_m/fimmu-17-1771593-g005.jpg Figure 5. Proliferation of CD8+ T cells, dendritic cells, and natural killer cells in the spleen. (A https://www.frontiersin.org/files/Articles/1771593/fimmu-17-1771593-HTML/image_m/fimmu-17-1771593-g006.jpg Figure 6. Splenic MDSC, tumor-associated neutrophils and macrophages. (A, B) MDSC cells (CD11b+Gr-1+ https://www.frontiersin.org/files/Articles/1771593/fimmu-17-1771593-HTML/image_m/fimmu-17-1771593-g007.jpg Figure 7. CXCL10, IFN-γ, IL-2, and IL-10 levels in blood and tumor growth on the non-radiated side. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1759463/full https://www.frontiersin.org/files/Articles/1759463/fgene-17-1759463-HTML/image_m/fgene-17-1759463-g001.jpg Figure 1. Relationship between ECHDC2 expression and clinicopathological characteristics in GBM. (A– https://www.frontiersin.org/files/Articles/1759463/fgene-17-1759463-HTML/image_m/fgene-17-1759463-g002.jpg Figure 2. Single-cell expression atlas of ECHDC2 and its prognostic significance in GBM. (A) UMAP pl https://www.frontiersin.org/files/Articles/1759463/fgene-17-1759463-HTML/image_m/fgene-17-1759463-g003.jpg Figure 3. ESTIMATE analysis and immune-infiltration landscape associated with ECHDC2 expression in G https://www.frontiersin.org/files/Articles/1759463/fgene-17-1759463-HTML/image_m/fgene-17-1759463-g004.jpg Figure 4. Associations between ECHDC2 expression and immunological features in the GSE83300 cohort. https://www.frontiersin.org/files/Articles/1759463/fgene-17-1759463-HTML/image_m/fgene-17-1759463-g005.jpg Figure 5. Functional enrichment analyses and impact of ECHDC2 on the PI3K/Akt signaling pathway. (A) https://www.frontiersin.org/files/Articles/1759463/fgene-17-1759463-HTML/image_m/fgene-17-1759463-g006.jpg Figure 6. ECHDC2 promotes GBM cell migration. (A) Wound healing assays were performed to assess the https://www.frontiersin.org/files/Articles/1759463/fgene-17-1759463-HTML/image_m/fgene-17-1759463-g007.jpg Figure 7. ECHDC2 promotes GBM cell proliferation. (A) Cell proliferation was assessed in U251 and A1 https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1744587/full https://www.frontiersin.org/files/Articles/1744587/fmicb-17-1744587-HTML/image_m/fmicb-17-1744587-g001.jpg Figure 1. Phylogenetic analysis of 348 HEV strains in Asia. https://www.frontiersin.org/files/Articles/1744587/fmicb-17-1744587-HTML/image_m/fmicb-17-1744587-g002.jpg Figure 2. Genomic similarity analysis of 13 representative HEV isolates across genotypes in Asia. https://www.frontiersin.org/files/Articles/1744587/fmicb-17-1744587-HTML/image_m/fmicb-17-1744587-g003.jpg Figure 3. Genomic structure of HEV and recombination map of HEV genomes. https://www.frontiersin.org/files/Articles/1744587/fmicb-17-1744587-HTML/image_m/fmicb-17-1744587-t001.jpg Table 1. Detailed information on potential recombination events identified in the full-length genome https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1673508/full https://www.frontiersin.org/files/Articles/1673508/fimmu-16-1673508-HTML/image_m/fimmu-16-1673508-t001.jpg Table 1. Patient’s characteristics. https://www.frontiersin.org/files/Articles/1673508/fimmu-16-1673508-HTML/image_m/fimmu-16-1673508-g001.jpg Figure 1. Comparison of immunoglobulin heavy-chain isotype proportion. Proportion of BCR sequences f https://www.frontiersin.org/files/Articles/1673508/fimmu-16-1673508-HTML/image_m/fimmu-16-1673508-g002.jpg Figure 2. Comparison of clonality and diversity of the BCR repertoire. (A) Comparison of the D50 div https://www.frontiersin.org/files/Articles/1673508/fimmu-16-1673508-HTML/image_m/fimmu-16-1673508-g003.jpg Figure 3. Somatic hypermutation frequency. (A) Overall mean somatic hypermutation (SHM) frequency ac https://www.frontiersin.org/files/Articles/1673508/fimmu-16-1673508-HTML/image_m/fimmu-16-1673508-g004.jpg Figure 4. IGHV and IGHJ gene usage. (A) Overall frequency of IGHV gene segments among total IGH sequ https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1784176/full https://www.frontiersin.org/files/Articles/1784176/fpls-17-1784176-HTML/image_m/fpls-17-1784176-g001.jpg Figure 1. Map-based cloning GSE3.1. (A) mature rice grains of ZH11 and DLG. (B) grain width of ZH11 https://www.frontiersin.org/files/Articles/1784176/fpls-17-1784176-HTML/image_m/fpls-17-1784176-g002.jpg Figure 2. GSE3.1 positively regulates grain size and grain weight. (A) mature rice grains of ZH11, g https://www.frontiersin.org/files/Articles/1784176/fpls-17-1784176-HTML/image_m/fpls-17-1784176-g003.jpg Figure 3. GSE3.1 regulates grain size by promoting cell division in spikelet hulls. (A–C) global out https://www.frontiersin.org/files/Articles/1784176/fpls-17-1784176-HTML/image_m/fpls-17-1784176-g004.jpg Figure 4. Expression pattern of GSE3.1 and subcellular localization of GSE3.1. (A) expression levels https://www.frontiersin.org/files/Articles/1784176/fpls-17-1784176-HTML/image_m/fpls-17-1784176-g005.jpg Figure 5. NIL-GSE3.1DLG increases grain yield in rice. (A) plant morphology of NIL-GSE3.1ZH11 and NI https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1716128/full https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-g001.jpg Figure 1. Flow diagram of the study process. https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-t001.jpg Table 1. Characteristics of the observational studies included in this meta-analysis. https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-g002.jpg Figure 2. Meta-analysis of the 50% responder rate. https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-g003.jpg Figure 3. Meta-analysis of the seizure-free rate. https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-g004.jpg Figure 4. Efficacy of BRV in patients with learning disability. (A) 50% responder rate of BRV in pat https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-g005.jpg Figure 5. Impact of previous levetiracetam-exposure on BRV efficacy. (A) 50% responder rate of BRV i https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-g006.jpg Figure 6. Efficacy of an overnight switch from LEV to BRV. (A) 50% responder rate in patients who sw https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-g007.jpg Figure 7. Efficacy of BRV as monotherapy. (A) Pooled 50% responder rate of PER as monotherapy. (B) P https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-g008.jpg Figure 8. Meta-analysis of the adverse events rate. https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-g009.jpg Figure 9. Meta-analysis of the withdrawal rate due to adverse events. https://www.frontiersin.org/files/Articles/1716128/fphar-16-1716128-HTML/image_m/fphar-16-1716128-t002.jpg Table 2. Summary of AEs reported in ≥2 studies in the meta-analysis. https://www.frontiersin.org/journals/genome-editing/articles/10.3389/fgeed.2025.1596600/full https://www.frontiersin.org/files/Articles/1596600/fgeed-07-1596600-HTML/image_m/fgeed-07-1596600-g001.jpg Figure 1. Diagram of the CRISPR-Cas9 system. https://www.frontiersin.org/files/Articles/1596600/fgeed-07-1596600-HTML/image_m/fgeed-07-1596600-g002.jpg Figure 2. Activation tagging schematic diagram. https://www.frontiersin.org/files/Articles/1596600/fgeed-07-1596600-HTML/image_m/fgeed-07-1596600-g003.jpg Figure 3. Illustration of CRISPR/dCas9-mediated transcriptional activation. The dCas9 domain is fuse https://www.frontiersin.org/files/Articles/1596600/fgeed-07-1596600-HTML/image_m/fgeed-07-1596600-g004.jpg Figure 4. Illustration of the CRISPRa workflow in plant disease investigation. https://www.frontiersin.org/files/Articles/1596600/fgeed-07-1596600-HTML/image_m/fgeed-07-1596600-g005.jpg Figure 5. Comparison of dCas9-based Transcriptional Activation Systems. Six different strategies for https://www.frontiersin.org/files/Articles/1596600/fgeed-07-1596600-HTML/image_m/fgeed-07-1596600-t001.jpg Table 1. Examples of Commercially Approved and/or Released Gene-Edited Crops. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1768005/full https://www.frontiersin.org/files/Articles/1768005/frai-09-1768005-HTML-r1/image_m/frai-09-1768005-g001.jpg Figure 1. Research workflow. https://www.frontiersin.org/files/Articles/1768005/frai-09-1768005-HTML-r1/image_m/frai-09-1768005-t001.jpg Table 1. Criteria for evaluating LLM summaries. https://www.frontiersin.org/files/Articles/1768005/frai-09-1768005-HTML-r1/image_m/frai-09-1768005-t002.jpg Table 2. Characteristics of models used as LLM-as-a-Judge. https://www.frontiersin.org/files/Articles/1768005/frai-09-1768005-HTML-r1/image_m/frai-09-1768005-t003.jpg Table 3. Example prompt configurations for varying levels of granularity. https://www.frontiersin.org/files/Articles/1768005/frai-09-1768005-HTML-r1/image_m/frai-09-1768005-t004.jpg Table 4. Average expert estimates and automatic metrics grouped by the models that performed the sum https://www.frontiersin.org/files/Articles/1768005/frai-09-1768005-HTML-r1/image_m/frai-09-1768005-t005.jpg Table 5. The consistency of experts with the LLM-as-a-Judge’s assessments at different rating scale https://www.frontiersin.org/files/Articles/1768005/frai-09-1768005-HTML-r1/image_m/frai-09-1768005-t006.jpg Table 6. Consistency between expert evaluations and LLM-as-a-Judge assessments across varying prompt https://www.frontiersin.org/files/Articles/1768005/frai-09-1768005-HTML-r1/image_m/frai-09-1768005-t007.jpg Table 7. Consistency between expert evaluations and LLM-as-a-Judge assessments in detecting hallucin https://www.frontiersin.org/files/Articles/1768005/frai-09-1768005-HTML-r1/image_m/frai-09-1768005-t008.jpg Table 8. Examples of LLM-as-a-Judge work with hallucinations. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1626523/full https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-t001.jpg Table 1. Picos criteria of this study. https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-g001.jpg Figure 1. Flowchart of study selection process for the meta-analysis of DII and CVDs risk/mortality. https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-t002.jpg Table 2. Basic characteristics and quality assessment of studies on the risk of CVDs associated with https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-t003.jpg Table 3. Basic characteristics and quality assessment of studies on the risk of CVDs mortality assoc https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-g002.jpg Figure 2. Forest plot of the association between DII and CVDs risk. https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-t004.jpg Table 4. Subgroup analysis of the association between DII and CVDs risk. https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-g003.jpg Figure 3. Forest plot of the association between DII and CVDs mortality. https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-t005.jpg Table 5. Subgroup analysis of the association between DII and CVDs mortality. https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-g004.jpg Figure 4. Sensitivity analysis of DII and CVDs risk. https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-g005.jpg Figure 5. Sensitivity analysis of DII and CVDs mortality. https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-g006.jpg Figure 6. Trim-and-fill funnel plot for the association between DII and CVDs risk. https://www.frontiersin.org/files/Articles/1626523/fcvm-12-1626523-HTML-r1/image_m/fcvm-12-1626523-g007.jpg Figure 7. Trim-and-fill funnel plot for the association between DII and CVDs mortality. https://www.frontiersin.org/journals/conservation-science/articles/10.3389/fcosc.2025.1671488/full https://www.frontiersin.org/files/Articles/1671488/fcosc-06-1671488-HTML/image_m/fcosc-06-1671488-t001.jpg Table 1. Plasma polymerisation conditions of the three monomers. https://www.frontiersin.org/files/Articles/1671488/fcosc-06-1671488-HTML/image_m/fcosc-06-1671488-g001.jpg Figure 1. Representation of the 20 ft shipping container configurations tested: (a) extraction-only https://www.frontiersin.org/files/Articles/1671488/fcosc-06-1671488-HTML/image_m/fcosc-06-1671488-t002.jpg Table 2. Species-specific primers considered in this study (derived from Henger et al., 2023), inclu https://www.frontiersin.org/files/Articles/1671488/fcosc-06-1671488-HTML/image_m/fcosc-06-1671488-g002.jpg Figure 2. Summary of the surface characterisation of the three plasma polymer coatings used for filt https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1674412/full https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-g001.jpg Figure 1. Flowchart of the different steps in microplastic sampling. https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-g002.jpg Figure 2. An example of the diversity of collection methods for water, sediment, biota, and air that https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-t001.jpg Table 1. Summary table of reagents for chemical digestion, their recommended concentrations, tempera https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-t002.jpg Table 2. Commonly used density separation reagents for microplastics analysis, including their densi https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-t003.jpg Table 3. Size classifications are used to categorise plastic, from macroplastics (> 25 mm) to microp https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-t004.jpg Table 4. Description and classification of various plastic morphologies found in environmental sampl https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-t005.jpg Table 5. Summary table of polymer identification instrumentation and their advantages, limitations, https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-t006.jpg Table 6. A description of the type of blanks and controls that are recommended throughout microplast https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-t007.jpg Table 7. Example databases established to collate and disseminate environmental information on the a https://www.frontiersin.org/files/Articles/1674412/fmars-12-1674412-HTML-r2/image_m/fmars-12-1674412-t008.jpg Table 8. Essential and desirable reporting parameters for microplastic load, physical characteristic https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1704317/full https://www.frontiersin.org/files/Articles/1704317/fneur-17-1704317-HTML/image_m/fneur-17-1704317-g001.jpg Figure 1. A general overview of the proposed lesion segmentation network. Our approach produces a 3D https://www.frontiersin.org/files/Articles/1704317/fneur-17-1704317-HTML/image_m/fneur-17-1704317-g002.jpg Figure 2. Boxplots of evaluated metrics obtained from the Proposed method (blue box), BIANCA (purple https://www.frontiersin.org/files/Articles/1704317/fneur-17-1704317-HTML/image_m/fneur-17-1704317-g003.jpg Figure 3. Visual comparison of results for all described methods. The first column represented subje https://www.frontiersin.org/files/Articles/1704317/fneur-17-1704317-HTML/image_m/fneur-17-1704317-g004.jpg Figure 4. Heatmap of the summarized misclassified voxels in a sample fold normalized to [0, 1] range https://www.frontiersin.org/files/Articles/1704317/fneur-17-1704317-HTML/image_m/fneur-17-1704317-t001.jpg Table 1. Experimental results using the MICCAI 2016 challenge on the multiple sclerosis lesion segme https://www.frontiersin.org/files/Articles/1704317/fneur-17-1704317-HTML/image_m/fneur-17-1704317-t002.jpg Table 2. Mean Diceobj detection rate of lesion volumes measured on the MICCAI 2016 dataset. https://www.frontiersin.org/files/Articles/1704317/fneur-17-1704317-HTML/image_m/fneur-17-1704317-g005.jpg Figure 5. Qualitative assessment of lesion classification approach. The color code for automatically https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1752870/full https://www.frontiersin.org/files/Articles/1752870/fendo-17-1752870-HTML-r1/image_m/fendo-17-1752870-t001.jpg Table 1. Clinical characteristics of patients with or without endometriosis. https://www.frontiersin.org/files/Articles/1752870/fendo-17-1752870-HTML-r1/image_m/fendo-17-1752870-g001.jpg Figure 1. Comparison of ERα36 expression between endometriosis vs. non- endometriosis tissues. (A) P https://www.frontiersin.org/files/Articles/1752870/fendo-17-1752870-HTML-r1/image_m/fendo-17-1752870-t002.jpg Table 2. Pathological characteristics of endometriosis patients with ERα36+ or ERα36- expression. https://www.frontiersin.org/files/Articles/1752870/fendo-17-1752870-HTML-r1/image_m/fendo-17-1752870-g002.jpg Figure 2. Expression of PGP9.5 in eutopic and ectopic endometrium measured by Immunohistochemistry. https://www.frontiersin.org/files/Articles/1752870/fendo-17-1752870-HTML-r1/image_m/fendo-17-1752870-g003.jpg Figure 3. Nerve fiber density in endometriotic lesions. Quantification of nerve fiber densities (mm² https://www.frontiersin.org/files/Articles/1752870/fendo-17-1752870-HTML-r1/image_m/fendo-17-1752870-t003.jpg Table 3. Correlation between ERα36 and PGP9.5 expression in endometriosis endometrium. https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2026.1720114/full https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-t001.jpg Table 1. Phases of political communication. https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-t002.jpg Table 2. Party press in Portugal. https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-g001.jpg Figure 1. Electoral results of CHEGA. Source: www.cne.pt. Consulted on 10 July 2025. https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-t003.jpg Table 3. Folha Nacional front page’s main image. https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-g002.jpg Figure 2. Main political actors on the front page of Folha Nacional. https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-g003.jpg Figure 3. Cover 5 January 2024. Reprinted with permission from Folha Nacional, https://folhanacional https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-g004.jpg Figure 4. Cover 10 May 2024. Reprinted with permission from Folha Nacional, https://folhanacional.pt https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-g005.jpg Figure 5. Folha Nacional front page’s main topics/themes. https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-t004.jpg Table 4. Top story and headline. https://www.frontiersin.org/files/Articles/1720114/fpos-08-1720114-HTML/image_m/fpos-08-1720114-g006.jpg Figure 6. Typology of the main contents of the covers. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1694807/full https://www.frontiersin.org/files/Articles/1694807/fpubh-14-1694807-HTML/image_m/fpubh-14-1694807-t001.jpg Table 1. Prevalence of otitis media in 1990 and 2021. https://www.frontiersin.org/files/Articles/1694807/fpubh-14-1694807-HTML/image_m/fpubh-14-1694807-t002.jpg Table 2. Incidence of otitis media in 1990 and 2021. https://www.frontiersin.org/files/Articles/1694807/fpubh-14-1694807-HTML/image_m/fpubh-14-1694807-t003.jpg Table 3. YLDs of otitis media in 1990 and 2021. https://www.frontiersin.org/files/Articles/1694807/fpubh-14-1694807-HTML/image_m/fpubh-14-1694807-g001.jpg Figure 1. Global distribution of age-standardized rates of otitis media (OM) burden in 2021. (A) Age https://www.frontiersin.org/files/Articles/1694807/fpubh-14-1694807-HTML/image_m/fpubh-14-1694807-g002.jpg Figure 2. Correlation between socio-demographic index and age-standardized rates of otitis media bur https://www.frontiersin.org/files/Articles/1694807/fpubh-14-1694807-HTML/image_m/fpubh-14-1694807-g003.jpg Figure 3. Age-period-cohort analysis of otitis media disease burden across global and Socio-demograp https://www.frontiersin.org/files/Articles/1694807/fpubh-14-1694807-HTML/image_m/fpubh-14-1694807-g004.jpg Figure 4. Frontier analysis of global otitis media burden by Socio-demographic Index, 1990–2021. Eff https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1773453/full https://www.frontiersin.org/files/Articles/1773453/fmicb-17-1773453-HTML/image_m/fmicb-17-1773453-g001.jpg Figure 1. (A) Field view indicating the vertical profile of the central lake water column (0,−11 m) https://www.frontiersin.org/files/Articles/1773453/fmicb-17-1773453-HTML/image_m/fmicb-17-1773453-t001.jpg Table 1. Temperature, pH, and electrical conductivity measured during both sampling, and nutrient co https://www.frontiersin.org/files/Articles/1773453/fmicb-17-1773453-HTML/image_m/fmicb-17-1773453-g002.jpg Figure 2. Relative abundance (%) of the most prevalent phyla of Bacteria and Archaea in water sample https://www.frontiersin.org/files/Articles/1773453/fmicb-17-1773453-HTML/image_m/fmicb-17-1773453-g003.jpg Figure 3. (A) CLSM combined images showing the spatial distribution of the Plantomycetota phylum (gr https://www.frontiersin.org/files/Articles/1773453/fmicb-17-1773453-HTML/image_m/fmicb-17-1773453-g004.jpg Figure 4. (A) Relative abundance (%) of the most prevalent phyla of Bacteria and Archaea in water an https://www.frontiersin.org/files/Articles/1773453/fmicb-17-1773453-HTML/image_m/fmicb-17-1773453-g005.jpg Figure 5. (A) Overlap of OTUs among surface-water communities from the central lake (CL0m), the hydr https://www.frontiersin.org/files/Articles/1773453/fmicb-17-1773453-HTML/image_m/fmicb-17-1773453-t002.jpg Table 2. Alpha diversity indices (observed richness, Shannon diversity, Simpson diversity, and Pielo https://www.frontiersin.org/files/Articles/1773453/fmicb-17-1773453-HTML/image_m/fmicb-17-1773453-g006.jpg Figure 6. Principal Coordinates Analysis (PCoA) of microbial community composition based on Bray–Cur https://www.frontiersin.org/files/Articles/1773453/fmicb-17-1773453-HTML/image_m/fmicb-17-1773453-g007.jpg Figure 7. Distribution of metabolic functions obtained from FAPROTAX. The matrix was row-normalized https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1659900/full https://www.frontiersin.org/files/Articles/1659900/fpubh-14-1659900-HTML/image_m/fpubh-14-1659900-g001.jpg Figure 1. Graphical representation of the relationship between the variables of interest. Path (a) r https://www.frontiersin.org/files/Articles/1659900/fpubh-14-1659900-HTML/image_m/fpubh-14-1659900-g002.jpg Figure 2. Trend of self-reported health, Italy, 2013–2021. Trend of self-reported health, Italy, 201 https://www.frontiersin.org/files/Articles/1659900/fpubh-14-1659900-HTML/image_m/fpubh-14-1659900-t001.jpg Table 1. Logistic regression: average marginal effects (AME) for probability of being healthy (selec https://www.frontiersin.org/files/Articles/1659900/fpubh-14-1659900-HTML/image_m/fpubh-14-1659900-t002.jpg Table 2. Mediation effects, percentage of the total effect of the education attainment due to each m https://www.frontiersin.org/files/Articles/1659900/fpubh-14-1659900-HTML/image_m/fpubh-14-1659900-t003.jpg Table 3. Mediation effects, percentage of total effect of education due to economic resources and ph https://www.frontiersin.org/files/Articles/1659900/fpubh-14-1659900-HTML/image_m/fpubh-14-1659900-t004.jpg Table 4. Mediation effects, percentage of total effect of education due to economic resources and ph https://www.frontiersin.org/files/Articles/1659900/fpubh-14-1659900-HTML/image_m/fpubh-14-1659900-t005.jpg Table 5. Mediation effects, percentage of total effect of education due to economic resources and ph https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1783075/full https://www.frontiersin.org/files/Articles/1783075/fpsyt-17-1783075-HTML-r1/image_m/fpsyt-17-1783075-t001.jpg Table 1. Descriptive statistics of participant characteristics by group. https://www.frontiersin.org/files/Articles/1783075/fpsyt-17-1783075-HTML-r1/image_m/fpsyt-17-1783075-g001.jpg Figure 1. Procedure for administering sensory stimuli. (A) Illustration of the participant’s setup f https://www.frontiersin.org/files/Articles/1783075/fpsyt-17-1783075-HTML-r1/image_m/fpsyt-17-1783075-t002.jpg Table 2. Definitions of the four electrodermal parameters. https://www.frontiersin.org/files/Articles/1783075/fpsyt-17-1783075-HTML-r1/image_m/fpsyt-17-1783075-g002.jpg Figure 2. Overview of statistical comparisons across sensory conditions and exposure levels for auti https://www.frontiersin.org/files/Articles/1783075/fpsyt-17-1783075-HTML-r1/image_m/fpsyt-17-1783075-t003.jpg Table 3. Results of the functional permutation test (FP-test) and Wilcoxon-Mann-Whitney U-test compa https://www.frontiersin.org/files/Articles/1783075/fpsyt-17-1783075-HTML-r1/image_m/fpsyt-17-1783075-t004.jpg Table 4. Results of the functional permutation test (FP-test) for within-group comparisons in autist https://www.frontiersin.org/files/Articles/1783075/fpsyt-17-1783075-HTML-r1/image_m/fpsyt-17-1783075-g003.jpg Figure 3. Mean SCR signal plots for each stimulus type (visual, auditory, and audiovisual) and the b https://www.frontiersin.org/files/Articles/1783075/fpsyt-17-1783075-HTML-r1/image_m/fpsyt-17-1783075-g004.jpg Figure 4. Mean SCR signal plots for each stimulus type (visual, auditory, and audiovisual) and the b https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1594757/full https://www.frontiersin.org/files/Articles/1594757/fphar-16-1594757-HTML/image_m/fphar-16-1594757-g008.jpg GRAPHICAL ABSTRACT | Schematic representation of Quercetin’s anti-fibrotic effects through the FSTL1 https://www.frontiersin.org/files/Articles/1594757/fphar-16-1594757-HTML/image_m/fphar-16-1594757-t001.jpg Table 1. Sequences of the primers for qRT-PCR. https://www.frontiersin.org/files/Articles/1594757/fphar-16-1594757-HTML/image_m/fphar-16-1594757-g001.jpg Figure 1. Quercetin alleviates BLM-induced pulmonary fibrosis and inflammation in pulmonary fibrosis https://www.frontiersin.org/files/Articles/1594757/fphar-16-1594757-HTML/image_m/fphar-16-1594757-g002.jpg Figure 2. Quercetin alleviates BLM-induced EMT via FSTL1/NF-κB signaling pathway in a pulmonary fibr https://www.frontiersin.org/files/Articles/1594757/fphar-16-1594757-HTML/image_m/fphar-16-1594757-g003.jpg Figure 3. TGF-β1 induces EMT via the FSTL1/NF-κB signaling pathway in vitro. (A) Protein expression https://www.frontiersin.org/files/Articles/1594757/fphar-16-1594757-HTML/image_m/fphar-16-1594757-g004.jpg Figure 4. Overexpression of FSTL1 induces TGF-β1 expression and EMT in A549 cells. (A) Analysis of F https://www.frontiersin.org/files/Articles/1594757/fphar-16-1594757-HTML/image_m/fphar-16-1594757-g005.jpg Figure 5. FSTL1 knockout attenuates TGF-β1-induced pulmonary fibrosis and inflammation in A549 cells https://www.frontiersin.org/files/Articles/1594757/fphar-16-1594757-HTML/image_m/fphar-16-1594757-g006.jpg Figure 6. Quercetin alleviates TGF-β1-induced EMT via the FSTL1/NF-κB signaling pathway in vitro. (A https://www.frontiersin.org/files/Articles/1594757/fphar-16-1594757-HTML/image_m/fphar-16-1594757-g007.jpg Figure 7. FSTL1 knockout abrogates the anti-fibrotic and anti-inflammatory effects of quercetin in A https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1790974/full https://www.frontiersin.org/files/Articles/1790974/fpsyg-17-1790974-HTML/image_m/fpsyg-17-1790974-g001.jpg Figure 1. Experimental setting. The two players sat in front of a desk with a tic-tac-toe board plac https://www.frontiersin.org/files/Articles/1790974/fpsyg-17-1790974-HTML/image_m/fpsyg-17-1790974-g002.jpg Figure 2. Affective questionnaire. Every three matches the players had to answer two questions: the https://www.frontiersin.org/files/Articles/1790974/fpsyg-17-1790974-HTML/image_m/fpsyg-17-1790974-g003.jpg Figure 3. Kinematic parameters. The first two graphs showed the kinematic differences in the reachin https://www.frontiersin.org/files/Articles/1790974/fpsyg-17-1790974-HTML/image_m/fpsyg-17-1790974-t001.jpg Table 1. Results and statistical values of the main kinematic features analyzed during the experimen https://www.frontiersin.org/files/Articles/1790974/fpsyg-17-1790974-HTML/image_m/fpsyg-17-1790974-g004.jpg Figure 4. The graphs represent an example of how each participant’s peak velocity varied across the https://www.frontiersin.org/files/Articles/1790974/fpsyg-17-1790974-HTML/image_m/fpsyg-17-1790974-g005.jpg Figure 5. The EQ scores were correlated with the VFs effect of different kinematic parameters. Parti https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1780231/full https://www.frontiersin.org/files/Articles/1780231/fgene-17-1780231-HTML/image_m/fgene-17-1780231-g001.jpg Figure 1. Principal component analysis reveals distinct primary and secondary transcriptional progra https://www.frontiersin.org/files/Articles/1780231/fgene-17-1780231-HTML/image_m/fgene-17-1780231-g002.jpg Figure 2. Targeted transcriptomic profiling of the top 25 differentially expressed genes (DEGs) in c https://www.frontiersin.org/files/Articles/1780231/fgene-17-1780231-HTML/image_m/fgene-17-1780231-g003.jpg Figure 3. PDK4 emerged at xenobiotic metabolism (a) Gene Set Enrichment Analysis (GSEA) of bulk RNA- https://www.frontiersin.org/files/Articles/1780231/fgene-17-1780231-HTML/image_m/fgene-17-1780231-g004.jpg Figure 4. Comparative genomic features underlying relative mutability of PDK4 and stress-response ge https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1759043/full https://www.frontiersin.org/files/Articles/1759043/fmed-13-1759043-HTML/image_m/fmed-13-1759043-t001.jpg Table 1. Baseline characteristics of included patients. https://www.frontiersin.org/files/Articles/1759043/fmed-13-1759043-HTML/image_m/fmed-13-1759043-t002.jpg Table 2. Eradication rate and symptom relief rate. https://www.frontiersin.org/files/Articles/1759043/fmed-13-1759043-HTML/image_m/fmed-13-1759043-t003.jpg Table 3. Correlation between Helicobacter pylori eradication and symptom improvement. https://www.frontiersin.org/files/Articles/1759043/fmed-13-1759043-HTML/image_m/fmed-13-1759043-g001.jpg Figure 1. Correlation between Helicobacter pylori eradication and symptom improvement. https://www.frontiersin.org/files/Articles/1759043/fmed-13-1759043-HTML/image_m/fmed-13-1759043-t004.jpg Table 4. Incidence of adverse events. https://www.frontiersin.org/files/Articles/1759043/fmed-13-1759043-HTML/image_m/fmed-13-1759043-g002.jpg Figure 2. Total eradication rate, symptom relief rate, and incidence of adverse events. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1644411/full https://www.frontiersin.org/files/Articles/1644411/fonc-15-1644411-HTML/image_m/fonc-15-1644411-g001.jpg Figure 1. Schematic diagram of average peak enhancement rate parameters. https://www.frontiersin.org/files/Articles/1644411/fonc-15-1644411-HTML/image_m/fonc-15-1644411-g002.jpg Figure 2. Grading of spatial distribution characteristics of DIC parameters based on average peak en https://www.frontiersin.org/files/Articles/1644411/fonc-15-1644411-HTML/image_m/fonc-15-1644411-t001.jpg Table 1. Baseline data of patients. https://www.frontiersin.org/files/Articles/1644411/fonc-15-1644411-HTML/image_m/fonc-15-1644411-g003.jpg Figure 3. Stacked bar chart of the ratio of benign to malignant with changes in grading. https://www.frontiersin.org/files/Articles/1644411/fonc-15-1644411-HTML/image_m/fonc-15-1644411-t002.jpg Table 2. Intra-group consistency analysis. https://www.frontiersin.org/files/Articles/1644411/fonc-15-1644411-HTML/image_m/fonc-15-1644411-t003.jpg Table 3. Intergroup consistency (Kappa) analysis. https://www.frontiersin.org/files/Articles/1644411/fonc-15-1644411-HTML/image_m/fonc-15-1644411-g004.jpg Figure 4. ROC curves for the four diagnostic scenarios. https://www.frontiersin.org/files/Articles/1644411/fonc-15-1644411-HTML/image_m/fonc-15-1644411-t004.jpg Table 4. Diagnostic performance metrics and ROC analysis. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1648347/full https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t001.jpg Table 1. Elution gradient. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t002.jpg Table 2. MRM parameter data. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t003.jpg Table 3. Calibration standard C1-C6 concentration data. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-g001.jpg Figure 1. Calibration curves of IGF-1 (10–500 ng/mL). https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t004.jpg Table 4. Linear reference standard detection results. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t005.jpg Table 5. Detection limit detection results. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t006.jpg Table 6. Spiked sample recovery. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t007.jpg Table 7. Intraday precision. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t008.jpg Table 8. Inter-operator precision. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t009.jpg Table 9. Inter-Participant Sampling results. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t010.jpg Table 10. Thermal Stability results. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t011.jpg Table 11. Sample Stability results. https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-g002.jpg Figure 2. Comparison of IGF-I in QAMS and plasma. The correlations of venous blood plasma and corres https://www.frontiersin.org/files/Articles/1648347/fbioe-13-1648347-HTML-r1/image_m/fbioe-13-1648347-t012.jpg Table 12. Hematocrit (HCT) Effect results. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1758644/full https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-g001.jpg Figure 1. Representative ultrasound and histopathology of ovarian tumors. (A) Benign mucinous cystad https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-t001.jpg Table 1. Clinical characteristics of patients with different pathological types of tumors. https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-t002.jpg Table 2. Serological markers of patients with different pathological types of tumors. https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-g002.jpg Figure 2. Two side-by-side receiver operating characteristic (ROC) curve graphs compare diagnostic p https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-t003.jpg Table 3. Ultrasonographic features of patients with different pathological types of tumors. https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-g003.jpg Figure 3. Two side-by-side ROC curve charts labeled A and B display sensitivity versus one minus spe https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-t004.jpg Table 4. Diagnostic efficacy of serological markers for BOETs. https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-t005.jpg Table 5. Diagnostic efficacy of sonographic features for BOETs. https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-t006.jpg Table 6. Diagnostic efficacy of combined serological markers and sonographic features for BOETs. https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-g004.jpg Figure 4. Two side-by-side ROC curve charts labeled A and B compare diagnostic performance. Both cha https://www.frontiersin.org/files/Articles/1758644/fonc-16-1758644-HTML/image_m/fonc-16-1758644-t007.jpg Table 7. Comparison of BOETs by different pathological subtypes (serous and mucinous). https://www.frontiersin.org/journals/organizational-psychology/articles/10.3389/forgp.2025.1525043/full https://www.frontiersin.org/files/Articles/1525043/forgp-03-1525043-HTML/image_m/forgp-03-1525043-g001.jpg Figure 1. PRISMA diagram of the review. https://www.frontiersin.org/files/Articles/1525043/forgp-03-1525043-HTML/image_m/forgp-03-1525043-t001.jpg Table 1. Articles included for attribute analysis.a https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2026.1762332/full https://www.frontiersin.org/files/Articles/1762332/fcomp-08-1762332-HTML-r1/image_m/fcomp-08-1762332-g001.jpg Figure 1. Taxonomy of XAI methods for cyber defenses (Moustafa et al., 2023). https://www.frontiersin.org/files/Articles/1762332/fcomp-08-1762332-HTML-r1/image_m/fcomp-08-1762332-g002.jpg Figure 2. XAI key concepts and relationships (Gummadi et al., 2024). https://www.frontiersin.org/files/Articles/1762332/fcomp-08-1762332-HTML-r1/image_m/fcomp-08-1762332-t001.jpg Table 1. XAI applications to cybersecurity from recently published papers. https://www.frontiersin.org/files/Articles/1762332/fcomp-08-1762332-HTML-r1/image_m/fcomp-08-1762332-t002.jpg Table 2. Comparison of XAI techniques from recently published papers. https://www.frontiersin.org/files/Articles/1762332/fcomp-08-1762332-HTML-r1/image_m/fcomp-08-1762332-g003.jpg Figure 3. Evaluation framework for XAI effectiveness in cybersecurity. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1676192/full https://www.frontiersin.org/files/Articles/1676192/fpubh-13-1676192-HTML/image_m/fpubh-13-1676192-g001.jpg Figure 1. The COM-B model (A) and the hypothetical model (B). https://www.frontiersin.org/files/Articles/1676192/fpubh-13-1676192-HTML/image_m/fpubh-13-1676192-g002.jpg Figure 2. Composition of chronic disease types among participants. https://www.frontiersin.org/files/Articles/1676192/fpubh-13-1676192-HTML/image_m/fpubh-13-1676192-t001.jpg Table 1. Fundamental characteristics of participants (N = 1,550). https://www.frontiersin.org/files/Articles/1676192/fpubh-13-1676192-HTML/image_m/fpubh-13-1676192-t002.jpg Table 2. Descriptive analysis of variables (N = 1,550). https://www.frontiersin.org/files/Articles/1676192/fpubh-13-1676192-HTML/image_m/fpubh-13-1676192-g003.jpg Figure 3. Correlations among the main study variables (N = 1,550). https://www.frontiersin.org/files/Articles/1676192/fpubh-13-1676192-HTML/image_m/fpubh-13-1676192-t003.jpg Table 3. Standardized effects of the mediating model of walking (N = 1,550). https://www.frontiersin.org/files/Articles/1676192/fpubh-13-1676192-HTML/image_m/fpubh-13-1676192-g004.jpg Figure 4. Mediation model of HL on the relationship between PSS, GSE, and walking in patients with c https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1759268/full https://www.frontiersin.org/files/Articles/1759268/fneur-17-1759268-HTML/image_m/fneur-17-1759268-g001.jpg Figure 1. Normal platelet count and size but increased Aß binding to platelets from AD patients. (A) https://www.frontiersin.org/files/Articles/1759268/fneur-17-1759268-HTML/image_m/fneur-17-1759268-g002.jpg Figure 2. Unaltered glycoprotein expression at the surface of platelets from AD patients. Glycoprote https://www.frontiersin.org/files/Articles/1759268/fneur-17-1759268-HTML/image_m/fneur-17-1759268-g003.jpg Figure 3. Different number of granules in platelets from AD patients. Analysis of the number of (A) https://www.frontiersin.org/files/Articles/1759268/fneur-17-1759268-HTML/image_m/fneur-17-1759268-g004.jpg Figure 4. Significantly increased open canalicular system in platelets from AD patients. The area of https://www.frontiersin.org/files/Articles/1759268/fneur-17-1759268-HTML/image_m/fneur-17-1759268-g005.jpg Figure 5. Platelets from AD patients show no major differences in platelet integrin activation and d https://www.frontiersin.org/files/Articles/1759268/fneur-17-1759268-HTML/image_m/fneur-17-1759268-g006.jpg Figure 6. Sex-specific analysis of platelet activation revealed a reduced activation profile in fema https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1716917/full https://www.frontiersin.org/files/Articles/1716917/fcvm-12-1716917-HTML/image_m/fcvm-12-1716917-g001.jpg Figure 1. ECG and coronary angiography findings of case1: (A) baseline ECG (pre-exercise). (B) ECG a https://www.frontiersin.org/files/Articles/1716917/fcvm-12-1716917-HTML/image_m/fcvm-12-1716917-g002.jpg Figure 2. ECG and coronary angiography findings of case 2: (A) ECG during an asymptomatic period, sh https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1646640/full https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-t001.jpg Table 1. Baseline characteristics of all patients included in this study. https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-g001.jpg Figure 1. Association of EASIX, s-EASIX and m-EASIX scores and main outcomes in the training cohort https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-g002.jpg Figure 2. Overall survival and Relapse-free survival according to D7-m-EASIX score in the training c https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-t002.jpg Table 2. Univariate analysis of risk factors for main outcomes in the training cohort. https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-t003.jpg Table 3. Multivariate analysis of risk factors for main outcomes in the training cohort. https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-g003.jpg Figure 3. The cumulative incidences of relapse and non-relapse morality according to D7-m-EASIX scor https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-g004.jpg Figure 4. Distribution of D7-m-EASIX scores across II-IV aGVHD in the training cohort (aGVHD: n = 58 https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-g005.jpg Figure 5. The impact of D7-m-EASIX score on GVHD in the training cohort. (a) cumulative incidence of https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-t004.jpg Table 4. Univariate analysis and multivariate analysis of II-IV aGVHD in the training cohort. https://www.frontiersin.org/files/Articles/1646640/fimmu-16-1646640-HTML/image_m/fimmu-16-1646640-g006.jpg Figure 6. The correlation between age and D7-m-EASIX score in the training cohort. A scatter plot di https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1776361/full https://www.frontiersin.org/files/Articles/1776361/fgene-17-1776361-HTML/image_m/fgene-17-1776361-g001.jpg Figure 1. FAP pedigree and Sanger sequencing of APC c.3799dup variant (A). The FAP pedigree. The arr https://www.frontiersin.org/files/Articles/1776361/fgene-17-1776361-HTML/image_m/fgene-17-1776361-t001.jpg Table 1. Primer sequences used for mutation sequencing of APC gene. https://www.frontiersin.org/files/Articles/1776361/fgene-17-1776361-HTML/image_m/fgene-17-1776361-g002.jpg Figure 2. Clinical and histopathological manifestations of the colon in FAP family members. (A) Mult https://www.frontiersin.org/files/Articles/1776361/fgene-17-1776361-HTML/image_m/fgene-17-1776361-t002.jpg Table 2. Clinical and genetic characteristics of participants in the family https://www.frontiersin.org/files/Articles/1776361/fgene-17-1776361-HTML/image_m/fgene-17-1776361-g003.jpg Figure 3. Effect of wild-type and mutant APC on β-catenin protein expression in SW480 cells. (A) Rep https://www.frontiersin.org/files/Articles/1776361/fgene-17-1776361-HTML/image_m/fgene-17-1776361-g004.jpg Figure 4. APC protein structure, variant location, and genotype–phenotype correlations. (A) Schemati https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1666522/full https://www.frontiersin.org/files/Articles/1666522/fmed-12-1666522-HTML-r1/image_m/fmed-12-1666522-g002.jpg Graphical Abstract https://www.frontiersin.org/files/Articles/1666522/fmed-12-1666522-HTML-r1/image_m/fmed-12-1666522-t001.jpg Table 1. Monogenic causes and phenotypes of lymphedema. https://www.frontiersin.org/files/Articles/1666522/fmed-12-1666522-HTML-r1/image_m/fmed-12-1666522-t002.jpg Table 2. Biomarkers or pathways of lymphedema. https://www.frontiersin.org/files/Articles/1666522/fmed-12-1666522-HTML-r1/image_m/fmed-12-1666522-t003.jpg Table 3. Clinical manifestations and sorting criteria. https://www.frontiersin.org/files/Articles/1666522/fmed-12-1666522-HTML-r1/image_m/fmed-12-1666522-t004.jpg Table 4. Staging systems. https://www.frontiersin.org/files/Articles/1666522/fmed-12-1666522-HTML-r1/image_m/fmed-12-1666522-t005.jpg Table 5. Imaging technologies. https://www.frontiersin.org/files/Articles/1666522/fmed-12-1666522-HTML-r1/image_m/fmed-12-1666522-g001.jpg Figure 1. Diagnostic decision making flow chart for lymphedema. https://www.frontiersin.org/files/Articles/1666522/fmed-12-1666522-HTML-r1/image_m/fmed-12-1666522-t006.jpg Table 6. Clinical evidence table of main treatment methods. https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1746644/full https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g001.jpg Figure 1. Conventional Metal−Ligand Cooperation mechanism of strongly bonded (a) and hemilabile (b) https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g006.jpg Scheme 1. Selective HY of dimethyl oxalate to 2-hydroxyacetate catalyzed by Ru (acac)3/TriSulfBu 1 a https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g007.jpg Scheme 2. Phosphorus- and sulfur-amino Ru-complexes 2a-k and Ru-MACHO® for C=C, C=N and C=O HY. a) T https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g008.jpg Scheme 3. Comparative performances of phosphine- and thioether-based complexes 2a-j in the selective https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g009.jpg Scheme 4. Tunable chemoselective C=C and C=O HY of α,β-unsaturated aldehydes and esters catalyzed by https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g010.jpg Scheme 5. HY of aromatic ketones catalyzed by complex 6. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g011.jpg Scheme 6. Semi HY of methyl trifluoroacetate to its methyl hemiacetal catalyzed by complexes 2a, 6–8 https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g012.jpg Scheme 7. HY of ketones, benzaldehyde and (E)-N,1-diphenylmethanimine catalyzed by half-sandwich thi https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g013.jpg Scheme 8. Indirect and direct HY of CO2 assisted by cyclic amines catalyzed by 12, 13 and assisted b https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g014.jpg Scheme 9. HY of C–C multiple bonds catalyzed by 14. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g002.jpg Figure 2. Thioether-based half-sandwich complexes bearing heterocycles. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-t001.jpg Table 1. Comparison of catalytic activities of complexes 15–23 in the TH of acetophenone. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g015.jpg Scheme 10. TH of acetophenone catalyzed by 24–27. a) TON values are calculated as (moles of products https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g016.jpg Scheme 11. TY of acetophenone catalyzed by thiolato complexes 28 and 29 a) TON values are calculated https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g017.jpg Scheme 12. TH of acetophenone catalyzed by catalysts 30a,b and 31a,b. a) TON values are calculated a https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g003.jpg Figure 3. Thioamide and thiourea coordinating complexes. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-t002.jpg Table 2. TH of acetophenone and nitrobenzene catalyzed by complexes 32–42. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g018.jpg Scheme 13. Double TH catalyzed by manganese SNN complexes 43 and 44. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g019.jpg Scheme 14. Generation of the catalytically active M–H species from complex 45 and hydrosilylation of https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g020.jpg Scheme 15. Dehydrogenative silylation of ketones, hydrosylilation of CO2 and pyridines and hydrodefl https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g021.jpg Scheme 16. Selectivity in the hydrosilylation of ketimines with Me2PhSiH catalyzed by complexes 9a,c https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g022.jpg Scheme 17. Silane-promoted TH of imines, ketones and nitroarenes catalyzed by complexes 46–48 and hy https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g023.jpg Scheme 18. Hydrosilylation of acetophenone mediated by complex 49 and hydroboration of acetophenone https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g024.jpg Scheme 19. Enantioselective TH of ketones to optically active carbinols catalyzed by in situ generat https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g025.jpg Scheme 20. Enantioselective TH of ketones to optically active carbinols promoted by iPrOH and cataly https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g026.jpg Scheme 21. Enantioselective HY of β-ketoesters, ketones and α-alkyl-β-ketosulfonamides catalyzed by https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g027.jpg Scheme 22. Enantioselective TH of ketones to optically active carbinols promoted by iPrOH and cataly https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g028.jpg Scheme 23. Dehydrogenative silylation of imines and ketones with silanes catalyzed by complex 9d. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g029.jpg Scheme 24. Representation of a generic borrowing hydrogen process mediated by a transition metal com https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g030.jpg Scheme 25. N-Benzylation of aniline with benzyl alcohol catalyzed by 65, 66 or 30, 31 and 67. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g031.jpg Scheme 26. N-Alkylation reactions catalyzed by complex 68. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g032.jpg Scheme 27. Acid/base modulation of N-acetonylation and N-hydroxyethylation of secondary amines using https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g033.jpg Scheme 28. BH reactions catalyzed by Mn-based catalysts 43 and 44. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g034.jpg Scheme 29. Mechanism of thiolate-based MLC for H-H and Si-H activation. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g004.jpg Figure 4. Principal mechanistic pathways for sulfur-based metal complexes. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-g005.jpg Figure 5. Mechanism of thioester-based MLC for hydroborination reaction with catalysts. https://www.frontiersin.org/files/Articles/1746644/fchem-13-1746644-HTML-r1/image_m/fchem-13-1746644-t003.jpg Table 3. Advantages and Disadvantages of sulfur Ligands. https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2026.1731940/full https://www.frontiersin.org/files/Articles/1731940/fresc-07-1731940-HTML/image_m/fresc-07-1731940-t001.jpg Table 1. General characteristics of the study population. https://www.frontiersin.org/files/Articles/1731940/fresc-07-1731940-HTML/image_m/fresc-07-1731940-t002.jpg Table 2. Vital signs and perceived exertion in different functional tests. https://www.frontiersin.org/files/Articles/1731940/fresc-07-1731940-HTML/image_m/fresc-07-1731940-g001.jpg Figure 1. Correlation between functional tests and aerobic capacity estimated by NASA MET and VO₂. https://www.frontiersin.org/files/Articles/1731940/fresc-07-1731940-HTML/image_m/fresc-07-1731940-g002.jpg Figure 2. Comparison of the hemodynamic effort index across the four functional tests. https://www.frontiersin.org/files/Articles/1731940/fresc-07-1731940-HTML/image_m/fresc-07-1731940-g003.jpg Figure 3. Correlation between estimated VO₂ using the NASA formula and the hemodynamic effort index https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1751628/full https://www.frontiersin.org/files/Articles/1751628/fonc-16-1751628-HTML/image_m/fonc-16-1751628-g001.jpg Figure 1. PRISMA flow diagram of the systematic review process (18). A total of 318 records were ide https://www.frontiersin.org/files/Articles/1751628/fonc-16-1751628-HTML/image_m/fonc-16-1751628-t001.jpg Table 1. Summary of included clinical studies evaluating pharmacotherapeutic agents for the preventi https://www.frontiersin.org/files/Articles/1751628/fonc-16-1751628-HTML/image_m/fonc-16-1751628-g002.jpg Figure 2. (A–C) present graphical summaries of the risk of bias assessments for all included studies https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1645991/full https://www.frontiersin.org/files/Articles/1645991/fimmu-16-1645991-HTML/image_m/fimmu-16-1645991-g001.jpg Figure 1. Figure 1 presents a multidimensional risk factor framework for HIV-associated depression. https://www.frontiersin.org/files/Articles/1645991/fimmu-16-1645991-HTML/image_m/fimmu-16-1645991-g002.jpg Figure 2. Figure 2 illustrates the complex interaction between inflammation and metabolic dysregulat https://www.frontiersin.org/files/Articles/1645991/fimmu-16-1645991-HTML/image_m/fimmu-16-1645991-g003.jpg Figure 3. Treatment strategies for depression in HIV infection. This figure delineates treatment app https://www.frontiersin.org/journals/food-science-and-technology/articles/10.3389/frfst.2025.1692329/full https://www.frontiersin.org/files/Articles/1692329/frfst-05-1692329-HTML/image_m/frfst-05-1692329-g004.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1692329/frfst-05-1692329-HTML/image_m/frfst-05-1692329-g001.jpg Figure 1. Fluorescence spectra of binding interactions between (a) whey and pea protein nanofibrils https://www.frontiersin.org/files/Articles/1692329/frfst-05-1692329-HTML/image_m/frfst-05-1692329-g002.jpg Figure 2. Molecular interactions of linear and curly nanofibrils prepared from pea (a) and whey (b) https://www.frontiersin.org/files/Articles/1692329/frfst-05-1692329-HTML/image_m/frfst-05-1692329-g003.jpg Figure 3. Morphology of protein nanofibrils synthesized from pea and whey protein isolate and measur https://www.frontiersin.org/files/Articles/1692329/frfst-05-1692329-HTML/image_m/frfst-05-1692329-t001.jpg Table 1. Structural properties of nanofibrils as analyzed using AFM imaging. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1692281/full https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-t001.jpg Table 1. Risk of bias summary for observational studies (Newcastle–Ottawa Scale). https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-t002.jpg Table 2. Risk of bias summary for randomized trials (Cochrane RoB 2). https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-t003.jpg Table 3. Evidence on VDR polymorphisms, vitamin D status, and pulmonary TB outcomes organized by dom https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-g001.jpg Figure 1. Role and importance of vitamin D in human body. The figure depicts the vitamin D–VDR pathw https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-g002.jpg Figure 2. Metabolic function of vitamin D in human body. Vitamin D metabolism in humans: UVB-driven https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-g003.jpg Figure 3. Gene arrangements and restriction sites of the vitamin D receptor (VDR) gene. Genomic orga https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-g004.jpg Figure 4. Flow diagram for new systematic review on VDR/vitamin D and MDR−PTB outcomes. https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-t004.jpg Table 4. Comparison of VDR genotype frequencies in pulmonary tuberculosis in various studies. https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-g005.jpg Figure 5. Host vitamin D signaling and VDR polymorphisms shaping immune response and MDR-TB outcomes https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-t005.jpg Table 5A. Restriction enzyme recognition based on SNPs in VDR gene. https://www.frontiersin.org/files/Articles/1692281/fcimb-16-1692281-HTML/image_m/fcimb-16-1692281-t006.jpg Table 5B. Summary of key studies on VDR polymorphisms relevant to MDR-TB. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1656350/full https://www.frontiersin.org/files/Articles/1656350/feduc-11-1656350-HTML/image_m/feduc-11-1656350-t001.jpg Table 1. Participant demographic information. https://www.frontiersin.org/files/Articles/1656350/feduc-11-1656350-HTML/image_m/feduc-11-1656350-t002.jpg Table 2. Questions, descriptions, and example codes. https://www.frontiersin.org/files/Articles/1656350/feduc-11-1656350-HTML/image_m/feduc-11-1656350-t003.jpg Table 3. Alignment between research questions, themes, and codes. https://www.frontiersin.org/files/Articles/1656350/feduc-11-1656350-HTML/image_m/feduc-11-1656350-g001.jpg Figure 1. Sources and outcomes shaping engineering doctoral students’ teaching philosophy. https://www.frontiersin.org/files/Articles/1656350/feduc-11-1656350-HTML/image_m/feduc-11-1656350-g002.jpg Figure 2. Sources and outcomes shaping engineering doctoral students’ mentoring philosophy. https://www.frontiersin.org/files/Articles/1656350/feduc-11-1656350-HTML/image_m/feduc-11-1656350-t004.jpg Table 4. Mapping emergent themes to guiding theoretical perspectives. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1769050/full https://www.frontiersin.org/files/Articles/1769050/fmolb-13-1769050-HTML/image_m/fmolb-13-1769050-g002.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1769050/fmolb-13-1769050-HTML/image_m/fmolb-13-1769050-t001.jpg Table 1. Characteristics of the population sample revealing univariate differences between the two g https://www.frontiersin.org/files/Articles/1769050/fmolb-13-1769050-HTML/image_m/fmolb-13-1769050-t002.jpg Table 2. Additional clinical characteristics of the study group. https://www.frontiersin.org/files/Articles/1769050/fmolb-13-1769050-HTML/image_m/fmolb-13-1769050-t003.jpg Table 3. Firth penalized logistic regression model investigating the association of the clinical fea https://www.frontiersin.org/files/Articles/1769050/fmolb-13-1769050-HTML/image_m/fmolb-13-1769050-g001.jpg Figure 1. Forest plot featuring odds ratios (OR) of being in the study group vs. control according t https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1752899/full https://www.frontiersin.org/files/Articles/1752899/fvets-13-1752899-HTML-r1/image_m/fvets-13-1752899-t001.jpg Table 1. Shocks to U.S. hog and pork production and exports. https://www.frontiersin.org/files/Articles/1752899/fvets-13-1752899-HTML-r1/image_m/fvets-13-1752899-g001.jpg Figure 1. Percentage changes in U.S. output. Source: Authors’ simulations. Scenario SO1 considers a https://www.frontiersin.org/files/Articles/1752899/fvets-13-1752899-HTML-r1/image_m/fvets-13-1752899-g002.jpg Figure 2. Percentage changes in U.S. producer prices. Source: Authors’ simulations. Scenario SO1 con https://www.frontiersin.org/files/Articles/1752899/fvets-13-1752899-HTML-r1/image_m/fvets-13-1752899-g003.jpg Figure 3. Percentage changes in world prices. Source: Authors’ simulations. Scenario SO1 considers a https://www.frontiersin.org/files/Articles/1752899/fvets-13-1752899-HTML-r1/image_m/fvets-13-1752899-t002.jpg Table 2. Changes in welfare (equivalent variation, $US million). https://www.frontiersin.org/files/Articles/1752899/fvets-13-1752899-HTML-r1/image_m/fvets-13-1752899-g004.jpg Figure 4. Percentage changes in hog exports. Source: Authors’ simulations. Scenario SO1 considers a https://www.frontiersin.org/files/Articles/1752899/fvets-13-1752899-HTML-r1/image_m/fvets-13-1752899-g005.jpg Figure 5. Percentage changes in pork exports. Source: Authors’ simulations. Scenario SO1 considers a https://www.frontiersin.org/files/Articles/1752899/fvets-13-1752899-HTML-r1/image_m/fvets-13-1752899-t003.jpg Table 3. SSA with variation in shocks to production and exports, 95% confidence interval. https://www.frontiersin.org/files/Articles/1752899/fvets-13-1752899-HTML-r1/image_m/fvets-13-1752899-t004.jpg Table 4. SSA with variation in elasticity of substitution for hog and pork sectors, 95% confidence i https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2026.1687194/full https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t001.jpg Table 1. Comparison of reliability measures. https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t010.jpg Algorithm 1. Monotone Delta (δ). https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t002.jpg Table 2. Comparison of reliability measures vs. the theoretical reliability across four synthetic sc https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t003.jpg Table 3. Visual verity questionnaire. https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-g001.jpg Figure 1. Comparison of participant responses across models (Camera, DALL·E2, Stable Diffusion, and https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t004.jpg Table 4. Mean participant responses (out of 5). https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-g002.jpg Figure 2. Readability scores under ideal conditions show that Monotone delta performs similarly to C https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t005.jpg Table 5. Reliability measures across datasets under ideal conditions. https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t006.jpg Table 6. Reliability measures across datasets under redundancy scenario. https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t007.jpg Table 7. Reliability measures across datasets under multidimensionality scenario. https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t008.jpg Table 8. Reliability measures across datasets under non-normal and correlated errors scenario. https://www.frontiersin.org/files/Articles/1687194/fams-12-1687194-HTML/image_m/fams-12-1687194-t009.jpg Table 9. Computation times for reliability measures (seconds). https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2025.1677414/full https://www.frontiersin.org/files/Articles/1677414/fncel-19-1677414-HTML/image_m/fncel-19-1677414-g001.jpg Figure 1. Development and colonization of border-associated macrophages (BAMs) in the embryonic mous https://www.frontiersin.org/files/Articles/1677414/fncel-19-1677414-HTML/image_m/fncel-19-1677414-t001.jpg Table 1. Brain-resident myeloid cell subtypes during development, health, and disease. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1643292/full https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-t001.jpg Table 1. Distribution of fraudulent and non-fraudulent transactions. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-g001.jpg Figure 1. The relation between fraud and amount. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-g002.jpg Figure 2. Proposed methodology. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-g003.jpg Figure 3. Comparison of class imbalance before and after SMOTE oversampling. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-t002.jpg Table 2. Hyperparameter search space for grid search tuning. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-g004.jpg Figure 4. Confusion matrices of the evaluated models. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-t003.jpg Table 3. Performance metrics of machine learning. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-g005.jpg Figure 5. Bar chart comparing the performance metrics. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-g006.jpg Figure 6. Training and validation accuracy and loss curves. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-t004.jpg Table 4. Inference time and real-time suitability. https://www.frontiersin.org/files/Articles/1643292/frai-08-1643292-HTML/image_m/frai-08-1643292-t005.jpg Table 5. p-values for pairwise statistical comparisons between the proposed model and baseline model https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1706103/full https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-g001.jpg Figure 1. Gin A inhibits HG-induced proliferation of A10 cells (A) Structural formula of Gingerenone https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-g002.jpg Figure 2. Gin A reduces HG-induced migration of A10 cells (A) Representative Transwell images showin https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-g003.jpg Figure 3. Gin A attenuates HG-induced oxidative stress in A10 cells (A) Intracellular ROS levels in https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-g004.jpg Figure 4. Gin A activates AMPK and suppresses mTOR/S6K1 signaling in A10 cells (A) Concentration-dep https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-g005.jpg Figure 5. Role of AMPK activation in the Gin A-mediated inhibition of the proliferation and migratio https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-g006.jpg Figure 6. Experimental validation in primary HASMCs with siRNA knockdown and osmotic stress controls https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-g007.jpg Figure 7. Gin A attenuates neointimal hyperplasia and restores AMPK activation in diabetic rats afte https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-t001.jpg Table 1. Noncompartmental pharmacokinetic parameters of Gin A in rats. https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-g008.jpg Figure 8. Preliminary pharmacokinetics and 14-day safety evaluation of Gin A in rats (A) Linear plas https://www.frontiersin.org/files/Articles/1706103/fphar-17-1706103-HTML/image_m/fphar-17-1706103-t002.jpg Table 2. Serum biochemical markers for toxicological evaluation of Gin A in rats. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1737893/full https://www.frontiersin.org/files/Articles/1737893/fsurg-13-1737893-HTML/image_m/fsurg-13-1737893-g001.jpg Figure 1. Patient clinical history timeline. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1745820/full https://www.frontiersin.org/files/Articles/1745820/fnins-19-1745820-HTML-r1/image_m/fnins-19-1745820-g001.jpg Figure 1. Flowchart of patients included. HIS, hospital information systems; VM, vestibular migraine https://www.frontiersin.org/files/Articles/1745820/fnins-19-1745820-HTML-r1/image_m/fnins-19-1745820-t001.jpg Table 1. Demographic information and clinical characteristics for patients with VM and MD. https://www.frontiersin.org/files/Articles/1745820/fnins-19-1745820-HTML-r1/image_m/fnins-19-1745820-t002.jpg Table 2. Nystagmus characteristics of patients with VM and MD. https://www.frontiersin.org/files/Articles/1745820/fnins-19-1745820-HTML-r1/image_m/fnins-19-1745820-g002.jpg Figure 2. Comparison of spontaneous nystagmus, nystagmus of Roll-test, Dix-Hallpike test, and deep h https://www.frontiersin.org/files/Articles/1745820/fnins-19-1745820-HTML-r1/image_m/fnins-19-1745820-t003.jpg Table 3. Nystagmus characteristics in VM and MD patients: slow-phase velocity and torsional componen https://www.frontiersin.org/files/Articles/1745820/fnins-19-1745820-HTML-r1/image_m/fnins-19-1745820-t004.jpg Table 4. Caloric test of patients with VM and MD. https://www.frontiersin.org/files/Articles/1745820/fnins-19-1745820-HTML-r1/image_m/fnins-19-1745820-g003.jpg Figure 3. Comparison of canal paresis in VM and MD patients. VM, vestibular migraine; MD, Meniere's https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1759986/full https://www.frontiersin.org/files/Articles/1759986/fmed-13-1759986-HTML/image_m/fmed-13-1759986-g001.jpg Figure 1. Flowchart. https://www.frontiersin.org/files/Articles/1759986/fmed-13-1759986-HTML/image_m/fmed-13-1759986-t001.jpg Table 1. Clinical features of all CBDS patients. https://www.frontiersin.org/files/Articles/1759986/fmed-13-1759986-HTML/image_m/fmed-13-1759986-t002.jpg Table 2. Analysis of the differences between success group and failure group. https://www.frontiersin.org/files/Articles/1759986/fmed-13-1759986-HTML/image_m/fmed-13-1759986-t003.jpg Table 3. Independent risk factors for ERCP-guided stone extraction failure: binary multivariate logi https://www.frontiersin.org/files/Articles/1759986/fmed-13-1759986-HTML/image_m/fmed-13-1759986-g002.jpg Figure 2. ROC curve and area under the curve (AUC) for predicting failure of endoscopic retrograde c https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1646494/full https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-t001.jpg Table 1. Perceived pros and cons of screening among participants. https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-t002.jpg Table 2. Demographic summary of research sample (N = 360). https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-t003.jpg Table 3. Reasons for participation in the ProstaPilot study (N = 360). https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-t004.jpg Table 4. Decision timeline for prostate cancer preventive examination participation. https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-t005.jpg Table 5. Knowledge-based results on prostate cancer and screening. https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-g001.jpg Figure 1. Perceptions of risk and preventive screening. https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-t006.jpg Table 6. Communication with healthcare providers regarding screening. https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-t007.jpg Table 7. Sources of information about prostate cancer prevention and Figure 2. https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-g002.jpg Figure 2. Information about the risk and prevention of prostate cancer. https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-g003.jpg Figure 3. Barriers, beliefs, and social influences. https://www.frontiersin.org/files/Articles/1646494/fpubh-13-1646494-HTML/image_m/fpubh-13-1646494-t008.jpg Table 8. Source of discomfort/barrier. https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2026.1720758/full https://www.frontiersin.org/files/Articles/1720758/fevo-14-1720758-HTML/image_m/fevo-14-1720758-g001.jpg Figure 1. Insect community composition: (A) Total abundance by taxonomic order; (B) Monthly variatio https://www.frontiersin.org/files/Articles/1720758/fevo-14-1720758-HTML/image_m/fevo-14-1720758-g002.jpg Figure 2. (A) Monthly counts of observed bird species (including migratory and resident birds); (B) https://www.frontiersin.org/files/Articles/1720758/fevo-14-1720758-HTML/image_m/fevo-14-1720758-g003.jpg Figure 3. Relative abundance of different samples: [(A)-1] animal-based foods and [(A)-2] food chain https://www.frontiersin.org/files/Articles/1720758/fevo-14-1720758-HTML/image_m/fevo-14-1720758-g004.jpg Figure 4. Trends of animal food and food-chain related plant flow. https://www.frontiersin.org/files/Articles/1720758/fevo-14-1720758-HTML/image_m/fevo-14-1720758-t001.jpg Table 1. Comparison of dietary composition among studied species, and Swinhoe’s Snipe and Grey Night https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1723075/full https://www.frontiersin.org/files/Articles/1723075/fpubh-13-1723075-HTML-r1/image_m/fpubh-13-1723075-t001.jpg Table 1. Occupational burnout among employees with different basic characteristics. https://www.frontiersin.org/files/Articles/1723075/fpubh-13-1723075-HTML-r1/image_m/fpubh-13-1723075-g001.jpg Figure 1. Correlations between occupational burnout, sleep quality, and anxiety. The figure presents https://www.frontiersin.org/files/Articles/1723075/fpubh-13-1723075-HTML-r1/image_m/fpubh-13-1723075-t002.jpg Table 2. Association between occupational burnout and sleep quality. https://www.frontiersin.org/files/Articles/1723075/fpubh-13-1723075-HTML-r1/image_m/fpubh-13-1723075-g002.jpg Figure 2. The relationship between occupational burnout and sleep quality. The red curve represents https://www.frontiersin.org/files/Articles/1723075/fpubh-13-1723075-HTML-r1/image_m/fpubh-13-1723075-t003.jpg Table 3. Correlation analysis between occupational burnout and anxiety. https://www.frontiersin.org/files/Articles/1723075/fpubh-13-1723075-HTML-r1/image_m/fpubh-13-1723075-t004.jpg Table 4. Correlation analysis between sleep quality and anxiety. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1658235/full https://www.frontiersin.org/files/Articles/1658235/fpubh-14-1658235-HTML/image_m/fpubh-14-1658235-g001.jpg Figure 1. Flowchart of participant selection in the study. https://www.frontiersin.org/files/Articles/1658235/fpubh-14-1658235-HTML/image_m/fpubh-14-1658235-t001.jpg Table 1. Basic characteristics of the participants. https://www.frontiersin.org/files/Articles/1658235/fpubh-14-1658235-HTML/image_m/fpubh-14-1658235-t002.jpg Table 2. Associations between body mass index and cardiometabolic risk factors. https://www.frontiersin.org/files/Articles/1658235/fpubh-14-1658235-HTML/image_m/fpubh-14-1658235-g002.jpg Figure 2. Dose–response pattern between body mass index and cardiometabolic risk factors using RCS m https://www.frontiersin.org/files/Articles/1658235/fpubh-14-1658235-HTML/image_m/fpubh-14-1658235-g003.jpg Figure 3. Stratified analyses of the associations between body mass index and six cardiometabolic ri https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1766524/full https://www.frontiersin.org/files/Articles/1766524/fnut-13-1766524-HTML/image_m/fnut-13-1766524-g001.jpg Figure 1. Flow diagram of participants included in this study. https://www.frontiersin.org/files/Articles/1766524/fnut-13-1766524-HTML/image_m/fnut-13-1766524-t001.jpg Table 1. Baseline characteristics of participants. https://www.frontiersin.org/files/Articles/1766524/fnut-13-1766524-HTML/image_m/fnut-13-1766524-t002.jpg Table 2. Associations between TyG-derived indices and the risk of hyperuricemia among oilfield worke https://www.frontiersin.org/files/Articles/1766524/fnut-13-1766524-HTML/image_m/fnut-13-1766524-g002.jpg Figure 2. Restricted cubic spline analyses of the associations between TyG-derived indices and the r https://www.frontiersin.org/files/Articles/1766524/fnut-13-1766524-HTML/image_m/fnut-13-1766524-g003.jpg Figure 3. Comparison of AUC values of TyG-derived indices for predicting hyperuricemia. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1758892/full https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-g001.jpg Figure 1. Flowchart of the data cleaning pipeline for drug-induced cataract data from the FAERS data https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-t001.jpg Table 1. Four-grid table of disproportionality analysis method. https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-t002.jpg Table 2. Principle of disproportionality analysis and standard of signal detection. https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-g002.jpg Figure 2. Distribution of baseline data for patients reporting adverse events of cataract in the FAE https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-t003.jpg Table 3. Baseline characteristics of patients with drug-induced cataract. https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-t004.jpg Table 4. Disproportionality analysis of positive signal drugs associated with cataract. https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-g003.jpg Figure 3. Forest plot of ROR-based positive signals for drug-induced cataract from the FAERS databas https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-g004.jpg Figure 4. Distribution of risk and case count for drug-induced cataract. (A) Signals ranking of drug https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-g005.jpg Figure 5. Onset time of adverse reactions in drug-induced cataract. (A) Analysis of cumulative repor https://www.frontiersin.org/files/Articles/1758892/fmed-13-1758892-HTML-r1/image_m/fmed-13-1758892-g006.jpg Figure 6. Multivariate logistic regression results of the top 20 drugs associated with cataract adve https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1733633/full https://www.frontiersin.org/files/Articles/1733633/fnins-20-1733633-HTML-r2/image_m/fnins-20-1733633-g001.jpg Figure 1. (A) Block tracking experiment. During the task, the patient moves a ball up and down by sq https://www.frontiersin.org/files/Articles/1733633/fnins-20-1733633-HTML-r2/image_m/fnins-20-1733633-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1733633/fnins-20-1733633-HTML-r2/image_m/fnins-20-1733633-g002.jpg Figure 2. Effect of motor state, stimulation amplitude, and somatotopy (i.e., muscle) on MEP prevale https://www.frontiersin.org/files/Articles/1733633/fnins-20-1733633-HTML-r2/image_m/fnins-20-1733633-g003.jpg Figure 3. Representative MEP waveform morphologies and onset/offset latencies at SoC stimulation amp https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1785033/full https://www.frontiersin.org/files/Articles/1785033/fspor-08-1785033-HTML/image_m/fspor-08-1785033-g001.jpg Figure 1. PRISMA 2020 flow diagram. https://www.frontiersin.org/files/Articles/1785033/fspor-08-1785033-HTML/image_m/fspor-08-1785033-t001.jpg Table 1. Statistics of high-frequency keywords. https://www.frontiersin.org/files/Articles/1785033/fspor-08-1785033-HTML/image_m/fspor-08-1785033-t002.jpg Table 2. Co-occurrence matrix (partial). https://www.frontiersin.org/files/Articles/1785033/fspor-08-1785033-HTML/image_m/fspor-08-1785033-t003.jpg Table 3. Word-Document matrix (partial). https://www.frontiersin.org/files/Articles/1785033/fspor-08-1785033-HTML/image_m/fspor-08-1785033-t004.jpg Table 4. Similarity matrix (partial). https://www.frontiersin.org/files/Articles/1785033/fspor-08-1785033-HTML/image_m/fspor-08-1785033-t005.jpg Table 5. Top 10 nodes by betweenness centrality in the high-frequency keyword Co-occurrence network. https://www.frontiersin.org/files/Articles/1785033/fspor-08-1785033-HTML/image_m/fspor-08-1785033-t006.jpg Table 6. Top 10 nodes by closeness centrality in the high-frequency keyword Co-occurrence network. https://www.frontiersin.org/files/Articles/1785033/fspor-08-1785033-HTML/image_m/fspor-08-1785033-g002.jpg Figure 2. Social network graph of high-frequency keywords in digital sports. Node size represents th https://www.frontiersin.org/files/Articles/1785033/fspor-08-1785033-HTML/image_m/fspor-08-1785033-g003.jpg Figure 3. Cluster dendrogram of high-frequency keywords. The clustering method using between-groups https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1787732/full https://www.frontiersin.org/files/Articles/1787732/fvets-13-1787732-HTML-r1/image_m/fvets-13-1787732-g001.jpg Figure 1. Distribution of outbreaks and cases of HPAI 2021–2023 in Germany with closer look for the https://www.frontiersin.org/files/Articles/1787732/fvets-13-1787732-HTML-r1/image_m/fvets-13-1787732-g002.jpg Figure 2. Distribution and number of geese held on pastures and ducks held in barns that were assess https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1748418/full https://www.frontiersin.org/files/Articles/1748418/fnagi-18-1748418-HTML/image_m/fnagi-18-1748418-t001.jpg Table 1. Key neuroimmune-related risk genes in Alzheimer’s disease. https://www.frontiersin.org/files/Articles/1748418/fnagi-18-1748418-HTML/image_m/fnagi-18-1748418-t002.jpg Table 2. Glial cell states and functions in Alzheimer’s disease. https://www.frontiersin.org/files/Articles/1748418/fnagi-18-1748418-HTML/image_m/fnagi-18-1748418-g001.jpg Figure 1. Schematic illustration of the mechanistic role of the neuroimmune network in the core path https://www.frontiersin.org/files/Articles/1748418/fnagi-18-1748418-HTML/image_m/fnagi-18-1748418-t003.jpg Table 3. Emerging neuroimmune-targeted therapies for Alzheimer’s Disease. https://www.frontiersin.org/files/Articles/1748418/fnagi-18-1748418-HTML/image_m/fnagi-18-1748418-t004.jpg Table 4. Core neuroimmune-related biomarkers in Alzheimer’s Disease. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1651506/full https://www.frontiersin.org/files/Articles/1651506/fpsyt-16-1651506-HTML/image_m/fpsyt-16-1651506-t001.jpg Table 1. Descriptive statistics of measurement items. https://www.frontiersin.org/files/Articles/1651506/fpsyt-16-1651506-HTML/image_m/fpsyt-16-1651506-g001.jpg Figure 1. Network structure of emotional resilience and symptoms of anxiety and depression in adoles https://www.frontiersin.org/files/Articles/1651506/fpsyt-16-1651506-HTML/image_m/fpsyt-16-1651506-g002.jpg Figure 2. Centrality indices of the network structure for emotional resilience and symptoms of anxie https://www.frontiersin.org/files/Articles/1651506/fpsyt-16-1651506-HTML/image_m/fpsyt-16-1651506-g003.jpg Figure 3. Bridge network structure of emotional resilience and symptoms of anxiety and depression in https://www.frontiersin.org/files/Articles/1651506/fpsyt-16-1651506-HTML/image_m/fpsyt-16-1651506-g004.jpg Figure 4. Bridge expected influence (1-step) of the Network Structure of Adolescent Emotional Resili https://www.frontiersin.org/files/Articles/1651506/fpsyt-16-1651506-HTML/image_m/fpsyt-16-1651506-g005.jpg Figure 5. Stability of centrality and bridge centrality indices using case-dropping bootstrap. The x https://www.frontiersin.org/files/Articles/1651506/fpsyt-16-1651506-HTML/image_m/fpsyt-16-1651506-g006.jpg Figure 6. Network comparison of emotional resilience and symptoms of anxiety and depression between https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1728556/full https://www.frontiersin.org/files/Articles/1728556/fpsyt-17-1728556-HTML-r1/image_m/fpsyt-17-1728556-t001.jpg Table 1. Demographic characteristics of the study participants (N = 12,537). https://www.frontiersin.org/files/Articles/1728556/fpsyt-17-1728556-HTML-r1/image_m/fpsyt-17-1728556-t002.jpg Table 2. Descriptive statistics for MHKQ and stigma (PDD). https://www.frontiersin.org/files/Articles/1728556/fpsyt-17-1728556-HTML-r1/image_m/fpsyt-17-1728556-g001.jpg Figure 1. Association network of MHKQ and stigma (PDD). https://www.frontiersin.org/files/Articles/1728556/fpsyt-17-1728556-HTML-r1/image_m/fpsyt-17-1728556-g002.jpg Figure 2. Bridge centrality indices of 32 nodes shown as standardized values z scores. https://www.frontiersin.org/files/Articles/1728556/fpsyt-17-1728556-HTML-r1/image_m/fpsyt-17-1728556-g003.jpg Figure 3. Stability of centrality indices by case dropping subset bootstrap. https://www.frontiersin.org/files/Articles/1728556/fpsyt-17-1728556-HTML-r1/image_m/fpsyt-17-1728556-g004.jpg Figure 4. Simulation of aggravating (A) and alleviating (B). https://www.frontiersin.org/journals/hematology/articles/10.3389/frhem.2026.1802350/full https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-t001.jpg Table 1. Mice, reagents and equipment. https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-t002.jpg Table 2. Antibodies and titration. https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-g001.jpg Figure 1. Schematic representation of the experimental procedure. https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-g002.jpg Figure 2. Cell surface markers of bone marrow cell populations. (A) The schema pictures a simplified https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-g003.jpg Figure 3. Spectral flow cytometry panel design. (A) The spectral cytometer used in this study is a C https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-g004.jpg Figure 4. Identification of HSC and progenitors. (A) Gating strategy for lineage negative for HSC an https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-g005.jpg Figure 5. Identification of erythroid cells. (A) Gating strategy for lineage negative for erythroid https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-g006.jpg Figure 6. Identification of mature immune cells. (A) Gating strategy of lineage negative for myeloid https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-g007.jpg Figure 7. UMAP of all cells. UMAP analysis (left panel) was performed on 100,000 CD45+ Live cells us https://www.frontiersin.org/files/Articles/1802350/frhem-05-1802350-HTML-r1/image_m/frhem-05-1802350-g008.jpg Figure 8. Application on different mouse models. (A) Analysis of HSC and progenitor cell frequencies https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1658444/full https://www.frontiersin.org/files/Articles/1658444/fimmu-16-1658444-HTML/image_m/fimmu-16-1658444-g001.jpg Figure 1. Clinical findings. (A–F). Changes in the patient’s lymphocytes (A), white blood cell count https://www.frontiersin.org/files/Articles/1658444/fimmu-16-1658444-HTML/image_m/fimmu-16-1658444-g002.jpg Figure 2. Genetic and protein expression analysis. (A) Whole-exome sequencing identified a heterozyg https://www.frontiersin.org/files/Articles/1658444/fimmu-16-1658444-HTML/image_m/fimmu-16-1658444-t001.jpg Table 1. Patient immunological profile. https://www.frontiersin.org/files/Articles/1658444/fimmu-16-1658444-HTML/image_m/fimmu-16-1658444-g003.jpg Figure 3. T and B cell subset phenotyping before and after treatment and comparison with healthy con https://www.frontiersin.org/files/Articles/1658444/fimmu-16-1658444-HTML/image_m/fimmu-16-1658444-g004.jpg Figure 4. Cytokine profile analysis. (A) Cytokine profiles of the patient (measured monthly over fiv https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1676687/full https://www.frontiersin.org/files/Articles/1676687/fonc-16-1676687-HTML/image_m/fonc-16-1676687-g001.jpg Figure 1. Participating countries for the EMPOWER-Lung 1 and EMPOWER-Lung 3 clinical trials. https://www.frontiersin.org/files/Articles/1676687/fonc-16-1676687-HTML/image_m/fonc-16-1676687-t001.jpg Table 1. Risk burden derived from a single PRO scale. https://www.frontiersin.org/files/Articles/1676687/fonc-16-1676687-HTML/image_m/fonc-16-1676687-t002.jpg Table 2. Risk burden derived from a baseline composite PRO measure. https://www.frontiersin.org/files/Articles/1676687/fonc-16-1676687-HTML/image_m/fonc-16-1676687-t003.jpg Table 3. Top 10 overall survival HRs of composite and single PROs at baseline from a Cox proportiona https://www.frontiersin.org/files/Articles/1676687/fonc-16-1676687-HTML/image_m/fonc-16-1676687-g002.jpg Figure 2. An example of a Kaplan-Meier plot: estimate of OS by risk of role functioning and LC-dyspn https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1630283/full https://www.frontiersin.org/files/Articles/1630283/fchem-13-1630283-HTML-r1/image_m/fchem-13-1630283-g001.jpg Figure 1. Integrative workflow of network toxicology and molecular docking analysis for this study. https://www.frontiersin.org/files/Articles/1630283/fchem-13-1630283-HTML-r1/image_m/fchem-13-1630283-g002.jpg Figure 2. The common PBDE congeners identification and analysis of their effect on breast cancer. (A https://www.frontiersin.org/files/Articles/1630283/fchem-13-1630283-HTML-r1/image_m/fchem-13-1630283-t001.jpg Table 1. General characteristics and breast cancer risk factors for cases and controls. https://www.frontiersin.org/files/Articles/1630283/fchem-13-1630283-HTML-r1/image_m/fchem-13-1630283-g003.jpg Figure 3. Candidate genes screening co-targeted by chemicals and breast cancer. (A) Relationship dia https://www.frontiersin.org/files/Articles/1630283/fchem-13-1630283-HTML-r1/image_m/fchem-13-1630283-t002.jpg Table 2. Topological measurements of top 20 genes in the PPI networks, identified utilizing the Cent https://www.frontiersin.org/files/Articles/1630283/fchem-13-1630283-HTML-r1/image_m/fchem-13-1630283-g004.jpg Figure 4. The core target genes identified by machine learning and TCGA-BRCA database. (A) Nine cand https://www.frontiersin.org/files/Articles/1630283/fchem-13-1630283-HTML-r1/image_m/fchem-13-1630283-g005.jpg Figure 5. Molecular docking of the five core genes/controls with the major PBDEs. (A) The overview o https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1526053/full https://www.frontiersin.org/files/Articles/1526053/fnut-12-1526053-HTML/image_m/fnut-12-1526053-t001.jpg Table 1. The demographics and obstetrics characteristics of HBsAg-positive mothers and their offspri https://www.frontiersin.org/files/Articles/1526053/fnut-12-1526053-HTML/image_m/fnut-12-1526053-t002.jpg Table 2. The effects of different folic acid supplementation on immune cells proportion of cord bloo https://www.frontiersin.org/files/Articles/1526053/fnut-12-1526053-HTML/image_m/fnut-12-1526053-t003.jpg Table 3. The effects of different folic acid supplementation on the expression levels of innate immu https://www.frontiersin.org/files/Articles/1526053/fnut-12-1526053-HTML/image_m/fnut-12-1526053-t004.jpg Table 4. The effects of STING and pNF-κB expression in cord blood on anti-HBs level of infants born https://www.frontiersin.org/files/Articles/1526053/fnut-12-1526053-HTML/image_m/fnut-12-1526053-t005.jpg Table 5. The effects of folic acid supplementation on anti-HBs level of infants born to HBsAg-positi https://www.frontiersin.org/files/Articles/1526053/fnut-12-1526053-HTML/image_m/fnut-12-1526053-t006.jpg Table 6. The impact of preconception folic acid supplementation on anti-HBs level of infants born to https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1762922/full https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-g001.jpg Figure 1. Flow chart of literature screening and inclusion. https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-t001.jpg Table 1. Characteristics of included studies (n = 12). https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-g002.jpg Figure 2. Forest plots for the acute effects of esports on RMSSD. https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-g003.jpg Figure 3. Forest plots for the acute effects of esports on SDNN. https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-g004.jpg Figure 4. Forest plots for the acute effects of esports on pNN50. https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-g005.jpg Figure 5. Forest plots for the acute effects of esports on HF. https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-g006.jpg Figure 6. Forest plots for the acute effects of esports on LF. https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-g007.jpg Figure 7. Forest plots for the acute effects of esports on LF/HF. https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-t002.jpg Table 2. Subgroup analysis based on game genre. https://www.frontiersin.org/files/Articles/1762922/fphys-17-1762922-HTML/image_m/fphys-17-1762922-t003.jpg Table 3. Subgroup analysis based on the duration of HRV analysis. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1642209/full https://www.frontiersin.org/files/Articles/1642209/fimmu-16-1642209-HTML-r1/image_m/fimmu-16-1642209-g001.jpg Figure 1. Weissella cibaria LAB_Weis_Camel_L4 isolation and identification. (A) Lactic acid bacteria https://www.frontiersin.org/files/Articles/1642209/fimmu-16-1642209-HTML-r1/image_m/fimmu-16-1642209-t001.jpg Table 1. The biochemical identification results of the bacterial strains. https://www.frontiersin.org/files/Articles/1642209/fimmu-16-1642209-HTML-r1/image_m/fimmu-16-1642209-g002.jpg Figure 2. Weissella cibaria LAB_Weis_Camel L4 growth characteristics. (A) Growth curve. (B) Acid pro https://www.frontiersin.org/files/Articles/1642209/fimmu-16-1642209-HTML-r1/image_m/fimmu-16-1642209-g003.jpg Figure 3. Weissella cibaria LAB_Weis_Camel_L4 safety evaluation. (A) H2O2 production assay. (B) No h https://www.frontiersin.org/files/Articles/1642209/fimmu-16-1642209-HTML-r1/image_m/fimmu-16-1642209-t002.jpg Table 2. The in vitro antibacterial activity of LAB_Weis_Camel_L4 against standard bacterial. https://www.frontiersin.org/files/Articles/1642209/fimmu-16-1642209-HTML-r1/image_m/fimmu-16-1642209-g004.jpg Figure 4. Weissella cibaria LAB_Weis_Camel_L4 Alleviated inflammation and prolonged the survival tim https://www.frontiersin.org/files/Articles/1642209/fimmu-16-1642209-HTML-r1/image_m/fimmu-16-1642209-g005.jpg Figure 5. Weissella cibaria LAB Weis Camel L4 Alleviated intestinal pathological damage in E. coli-i https://www.frontiersin.org/files/Articles/1642209/fimmu-16-1642209-HTML-r1/image_m/fimmu-16-1642209-g006.jpg Figure 6. Weissella cibaria LAB_Weis_Camel_L4 enhance the species richness of intestinal microbiota https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1594224/full https://www.frontiersin.org/files/Articles/1594224/fimmu-16-1594224-HTML/image_m/fimmu-16-1594224-g001.jpg Figure 1. The structure of the intestinal mucosal barrier. https://www.frontiersin.org/files/Articles/1594224/fimmu-16-1594224-HTML/image_m/fimmu-16-1594224-g002.jpg Figure 2. The composition and function of the respiratory barrier. https://www.frontiersin.org/files/Articles/1594224/fimmu-16-1594224-HTML/image_m/fimmu-16-1594224-t001.jpg Table 1. Mechanism of natural products and their extracts in the treatment of mucosal barrier relate https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1700857/full https://www.frontiersin.org/files/Articles/1700857/fpsyg-16-1700857-HTML-r1/image_m/fpsyg-16-1700857-t001.jpg Table 1. Demographics of participants with Crohn’s disease based on gender. https://www.frontiersin.org/files/Articles/1700857/fpsyg-16-1700857-HTML-r1/image_m/fpsyg-16-1700857-g001.jpg Figure 1. Regularized network analysis of the relationships between social support, disease acceptan https://www.frontiersin.org/files/Articles/1700857/fpsyg-16-1700857-HTML-r1/image_m/fpsyg-16-1700857-g002.jpg Figure 2. Standardized centrality indices (z-scores) for all nodes in Crohn’s disease patients, stra https://www.frontiersin.org/files/Articles/1700857/fpsyg-16-1700857-HTML-r1/image_m/fpsyg-16-1700857-g003.jpg Figure 3. Standardized bridging strength centrality scores for nodes in the networks of social suppo https://www.frontiersin.org/files/Articles/1700857/fpsyg-16-1700857-HTML-r1/image_m/fpsyg-16-1700857-g004.jpg Figure 4. Stability analysis of centrality measures in the overall sample of Crohn’s disease patient https://www.frontiersin.org/files/Articles/1700857/fpsyg-16-1700857-HTML-r1/image_m/fpsyg-16-1700857-g005.jpg Figure 5. Bootstrapped 95% confidence intervals for the estimated edge weights in the network of soc https://www.frontiersin.org/files/Articles/1700857/fpsyg-16-1700857-HTML-r1/image_m/fpsyg-16-1700857-g006.jpg Figure 6. Bootstrapped difference tests (α = 0.05) for social support, disease acceptance, and disea https://www.frontiersin.org/files/Articles/1700857/fpsyg-16-1700857-HTML-r1/image_m/fpsyg-16-1700857-t002.jpg Table 2. Comparison of global network features and structural characteristics across marital status https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1703784/full https://www.frontiersin.org/files/Articles/1703784/fnut-12-1703784-HTML-r1/image_m/fnut-12-1703784-t001.jpg Table 1. Demographic information (N = 16). https://www.frontiersin.org/files/Articles/1703784/fnut-12-1703784-HTML-r1/image_m/fnut-12-1703784-g001.jpg Figure 1. Antibody concentrations differ among tissue types. Anti-RBD antibodies (IgG and IgA) again https://www.frontiersin.org/files/Articles/1703784/fnut-12-1703784-HTML-r1/image_m/fnut-12-1703784-g002.jpg Figure 2. No correlation between antibody level and time since event. The time since the most recent https://www.frontiersin.org/files/Articles/1703784/fnut-12-1703784-HTML-r1/image_m/fnut-12-1703784-t002.jpg Table 2. Cytokines in human milk. https://www.frontiersin.org/files/Articles/1703784/fnut-12-1703784-HTML-r1/image_m/fnut-12-1703784-g003.jpg Figure 3. Cytokine concentrations in milk vary among participants and breasts. Concentrations of IL- https://www.frontiersin.org/files/Articles/1703784/fnut-12-1703784-HTML-r1/image_m/fnut-12-1703784-g004.jpg Figure 4. Mammary epithelium permeability varies little between breasts. Na: K, an indicator of mamm https://www.frontiersin.org/files/Articles/1703784/fnut-12-1703784-HTML-r1/image_m/fnut-12-1703784-g005.jpg Figure 5. No association between mammary epithelium permeability and antibody concentration. The ass https://www.frontiersin.org/files/Articles/1703784/fnut-12-1703784-HTML-r1/image_m/fnut-12-1703784-g006.jpg Figure 6. Associations between mammary epithelium permeability and pro-inflammatory cytokines in mil https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1658947/full https://www.frontiersin.org/files/Articles/1658947/fpubh-14-1658947-HTML/image_m/fpubh-14-1658947-t001.jpg Table 1. Sociodemographic characteristics of study participants. https://www.frontiersin.org/files/Articles/1658947/fpubh-14-1658947-HTML/image_m/fpubh-14-1658947-t002.jpg Table 2. Bivariate analysis of factors associated with SKT and STAT scores. https://www.frontiersin.org/files/Articles/1658947/fpubh-14-1658947-HTML/image_m/fpubh-14-1658947-t003.jpg Table 3. Multivariate linear regression analysis of factors associated with SKT and STAT scores. https://www.frontiersin.org/files/Articles/1658947/fpubh-14-1658947-HTML/image_m/fpubh-14-1658947-g001.jpg Figure 1. Sources of information about stroke incidence. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1754983/full https://www.frontiersin.org/files/Articles/1754983/fimmu-17-1754983-HTML/image_m/fimmu-17-1754983-g001.jpg Figure 1. Signal transduction processes within airway smooth muscle cells under mechanical pressure. https://www.frontiersin.org/files/Articles/1754983/fimmu-17-1754983-HTML/image_m/fimmu-17-1754983-g002.jpg Figure 2. Schematic illustration of signal transduction and cellular response Induced by mechanical https://www.frontiersin.org/files/Articles/1754983/fimmu-17-1754983-HTML/image_m/fimmu-17-1754983-g003.jpg Figure 3. Therapeutic strategies of VILI. Major therapeutic directions for ventilator-induced lung i https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1740433/full https://www.frontiersin.org/files/Articles/1740433/fimmu-16-1740433-HTML/image_m/fimmu-16-1740433-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1740433/fimmu-16-1740433-HTML/image_m/fimmu-16-1740433-g001.jpg Figure 1. Role of altered mitochondrial bioenergetics and biogenesis leads to disease pathology. Thi https://www.frontiersin.org/files/Articles/1740433/fimmu-16-1740433-HTML/image_m/fimmu-16-1740433-g002.jpg Figure 2. Mitochondrial dysfunction pathways contributing to Graft Versus Host Disease. This figure https://www.frontiersin.org/files/Articles/1740433/fimmu-16-1740433-HTML/image_m/fimmu-16-1740433-t001.jpg Table 1. Key preclinical findings on the role of mitochondrial metabolism in GVHD. https://www.frontiersin.org/files/Articles/1740433/fimmu-16-1740433-HTML/image_m/fimmu-16-1740433-g003.jpg Figure 3. Mitochondrial metabolic pathways and their therapeutic targets in immune cells. This figur https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1643048/full https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-g001.jpg Figure 1. Inflammatory mechanisms in septic cardiac injury. Cardiac injury in sepsis is driven by in https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-g002.jpg Figure 2. Immunosuppressive mechanisms in septic cardiac injury.The early phase of sepsis is charact https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-t001.jpg Table 1. Septic myocardial disease is mainly induced by infections from the following pathogens, pre https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-g003.jpg Figure 3. Oxidative stress mechanisms in septic cardiac injury. Cardiac tissue from non-surviving se https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-g004.jpg Figure 4. Pathological mechanisms of ANS dysregulation in septic cardiac injury. The ANS maintains s https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-g005.jpg Figure 5. Molecular mechanisms of complement system hyperactivation in septic cardiac injury. The co https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-g006.jpg Figure 6. Roles of extracellular vesicles in sepsis-induced myocardial dysfunction.This figure illus https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-g007.jpg Figure 7. Multidimensional therapeutic strategies for sepsis-induced myocardial injury. 1. Anti-infl https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-t002.jpg Table 2. Potential therapeutic targets and strategies for septic cardiomyopathy. https://www.frontiersin.org/files/Articles/1643048/fimmu-16-1643048-HTML/image_m/fimmu-16-1643048-t003.jpg Table 3. Translational clinical research on sepsis and its cardiac injury. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1613156/full https://www.frontiersin.org/files/Articles/1613156/fphar-16-1613156-HTML-r2/image_m/fphar-16-1613156-t001.jpg Table 1. Electronic search strategies. https://www.frontiersin.org/files/Articles/1613156/fphar-16-1613156-HTML-r2/image_m/fphar-16-1613156-g001.jpg Figure 1. Flow chart of the retrieval. https://www.frontiersin.org/files/Articles/1613156/fphar-16-1613156-HTML-r2/image_m/fphar-16-1613156-g002.jpg Figure 2. Botanical features and key bioactive compounds of Lycii Fructus. (a) Goji tree with mature https://www.frontiersin.org/files/Articles/1613156/fphar-16-1613156-HTML-r2/image_m/fphar-16-1613156-g003.jpg Figure 3. The protective effect of Lycii Fructus on male reproductive function. Abbreviation: cyt-c, https://www.frontiersin.org/files/Articles/1613156/fphar-16-1613156-HTML-r2/image_m/fphar-16-1613156-t002.jpg Table 2. Experiments on the effect of Lycii Fructus on male infertility. https://www.frontiersin.org/files/Articles/1613156/fphar-16-1613156-HTML-r2/image_m/fphar-16-1613156-g004.jpg Figure 4. Mechanisms of the protective effects of Lycii Fructus in the treatment of male infertility https://www.frontiersin.org/files/Articles/1613156/fphar-16-1613156-HTML-r2/image_m/fphar-16-1613156-g005.jpg Figure 5. Mechanism of Lycii Fructus in repairing the blood-testis barrier. Abbreviations: LBP, Lyci https://www.frontiersin.org/files/Articles/1613156/fphar-16-1613156-HTML-r2/image_m/fphar-16-1613156-t003.jpg Table 3. Clinical trials of the effects of Lycii Fructus on male infertility. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1749806/full https://www.frontiersin.org/files/Articles/1749806/fmed-13-1749806-HTML-r1/image_m/fmed-13-1749806-g001.jpg Figure 1. Study flowchart. https://www.frontiersin.org/files/Articles/1749806/fmed-13-1749806-HTML-r1/image_m/fmed-13-1749806-t001.jpg Table 1. A process chart of the trial. https://www.frontiersin.org/files/Articles/1749806/fmed-13-1749806-HTML-r1/image_m/fmed-13-1749806-g002.jpg Figure 2. Anatomical locations of acupuncture and sham acupuncture acupoints. Created in BioRender. https://www.frontiersin.org/files/Articles/1749806/fmed-13-1749806-HTML-r1/image_m/fmed-13-1749806-t002.jpg Table 2. Intervention in the acupuncture group. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1696372/full https://www.frontiersin.org/files/Articles/1696372/fcell-13-1696372-HTML/image_m/fcell-13-1696372-t001.jpg Table 1. Characteristics of different dyes used in this study. https://www.frontiersin.org/files/Articles/1696372/fcell-13-1696372-HTML/image_m/fcell-13-1696372-g001.jpg Figure 1. Identification of a fluorescent probe for spatial tracing. (A) Staining patterns using dif https://www.frontiersin.org/files/Articles/1696372/fcell-13-1696372-HTML/image_m/fcell-13-1696372-g002.jpg Figure 2. Spatial patterns of hPSCs on the Matrigel-coated surface during maintenance and differenti https://www.frontiersin.org/files/Articles/1696372/fcell-13-1696372-HTML/image_m/fcell-13-1696372-g003.jpg Figure 3. Transcriptome analysis of spatial gene expression in hPSC colonies. (A) Schematic illustra https://www.frontiersin.org/files/Articles/1696372/fcell-13-1696372-HTML/image_m/fcell-13-1696372-g004.jpg Figure 4. Cell adhesion-promoting factors affect metabolic and cell fate patterns. (A,B) Effect of t https://www.frontiersin.org/files/Articles/1696372/fcell-13-1696372-HTML/image_m/fcell-13-1696372-g005.jpg Figure 5. mTOR and ROCK modulate metabolic and cell fate pattern formation in hPSC colonies. (A,B) J https://www.frontiersin.org/files/Articles/1696372/fcell-13-1696372-HTML/image_m/fcell-13-1696372-g006.jpg Figure 6. Distinct pattern regulation of metabolism and differentiation in confined colonies. (A,B) https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1770282/full https://www.frontiersin.org/files/Articles/1770282/fpubh-14-1770282-HTML/image_m/fpubh-14-1770282-t001.jpg Table 1. Total number of social housing sites and tenant types for the regions of Ontario. https://www.frontiersin.org/files/Articles/1770282/fpubh-14-1770282-HTML/image_m/fpubh-14-1770282-t002.jpg Table 2. Distribution of social housing sites based on type of postal code for regions of Ontario. https://www.frontiersin.org/files/Articles/1770282/fpubh-14-1770282-HTML/image_m/fpubh-14-1770282-t003.jpg Table 3. Distribution of social housing sites based on type of housing provider for regions of Ontar https://www.frontiersin.org/files/Articles/1770282/fpubh-14-1770282-HTML/image_m/fpubh-14-1770282-t004.jpg Table 4. Distribution of social housing sites based on the type of housing building for regions of O https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1651694/full https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g001.jpg Figure 1. Recruitment pathway for eligible patients in this study. https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g002.jpg Figure 2. Radiomics workflow diagram. https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g003.jpg Figure 3. Framework diagram of the 3D-Unet automatic segmentation model developed in this study. https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-t001.jpg Table 1. The baseline characteristics of the enrolled patients in the training and test cohorts. https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-t002.jpg Table 2. The seven retained radiomic features and their coefficients. https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g004.jpg Figure 4. Distribution of radscores in both the training and test sets. (A,B) Box plot of radscores https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g005.jpg Figure 5. Forest plot showing characteristics identified through univariate logistic regression anal https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g006.jpg Figure 6. Patient characteristic selection using the LASSO for binary logistic regression model. (A) https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g007.jpg Figure 7. ROC curve of 8 machine learning models. (A) The training set. (B) The test set. https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g008.jpg Figure 8. Calibration curve of 8 machine learning models. (A) The training set. (B) The test set. https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g009.jpg Figure 9. Precision-recall curve of 8 machine learning models. (A) The training set. (B) The test se https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g010.jpg Figure 10. Radar chart of confusion matrix evaluation indexes for 8 machine learning models. (A) The https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g011.jpg Figure 11. Nomogram constructed from characteristics jointly selected through multivariate logistic https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g012.jpg Figure 12. Decision curve showing the clinical net benefit of the model across different risk thresh https://www.frontiersin.org/files/Articles/1651694/fneur-16-1651694-HTML/image_m/fneur-16-1651694-g013.jpg Figure 13. Clinical impact curve depicting the relationship between predicted high-risk individuals https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1653460/full https://www.frontiersin.org/files/Articles/1653460/fneur-17-1653460-HTML/image_m/fneur-17-1653460-g001.jpg Figure 1. Screening diagram of literature process. https://www.frontiersin.org/files/Articles/1653460/fneur-17-1653460-HTML/image_m/fneur-17-1653460-t001.jpg Table 1. Basic characteristics of included studies, stratified by outcome definition (SAD vs. SAE). https://www.frontiersin.org/files/Articles/1653460/fneur-17-1653460-HTML/image_m/fneur-17-1653460-t002.jpg Table 2. Outcome definitions and ascertainment criteria of included studies, stratified by outcome t https://www.frontiersin.org/files/Articles/1653460/fneur-17-1653460-HTML/image_m/fneur-17-1653460-g002.jpg Figure 2. Reported AUC (c-statistic) values of included SABD risk prediction models (SAD vs. SAE). a https://www.frontiersin.org/files/Articles/1653460/fneur-17-1653460-HTML/image_m/fneur-17-1653460-t003.jpg Table 3. Basic characteristics of included risk prediction models, stratified by outcome type (SAD v https://www.frontiersin.org/files/Articles/1653460/fneur-17-1653460-HTML/image_m/fneur-17-1653460-g003.jpg Figure 3. Predictors of included SABD risk prediction models (SAD vs. SAE). 1, Age; 2, Temperature; https://www.frontiersin.org/files/Articles/1653460/fneur-17-1653460-HTML/image_m/fneur-17-1653460-t004.jpg Table 4. Risk of bias and applicability assessment of included prediction models, stratified by outc https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1682842/full https://www.frontiersin.org/files/Articles/1682842/fneur-16-1682842-HTML/image_m/fneur-16-1682842-g001.jpg Figure 1. Experimental flowchart. https://www.frontiersin.org/files/Articles/1682842/fneur-16-1682842-HTML/image_m/fneur-16-1682842-t001.jpg Table 1. Comparison of basic clinical characteristics of acute ischemic stroke patients in training https://www.frontiersin.org/files/Articles/1682842/fneur-16-1682842-HTML/image_m/fneur-16-1682842-t002.jpg Table 2. Eight features screened based on radiomics features by LASSO dimensionality reduction. https://www.frontiersin.org/files/Articles/1682842/fneur-16-1682842-HTML/image_m/fneur-16-1682842-g002.jpg Figure 2. Feature importance ranking of the eight selected radiomics features. The ranking was gener https://www.frontiersin.org/files/Articles/1682842/fneur-16-1682842-HTML/image_m/fneur-16-1682842-g003.jpg Figure 3. The left graph shows the ROC curves and AUC values of the KNN algorithm for the three mode https://www.frontiersin.org/files/Articles/1682842/fneur-16-1682842-HTML/image_m/fneur-16-1682842-g004.jpg Figure 4. Calibration (Left) curves and decision curve analysis (Right) of three models using the KN https://www.frontiersin.org/files/Articles/1682842/fneur-16-1682842-HTML/image_m/fneur-16-1682842-t003.jpg Table 3. Training dataset-test dataset performance comparison of multiple models in clinical, radiom https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1605231/full https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-g001.jpg Figure 1. Flowchart of the study. Web of Science (WoS). https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-g002.jpg Figure 2. General analysis. (A) Distribution of the 100 articles by publication year. (B) Proportion https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-g003.jpg Figure 3. Author analyses. (A) Visualized connections of the authors. (B) Top eight authors of publi https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-g004.jpg Figure 4. Country analyses. (A) Visualized connections between the countries/regions. (B) Top 10 cou https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-g005.jpg Figure 5. Institution analyses. (A) Visualized connections between the Institutions. (B) Top 13 inst https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-g006.jpg Figure 6. Journal analyses. (A) Visualized connections between the journals. (B) Top 11 journals of https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-t001.jpg Table 1. The 10 journals with the highest impact factor (IF). https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-g007.jpg Figure 7. Keywords analyses. (A) Visualized connections between the keywords. (B) Top 12 keywords of https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-g008.jpg Figure 8. Frequency of the 50 specific AI methods. This figure presents a statistical analysis of 50 https://www.frontiersin.org/files/Articles/1605231/fnagi-17-1605231-HTML/image_m/fnagi-17-1605231-t002.jpg Table 2. Twelve AI methods ranked by occurrence. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1725724/full https://www.frontiersin.org/files/Articles/1725724/fmed-13-1725724-HTML/image_m/fmed-13-1725724-g001.jpg Figure 1. Schematic illustration of the pathophysiological mechanisms contributing to early HE in sp https://www.frontiersin.org/files/Articles/1725724/fmed-13-1725724-HTML/image_m/fmed-13-1725724-t001.jpg Table 1. Comparative overview of major randomized controlled trials evaluating intensive blood press https://www.frontiersin.org/files/Articles/1725724/fmed-13-1725724-HTML/image_m/fmed-13-1725724-t002.jpg Table 2. Summary of key randomized controlled trials assessing prehospital antihypertensive interven https://www.frontiersin.org/files/Articles/1725724/fmed-13-1725724-HTML/image_m/fmed-13-1725724-t003.jpg Table 3. Developmental stages of emerging tools for prehospital management of intracerebral hemorrha https://www.frontiersin.org/files/Articles/1725724/fmed-13-1725724-HTML/image_m/fmed-13-1725724-t004.jpg Table 4. Key recommendations for prehospital BP management in suspected ICH. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1690638/full https://www.frontiersin.org/files/Articles/1690638/fneur-16-1690638-HTML-r1/image_m/fneur-16-1690638-g001.jpg Figure 1. Incorporate into the patient process. https://www.frontiersin.org/files/Articles/1690638/fneur-16-1690638-HTML-r1/image_m/fneur-16-1690638-t001.jpg Table 1. Baseline characteristics of participants. https://www.frontiersin.org/files/Articles/1690638/fneur-16-1690638-HTML-r1/image_m/fneur-16-1690638-t002.jpg Table 2. Comparison of Baseline Characteristics Between the Internal Development Cohort and the Exte https://www.frontiersin.org/files/Articles/1690638/fneur-16-1690638-HTML-r1/image_m/fneur-16-1690638-g002.jpg Figure 2. Screening process of characteristic variables. (A) Boruta algorithm feature selection impo https://www.frontiersin.org/files/Articles/1690638/fneur-16-1690638-HTML-r1/image_m/fneur-16-1690638-t003.jpg Table 3. Benchmark test results. https://www.frontiersin.org/files/Articles/1690638/fneur-16-1690638-HTML-r1/image_m/fneur-16-1690638-g003.jpg Figure 3. Comprehensive performance evaluation of different machine learning models. https://www.frontiersin.org/files/Articles/1690638/fneur-16-1690638-HTML-r1/image_m/fneur-16-1690638-g004.jpg Figure 4. Internal testing and performance evaluation of the model. (A) Receiver operating character https://www.frontiersin.org/files/Articles/1690638/fneur-16-1690638-HTML-r1/image_m/fneur-16-1690638-g005.jpg Figure 5. External testing and performance evaluation of the model. (A) Receiver operating character https://www.frontiersin.org/files/Articles/1690638/fneur-16-1690638-HTML-r1/image_m/fneur-16-1690638-g006.jpg Figure 6. SHapley Additive exPlanations analysis of the Extra Trees Classifier model predicting gast https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1721907/full https://www.frontiersin.org/files/Articles/1721907/fpsyg-17-1721907-HTML/image_m/fpsyg-17-1721907-t001.jpg Table 1. Comparative overview of scales assessing human-nature relationships. https://www.frontiersin.org/files/Articles/1721907/fpsyg-17-1721907-HTML/image_m/fpsyg-17-1721907-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1721907/fpsyg-17-1721907-HTML/image_m/fpsyg-17-1721907-t002.jpg Table 2. Frequency of NC-specific data collection methods used in the reviewed studies. https://www.frontiersin.org/files/Articles/1721907/fpsyg-17-1721907-HTML/image_m/fpsyg-17-1721907-t003.jpg Table 3. Analysis and synthesis of dimensions of NC. https://www.frontiersin.org/files/Articles/1721907/fpsyg-17-1721907-HTML/image_m/fpsyg-17-1721907-t004.jpg Table 4. Integrated nature connectedness framework. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1698741/full https://www.frontiersin.org/files/Articles/1698741/fneur-16-1698741-HTML/image_m/fneur-16-1698741-g001.jpg Figure 1. Flowchart of patient selection and cohort assignment. https://www.frontiersin.org/files/Articles/1698741/fneur-16-1698741-HTML/image_m/fneur-16-1698741-g002.jpg Figure 2. Architecture of the TabPFN predictive model. (A) TabPFN employs a bidirectional attention https://www.frontiersin.org/files/Articles/1698741/fneur-16-1698741-HTML/image_m/fneur-16-1698741-t001.jpg Table 1. Patient demographics and baseline characteristics. https://www.frontiersin.org/files/Articles/1698741/fneur-16-1698741-HTML/image_m/fneur-16-1698741-t002.jpg Table 2. Performance metrics of predictive models without class-imbalance correction. https://www.frontiersin.org/files/Articles/1698741/fneur-16-1698741-HTML/image_m/fneur-16-1698741-g003.jpg Figure 3. Comparison of the six models without class-imbalance correction in the ROC curves, calibra https://www.frontiersin.org/files/Articles/1698741/fneur-16-1698741-HTML/image_m/fneur-16-1698741-g004.jpg Figure 4. Comparison of the six models without class-imbalance correction in the ROC curves, calibra https://www.frontiersin.org/files/Articles/1698741/fneur-16-1698741-HTML/image_m/fneur-16-1698741-g005.jpg Figure 5. SHAP summary plot for global feature importance in the TabPFN model. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1734264/full https://www.frontiersin.org/files/Articles/1734264/fneur-17-1734264-HTML/image_m/fneur-17-1734264-g001.jpg Figure 1. The overall flowchart of the study. https://www.frontiersin.org/files/Articles/1734264/fneur-17-1734264-HTML/image_m/fneur-17-1734264-t001.jpg Table 1. Comparison of baseline data between training set and test set. https://www.frontiersin.org/files/Articles/1734264/fneur-17-1734264-HTML/image_m/fneur-17-1734264-t002.jpg Table 2. Comparison of variables between sepsis and non-sepsis groups. https://www.frontiersin.org/files/Articles/1734264/fneur-17-1734264-HTML/image_m/fneur-17-1734264-g002.jpg Figure 2. LASSO regression analysis results. (A) LASSO regression model feature selection: The left https://www.frontiersin.org/files/Articles/1734264/fneur-17-1734264-HTML/image_m/fneur-17-1734264-g003.jpg Figure 3. Performance comparison of 14 predictive models. (A) ROC curves for the training set; (B) R https://www.frontiersin.org/files/Articles/1734264/fneur-17-1734264-HTML/image_m/fneur-17-1734264-t003.jpg Table 3. Performance metrics of machine learning models on the training set. https://www.frontiersin.org/files/Articles/1734264/fneur-17-1734264-HTML/image_m/fneur-17-1734264-t004.jpg Table 4. Performance metrics of machine learning models on the test set. https://www.frontiersin.org/files/Articles/1734264/fneur-17-1734264-HTML/image_m/fneur-17-1734264-g004.jpg Figure 4. SHAP-based model interpretation. (A) SHAP feature importance (mean absolute SHAP value): B https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1716984/full https://www.frontiersin.org/files/Articles/1716984/fneur-16-1716984-HTML/image_m/fneur-16-1716984-g001.jpg Figure 1. Research flow chart. This figure shows the machine learning modeling process of this study https://www.frontiersin.org/files/Articles/1716984/fneur-16-1716984-HTML/image_m/fneur-16-1716984-g002.jpg Figure 2. (a) Variable correlation heat map; (b) Model performance bar graph (Accuracy, Recall, F1, https://www.frontiersin.org/files/Articles/1716984/fneur-16-1716984-HTML/image_m/fneur-16-1716984-g003.jpg Figure 3. (a) Comparison of ROC curves of multiple models; (b) PR curve. https://www.frontiersin.org/files/Articles/1716984/fneur-16-1716984-HTML/image_m/fneur-16-1716984-g004.jpg Figure 4. Confusion moment comparison chart. https://www.frontiersin.org/files/Articles/1716984/fneur-16-1716984-HTML/image_m/fneur-16-1716984-g005.jpg Figure 5. SHAP summary composite image. (a) MLP; (b) XGBoost; (c) CatBoost. https://www.frontiersin.org/files/Articles/1716984/fneur-16-1716984-HTML/image_m/fneur-16-1716984-g006.jpg Figure 6. (a) Top-N average SHAP value histogram; (b) SHAP force plot. https://www.frontiersin.org/files/Articles/1716984/fneur-16-1716984-HTML/image_m/fneur-16-1716984-g007.jpg Figure 7. Comparison of model training time and parameter quantity. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1654147/full https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-g001.jpg Figure 1. Study design flowchart. https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-t001.jpg Table 1. A comparison of the baseline characteristics between the training and validation cohorts (a https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-g002.jpg Figure 2. LASSO variable selection process for 28 candidate predictors. (A) Coefficient profile plot https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-g003.jpg Figure 3. Results of multivariable logistic regression of hypostatic pneumonia after EVT. https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-g004.jpg Figure 4. Nomogram for predicting HP risk in EVT patients. (A) Nomogram: variables assigned scores; https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-t002.jpg Table 2. Diagnostic performance indicators of the model based on preset clinical decision thresholds https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-g005.jpg Figure 5. The ROC curves for the HP predictive nomogram in AIS-LVO patients after EVT. In the traini https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-t003.jpg Table 3. Results of cross-validation and bootstrap analysis. https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-g006.jpg Figure 6. Calibration curves for the HP nomogram. In the training cohort (A), the calibration curve https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-g007.jpg Figure 7. The DCA of the nomogram for predicting HP in AIS-LVO patients after EVT. In the training c https://www.frontiersin.org/files/Articles/1654147/fneur-16-1654147-HTML/image_m/fneur-16-1654147-g008.jpg Figure 8. The CIC of the nomogram for predicting HP in AIS-LVO patients after EVT. In the training c https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1658247/full https://www.frontiersin.org/files/Articles/1658247/fnins-19-1658247-HTML/image_m/fnins-19-1658247-t001.jpg Table 1. Determinants of subjective cognitive function. https://www.frontiersin.org/files/Articles/1658247/fnins-19-1658247-HTML/image_m/fnins-19-1658247-t002.jpg Table 2. Baseline characteristics of all participants. https://www.frontiersin.org/files/Articles/1658247/fnins-19-1658247-HTML/image_m/fnins-19-1658247-g001.jpg Figure 1. LASSO regression analysis results. (A) Coefficient profile plot of the LASSO model; (B) Cr https://www.frontiersin.org/files/Articles/1658247/fnins-19-1658247-HTML/image_m/fnins-19-1658247-g002.jpg Figure 2. Receiver operating characteristic (ROC) curves of the machine learning models. (A) ROC cur https://www.frontiersin.org/files/Articles/1658247/fnins-19-1658247-HTML/image_m/fnins-19-1658247-g003.jpg Figure 3. Receiver operating characteristic (ROC) curves of the SVM model. (A) ROC curve for the tra https://www.frontiersin.org/files/Articles/1658247/fnins-19-1658247-HTML/image_m/fnins-19-1658247-t003.jpg Table 3. Comparison of predictive performance across multiple models. https://www.frontiersin.org/files/Articles/1658247/fnins-19-1658247-HTML/image_m/fnins-19-1658247-g004.jpg Figure 4. Calibration and decision curve analysis (DCA) of the SVM model in the test set. (A) Calibr https://www.frontiersin.org/files/Articles/1658247/fnins-19-1658247-HTML/image_m/fnins-19-1658247-g005.jpg Figure 5. Shapley Additive Explanations (SHAP)-based interpretability analysis of the SVM model. (A) https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1782742/full https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g001.jpg Figure 1. Location of sampling sites in the Pailugou watershed. (A) Qilian Mountains National Park. https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-t001.jpg Table 1. Basic characteristics of sample plots of Picea crassifolia forest. https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g002.jpg Figure 2. Tree-ring indices of Picea crassifolia at the five different elevations of the Pailugou wa https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g003.jpg Figure 3. Monthly variations in temperature and precipitation in the Pailugou Watershed of the Qilia https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g004.jpg Figure 4. Trends in mean annual temperature and annual total precipitation in the Pailugou Watershed https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-t002.jpg Table 2. Statistical characteristics of standard chronologies for Picea crassifolia at different ele https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-t003.jpg Table 3. Correlation coefficients between Picea crassifolia chronologies at different elevations. https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g005.jpg Figure 5. Correlations between Picea crassifolia chronologies at different elevations and monthly cl https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g006.jpg Figure 6. Moving correlations between the standardized chronology of tree-ring width for Picea crass https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g007.jpg Figure 7. Moving correlations between the standardized chronology of tree-ring width for Picea crass https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g008.jpg Figure 8. Moving correlations between the standardized chronology of tree-ring width for Picea crass https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g009.jpg Figure 9. Moving correlations between the standardized chronology of tree-ring width for Picea crass https://www.frontiersin.org/files/Articles/1782742/fpls-17-1782742-HTML/image_m/fpls-17-1782742-g010.jpg Figure 10. Moving correlations between the standardized chronology of tree-ring width for Picea cras https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1560032/full https://www.frontiersin.org/files/Articles/1560032/fnagi-17-1560032-HTML-r1/image_m/fnagi-17-1560032-g001.jpg Figure 1. Literature screening process. https://www.frontiersin.org/files/Articles/1560032/fnagi-17-1560032-HTML-r1/image_m/fnagi-17-1560032-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1560032/fnagi-17-1560032-HTML-r1/image_m/fnagi-17-1560032-g002.jpg Figure 2. Risk of bias assessment for included studies. https://www.frontiersin.org/files/Articles/1560032/fnagi-17-1560032-HTML-r1/image_m/fnagi-17-1560032-g003.jpg Figure 3. Forest plots of result of meta analysis. https://www.frontiersin.org/files/Articles/1560032/fnagi-17-1560032-HTML-r1/image_m/fnagi-17-1560032-g004.jpg Figure 4. Potential mechanisms of acupuncture in the treatment of cognitive impairment caused by sle https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1772485/full https://www.frontiersin.org/files/Articles/1772485/fcvm-13-1772485-HTML/image_m/fcvm-13-1772485-t001.jpg Table 1. Sociodemographic characteristics of CHF patients (N = 15). https://www.frontiersin.org/files/Articles/1772485/fcvm-13-1772485-HTML/image_m/fcvm-13-1772485-t002.jpg Table 2. Professional characteristics of clinical practitioners (N = 6). https://www.frontiersin.org/files/Articles/1772485/fcvm-13-1772485-HTML/image_m/fcvm-13-1772485-t003.jpg Table 3. Core themes and subthemes in discharge preparedness (dyadic analysis). https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1751293/full https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g010.jpg GRAPHICAL ABSTRACT | Schematic of Smart Hydrogel Targeting Pathogenic Mechanisms to Treat Osteoarthr https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-t001.jpg Table 1. Sensitive bonds and responsive units used in smart responsive hydrogels for OA applications https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-t002.jpg Table 2. Endogenous stimuli-responsive hydrogels targeting inflammation. https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g001.jpg Figure 1. pH and ROS responsive hydrogels targeting inflammatory responses. (A) A dual pH/ROS respon https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g002.jpg Figure 2. Enzyme responsive and thermosensitive hydrogels targeting inflammatory responses. (A) LFDC https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-t003.jpg Table 3. Exogenous stimuli-responsive hydrogels targeting inflammation. https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g003.jpg Figure 3. Exogenous stimuli-responsive hydrogels targeting inflammatory responses. (A) UV crosslinke https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-t004.jpg Table 4. Endogenous stimuli-responsive hydrogels targeting mitochondrial function. https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g004.jpg Figure 4. Endogenous stimuli-responsive hydrogel platforms for regulating mitochondrial function. (A https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-t005.jpg Table 5. Endogenous stimuli-responsive hydrogels targeting cartilage damage. https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g005.jpg Figure 5. pH and ROS responsive hydrogels targeting cartilage injury. (A) OCCG-4LF@saRNA: a chitosan https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g006.jpg Figure 6. Enzyme responsive and thermosensitive hydrogels targeting cartilage injury. (A) strZPM Gel https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-t006.jpg Table 6. Exogenous stimuli-responsive hydrogels targeting cartilage damage. https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g007.jpg Figure 7. Exogenous stimuli-responsive hydrogels targeting cartilage injury. (A) Light responsive HA https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g008.jpg Figure 8. Targeting the Neurovascular Axis. (A) A dual responsive polycationic hydrogel (OSPPB) reve https://www.frontiersin.org/files/Articles/1751293/fbioe-14-1751293-HTML/image_m/fbioe-14-1751293-g009.jpg Figure 9. Inhibiting Aberrant Neurogenesis or Angiogenesis. (A) pH/ROS Dual-Responsive TA-Mg@Bev iHG https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1781458/full https://www.frontiersin.org/files/Articles/1781458/fmed-13-1781458-HTML-r1/image_m/fmed-13-1781458-g001.jpg Figure 1. Gut microbiome–driven mechanisms of colorectal anastomotic healing versus leakage. Concept https://www.frontiersin.org/files/Articles/1781458/fmed-13-1781458-HTML-r1/image_m/fmed-13-1781458-t001.jpg Table 1. Microbiome-associated mechanisms contributing to colorectal anastomotic healing and leakage https://www.frontiersin.org/files/Articles/1781458/fmed-13-1781458-HTML-r1/image_m/fmed-13-1781458-t002.jpg Table 2. Candidate biomarkers for microbiome-informed risk stratification of colorectal anastomotic https://www.frontiersin.org/files/Articles/1781458/fmed-13-1781458-HTML-r1/image_m/fmed-13-1781458-t003.jpg Table 3. Perioperative microbiome-directed strategies for prevention of colorectal anastomotic leaka https://www.frontiersin.org/files/Articles/1781458/fmed-13-1781458-HTML-r1/image_m/fmed-13-1781458-g002.jpg Figure 2. Conceptual workflow for microbe-informed risk stratification and prevention of anastomotic https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1781510/full https://www.frontiersin.org/files/Articles/1781510/fonc-16-1781510-HTML/image_m/fonc-16-1781510-g001.jpg Figure 1. Flow diagram illustrating the study selection process for the meta-analysis, showing studi https://www.frontiersin.org/files/Articles/1781510/fonc-16-1781510-HTML/image_m/fonc-16-1781510-t001.jpg Table 1. Characteristics of the patients. https://www.frontiersin.org/files/Articles/1781510/fonc-16-1781510-HTML/image_m/fonc-16-1781510-g002.jpg Figure 2. Characteristics of the observed mutations. (A) Bar chart showing the mean number of mutati https://www.frontiersin.org/files/Articles/1781510/fonc-16-1781510-HTML/image_m/fonc-16-1781510-t002.jpg Table 2. Effect of mutation on the coding sequence. https://www.frontiersin.org/files/Articles/1781510/fonc-16-1781510-HTML/image_m/fonc-16-1781510-g003.jpg Figure 3. Lollipop Plots of most frequently mutated genes: Schematic representation of the linear di https://www.frontiersin.org/files/Articles/1781510/fonc-16-1781510-HTML/image_m/fonc-16-1781510-t003.jpg Table 3. Estimated frequency of the most mutated genes. https://www.frontiersin.org/files/Articles/1781510/fonc-16-1781510-HTML/image_m/fonc-16-1781510-g004.jpg Figure 4. Estimation of the frequency of mutation of the most mutated genes in TET. Panel A presents https://www.frontiersin.org/files/Articles/1781510/fonc-16-1781510-HTML/image_m/fonc-16-1781510-g005.jpg Figure 5. Characterization of genomic alterations and oncogenic signaling pathways. (A) Bar chart sh https://www.frontiersin.org/files/Articles/1781510/fonc-16-1781510-HTML/image_m/fonc-16-1781510-g006.jpg Figure 6. Mutational signature profiles across subgroups. (A) Global mutational profile: Distributio https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1633187/full https://www.frontiersin.org/files/Articles/1633187/fimmu-16-1633187-HTML/image_m/fimmu-16-1633187-g001.jpg Figure 1. Flowchart of the study design and timeline for assessing the immunological responses in tr https://www.frontiersin.org/files/Articles/1633187/fimmu-16-1633187-HTML/image_m/fimmu-16-1633187-t001.jpg Table 1. Demographic and clinical characteristics of onchocerciasis participants. https://www.frontiersin.org/files/Articles/1633187/fimmu-16-1633187-HTML/image_m/fimmu-16-1633187-g002.jpg Figure 2. SARS-CoV-2 seroprevalence, antibody response and neutralizing potential in Ghanaian onchoc https://www.frontiersin.org/files/Articles/1633187/fimmu-16-1633187-HTML/image_m/fimmu-16-1633187-g003.jpg Figure 3. SARS-CoV-2 antibody response after complete vaccination with mRNA or vector-based COVID-19 https://www.frontiersin.org/files/Articles/1633187/fimmu-16-1633187-HTML/image_m/fimmu-16-1633187-g004.jpg Figure 4. Influence of microfilaria positivity and load onto COVID-19 vaccine-induced antibody respo https://www.frontiersin.org/files/Articles/1633187/fimmu-16-1633187-HTML/image_m/fimmu-16-1633187-g005.jpg Figure 5. SARS-CoV-2 IgG subclass response depending on the parasitic status of Ghanaian onchocercia https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1727049/full https://www.frontiersin.org/files/Articles/1727049/fimmu-16-1727049-HTML/image_m/fimmu-16-1727049-t001.jpg Table 1. Demographic and clinical characteristics of COVID-19 vaccinated participants. https://www.frontiersin.org/files/Articles/1727049/fimmu-16-1727049-HTML/image_m/fimmu-16-1727049-t002.jpg Table 2. Overview COVID-19 vaccines compared throughout this work. https://www.frontiersin.org/files/Articles/1727049/fimmu-16-1727049-HTML/image_m/fimmu-16-1727049-g001.jpg Figure 1. Time course of SARS-CoV-2-specific antibodies at different time points and following vacci https://www.frontiersin.org/files/Articles/1727049/fimmu-16-1727049-HTML/image_m/fimmu-16-1727049-t003.jpg Table 3. SARS-CoV-2 antibody kinetics among various time points and different vaccine groups. https://www.frontiersin.org/files/Articles/1727049/fimmu-16-1727049-HTML/image_m/fimmu-16-1727049-g002.jpg Figure 2. SARS-CoV-2 vaccine-induced antibody response and level of reported systemic adverse reacti https://www.frontiersin.org/files/Articles/1727049/fimmu-16-1727049-HTML/image_m/fimmu-16-1727049-g003.jpg Figure 3. SARS-CoV-2 neutralizing antibody seropositivity among different vaccine groups and upon di https://www.frontiersin.org/files/Articles/1727049/fimmu-16-1727049-HTML/image_m/fimmu-16-1727049-g004.jpg Figure 4. SARS-CoV-2-specific IgG subclass response after multiple vaccinations with different COVID https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1713278/full https://www.frontiersin.org/files/Articles/1713278/fpubh-13-1713278-HTML/image_m/fpubh-13-1713278-g001.jpg Figure 1. Carbon emission data of Hangzhou City. https://www.frontiersin.org/files/Articles/1713278/fpubh-13-1713278-HTML/image_m/fpubh-13-1713278-g002.jpg Figure 2. Carbon emission data of various districts in Hangzhou. https://www.frontiersin.org/files/Articles/1713278/fpubh-13-1713278-HTML/image_m/fpubh-13-1713278-g003.jpg Figure 3. Prediction model structure. https://www.frontiersin.org/files/Articles/1713278/fpubh-13-1713278-HTML/image_m/fpubh-13-1713278-g004.jpg Figure 4. The relationship between predictive performance, prediction quantity, and model structure. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1702448/full https://www.frontiersin.org/files/Articles/1702448/fphar-16-1702448-HTML-r1/image_m/fphar-16-1702448-g001.jpg Figure 1. Flowchart illustrating the study selection process for a review. Identification shows 2,86 https://www.frontiersin.org/files/Articles/1702448/fphar-16-1702448-HTML-r1/image_m/fphar-16-1702448-t001.jpg Table 1. Summary of all clinical studies investigating the effects of GLP-1 receptor agonists (GLP-1 https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1705102/full https://www.frontiersin.org/files/Articles/1705102/fcvm-12-1705102-HTML/image_m/fcvm-12-1705102-t001.jpg Table 1. The timeline of the patient's medical history. https://www.frontiersin.org/files/Articles/1705102/fcvm-12-1705102-HTML/image_m/fcvm-12-1705102-g001.jpg Figure 1. Electrocardiogram showed sinus rhythm, first-degree atrioventricular block, right axis dev https://www.frontiersin.org/files/Articles/1705102/fcvm-12-1705102-HTML/image_m/fcvm-12-1705102-g002.jpg Figure 2. Chest CT demonstrated a mass lesion in the right lower lobe of the lung (maximum cross-sec https://www.frontiersin.org/files/Articles/1705102/fcvm-12-1705102-HTML/image_m/fcvm-12-1705102-g003.jpg Figure 3. Echocardiography demonstrated (A) an enlarged right ventricle (right ventricular anteropos https://www.frontiersin.org/files/Articles/1705102/fcvm-12-1705102-HTML/image_m/fcvm-12-1705102-t002.jpg Table 2. Changes in indicators of PAH before and after treatment. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1752211/full https://www.frontiersin.org/files/Articles/1752211/fendo-17-1752211-HTML/image_m/fendo-17-1752211-g001.jpg Figure 1. Flow diagram of patient inclusion and exclusion in the study. https://www.frontiersin.org/files/Articles/1752211/fendo-17-1752211-HTML/image_m/fendo-17-1752211-g002.jpg Figure 2. Stepwise framework for developing a context-specific antimicrobial guidelines for diabetic https://www.frontiersin.org/files/Articles/1752211/fendo-17-1752211-HTML/image_m/fendo-17-1752211-t001.jpg Table 1. Demographic and sample characteristics of patients with diabetic foot infections. https://www.frontiersin.org/files/Articles/1752211/fendo-17-1752211-HTML/image_m/fendo-17-1752211-t002.jpg Table 2. Frequency and distribution of microbial isolates from culture-positive diabetic foot infect https://www.frontiersin.org/files/Articles/1752211/fendo-17-1752211-HTML/image_m/fendo-17-1752211-t003.jpg Table 3. Antimicrobial susceptibility patterns of major bacterial isolates in diabetic foot infectio https://www.frontiersin.org/files/Articles/1752211/fendo-17-1752211-HTML/image_m/fendo-17-1752211-g003.jpg Figure 3. Heat map showing antimicrobial susceptibility patterns of major bacterial isolates causing https://www.frontiersin.org/files/Articles/1752211/fendo-17-1752211-HTML/image_m/fendo-17-1752211-t004.jpg Table 4. Antimicrobial susceptibility of major DFI pathogens, organized by WHO AWaRe Classification. https://www.frontiersin.org/files/Articles/1752211/fendo-17-1752211-HTML/image_m/fendo-17-1752211-t005.jpg Table 5. Recommended severity specific empirical antibiotic regimens for diabetic foot infections in https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2026.1763987/full https://www.frontiersin.org/files/Articles/1763987/fclim-08-1763987-HTML/image_m/fclim-08-1763987-g001.jpg Figure 1. PRISMA flow diagram for studies selection. https://www.frontiersin.org/files/Articles/1763987/fclim-08-1763987-HTML/image_m/fclim-08-1763987-g002.jpg Figure 2. Trend of studies by topics over years. https://www.frontiersin.org/files/Articles/1763987/fclim-08-1763987-HTML/image_m/fclim-08-1763987-t001.jpg Table 1. Summary of studies by climatic stressors. https://www.frontiersin.org/files/Articles/1763987/fclim-08-1763987-HTML/image_m/fclim-08-1763987-g003.jpg Figure 3. The top ten mentioned diseases. https://www.frontiersin.org/files/Articles/1763987/fclim-08-1763987-HTML/image_m/fclim-08-1763987-g004.jpg Figure 4. Breeding techniques and methods reviewed. https://www.frontiersin.org/files/Articles/1763987/fclim-08-1763987-HTML/image_m/fclim-08-1763987-t002.jpg Table 2. Climatic factors and their impact on bean crop. https://www.frontiersin.org/files/Articles/1763987/fclim-08-1763987-HTML/image_m/fclim-08-1763987-t003.jpg Table 3. Identified diseases and resistant genes and cultivars. https://www.frontiersin.org/files/Articles/1763987/fclim-08-1763987-HTML/image_m/fclim-08-1763987-t004.jpg Table 4. Breeding strategies and their impacts in crop productivity. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1665145/full https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-g001.jpg Figure 1. The framework of MSGM. The multi-scale feature tensors from Temporal Multi-Scale Feature E https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-g002.jpg Figure 2. The schematic of Temporal Multi-Scale Feature Extraction. The process begins with raw EEG https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-g003.jpg Figure 3. The division method of 62-channel and 32-channel EEG. The same color represents the same r https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-t001.jpg Table 1. Training hyperparameters of the MSGM model. https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-t002.jpg Table 2. Architectural hyperparameters of the MSGM model. https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-t003.jpg Table 3. Hardware specifications for training and deployment. https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-t004.jpg Table 4. The accuracies and F1 scores (mean ± SD) on the SEED, THU-EP, and FACED datasets. https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-t005.jpg Table 5. Statistical significance analysis of MSGM compared to key baseline methods using Welch's t- https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-t006.jpg Table 6. Generalized emotion classification results of ablation studies on the SEED and THU-EP datas https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-g004.jpg Figure 4. (a) Effect of feature types on emotion classification performances using SEED. (b) Effect https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-g005.jpg Figure 5. Three methods for dividing 62 EEG channels into different regions, comprising 7, 10, and 1 https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-g006.jpg Figure 6. NVIDIA Jetson Xavier NX. https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-g007.jpg Figure 7. Performance comparison between the proposed MSGM and EmT. (a) Comparison of classification https://www.frontiersin.org/files/Articles/1665145/fnins-20-1665145-HTML/image_m/fnins-20-1665145-g008.jpg Figure 8. (a) Connectivity between the electrodes of the initial Local Graph. (b) Connectivity betwe https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1724059/full https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g001.jpg Figure 1. (a) Generalized main continental blocks in the eastern Tethyan tectonic domain and the pos https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g002.jpg Figure 2. Geological sketch map of the Mangzhang-Jiucheng area (modified after YIGS, 2020). 1. Quate https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g003.jpg Figure 3. Field Photos and representative photomicrographs of the granitoids for sample PM017-31-1 ( https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g004.jpg Figure 4. Discrimination diagram of parental magma of granitoids in the Mangzhang-Jiucheng area. (a) https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g005.jpg Figure 5. Chondrite-normalized REE and primitive-mantle-normalized trace element patterns for the La https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g006.jpg Figure 6. Cathodoluminescence images labeled with 206Pb/238U ages of the zircons separated from gran https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g007.jpg Figure 7. Concordia diagrams, chondrite-normalized rare-earth element patterns, and histograms of Ea https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g008.jpg Figure 8. Concordia diagrams, chondrite-normalized rare-earth element patterns, and histograms of Ea https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g009.jpg Figure 9. Histogram of reported magmatic zircon ages from the Tengchong Terrane. Data sourced from t https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g010.jpg Figure 10. I-type, S-type and A-type granite discrimination diagram for the granitoids from the Mang https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g011.jpg Figure 11. The discrimination diagrams of tectonic setting. (a) Rb vs. Nb + Y, (b) Rb vs. Yb + Ta an https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g012.jpg Figure 12. Ti-in-Zircon (Tzr) temperatures of magmatic zircons from the granitoids of the Mangzhang- https://www.frontiersin.org/files/Articles/1724059/feart-13-1724059-HTML/image_m/feart-13-1724059-g013.jpg Figure 13. Conceptual frameworks illustrating the Early-Middle Triassic (a), Late Triassic (b) and E https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1702233/full https://www.frontiersin.org/files/Articles/1702233/fonc-16-1702233-HTML/image_m/fonc-16-1702233-t001.jpg Table 1. General information of esophageal cancer patients (n=263). https://www.frontiersin.org/files/Articles/1702233/fonc-16-1702233-HTML/image_m/fonc-16-1702233-t002.jpg Table 2. Postoperative symptom incidence, severity and symptom group extraction results of esophagea https://www.frontiersin.org/files/Articles/1702233/fonc-16-1702233-HTML/image_m/fonc-16-1702233-g001.jpg Figure 1. Symptom network diagram. https://www.frontiersin.org/files/Articles/1702233/fonc-16-1702233-HTML/image_m/fonc-16-1702233-g002.jpg Figure 2. Centrality index of network nodes. https://www.frontiersin.org/files/Articles/1702233/fonc-16-1702233-HTML/image_m/fonc-16-1702233-g003.jpg Figure 3. Stability test of symptom network. https://www.frontiersin.org/files/Articles/1702233/fonc-16-1702233-HTML/image_m/fonc-16-1702233-g004.jpg Figure 4. Edge weight accuracy test of symptom network. https://www.frontiersin.org/files/Articles/1702233/fonc-16-1702233-HTML/image_m/fonc-16-1702233-g005.jpg Figure 5. Bridge strength of the symptom network. https://www.frontiersin.org/files/Articles/1702233/fonc-16-1702233-HTML/image_m/fonc-16-1702233-t003.jpg Table 3. Univariate analysis of postoperative fatigue in esophageal cancer patients. https://www.frontiersin.org/files/Articles/1702233/fonc-16-1702233-HTML/image_m/fonc-16-1702233-t004.jpg Table 4. Multivariate linear regression analysis of postoperative fatigue in esophageal cancer patie https://www.frontiersin.org/journals/environmental-engineering/articles/10.3389/fenve.2026.1766573/full https://www.frontiersin.org/files/Articles/1766573/fenve-05-1766573-HTML-r1/image_m/fenve-05-1766573-t001.jpg Table 1. Structured multi-tier framework for PFAS forensic fingerprinting under target-only datasets https://www.frontiersin.org/files/Articles/1766573/fenve-05-1766573-HTML-r1/image_m/fenve-05-1766573-g001.jpg Figure 1. Box plot graphic comparing total PFAS concentrations in nanograms per liter across four ar https://www.frontiersin.org/files/Articles/1766573/fenve-05-1766573-HTML-r1/image_m/fenve-05-1766573-g002.jpg Figure 2. Side-by-side pie chart maps display detected PFAS classes at multiple PFAS sampling locati https://www.frontiersin.org/files/Articles/1766573/fenve-05-1766573-HTML-r1/image_m/fenve-05-1766573-g003.jpg Figure 3. Side-by-side graphic shows groundwater PFAS concentrations at an industrial site for 2018 https://www.frontiersin.org/files/Articles/1766573/fenve-05-1766573-HTML-r1/image_m/fenve-05-1766573-g004.jpg Figure 4. Spatial interpolation maps of PFAS precursor classes in groundwater samples collected in 2 https://www.frontiersin.org/files/Articles/1766573/fenve-05-1766573-HTML-r1/image_m/fenve-05-1766573-g005.jpg Figure 5. Spatial interpolation of terminal PFAS classes in groundwater samples collected in 2018 an https://www.frontiersin.org/files/Articles/1766573/fenve-05-1766573-HTML-r1/image_m/fenve-05-1766573-g006.jpg Figure 6. PFAS profile changes at monitoring location 22X (2018 vs. 2024). A marked decline in 6:2 F https://www.frontiersin.org/files/Articles/1766573/fenve-05-1766573-HTML-r1/image_m/fenve-05-1766573-g007.jpg Figure 7. Unsupervised Partitioning Around Medoids (PAM) clustering of groundwater samples based on https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1628823/full https://www.frontiersin.org/files/Articles/1628823/fdgth-07-1628823-HTML-r1/image_m/fdgth-07-1628823-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/files/Articles/1628823/fdgth-07-1628823-HTML-r1/image_m/fdgth-07-1628823-g001.jpg Figure 1. User perspectives on PROfeel, an ESM-supported blended care intervention. Themes from pati https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1778827/full https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t001.jpg Table 1. Computation analysis of module parameters. https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t002.jpg Table 2. The impact of RCAC3 and SP-STR on the AodeMar’s computational load, detection accuracy, and https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-g001.jpg Figure 1. Structural diagrams contrasting the (a) Original convolution and (b) Ghost convolution, hi https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-g002.jpg Figure 2. Schematic diagram of the proposed eAodeMar framework, which features the lightweight G-SP- https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t003.jpg Table 3. Module-wise comparison of parameters and computational complexity before and after lightwei https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-g003.jpg Figure 3. Experimental devices and Jetson Xavier NX GPU. https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t004.jpg Table 4. Hardware parameters of Jetson Xaiver NX. https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-g004.jpg Figure 4. TensorRT interlayer integration strategy: (a) Original network. (b) Vertical fusion strate https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t005.jpg Table 5. The training environment configuration. https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-g005.jpg Figure 5. Example images from (a) SMD onboard video and (b) Linghai campus wharf video used for eval https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t006.jpg Table 6. Performance comparisons between AodeMar and its lightweight models. https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t007.jpg Table 7. Comparisons among eAodeMar, AodeMar and models with lightweight backbones. https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t008.jpg Table 8. Quantitative comparisons with typical lightweight models. https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t009.jpg Table 9. Performance comparison of models before and after deployment. https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-t010.jpg Table 10. Comparison of detection speed before and after acceleration on the actual collected and SM https://www.frontiersin.org/files/Articles/1778827/fmars-13-1778827-HTML/image_m/fmars-13-1778827-g006.jpg Figure 6. Comparison of detection results on challenging scenes from the Linghai campus wharf. (a) F https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1762569/full https://www.frontiersin.org/files/Articles/1762569/fmed-13-1762569-HTML/image_m/fmed-13-1762569-g001.jpg Figure 1. (A, B) Craniofacial appearance after surgical treatment: relatively flat facial profile, m https://www.frontiersin.org/files/Articles/1762569/fmed-13-1762569-HTML/image_m/fmed-13-1762569-g002.jpg Figure 2. (A, B) Bilateral medial upper eyelid entropion and trichiasis. https://www.frontiersin.org/files/Articles/1762569/fmed-13-1762569-HTML/image_m/fmed-13-1762569-g003.jpg Figure 3. Pre-operative baseline and postoperative bilateral fundus images. (A) (Pre-operative): 11- https://www.frontiersin.org/files/Articles/1762569/fmed-13-1762569-HTML/image_m/fmed-13-1762569-g004.jpg Figure 4. Bilateral axial length changes in a child with syndromic Pierre Robin sequence before and https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2026.1713699/full https://www.frontiersin.org/files/Articles/1713699/frsc-08-1713699-HTML/image_m/frsc-08-1713699-g001.jpg Figure 1. Normalized trends of urbanization rate, rail transit ridership, and network coverage index https://www.frontiersin.org/files/Articles/1713699/frsc-08-1713699-HTML/image_m/frsc-08-1713699-g002.jpg Figure 2. Study area around Daping Station, Chongqing. https://www.frontiersin.org/files/Articles/1713699/frsc-08-1713699-HTML/image_m/frsc-08-1713699-t001.jpg Table 1. Number and proportions of six categories of daily service POI data around Daping Station, 2 https://www.frontiersin.org/files/Articles/1713699/frsc-08-1713699-HTML/image_m/frsc-08-1713699-g003.jpg Figure 3. Space syntax analysis of street network integration values around Daping Station, 2014 and https://www.frontiersin.org/files/Articles/1713699/frsc-08-1713699-HTML/image_m/frsc-08-1713699-g004.jpg Figure 4. Kernel density maps of six functional POI categories around Daping Station in 2014 and 202 https://www.frontiersin.org/files/Articles/1713699/frsc-08-1713699-HTML/image_m/frsc-08-1713699-g005.jpg Figure 5. Spatial information entropy distribution of Daping Station URTSAs in 2024 (indicating func https://www.frontiersin.org/files/Articles/1713699/frsc-08-1713699-HTML/image_m/frsc-08-1713699-g006.jpg Figure 6. Diagram of the dual-directional vertical–horizontal functional coupling system. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1744951/full https://www.frontiersin.org/files/Articles/1744951/fimmu-17-1744951-HTML-r1/image_m/fimmu-17-1744951-t001.jpg Table 1. Overview of definitions of SRs to biologic therapy in psoriasis across published studies. https://www.frontiersin.org/files/Articles/1744951/fimmu-17-1744951-HTML-r1/image_m/fimmu-17-1744951-g001.jpg Figure 1. Predictive factors associated with super response to biologic therapy in psoriasis. Dots r https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1798373/full https://www.frontiersin.org/files/Articles/1798373/fpsyt-17-1798373-HTML/image_m/fpsyt-17-1798373-t001.jpg Table 1. Demographic characteristics and univariate analysis outcomes of the three psychological res https://www.frontiersin.org/files/Articles/1798373/fpsyt-17-1798373-HTML/image_m/fpsyt-17-1798373-t002.jpg Table 2. Latent profile model fit indices of psychological resilience among individuals from couples https://www.frontiersin.org/files/Articles/1798373/fpsyt-17-1798373-HTML/image_m/fpsyt-17-1798373-g001.jpg Figure 1. Characteristic distribution of the three psychological resilience latent profiles among in https://www.frontiersin.org/files/Articles/1798373/fpsyt-17-1798373-HTML/image_m/fpsyt-17-1798373-t003.jpg Table 3. Variable coding table. https://www.frontiersin.org/files/Articles/1798373/fpsyt-17-1798373-HTML/image_m/fpsyt-17-1798373-t004.jpg Table 4. Multivariate logistic regression analysis of psychological resilience latent profiles among https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1764795/full https://www.frontiersin.org/files/Articles/1764795/fneur-17-1764795-HTML/image_m/fneur-17-1764795-g001.jpg Figure 1. Representation of the Delphi rounds, including a timeline and key points for each round. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1765562/full https://www.frontiersin.org/files/Articles/1765562/fpubh-14-1765562-HTML/image_m/fpubh-14-1765562-t001.jpg Table 1. Demographic characteristics of participants (n = 39). https://www.frontiersin.org/journals/cancer-control-and-society/articles/10.3389/fcacs.2026.1752674/full https://www.frontiersin.org/files/Articles/1752674/fcacs-04-1752674-HTML/image_m/fcacs-04-1752674-t001.jpg Table 1. Deaths, AAMR, and APC related to respiratory failure in adult cancer patients in the United https://www.frontiersin.org/files/Articles/1752674/fcacs-04-1752674-HTML/image_m/fcacs-04-1752674-g001.jpg Figure 1. (A) Age-group trends in respiratory failure-related deaths among US adults with cancer, 19 https://www.frontiersin.org/files/Articles/1752674/fcacs-04-1752674-HTML/image_m/fcacs-04-1752674-g002.jpg Figure 2. Trends in respiratory failure-related deaths among adult cancer patients by race in the Un https://www.frontiersin.org/files/Articles/1752674/fcacs-04-1752674-HTML/image_m/fcacs-04-1752674-g003.jpg Figure 3. Trends in respiratory failure-related deaths among adult cancer patients in the United Sta https://www.frontiersin.org/files/Articles/1752674/fcacs-04-1752674-HTML/image_m/fcacs-04-1752674-g004.jpg Figure 4. Predicted mortality associated with respiratory failure among US adult cancer patients. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1728904/full https://www.frontiersin.org/files/Articles/1728904/fpubh-13-1728904-HTML/image_m/fpubh-13-1728904-t001.jpg Table 1. A literature review on LLs in Health. https://www.frontiersin.org/files/Articles/1728904/fpubh-13-1728904-HTML/image_m/fpubh-13-1728904-g001.jpg Figure 1. Prisma. https://www.frontiersin.org/files/Articles/1728904/fpubh-13-1728904-HTML/image_m/fpubh-13-1728904-t002.jpg Table 2. Stakeholder roles and collaborative activities. https://www.frontiersin.org/files/Articles/1728904/fpubh-13-1728904-HTML/image_m/fpubh-13-1728904-t003.jpg Table 3. LL as service integration platforms. https://www.frontiersin.org/files/Articles/1728904/fpubh-13-1728904-HTML/image_m/fpubh-13-1728904-g002.jpg Figure 2. The CLEA framework (collaborative living ecosystem approach). https://www.frontiersin.org/files/Articles/1728904/fpubh-13-1728904-HTML/image_m/fpubh-13-1728904-t004.jpg Table 4. Implications and further research. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1575617/full https://www.frontiersin.org/files/Articles/1575617/fpubh-13-1575617-HTML/image_m/fpubh-13-1575617-t001.jpg Table 1. Population by district/region, Volta Region, 2019 to 2023. https://www.frontiersin.org/files/Articles/1575617/fpubh-13-1575617-HTML/image_m/fpubh-13-1575617-t002.jpg Table 2. Age and sex specific populations, Volta Region, 2019–2023. https://www.frontiersin.org/files/Articles/1575617/fpubh-13-1575617-HTML/image_m/fpubh-13-1575617-t003.jpg Table 3. Characteristics of human bites reported to health facilities in the Volta Region, 2019–2023 https://www.frontiersin.org/files/Articles/1575617/fpubh-13-1575617-HTML/image_m/fpubh-13-1575617-g001.jpg Figure 1. Trend of human bites in the Volta Region, 2019–2023. https://www.frontiersin.org/files/Articles/1575617/fpubh-13-1575617-HTML/image_m/fpubh-13-1575617-t004.jpg Table 4. Incidence of human bites by sex and age, in the Volta Region, 2019–2023. https://www.frontiersin.org/files/Articles/1575617/fpubh-13-1575617-HTML/image_m/fpubh-13-1575617-g002.jpg Figure 2. Human bites incidence by district in the Volta Region, 2019–2023. https://www.frontiersin.org/files/Articles/1575617/fpubh-13-1575617-HTML/image_m/fpubh-13-1575617-t005.jpg Table 5. Relative risk (RR) of human bites by sex and age. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1744830/full https://www.frontiersin.org/files/Articles/1744830/fcvm-13-1744830-HTML-r1/image_m/fcvm-13-1744830-t001.jpg Table 1. LDLR-independent mechanisms of PCSK9 in different cell types. https://www.frontiersin.org/files/Articles/1744830/fcvm-13-1744830-HTML-r1/image_m/fcvm-13-1744830-g001.jpg Figure 1. The biosynthesis, secretion, and molecular interaction mechanisms of PCSK9 with LDLR. (A) https://www.frontiersin.org/files/Articles/1744830/fcvm-13-1744830-HTML-r1/image_m/fcvm-13-1744830-g002.jpg Figure 2. Pro-inflammatory and pro-atherosclerotic effect of PCSK9: local mechanisms within the vasc https://www.frontiersin.org/files/Articles/1744830/fcvm-13-1744830-HTML-r1/image_m/fcvm-13-1744830-g003.jpg Figure 3. From plaque to clot: the prothrombotic role of PCSK9. (A) Direct Platelet Priming via CD36 https://www.frontiersin.org/files/Articles/1744830/fcvm-13-1744830-HTML-r1/image_m/fcvm-13-1744830-g004.jpg Figure 4. Modulation of cardiomyocytes (CMs) by PCSK9. (A) Regulated Cell Death Pathways: (A1) Apopt https://www.frontiersin.org/files/Articles/1744830/fcvm-13-1744830-HTML-r1/image_m/fcvm-13-1744830-g005.jpg Figure 5. Modulation of cardiac fibroblasts (CFs) by PCSK9. (A) PCSK9-TLR4-NLRP3 Pathway: PCSK9 bind https://www.frontiersin.org/files/Articles/1744830/fcvm-13-1744830-HTML-r1/image_m/fcvm-13-1744830-g006.jpg Figure 6. PCSK9-driven osteogenic differentiation of valvular interstitial cells and therapeutic imp https://www.frontiersin.org/journals/electronics/articles/10.3389/felec.2026.1743265/full https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-g001.jpg Figure 1. N-input Double-Tree Single-Clock (DTSC) ACN design. The design consists of two sections: a https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-g002.jpg Figure 2. (a) A single capacitive SPDT synapse switch showing synapse capacitor, Ci, bias capacitor, https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-g003.jpg Figure 3. (a) Transistor-level diagram of the threshold logic showing two stages. The first stage is https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-t001.jpg Table 1. Post-layout rising offset voltage (mV) of the conventional (Conv) and the proposed (Prop) T https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-t002.jpg Table 2. Post-layout falling offset voltage (mV) of the Conv and (Prop) TL design across process cor https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-t003.jpg Table 3. DTSC N=12 ACN configuration with N+=5, I+={0,5,6,9,10} and N−=7, I−={1,2,3,4,7,8,11} where https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-g004.jpg Figure 4. (a) 12-input Double-Tree Single-Clock (DTSC) ACN design with 5 positive (top red rectangle https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-t004.jpg Table 4. Comparison between the theoretical model, proposed ACN and ACN using conventional TL design https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-t005.jpg Table 5. The comparison of the total synapse energy/operation between the proposed 12-input ACN and https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-g005.jpg Figure 5. (a) The figure presents three plots, shown in red, green, and blue. The red and green curv https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-t006.jpg Table 6. Power clock parameters at different frequencies at maximum loading (TV4: worst case synapse https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-g006.jpg Figure 6. Total synapse energy/operation versus operating frequency across 3 TVs for 12-input ACN an https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-g007.jpg Figure 7. The figure illustrates the total synapse energy per operation as a function of supply volt https://www.frontiersin.org/files/Articles/1743265/felec-07-1743265-HTML-r1/image_m/felec-07-1743265-g008.jpg Figure 8. Worst-case input vector, TV4 synapse energy distribution over 1,000 runs with mean, μ, and https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1749871/full https://www.frontiersin.org/files/Articles/1749871/fneur-17-1749871-HTML-r1/image_m/fneur-17-1749871-t001.jpg Table 1. Characteristics of the studied population at admission. https://www.frontiersin.org/files/Articles/1749871/fneur-17-1749871-HTML-r1/image_m/fneur-17-1749871-g001.jpg Figure 1. (A) C-reactive protein (CRP) levels from admission to the seventh day post-ictus in the st https://www.frontiersin.org/files/Articles/1749871/fneur-17-1749871-HTML-r1/image_m/fneur-17-1749871-g002.jpg Figure 2. C-reactive protein (CRP) levels from admission to the seventh day post-ictus in the study https://www.frontiersin.org/files/Articles/1749871/fneur-17-1749871-HTML-r1/image_m/fneur-17-1749871-t002.jpg Table 2. Logistic regression models to assess the association between the highest C-reactive protein https://www.frontiersin.org/files/Articles/1749871/fneur-17-1749871-HTML-r1/image_m/fneur-17-1749871-g003.jpg Figure 3. Neutrophil-to-lymphocyte ratio (N/L) levels from admission to the seventh day post-ictus i https://www.frontiersin.org/files/Articles/1749871/fneur-17-1749871-HTML-r1/image_m/fneur-17-1749871-t003.jpg Table 3. Logistic regression model to assess the association between the highest C-reactive protein https://www.frontiersin.org/files/Articles/1749871/fneur-17-1749871-HTML-r1/image_m/fneur-17-1749871-g004.jpg Figure 4. Evolution of (A) C-reactive protein (CRP) levels and (B) neutrophil-to-lymphocyte ratio (N https://www.frontiersin.org/files/Articles/1749871/fneur-17-1749871-HTML-r1/image_m/fneur-17-1749871-g005.jpg Figure 5. Evolution of (A) C-reactive protein (CRP) levels and (B) neutrophil-to-lymphocyte ratio (N https://www.frontiersin.org/files/Articles/1749871/fneur-17-1749871-HTML-r1/image_m/fneur-17-1749871-g006.jpg Figure 6. Evolution of (A) C-reactive protein (CRP) levels and (B) neutrophil-to-lymphocyte ratio (N https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1646084/full https://www.frontiersin.org/files/Articles/1646084/fpsyt-16-1646084-HTML/image_m/fpsyt-16-1646084-t001.jpg Table 1. Socio-demographic characteristics and comparison of participants’ different variables regar https://www.frontiersin.org/files/Articles/1646084/fpsyt-16-1646084-HTML/image_m/fpsyt-16-1646084-t002.jpg Table 2. Pearson’s correlation of positive coping style, resilience, and stigma in patients with Mei https://www.frontiersin.org/files/Articles/1646084/fpsyt-16-1646084-HTML/image_m/fpsyt-16-1646084-t003.jpg Table 3. Multiple linear regression analysis for factors associated with illness stigma in patients https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2026.1721770/full https://www.frontiersin.org/files/Articles/1721770/fcomm-11-1721770-HTML-r2/image_m/fcomm-11-1721770-g001.jpg Figure 1. Representation of the line of the activities held by researchers during BAW 2023. https://www.frontiersin.org/files/Articles/1721770/fcomm-11-1721770-HTML-r2/image_m/fcomm-11-1721770-t001.jpg Table 1. Characteristics of children that underwent knowledge assessment before and after the BAW 20 https://www.frontiersin.org/files/Articles/1721770/fcomm-11-1721770-HTML-r2/image_m/fcomm-11-1721770-g002.jpg Figure 2. Questionnaire scores before and after the Brain Awareness Week activities for everyone. Ba https://www.frontiersin.org/files/Articles/1721770/fcomm-11-1721770-HTML-r2/image_m/fcomm-11-1721770-g003.jpg Figure 3. Questionnaire scores before and after the Brain Awareness Week activities by gender. Basel https://www.frontiersin.org/files/Articles/1721770/fcomm-11-1721770-HTML-r2/image_m/fcomm-11-1721770-g004.jpg Figure 4. Questionnaire scores before and after the Brain Awareness Week activities by school grade. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1645979/full https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-t001.jpg Table 1. Experimental diets provided during the finishing phase for feedlot Nellore cattle. https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-g001.jpg Figure 1. Shannon’s diversity index and Chao richness index of rumen and cecum bacteria from Nellore https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-g002.jpg Figure 2. Difference of beta diversity in the two groups using principal coordinate analysis (PCoA) https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-g003.jpg Figure 3. Relative abundance of rumen bacteria, at phylum level, in Nellore cattle fed narasin. Valu https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-g004.jpg Figure 4. Relative abundance of cecum bacteria, at phylum level, in Nellore cattle fed narasin. Valu https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-g005.jpg Figure 5. Relative abundance of cecum bacteria (Clostridiales) in Nellore cattle fed narasin. https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-g006.jpg Figure 6. Heatmap of microbial abundance showing the main genera in the rumen of feedlot Nellore cat https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-g007.jpg Figure 7. Heatmap of microbial abundance showing the main genera in the cecum of feedlot Nellore cat https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-g008.jpg Figure 8. Co-occurrence networks of the ruminal microbiota in control and narasin-treated animals. G https://www.frontiersin.org/files/Articles/1645979/fmicb-17-1645979-HTML/image_m/fmicb-17-1645979-g009.jpg Figure 9. Co-occurrence networks of the cecal microbiota in Control and Narasin-treated animals. Gen https://www.frontiersin.org/journals/chemical-engineering/articles/10.3389/fceng.2025.1683078/full https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g001.jpg Figure 1. Characterization of black rice husk: (a) raw black rice husk ash, screened sample; (b) bal https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g002.jpg Figure 2. Microscopic visualization of the ash–liquid system in porous media: (a) measurement setup, https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g003.jpg Figure 3. Sand pack experiment (a) without sand pack and (b) with sand pack. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g004.jpg Figure 4. (a) Observation percentage of the different components of black rice husk. (b) Important p https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g005.jpg Figure 5. Particle size of black rice husk ash. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-t001.jpg Table 1. Pressure observation of foam generation. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-t002.jpg Table 2. Foam life in different time ranges. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-t003.jpg Table 3. Sand pack parameters. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-t004.jpg Table 4. Liquid pressure flow. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g006.jpg Figure 6. Displacing rig and flow chart. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g007.jpg Figure 7. Water saturation and oil saturation: (a) water saturation at different surfactant amounts https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g008.jpg Figure 8. Half-life predicted. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g009.jpg Figure 9. Residual by the predicted plot. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g010.jpg Figure 10. Studentized residuals. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-t005.jpg Table 5. Parameter estimates. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-t006.jpg Table 6. Effect tests. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g011.jpg Figure 11. Interaction profiles. https://www.frontiersin.org/files/Articles/1683078/fceng-07-1683078-HTML-r2/image_m/fceng-07-1683078-g012.jpg Figure 12. Microscopic visualization of foam: (a) initial stage, (b) middle stage, and (c) late stag https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1586990/full https://www.frontiersin.org/files/Articles/1586990/fmars-12-1586990-HTML-r1/image_m/fmars-12-1586990-g001.jpg Figure 1. Location of the moored sediment trap (400 m) at the P3 observation site (Northeast Scotia https://www.frontiersin.org/files/Articles/1586990/fmars-12-1586990-HTML-r1/image_m/fmars-12-1586990-g002.jpg Figure 2. Taxonomic (A.i, A.ii), ontogenetic stage (B.i, B.ii) composition of copepods present in a https://www.frontiersin.org/files/Articles/1586990/fmars-12-1586990-HTML-r1/image_m/fmars-12-1586990-g003.jpg Figure 3. Individual copepod length (A) and total distribution of copepod lengths (B) present in a s https://www.frontiersin.org/files/Articles/1586990/fmars-12-1586990-HTML-r1/image_m/fmars-12-1586990-g004.jpg Figure 4. Calanus simillimus (A), Metridia spp (B). and Pleuromamma robusta (C) with regards ontogen https://www.frontiersin.org/files/Articles/1586990/fmars-12-1586990-HTML-r1/image_m/fmars-12-1586990-g005.jpg Figure 5. Schematic of potential Calanus simillimus winter ecology (copepodite stage C5) with respec https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1658429/full https://www.frontiersin.org/files/Articles/1658429/fimmu-16-1658429-HTML/image_m/fimmu-16-1658429-t001.jpg Table 1. List of antibodies. https://www.frontiersin.org/files/Articles/1658429/fimmu-16-1658429-HTML/image_m/fimmu-16-1658429-g001.jpg Figure 1. NUPA10hd and Hoxb8 BM progenitors efficiently differentiate into DCs in vitro. (A) Scheme https://www.frontiersin.org/files/Articles/1658429/fimmu-16-1658429-HTML/image_m/fimmu-16-1658429-g002.jpg Figure 2. NUPA10hd and Hoxb8 mDCs show CCR7-dependent migration and activate T cells in vitro. (A) H https://www.frontiersin.org/files/Articles/1658429/fimmu-16-1658429-HTML/image_m/fimmu-16-1658429-g003.jpg Figure 3. NUPA10hd and Hoxb8 mDCs induce T-cell activation and proliferation in vivo. (A) Scheme of https://www.frontiersin.org/files/Articles/1658429/fimmu-16-1658429-HTML/image_m/fimmu-16-1658429-g004.jpg Figure 4. NUPA10hd, but not Hoxb8, progenitors reconstitute BM, spleen, and SI of RAGγc−/− mice. (A) https://www.frontiersin.org/files/Articles/1658429/fimmu-16-1658429-HTML/image_m/fimmu-16-1658429-g005.jpg Figure 5. NUPA10hd progenitors give rise to pDCs and cDCs in lymphoid and non-lymphoid organs. (A, B https://www.frontiersin.org/files/Articles/1658429/fimmu-16-1658429-HTML/image_m/fimmu-16-1658429-g006.jpg Figure 6. Generation of genetically engineered NUPA10hd mDCs to monitor Ag-specific T cell/DC intera https://www.frontiersin.org/files/Articles/1658429/fimmu-16-1658429-HTML/image_m/fimmu-16-1658429-g007.jpg Figure 7. Generation of genetically engineered ex vivo DCs using the NUPA10hd system to monitor the https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1594340/full https://www.frontiersin.org/files/Articles/1594340/fcell-13-1594340-HTML/image_m/fcell-13-1594340-t001.jpg Table 1. qRT-PCR Primers. https://www.frontiersin.org/files/Articles/1594340/fcell-13-1594340-HTML/image_m/fcell-13-1594340-g001.jpg Figure 1. Characterization of human iPSCs. Phase Contrast Micrograph of one representative human iPS https://www.frontiersin.org/files/Articles/1594340/fcell-13-1594340-HTML/image_m/fcell-13-1594340-g002.jpg Figure 2. Characterization of Hepatocyte-like cell differentiation. Phase Contrast Micrographs of on https://www.frontiersin.org/files/Articles/1594340/fcell-13-1594340-HTML/image_m/fcell-13-1594340-g003.jpg Figure 3. Proteomic analysis of iPSCs, GF HLCs, and SM HLCs compared to Controls (CTR). Diagrammatic https://www.frontiersin.org/files/Articles/1594340/fcell-13-1594340-HTML/image_m/fcell-13-1594340-g004.jpg Figure 4. Proteomic analysis of GF hepatocytes compared to SM hepatocytes. A Venn diagram illustrati https://www.frontiersin.org/files/Articles/1594340/fcell-13-1594340-HTML/image_m/fcell-13-1594340-g005.jpg Figure 5. Affected TCA cycle proteins in GF HLCs compared to SM HLCs. Graphical representation of th https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1763261/full https://www.frontiersin.org/files/Articles/1763261/fmed-13-1763261-HTML/image_m/fmed-13-1763261-g001.jpg Figure 1. Identification, screening, and inclusion of articles. https://www.frontiersin.org/files/Articles/1763261/fmed-13-1763261-HTML/image_m/fmed-13-1763261-t001.jpg Table 1. Chronological development of AMA, 2009–2025. https://www.frontiersin.org/files/Articles/1763261/fmed-13-1763261-HTML/image_m/fmed-13-1763261-g002.jpg Figure 2. Chronological development of the African regulatory ecosystem. https://www.frontiersin.org/files/Articles/1763261/fmed-13-1763261-HTML/image_m/fmed-13-1763261-t002.jpg Table 2. African Union Member States that have ratified the AMA treaty (as of December 2025). https://www.frontiersin.org/files/Articles/1763261/fmed-13-1763261-HTML/image_m/fmed-13-1763261-t003.jpg Table 3. Comparative overview: AMA vs. EMA vs. US FDA. https://www.frontiersin.org/files/Articles/1763261/fmed-13-1763261-HTML/image_m/fmed-13-1763261-g003.jpg Figure 3. Continental pathway for listing of human medicinal products. https://www.frontiersin.org/files/Articles/1763261/fmed-13-1763261-HTML/image_m/fmed-13-1763261-t004.jpg Table 4. Key challenges in the development and implementation of the African Medicines Agency (AMA). https://www.frontiersin.org/files/Articles/1763261/fmed-13-1763261-HTML/image_m/fmed-13-1763261-t005.jpg Table 5. Strategic future roles of the African Medicines Agency (AMA). https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1734096/full https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g001.jpg Figure 1. Fall detection among elderly persons (FDEP) model. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g002.jpg Figure 2. The architecture of fall detection of elderly persons (FDEP). https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g003.jpg Figure 3. Sample SimgFall images created from plotted IMU accelerometer and gyroscope signals. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g012.jpg ALGORITHM 1. SimgFall dataset creation. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g004.jpg Figure 4. CNN model working example. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g005.jpg Figure 5. (a) FallCNN_1 model and (b) summary of FallCNN_1 model. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g006.jpg Figure 6. (a) FallCNN_2 model and (b) summary of FallCNN_2 model. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g007.jpg Figure 7. (a) FallCNN_3 model and (b) summary of FallCNN_3 model. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g008.jpg Figure 8. (a) FallCNN_4 model and (b) summary of FallCNN_4 model. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-t001.jpg Table 1. Learning process recorded by the custom CNN (FallCNN) models, considering the number of epo https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-t002.jpg Table 2. Confusion matrix for FallCNN models. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-t003.jpg Table 3. Performance summary for FallCNN models. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-t004.jpg Table 4. Confusion matrix for MobileNetV2 models. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-t005.jpg Table 5. Performance summary for MobileNetV2 models. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-t006.jpg Table 6. Performance graph for FallCNN and MobileNetV2 models. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g009.jpg Figure 9. Feature explorer for FallCNN_3. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g010.jpg Figure 10. Feature explorer for FallCNN_4. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-g011.jpg Figure 11. Visualization of the most salient features in the convolution and pooling layers. https://www.frontiersin.org/files/Articles/1734096/frai-09-1734096-HTML-r1/image_m/frai-09-1734096-t007.jpg Table 7. Parameter comparison. https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2025.1628474/full https://www.frontiersin.org/files/Articles/1628474/fragi-06-1628474-HTML/image_m/fragi-06-1628474-g001.jpg Figure 1. Conceptual and methodological framework of kefir-aging literature mapping. (a) Schematic o https://www.frontiersin.org/files/Articles/1628474/fragi-06-1628474-HTML/image_m/fragi-06-1628474-g002.jpg Figure 2. Thematic distribution of kefir and aging studies based on AI-guided evidence mapping. (a) https://www.frontiersin.org/files/Articles/1628474/fragi-06-1628474-HTML/image_m/fragi-06-1628474-t001.jpg Table 1. Mechanistic pathways of kefir across aging domains. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1763975/full https://www.frontiersin.org/files/Articles/1763975/fpubh-14-1763975-HTML-r1/image_m/fpubh-14-1763975-g001.jpg Figure 1. Methodological framework and timeline for the evaluation of pre-emptive pharmacogenetic te https://www.frontiersin.org/files/Articles/1763975/fpubh-14-1763975-HTML-r1/image_m/fpubh-14-1763975-t001.jpg Table 1. Recommended actions and key stakeholders for the effective implementation of pre-emptive PG https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2026.1746577/full https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-g001.jpg Figure 1. Graphical abstract of the proposed framework. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-t007.jpg Algorithm 1. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-t008.jpg Algorithm 2. Adaptive autonomous reproduction of contact-rich assembly. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-g002.jpg Figure 2. Dataflow of the ART-based contact learning and classification. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-g003.jpg Figure 3. (Top) IndustRealKit parts, (Bottom) Disc brake parts. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-g004.jpg Figure 4. Output of force applied during contact exploration (Equation 10) with: fx=2.7Hz, fy=5.4Hz, https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-g005.jpg Figure 5. Exemplary data from the ART-based contact classification. From top to bottom: the six chan https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-t001.jpg Table 1. Hyperparameters selection. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-t002.jpg Table 2. Peg-in-hole: success rates. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-t003.jpg Table 3. Peg-in-hole: average completion time [s] of successful trials. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-t004.jpg Table 4. Plug insertion and car-parts assembly: success rates. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-t005.jpg Table 5. Plug insertion and car-parts assembly: average completion time [s] of successful trials. https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-g006.jpg Figure 6. Force measurements over the 47 assembly trials listed in Tables 2–5. (Top) 27x peg-in-hole https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-g007.jpg Figure 7. Snapshots of three assembly tasks following our policy. (Top) Medium cylinder peg, (Middle https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-g008.jpg Figure 8. Distance to final position (Left) and orientation (Right) goals. Results over the 47 assem https://www.frontiersin.org/files/Articles/1746577/frobt-13-1746577-HTML/image_m/frobt-13-1746577-t006.jpg Table 6. Overall success across all tasks. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1768485/full https://www.frontiersin.org/files/Articles/1768485/fgene-17-1768485-HTML/image_m/fgene-17-1768485-t001.jpg Table 1. Distribution of participants by age group. https://www.frontiersin.org/files/Articles/1768485/fgene-17-1768485-HTML/image_m/fgene-17-1768485-t002.jpg Table 2. Frequencies and percentages of chromosomal alterations (CAs) and chromosomal variants (CVs) https://www.frontiersin.org/files/Articles/1768485/fgene-17-1768485-HTML/image_m/fgene-17-1768485-g001.jpg Figure 1. Numerical and structural chromosomal alterations and heteromorphisms in irradiated (MI) an https://www.frontiersin.org/files/Articles/1768485/fgene-17-1768485-HTML/image_m/fgene-17-1768485-t003.jpg Table 3. Comparative frequencies of chromosomal alterations (CAs) and variants (CVs) in paired irrad https://www.frontiersin.org/files/Articles/1768485/fgene-17-1768485-HTML/image_m/fgene-17-1768485-t004.jpg Table 4. Individual micronuclei (MN) frequencies in irradiated samples (MI) and their corresponding https://www.frontiersin.org/files/Articles/1768485/fgene-17-1768485-HTML/image_m/fgene-17-1768485-g002.jpg Figure 2. Frequency of micronuclei (MN), nucleoplasmic bridges (NPB), and nuclear buds (NBUD) in irr https://www.frontiersin.org/files/Articles/1768485/fgene-17-1768485-HTML/image_m/fgene-17-1768485-g003.jpg Figure 3. Representative images of micronuclei (MN), nucleoplasmic bridges (NPB), and nuclear buds ( https://www.frontiersin.org/files/Articles/1768485/fgene-17-1768485-HTML/image_m/fgene-17-1768485-g004.jpg Figure 4. Micronuclei (MN) frequencies in non-irradiated vs. irradiated samples by sex. MN counts we https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1664231/full https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-g001.jpg Figure 1. Chemical structure of 1,2-benzisothiazol-3(2H)-one benzensulfonamides 6–24. https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-g008.jpg SCHEME 1. Synthesis of the key intermediates 1b–1d. Reagents and conditions: (a) NaNO2, HCl, 0°C; (b https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-g009.jpg SCHEME 2. Synthesis of the target compounds 6-24. Reagents and conditions: (a) SOCl2, reflux 3h; (b) https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-t001.jpg Table 1. Cytotoxicity and antiretroviral activity of compounds 6–24 and reference drugs. https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-t002.jpg Table 2. Comparative activity of compound 6 and reference compounds against HIV-1 and HIV-2 in cell- https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-g002.jpg Figure 2. Cytotoxicity and anti-HIV-1 activity of derivative 6. To evaluate antiviral activities of https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-g003.jpg Figure 3. Comparative activity of compound 6 (30 μM) and dextran sulphate (0.5 μM) in preventing syn https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-g004.jpg Figure 4. HIV-1 titres in supernatants of cultures treated with compound 6 and EFV during infection https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-g005.jpg Figure 5. Comparative virucidal effects of the derivative against HIV-1 (A) and HIV-2 (B). For viruc https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-g006.jpg Figure 6. Compound 6 concentration-dependent inactivation of cell-free infectious HIV-1 (A) and HIV- https://www.frontiersin.org/files/Articles/1664231/fmicb-16-1664231-HTML/image_m/fmicb-16-1664231-g007.jpg Figure 7. Amino acid sequence of NCp7 protein, with localization of amino acids (Phe-6 and Arg-32) i https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1607598/full https://www.frontiersin.org/files/Articles/1607598/fmed-12-1607598-HTML/image_m/fmed-12-1607598-t001.jpg Table 1. Cx26-mediated channel activity by automated SyncroPatch-Clamp analysis. https://www.frontiersin.org/files/Articles/1607598/fmed-12-1607598-HTML/image_m/fmed-12-1607598-g001.jpg Figure 1. (A) Automated voltage protocol as a function of time. (B) Typical current traces after app https://www.frontiersin.org/files/Articles/1607598/fmed-12-1607598-HTML/image_m/fmed-12-1607598-g002.jpg Figure 2. Human Cx26WT and mutations modeled by AlphaFold3 for docking experiments. (A) Cx26 DNA seq https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1730562/full https://www.frontiersin.org/files/Articles/1730562/fpubh-14-1730562-HTML-r1/image_m/fpubh-14-1730562-t001.jpg Table 1. Summary of themes, subthemes, illustrative excerpt from medical student reflection. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1723324/full https://www.frontiersin.org/files/Articles/1723324/fsurg-13-1723324-HTML-r1/image_m/fsurg-13-1723324-g001.jpg Figure 1. Schematic illustration of skeletal muscle measurement boundaries at the L3 vertebral level https://www.frontiersin.org/files/Articles/1723324/fsurg-13-1723324-HTML-r1/image_m/fsurg-13-1723324-t001.jpg Table 1. Comparison of the general data of the two groups. https://www.frontiersin.org/files/Articles/1723324/fsurg-13-1723324-HTML-r1/image_m/fsurg-13-1723324-t002.jpg Table 2. Comparison of intraoperative and postoperative conditions between the two groups. https://www.frontiersin.org/files/Articles/1723324/fsurg-13-1723324-HTML-r1/image_m/fsurg-13-1723324-t003.jpg Table 3. Univariate analysis of albumin levels after surgery. https://www.frontiersin.org/files/Articles/1723324/fsurg-13-1723324-HTML-r1/image_m/fsurg-13-1723324-t004.jpg Table 4. Multivariate linear regression analysis affecting postoperative albumin levels. https://www.frontiersin.org/files/Articles/1723324/fsurg-13-1723324-HTML-r1/image_m/fsurg-13-1723324-t005.jpg Table 5. Univariate analysis affecting the prolongation of postoperative gastrointestinal function r https://www.frontiersin.org/files/Articles/1723324/fsurg-13-1723324-HTML-r1/image_m/fsurg-13-1723324-t006.jpg Table 6. Results of multivariate logistic analysis affecting the prolongation of postoperative gastr https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1767123/full https://www.frontiersin.org/files/Articles/1767123/fpls-17-1767123-HTML/image_m/fpls-17-1767123-t001.jpg Table 1. Characteristics of the RS genes in barley and their protein physicochemical properties. https://www.frontiersin.org/files/Articles/1767123/fpls-17-1767123-HTML/image_m/fpls-17-1767123-g001.jpg Figure 1. The phylogenetic trees of RS proteins constructed using the maximum likelihood (ML) method https://www.frontiersin.org/files/Articles/1767123/fpls-17-1767123-HTML/image_m/fpls-17-1767123-g002.jpg Figure 2. Comparative collinearity analysis of the RS gene orthologs among eight plants. Collinear R https://www.frontiersin.org/files/Articles/1767123/fpls-17-1767123-HTML/image_m/fpls-17-1767123-g003.jpg Figure 3. Conserved motifs, conserved domains, and gene structures of HvRS genes. (A) Phylogenetic t https://www.frontiersin.org/files/Articles/1767123/fpls-17-1767123-HTML/image_m/fpls-17-1767123-g004.jpg Figure 4. Cis-acting elements in promoters of HvRS genes. https://www.frontiersin.org/files/Articles/1767123/fpls-17-1767123-HTML/image_m/fpls-17-1767123-g005.jpg Figure 5. Relative expression of HvRS genes in different tissues of barley. CAR15: Grain with bracts https://www.frontiersin.org/files/Articles/1767123/fpls-17-1767123-HTML/image_m/fpls-17-1767123-g006.jpg Figure 6. Relative expression levels of HvRS genes in the roots and shoots at the seedling stage und https://www.frontiersin.org/files/Articles/1767123/fpls-17-1767123-HTML/image_m/fpls-17-1767123-g007.jpg Figure 7. Allelic diversity and haplotype network analysis of HvRS2. (A) Haplotype analysis of HvRS2 https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1684484/full https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-t001.jpg Table 1. The brief summary of the state-of-the-art methods. https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-t002.jpg Table 2. The basic information about SDAD and MNDR dataset. https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-g001.jpg Figure 1. The overview of CGSDA framework. Embedding extracted by ChebNetII. https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-g002.jpg Figure 2. The overview of CGSDA framework. Embedding extracted by gated graph sequence neural networ https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-g003.jpg Figure 3. The overview of CGSDA framework. Embedding fusions and predicting potential snoRNA-disease https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-t003.jpg Table 3. Ten-fold cross-validation results performed by CGSDA based on SDAD and MNDR. https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-t004.jpg Table 4. The comparison results of CGSDA model and other state-of-the-art models based on ten-fold c https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-g004.jpg Figure 4. (A) The number of network layers in the ChebNetII and GatedGCN modules, where M_C and M_G https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-g005.jpg Figure 5. (A) The sequence length of the convolutional layer in the GatedGCN module, denoted by L. ( https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-g006.jpg Figure 6. Impact of hyperparameters on model performance. (A) Relationship between learning rate, we https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-t005.jpg Table 5. Ablation tests. https://www.frontiersin.org/files/Articles/1684484/fgene-16-1684484-HTML/image_m/fgene-16-1684484-t006.jpg Table 6. The top 15 predicted snoRNAs associated with lung cancer and breast cancer. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1674213/full https://www.frontiersin.org/files/Articles/1674213/fendo-16-1674213-HTML/image_m/fendo-16-1674213-g001.jpg Figure 1. Flow PRISMA diagram representing the selection process of eligible studies, using PRISMA20 https://www.frontiersin.org/files/Articles/1674213/fendo-16-1674213-HTML/image_m/fendo-16-1674213-g002.jpg Figure 2. Distribution of PTH2R-expressing neurons in the mouse brain (49). https://www.frontiersin.org/files/Articles/1674213/fendo-16-1674213-HTML/image_m/fendo-16-1674213-t001.jpg Table 1. Distribution of TIP39 in organs. https://www.frontiersin.org/files/Articles/1674213/fendo-16-1674213-HTML/image_m/fendo-16-1674213-g003.jpg Figure 3. Schematic diagrams demonstrate the distribution of TIP39 in the brain of mice (49). https://www.frontiersin.org/files/Articles/1674213/fendo-16-1674213-HTML/image_m/fendo-16-1674213-t002.jpg Table 2. Summary of the projections of TIP39 in the brain of mice. https://www.frontiersin.org/files/Articles/1674213/fendo-16-1674213-HTML/image_m/fendo-16-1674213-t003.jpg Table 3. Experimental findings for the endocrine roles of TIP39. https://www.frontiersin.org/files/Articles/1674213/fendo-16-1674213-HTML/image_m/fendo-16-1674213-g004.jpg Figure 4. Mechanism underlying TIP39-mediated maternal behavior. (A) Maternal rats receive stimuli f https://www.frontiersin.org/files/Articles/1674213/fendo-16-1674213-HTML/image_m/fendo-16-1674213-g005.jpg Figure 5. Potential mechanisms underlying TIP39 amelioration of postpartum depression. (A) Intrathec https://www.frontiersin.org/files/Articles/1674213/fendo-16-1674213-HTML/image_m/fendo-16-1674213-g006.jpg Figure 6. Overview of the function of TIP39 in the brain, including: 1) Pain. 2) Mood regulation. 3) https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1664799/full https://www.frontiersin.org/files/Articles/1664799/fbioe-13-1664799-HTML/image_m/fbioe-13-1664799-t001.jpg Table 1. Comparative evaluation of nanomaterials for osteoarthritis therapy. https://www.frontiersin.org/files/Articles/1664799/fbioe-13-1664799-HTML/image_m/fbioe-13-1664799-t002.jpg Table 2. Toxicity profiles of selected inorganic nanoparticles used in OA models. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1746280/full https://www.frontiersin.org/files/Articles/1746280/fnut-13-1746280-HTML/image_m/fnut-13-1746280-t001.jpg Table 1. Primer sequences used for gene expression analysis by qRT-PCR. https://www.frontiersin.org/files/Articles/1746280/fnut-13-1746280-HTML/image_m/fnut-13-1746280-g001.jpg Figure 1. UV–Vis spectra monitoring the formation of Pk-AgNps under reaction time (a) and temperatur https://www.frontiersin.org/files/Articles/1746280/fnut-13-1746280-HTML/image_m/fnut-13-1746280-g002.jpg Figure 2. SAED pattern (a) and XRD spectrum (b) collectively verify the crystalline structure of the https://www.frontiersin.org/files/Articles/1746280/fnut-13-1746280-HTML/image_m/fnut-13-1746280-g003.jpg Figure 3. FTIR spectra of Pk-AgNps and plant extract (a), DPPH radical scavenging activity of Pk-AgN https://www.frontiersin.org/files/Articles/1746280/fnut-13-1746280-HTML/image_m/fnut-13-1746280-t002.jpg Table 2. Diameter of inhibition zone (mm) of 30 μL purified Pk-AgNps. https://www.frontiersin.org/files/Articles/1746280/fnut-13-1746280-HTML/image_m/fnut-13-1746280-g004.jpg Figure 4. Cytotoxicity of Pk-AgNps in A549, MCF7, and AGS cells for 24 h treatment (a). Intracellula https://www.frontiersin.org/files/Articles/1746280/fnut-13-1746280-HTML/image_m/fnut-13-1746280-g005.jpg Figure 5. Hoechst staining showing nuclear morphology in A549, MCF7, and AGS cells after Pk-AgNps tr https://www.frontiersin.org/files/Articles/1746280/fnut-13-1746280-HTML/image_m/fnut-13-1746280-g006.jpg Figure 6. Expression level of EGFR, ELK-1, and MAPK14 genes in A549 (a), MCF7 (b), and AGS (c) cells https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1794046/full https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-t001.jpg Table 1. Conventional nutrient composition of walnut meal. https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-t002.jpg Table 2. Formulation of experimental diets containing walnut meal. https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-t003.jpg Table 3. Effects of different walnut meal replacement ratios on the growth performance of Diqing Tib https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-t004.jpg Table 4. Effects of different walnut meal replacement ratios on the carcass traits of Diqing Tibetan https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-t005.jpg Table 5. Effects of 50% walnut meal replacement on intramuscular fat content of Diqing Tibetan pigs. https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-g001.jpg Figure 1. Integrated analysis of cecum content metagenome with transcriptome and lipidome of back ad https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-g002.jpg Figure 2. Integrated analysis of cecum metagenomics, hepatic transcriptomics, and lipidomics. (A) Tr https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-g003.jpg Figure 3. Integrated analysis of muscle transcriptomics and metabolomics. (A) Transcriptomic analysi https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-g004.jpg Figure 4. Replacing 50% of soybean meal with walnut meal can significantly reduce subcutaneous fat a https://www.frontiersin.org/files/Articles/1794046/fmicb-17-1794046-HTML/image_m/fmicb-17-1794046-g005.jpg Figure 5. Molecular regulatory network of walnut meal replacing soybean meal-induced intramuscular f https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1584236/full https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-t001.jpg Table 1. Primers for real-time quantitative PCR. https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-g001.jpg Figure 1. PCA plot of LTP and STP. Each dot color represents a different time point and each dot rep https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-g002.jpg Figure 2. DEGs at three different developmental stages in LTP. (A) Volcano map of DEGs between E55 a https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-g003.jpg Figure 3. Expression changes of key pathway genes in LTP during development. (A) GO enrichment analy https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-g004.jpg Figure 4. DEGs at three different developmental stages in STP. (A) Volcano map of DEGs between E55 a https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-g005.jpg Figure 5. Expression changes of key pathway genes in STP during development. (A) GO enrichment analy https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-g006.jpg Figure 6. Identification of co-expression modules by WGCNA. (A) The determination of soft thresholdi https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-g007.jpg Figure 7. Module-trait relationships in large and small Diqing Tibetan pigs across three development https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-g008.jpg Figure 8. The interaction network of co-expression module genes significantly associated with three https://www.frontiersin.org/files/Articles/1584236/fvets-12-1584236-HTML-r1/image_m/fvets-12-1584236-g009.jpg Figure 9. qPCR results of DEGs. (A) qPCR validation of PDLIM3 expression. (B) qPCR validation of CMY https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1649636/full https://www.frontiersin.org/files/Articles/1649636/fonc-15-1649636-HTML/image_m/fonc-15-1649636-g001.jpg Figure 1. Contrast-enhanced MRI of the prostate. (a) Sagittal plane; (b) Transverse plane. https://www.frontiersin.org/files/Articles/1649636/fonc-15-1649636-HTML/image_m/fonc-15-1649636-g002.jpg Figure 2. Improved (port-free) single-site robot-assisted laparoscopic radical prostatectomy surgica https://www.frontiersin.org/files/Articles/1649636/fonc-15-1649636-HTML/image_m/fonc-15-1649636-g003.jpg Figure 3. Pathological examination and immunohistochemistry of surgically resected specimen: (a) (×2 https://www.frontiersin.org/files/Articles/1649636/fonc-15-1649636-HTML/image_m/fonc-15-1649636-g004.jpg Figure 4. Pathological consultation of previous specimens in our hospital. (a) (×20 HE), (b) (×100 H https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1744969/full https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g001.jpg Figure 1. Screening for potential targets. (A, B) DEGs were obtained using RNA-seq based on control https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g002.jpg Figure 2. Functional enrichment analysis. (A) GO enrichment analysis. (B) Sankey and dot plot of gen https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g003.jpg Figure 3. Network analysis of common targets and compound-target-signaling network construction. (A) https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g004.jpg Figure 4. Molecular docking and MD simulation analysis. (A) Heatmap of molecular docking binding ene https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g005.jpg Figure 5. QUE alleviates IL-1β-induced NP cell dysfunction. (A) Toluidine blue staining was used to https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g006.jpg Figure 6. QUE alleviates NP cell oxidative damage, ECM degradation, and inflammatory responses induc https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g007.jpg Figure 7. QUE alleviates IL-1β-caused apoptosis and senescence. (A) TUNEL assays were conducted to a https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g008.jpg Figure 8. Effects of QUE on the IL-1β-caused PI3K/Akt/eNOS pathway in NP cells. (A) WB analysis was https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g009.jpg Figure 9. QUE attenuates IDD in rats. (A) Overview of animal experiments. (B) X-ray was used to conf https://www.frontiersin.org/files/Articles/1744969/fimmu-17-1744969-HTML/image_m/fimmu-17-1744969-g010.jpg Figure 10. Schematic diagram of the potential therapeutic mechanism of ABR involved in IDD (by Figdr https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1720643/full https://www.frontiersin.org/files/Articles/1720643/fmicb-16-1720643-HTML/image_m/fmicb-16-1720643-g001.jpg Figure 1. (A) Study overview. GWAS, genome-wide association study; IVW, inverse-variance weighted. ( https://www.frontiersin.org/files/Articles/1720643/fmicb-16-1720643-HTML/image_m/fmicb-16-1720643-g002.jpg Figure 2. (A,B) The writhing scores and writhing latency of each group(n = 5). (C) Serum PGF2α level https://www.frontiersin.org/files/Articles/1720643/fmicb-16-1720643-HTML/image_m/fmicb-16-1720643-g003.jpg Figure 3. (A) Top 20 relative abundances of gut microbiota at the family level. (B) Top 20 relative https://www.frontiersin.org/files/Articles/1720643/fmicb-16-1720643-HTML/image_m/fmicb-16-1720643-g004.jpg Figure 4. (A) Overall effect size distribution of correlations between gut microbiota and pain-relat https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1746732/full https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-t001.jpg Table 1. Search terms deployed for pubMed. https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-t002.jpg Table 2. Descriptions of NICE DHT classification tiers [adapted from (40)]. https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-t003.jpg Table 3. Mapping of research objectives to the supporting methodologies undertaken in this review. https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-g001.jpg Figure 1. PRISMA-ScR flow diagram of paper selection. The figure shows the flow of papers throughout https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-g002.jpg Figure 2. Cumulative count of publications stratified by NICE DHT classification. The figure shows a https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-g003.jpg Figure 3. Count of papers by technology type and NICE DHT classification. Each technology type is pr https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-t004.jpg Table 4. Counts of papers by study design within each NICE DHT Category. https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-g004.jpg Figure 4. Four panels of stacked bar charts showing risk of bias (RoB) criterion scores for each pap https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-g005.jpg Figure 5. Stacked bar chart showing RoB criterion scores for observational and cross-sectional studi https://www.frontiersin.org/files/Articles/1746732/fdgth-08-1746732-HTML-r2/image_m/fdgth-08-1746732-g006.jpg Figure 6. Stacked bar chart showing RoB criterion scores for observational and cross-sectional studi https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1773101/full https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-t001.jpg Table 1. Measured constructs and pictures of neuropsychological tasks. https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-t002.jpg Table 2. Overview of tasks, constructs, and theoretical foundations used in the app. https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-t003.jpg Table 3. Sample characteristics and results of t-tests for each neuropsychological assessment variab https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-t004.jpg Table 4. Correlations among the study variables. https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-g001.jpg Figure 1. Scatterplot illustrating the correlation between Decision Variability and GAD-7 scores. https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-g002.jpg Figure 2. Scatterplot illustrating the correlation between Abandonment Tendency and GAD-7 scores. https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-g003.jpg Figure 3. Scatterplot illustrating the correlation between Motivational Deficit and PROMIS Depressio https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-t005.jpg Table 5. Classification results based on neuropsychological task indicators. https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-t006.jpg Table 6. Classification results based on neuropsychological task indicators and self-reports. https://www.frontiersin.org/files/Articles/1773101/fpsyt-17-1773101-HTML/image_m/fpsyt-17-1773101-t007.jpg Table 7. Standardized canonical discriminant function coefficients for the combined model. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1463237/full https://www.frontiersin.org/files/Articles/1463237/fpls-16-1463237-HTML-r1/image_m/fpls-16-1463237-t001.jpg Table 1. Variation in fourteen leaf and fine-root traits measured in 37 tree species. https://www.frontiersin.org/files/Articles/1463237/fpls-16-1463237-HTML-r1/image_m/fpls-16-1463237-g001.jpg Figure 1. Plant trait networks (PTNs) across species at six elevations: (A) 600 m, (B) 1100 m, (C) 1 https://www.frontiersin.org/files/Articles/1463237/fpls-16-1463237-HTML-r1/image_m/fpls-16-1463237-g002.jpg Figure 2. Betweenness centrality in plant trait networks across the elevational gradient. Trait abbr https://www.frontiersin.org/files/Articles/1463237/fpls-16-1463237-HTML-r1/image_m/fpls-16-1463237-g003.jpg Figure 3. Variation in (A–D) plant trait network metrics and (E–H) hub traits across the elevational https://www.frontiersin.org/files/Articles/1463237/fpls-16-1463237-HTML-r1/image_m/fpls-16-1463237-g004.jpg Figure 4. (A) Biplot from redundancy analysis (RDA) examining relationships between plant trait netw https://www.frontiersin.org/files/Articles/1463237/fpls-16-1463237-HTML-r1/image_m/fpls-16-1463237-g005.jpg Figure 5. (A) Biplot from redundancy analysis (RDA) examining relationships between species distribu https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1691912/full https://www.frontiersin.org/files/Articles/1691912/fpsyg-17-1691912-HTML/image_m/fpsyg-17-1691912-g001.jpg Figure 1. PRISMA flowchart of search yield. https://www.frontiersin.org/files/Articles/1691912/fpsyg-17-1691912-HTML/image_m/fpsyg-17-1691912-g002.jpg Figure 2. Convergence of peaks across studies showing greater GMV correlates of resilience. MKDA ran https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1722366/full https://www.frontiersin.org/files/Articles/1722366/fpubh-13-1722366-HTML/image_m/fpubh-13-1722366-g001.jpg Figure 1. Flowchart of analytic approach. https://www.frontiersin.org/files/Articles/1722366/fpubh-13-1722366-HTML/image_m/fpubh-13-1722366-g002.jpg Figure 2. Provincial-level maps of overweight prevalence, obesity prevalence, and number of days wit https://www.frontiersin.org/files/Articles/1722366/fpubh-13-1722366-HTML/image_m/fpubh-13-1722366-t001.jpg Table 1. Spatial autocorrelation of OLS/GWR residuals for overweight and obesity prevalence across 2 https://www.frontiersin.org/files/Articles/1722366/fpubh-13-1722366-HTML/image_m/fpubh-13-1722366-t002.jpg Table 2. Global model performance for OLS and GWR models. https://www.frontiersin.org/files/Articles/1722366/fpubh-13-1722366-HTML/image_m/fpubh-13-1722366-t003.jpg Table 3. GWR estimates for the association between cold days (<4 °C) and overweight/obesity prevalen https://www.frontiersin.org/files/Articles/1722366/fpubh-13-1722366-HTML/image_m/fpubh-13-1722366-g003.jpg Figure 3. Geographically weighted regression coefficients for the association between cold days (<4  https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1684313/full https://www.frontiersin.org/files/Articles/1684313/fimmu-16-1684313-HTML/image_m/fimmu-16-1684313-g001.jpg Figure 1. Flow chart of the patients included in the study N=533 patients were kidney engrafted betw https://www.frontiersin.org/files/Articles/1684313/fimmu-16-1684313-HTML/image_m/fimmu-16-1684313-t001.jpg Table 1. Recipients characteristics and kidney transplantation evolution. https://www.frontiersin.org/files/Articles/1684313/fimmu-16-1684313-HTML/image_m/fimmu-16-1684313-t002.jpg Table 2. Immune cells at the time of transplantation. https://www.frontiersin.org/files/Articles/1684313/fimmu-16-1684313-HTML/image_m/fimmu-16-1684313-g002.jpg Figure 2. Comparison of B cells subsets, T cells and NK cells in control (n=190), acute rejection (A https://www.frontiersin.org/files/Articles/1684313/fimmu-16-1684313-HTML/image_m/fimmu-16-1684313-t003.jpg Table 3. Univariable and multivariable analysis of risk factors for opportunistic infection within 2 https://www.frontiersin.org/files/Articles/1684313/fimmu-16-1684313-HTML/image_m/fimmu-16-1684313-t004.jpg Table 4. Comparison of recipients (clinical and biological data) with and without de novo DSA at 24 https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1707450/full https://www.frontiersin.org/files/Articles/1707450/fnut-13-1707450-HTML/image_m/fnut-13-1707450-t001.jpg Table 1. Baseline characteristics of participants included in the study. https://www.frontiersin.org/files/Articles/1707450/fnut-13-1707450-HTML/image_m/fnut-13-1707450-g001.jpg Figure 1. Cumulative incidence of DLBCL stratified by serum lipid levels: apolipoprotein A (A), apol https://www.frontiersin.org/files/Articles/1707450/fnut-13-1707450-HTML/image_m/fnut-13-1707450-t002.jpg Table 2. Associations between serum lipid and the risk of DLBCL in the UK Biobank. https://www.frontiersin.org/files/Articles/1707450/fnut-13-1707450-HTML/image_m/fnut-13-1707450-g002.jpg Figure 2. Restricted cubic spline models depicting the dose-response relationship of serum lipids wi https://www.frontiersin.org/files/Articles/1707450/fnut-13-1707450-HTML/image_m/fnut-13-1707450-g003.jpg Figure 3. Subgroup analyses for the association between serum lipid levels and DLBCL risk. Results a https://www.frontiersin.org/files/Articles/1707450/fnut-13-1707450-HTML/image_m/fnut-13-1707450-g004.jpg Figure 4. Trajectories of serum lipid levels prior to DLBCL diagnosis for apolipoprotein A (A), high https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1757548/full https://www.frontiersin.org/files/Articles/1757548/fimmu-17-1757548-HTML/image_m/fimmu-17-1757548-g001.jpg Figure 1. Study profile and baseline characteristics of kidney transplant recipients with urinary st https://www.frontiersin.org/files/Articles/1757548/fimmu-17-1757548-HTML/image_m/fimmu-17-1757548-g002.jpg Figure 2. Comparison of age and sex distribution between kidney transplant and control groups before https://www.frontiersin.org/files/Articles/1757548/fimmu-17-1757548-HTML/image_m/fimmu-17-1757548-t001.jpg Table 1. Demographic characteristics, comorbidities, and stone compositions by renal transplantation https://www.frontiersin.org/files/Articles/1757548/fimmu-17-1757548-HTML/image_m/fimmu-17-1757548-t002.jpg Table 2. Demographic characteristics, comorbidities, and stone compositions after propensity score m https://www.frontiersin.org/files/Articles/1757548/fimmu-17-1757548-HTML/image_m/fimmu-17-1757548-g003.jpg Figure 3. Comprehensive demographic and compositional profile of the 69 kidney transplant recipients https://www.frontiersin.org/files/Articles/1757548/fimmu-17-1757548-HTML/image_m/fimmu-17-1757548-g004.jpg Figure 4. Risk factors and subgroup analyses for calcium oxalate and carbonate apatite stones. (A) R https://www.frontiersin.org/files/Articles/1757548/fimmu-17-1757548-HTML/image_m/fimmu-17-1757548-g005.jpg Figure 5. Multivariate logistic regression analysis for calcium oxalate stones and carbonate apatite https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1710252/full https://www.frontiersin.org/files/Articles/1710252/fgene-16-1710252-HTML/image_m/fgene-16-1710252-t001.jpg Table 1. Knockdown target sequence. https://www.frontiersin.org/files/Articles/1710252/fgene-16-1710252-HTML/image_m/fgene-16-1710252-g001.jpg Figure 1. Pedigree analysis and Sanger sequencing results of the proband. (A) The family pedigree of https://www.frontiersin.org/files/Articles/1710252/fgene-16-1710252-HTML/image_m/fgene-16-1710252-t002.jpg Table 2. Bioinformatics predictions. https://www.frontiersin.org/files/Articles/1710252/fgene-16-1710252-HTML/image_m/fgene-16-1710252-g002.jpg Figure 2. The influence of mutations on the tertiary structure of proteins and the surface electrost https://www.frontiersin.org/files/Articles/1710252/fgene-16-1710252-HTML/image_m/fgene-16-1710252-t003.jpg Table 3. Hydrogen bond distances associated with the TTC21B c.1552T>C (p.C518R) mutation. https://www.frontiersin.org/files/Articles/1710252/fgene-16-1710252-HTML/image_m/fgene-16-1710252-g003.jpg Figure 3. The effects of mutations on the morphology of podocytes. (A) qRT-PCR verification of the k https://www.frontiersin.org/files/Articles/1710252/fgene-16-1710252-HTML/image_m/fgene-16-1710252-g004.jpg Figure 4. The effects of mutations on the cilia. (A) The impact of mutations on cilia formation. (B) https://www.frontiersin.org/files/Articles/1710252/fgene-16-1710252-HTML/image_m/fgene-16-1710252-g005.jpg Figure 5. PGT-assisted conception. SNP haplotype results as part of PGT-M analysis. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1708262/full https://www.frontiersin.org/files/Articles/1708262/fneur-16-1708262-HTML/image_m/fneur-16-1708262-g001.jpg Figure 1. Flowchart of patient exclusion and inclusion criteria. Overall, between 2017 and 2023, n = https://www.frontiersin.org/files/Articles/1708262/fneur-16-1708262-HTML/image_m/fneur-16-1708262-t001.jpg Table 1. Baseline characteristics for all patients grouped by direct-to-center (DC) and drip-and-shi https://www.frontiersin.org/files/Articles/1708262/fneur-16-1708262-HTML/image_m/fneur-16-1708262-g002.jpg Figure 2. Density plot of the calculated distance between home and thrombectomy center for direct-to https://www.frontiersin.org/files/Articles/1708262/fneur-16-1708262-HTML/image_m/fneur-16-1708262-t002.jpg Table 2. Distances between home addresses and thrombectomy center (based on zip codes) and time inte https://www.frontiersin.org/files/Articles/1708262/fneur-16-1708262-HTML/image_m/fneur-16-1708262-t003.jpg Table 3. Secondary analyses of correlations between geographical distance, time metrics, initial str https://www.frontiersin.org/files/Articles/1708262/fneur-16-1708262-HTML/image_m/fneur-16-1708262-t004.jpg Table 4. Clinical outcomes measured on the modified Rankin scale (mRS) and National Institutes of He https://www.frontiersin.org/files/Articles/1708262/fneur-16-1708262-HTML/image_m/fneur-16-1708262-g003.jpg Figure 3. Clinical outcomes based on the modified Rankin Scale (mRS) for direct-to-center (DC) and d https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1694651/full https://www.frontiersin.org/files/Articles/1694651/fimmu-17-1694651-HTML-r1/image_m/fimmu-17-1694651-g001.jpg Figure 1. Overall study design. (A) Pictorial depiction of the study protocol. (B) Flowchart of the https://www.frontiersin.org/files/Articles/1694651/fimmu-17-1694651-HTML-r1/image_m/fimmu-17-1694651-g002.jpg Figure 2. Principal component analysis (PCA). Two-dimensional PCA plots show the distribution of the https://www.frontiersin.org/files/Articles/1694651/fimmu-17-1694651-HTML-r1/image_m/fimmu-17-1694651-t001.jpg Table 1. Major tissue specific biofunctions. https://www.frontiersin.org/files/Articles/1694651/fimmu-17-1694651-HTML-r1/image_m/fimmu-17-1694651-g003.jpg Figure 3. Major cortical biofunctions. (A) Longitudinal profile of the z-score of two networks linke https://www.frontiersin.org/files/Articles/1694651/fimmu-17-1694651-HTML-r1/image_m/fimmu-17-1694651-g004.jpg Figure 4. Major cardiac biofunction. (A) Longitudinal profile of the z-score of two networks linked https://www.frontiersin.org/files/Articles/1694651/fimmu-17-1694651-HTML-r1/image_m/fimmu-17-1694651-g005.jpg Figure 5. Major pulmonary biofunction. (A) Longitudinal profile of the z-scores of two networks link https://www.frontiersin.org/files/Articles/1694651/fimmu-17-1694651-HTML-r1/image_m/fimmu-17-1694651-g006.jpg Figure 6. An overarching view of the tissue-specific dynamics of two networks, namely cell death and https://www.frontiersin.org/files/Articles/1694651/fimmu-17-1694651-HTML-r1/image_m/fimmu-17-1694651-t002.jpg Table 2. Correlation matrix. https://www.frontiersin.org/files/Articles/1694651/fimmu-17-1694651-HTML-r1/image_m/fimmu-17-1694651-g007.jpg Figure 7. List of the miRNA-mRNA showing largely consistent regulation across the doses and time sin https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1762884/full https://www.frontiersin.org/files/Articles/1762884/fbioe-14-1762884-HTML-r1/image_m/fbioe-14-1762884-g007.jpg Scheme 1. Schematic illustration of the Synthesis Process of PB@Pt and the CAT-like Activity of PB@P https://www.frontiersin.org/files/Articles/1762884/fbioe-14-1762884-HTML-r1/image_m/fbioe-14-1762884-g001.jpg Figure 1. Characterization of PB@Pt. (A) SEM and (B) (C) TEM images of PB@Pt. (D) The EDX elemental https://www.frontiersin.org/files/Articles/1762884/fbioe-14-1762884-HTML-r1/image_m/fbioe-14-1762884-g002.jpg Figure 2. (A) CAT-mimetic properties of the PB and PB@Pt. (B) Effect of Pt content on the CAT-mimeti https://www.frontiersin.org/files/Articles/1762884/fbioe-14-1762884-HTML-r1/image_m/fbioe-14-1762884-g003.jpg Figure 3. Effect of pH on the CL intensity of luminol-H2O2 system catalyzed by PB@Pt and HRP. https://www.frontiersin.org/files/Articles/1762884/fbioe-14-1762884-HTML-r1/image_m/fbioe-14-1762884-g004.jpg Figure 4. (A) UV−vis spectroscopy of different materials. (B) CL performance of PB@Pt and PB@Pt-Ab2. https://www.frontiersin.org/files/Articles/1762884/fbioe-14-1762884-HTML-r1/image_m/fbioe-14-1762884-g005.jpg Figure 5. Effects of (A) luminol concentration, (B) H2O2 concentration, (C) incubation time, and (D) https://www.frontiersin.org/files/Articles/1762884/fbioe-14-1762884-HTML-r1/image_m/fbioe-14-1762884-g006.jpg Figure 6. (A,B) The calibration curve of the proposed chemiluminescence immunosensor for VEGF detect https://www.frontiersin.org/journals/animal-science/articles/10.3389/fanim.2026.1768851/full https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-t001.jpg Table 1. Effect of two commercial lactic acid bacteria additives on the nutrient composition of whol https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-t002.jpg Table 2. Effect of two commercial lactic acid bacteria additives on dynamic changes in fermentation https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-t003.jpg Table 3. Effect of two commercial lactic acid bacteria additives on fermentation parameters of whole https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-t004.jpg Table 4. Effect of two commercial lactic acid bacteria additives on alpha diversity of whole-plant m https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-t005.jpg Table 5. Effect of two commercial lactic acid bacteria additives on bacterial alpha diversity after https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-g001.jpg Figure 1. Analysis of the beta diversity of bacterial communities during silage. https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-g002.jpg Figure 2. Analysis of the beta diversity of bacterial communities after aerobic exposure. https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-g003.jpg Figure 3. Phylum level of bacterial abundance across silage fermentation days. https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-g004.jpg Figure 4. Genus level of bacterial abundance across silage fermentation days. https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-g005.jpg Figure 5. Phylum level of bacterial abundance after aerobic exposure. https://www.frontiersin.org/files/Articles/1768851/fanim-07-1768851-HTML/image_m/fanim-07-1768851-g006.jpg Figure 6. Genus level of bacterial abundance after aerobic exposure. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1753404/full https://www.frontiersin.org/files/Articles/1753404/fpubh-14-1753404-HTML/image_m/fpubh-14-1753404-t001.jpg Table 1. Pediatricians' barriers of diagnosing CP. https://www.frontiersin.org/files/Articles/1753404/fpubh-14-1753404-HTML/image_m/fpubh-14-1753404-t002.jpg Table 2. Proposed intervention. https://www.frontiersin.org/files/Articles/1753404/fpubh-14-1753404-HTML/image_m/fpubh-14-1753404-t003.jpg Table 3. Intervention mapping. https://www.frontiersin.org/files/Articles/1753404/fpubh-14-1753404-HTML/image_m/fpubh-14-1753404-t004.jpg Table 4. Implementation strategies to target selected TDF barriers. https://www.frontiersin.org/journals/developmental-psychology/articles/10.3389/fdpys.2025.1722214/full https://www.frontiersin.org/files/Articles/1722214/fdpys-03-1722214-HTML/image_m/fdpys-03-1722214-g001.jpg Figure 1. Mediation model diagram. https://www.frontiersin.org/files/Articles/1722214/fdpys-03-1722214-HTML/image_m/fdpys-03-1722214-t001.jpg Table 1. Correlation analysis. https://www.frontiersin.org/files/Articles/1722214/fdpys-03-1722214-HTML/image_m/fdpys-03-1722214-t002.jpg Table 2. Mediation model test. https://www.frontiersin.org/files/Articles/1722214/fdpys-03-1722214-HTML/image_m/fdpys-03-1722214-g002.jpg Figure 2. Mediation model diagram. *** p < 0.001. https://www.frontiersin.org/files/Articles/1722214/fdpys-03-1722214-HTML/image_m/fdpys-03-1722214-t003.jpg Table 3. Mediation model path analysis. https://www.frontiersin.org/files/Articles/1722214/fdpys-03-1722214-HTML/image_m/fdpys-03-1722214-t004.jpg Table 4. Moderated mediation model tests. https://www.frontiersin.org/files/Articles/1722214/fdpys-03-1722214-HTML/image_m/fdpys-03-1722214-g003.jpg Figure 3. Moderated mediation model (***p < 0.001). https://www.frontiersin.org/files/Articles/1722214/fdpys-03-1722214-HTML/image_m/fdpys-03-1722214-t005.jpg Table 5. Predictive effects of different physical activity levels. https://www.frontiersin.org/files/Articles/1722214/fdpys-03-1722214-HTML/image_m/fdpys-03-1722214-g004.jpg Figure 4. (A) The moderating trends of anxiety on subjective well-being at different levels of physi https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1749570/full https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-g001.jpg Figure 1. Overall flowchart of the experiment. The participants' ECG signals are collected using the https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-t001.jpg Table 1. Nine features of the HRV dataset. https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-t002.jpg Table 2. Seven labels of the HRV dataset. https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-g002.jpg Figure 2. Illustration of the r-SMOTE (a) and r-ENN (b) principle. (a) r-SMOTE generates xnew1 by th https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-t003.jpg Table 3. Original data and refined SMOTE-ENN optimization data. https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-g003.jpg Figure 3. The architecture of the neural network. The network consists of one input layer, three hid https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-t004.jpg Table 4. Hyperparameters with optimization of the four methods. https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-t005.jpg Table 5. Classification results of the data using refined SMOTE-ENN optimization. https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-g004.jpg Figure 4. Confusion matrices of the four machine learning models. (a) Confusion matrix of SVM. (b) C https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-t006.jpg Table 6. Classification results of the data using traditional SMOTE optimization. https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-g005.jpg Figure 5. The ROC curves of the four machine learning models. (a) ROC curve of SVM. (b) ROC curve of https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-t007.jpg Table 7. Comparison results of this work with previous studies. https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-g006.jpg Figure 6. Feature importance distribution of the four machine learning models. (a) Feature importanc https://www.frontiersin.org/files/Articles/1749570/fdgth-08-1749570-HTML/image_m/fdgth-08-1749570-t008.jpg Table 8. Feature importance ranking of the four machine learning models. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1771808/full https://www.frontiersin.org/files/Articles/1771808/fcvm-13-1771808-HTML/image_m/fcvm-13-1771808-g001.jpg Figure 1. Distribution of the outcome (cardiac events) and exposure (congenital heart disease) among https://www.frontiersin.org/files/Articles/1771808/fcvm-13-1771808-HTML/image_m/fcvm-13-1771808-t001.jpg Table 1. Characteristics of cases and controls. https://www.frontiersin.org/files/Articles/1771808/fcvm-13-1771808-HTML/image_m/fcvm-13-1771808-t002.jpg Table 2. Association between congenital heart disease and recurrent cardiac events. https://www.frontiersin.org/files/Articles/1771808/fcvm-13-1771808-HTML/image_m/fcvm-13-1771808-g002.jpg Figure 2. Association between congenital heart disease and recurrent cardiac events, stratified by t https://www.frontiersin.org/files/Articles/1771808/fcvm-13-1771808-HTML/image_m/fcvm-13-1771808-g003.jpg Figure 3. Risk prediction of recurrent cardiac events in congenital heart disease (CHD) patients exp https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1643905/full https://www.frontiersin.org/files/Articles/1643905/fmed-12-1643905-HTML/image_m/fmed-12-1643905-g001.jpg Figure 1. Surgical procedure diagram. (a) The diagram indicates the surgical procedures conducted du https://www.frontiersin.org/files/Articles/1643905/fmed-12-1643905-HTML/image_m/fmed-12-1643905-t001.jpg Table 1. Modified An and Friedman scoring system. https://www.frontiersin.org/files/Articles/1643905/fmed-12-1643905-HTML/image_m/fmed-12-1643905-g002.jpg Figure 2. Physical markers of pyogenic infections. (a, b) Body weight and temperature of rats among https://www.frontiersin.org/files/Articles/1643905/fmed-12-1643905-HTML/image_m/fmed-12-1643905-g003.jpg Figure 3. Imaging findings of pyogenic infections. Micro-CT imaging shows bone destruction in differ https://www.frontiersin.org/files/Articles/1643905/fmed-12-1643905-HTML/image_m/fmed-12-1643905-g004.jpg Figure 4. Histological evidence of pyogenic spondylitis. (a) Hematoxylin and eosin (H&E) staining of https://www.frontiersin.org/files/Articles/1643905/fmed-12-1643905-HTML/image_m/fmed-12-1643905-g005.jpg Figure 5. Vital signs of rats within 28 days. (a) Body weight and (b) temperature of rats among diff https://www.frontiersin.org/journals/sustainable-resource-management/articles/10.3389/fsrma.2026.1773351/full https://www.frontiersin.org/files/Articles/1773351/fsrma-05-1773351-HTML/image_m/fsrma-05-1773351-t001.jpg Table 1. Taxonomy of sustainable development approaches in informal settlements. https://www.frontiersin.org/files/Articles/1773351/fsrma-05-1773351-HTML/image_m/fsrma-05-1773351-t002.jpg Table 2. Taxonomy of economic and legal aspects in informal settlement formalization. https://www.frontiersin.org/files/Articles/1773351/fsrma-05-1773351-HTML/image_m/fsrma-05-1773351-t003.jpg Table 3. Disaster and risk management strategies in informal settlements. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1780244/full https://www.frontiersin.org/files/Articles/1780244/fendo-17-1780244-HTML-r1/image_m/fendo-17-1780244-t001.jpg Table 1. Characteristics of included patients. https://www.frontiersin.org/files/Articles/1780244/fendo-17-1780244-HTML-r1/image_m/fendo-17-1780244-g001.jpg Figure 1. Single-surgeon trends for PG autotransplantation over time for (a) surgeon no.1 and (b) su https://www.frontiersin.org/files/Articles/1780244/fendo-17-1780244-HTML-r1/image_m/fendo-17-1780244-g002.jpg Figure 2. Single-surgeon trends for the level of systematic NIRAF application over time for (a) surg https://www.frontiersin.org/files/Articles/1780244/fendo-17-1780244-HTML-r1/image_m/fendo-17-1780244-g003.jpg Figure 3. Duration of surgery over time for (a) surgeon no.1 and (b) surgeon no. 2. By linear regres https://www.frontiersin.org/files/Articles/1780244/fendo-17-1780244-HTML-r1/image_m/fendo-17-1780244-g004.jpg Figure 4. Rates of postoperative hypoparathyroidism over time. Only total thyroidectomies are depict https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1594733/full https://www.frontiersin.org/files/Articles/1594733/fspor-07-1594733-HTML/image_m/fspor-07-1594733-g001.jpg Figure 1. CONSORT flowchart (42) of participants recruited for the study. https://www.frontiersin.org/files/Articles/1594733/fspor-07-1594733-HTML/image_m/fspor-07-1594733-t001.jpg Table 1. Baseline characteristics of the participants. https://www.frontiersin.org/files/Articles/1594733/fspor-07-1594733-HTML/image_m/fspor-07-1594733-t002.jpg Table 2. Clinical characteristics of study participants. https://www.frontiersin.org/files/Articles/1594733/fspor-07-1594733-HTML/image_m/fspor-07-1594733-t003.jpg Table 3. Anthropometric parameters of participants recorded at the beginning (T1) and the end (T2) o https://www.frontiersin.org/files/Articles/1594733/fspor-07-1594733-HTML/image_m/fspor-07-1594733-t004.jpg Table 4. Physical activity parameters of participants recorded at the beginning (T1) and at the end https://www.frontiersin.org/files/Articles/1594733/fspor-07-1594733-HTML/image_m/fspor-07-1594733-t005.jpg Table 5. Scores of total and particular domains of WHOQOL-BREF (60) in women participating in the st https://www.frontiersin.org/files/Articles/1594733/fspor-07-1594733-HTML/image_m/fspor-07-1594733-t006.jpg Table 6. Hormonal pattern of women at the beginning (T1) and at the end (T2) of intervention. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1754621/full https://www.frontiersin.org/files/Articles/1754621/fimmu-17-1754621-HTML/image_m/fimmu-17-1754621-t001.jpg Table 1. A comparative summary of mouse models for C. psittaci infection. https://www.frontiersin.org/files/Articles/1754621/fimmu-17-1754621-HTML/image_m/fimmu-17-1754621-g001.jpg Figure 1. Comparative advantages and disadvantages of animal models in C. psittaci infection. The fi https://www.frontiersin.org/files/Articles/1754621/fimmu-17-1754621-HTML/image_m/fimmu-17-1754621-g002.jpg Figure 2. Overview of animal models for C. psittaci. This image demonstrates a simple framework for https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1605282/full https://www.frontiersin.org/files/Articles/1605282/fonc-15-1605282-HTML/image_m/fonc-15-1605282-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1605282/fonc-15-1605282-HTML/image_m/fonc-15-1605282-g001.jpg Figure 1. Overall Survival (A) and Progression-Free Survival (B) in mRCC patients treated with first https://www.frontiersin.org/files/Articles/1605282/fonc-15-1605282-HTML/image_m/fonc-15-1605282-g002.jpg Figure 2. Overall Survival in mRCC patients treated with first-line nivolumab plus cabozantinib stra https://www.frontiersin.org/files/Articles/1605282/fonc-15-1605282-HTML/image_m/fonc-15-1605282-g003.jpg Figure 3. Overall Survival in mRCC patients treated with first-line cabozantinib plus nivolumab stra https://www.frontiersin.org/files/Articles/1605282/fonc-15-1605282-HTML/image_m/fonc-15-1605282-t002.jpg Table 2. Univariate and multivariate analysis in mRCC patients receiving first-line nivolumab plus c https://www.frontiersin.org/files/Articles/1605282/fonc-15-1605282-HTML/image_m/fonc-15-1605282-t003.jpg Table 3. Grade 3-Grade 4 (G3-G4) adverse events, drug interruptions and dose reductions. https://www.frontiersin.org/files/Articles/1605282/fonc-15-1605282-HTML/image_m/fonc-15-1605282-t004.jpg Table 4. Real-world data studies with nivolumab plus cabozantinib. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1798519/full https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g001.jpg Figure 1. Growth of LLM in hiring research. Source Scopus data (2020–2025). Percentage indicate perc https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g002.jpg Figure 2. Hiring stage at which LLM is used (Gen., generative; Class., classification; Summ., summar https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g003.jpg Figure 3. Change in LLM applications across hiring stages and task types. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g004.jpg Figure 4. Candidate-facing vs. employer-facing LLM applications. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g005.jpg Figure 5. Study design typology of LLM research in hiring [Pre and Post AI diffusion (2022)]. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g006.jpg Figure 6. Outcome distribution. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g007.jpg Figure 7. Outcome vs. study design matrix. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g008.jpg Figure 8. Distribution of documented risk. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-t001.jpg Table 1. Alignment between lexicon-derived risk categories and governance dimensions defined in the https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g009.jpg Figure 9. The mitigation maturity in LLM based hiring. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g010.jpg Figure 10. Mitigation maturity vs. risk in LLM based hiring. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g011.jpg Figure 11. The disciplinary depth per paper. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g012.jpg Figure 12. Conceptual focus of LLM-based hiring research by discipline. https://www.frontiersin.org/files/Articles/1798519/frai-09-1798519-HTML-r1/image_m/frai-09-1798519-g013.jpg Figure 13. Interdisciplinary co-occurrence in LLM based hiring. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1644091/full https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-t001.jpg Table 1. HNT/Al2O3-GO loading of embedded membranes. https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-g001.jpg Figure 1. The flow diagram in the ultrafiltration device. https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-g002.jpg Figure 2. Crystal structure of modified HNT and HNT/Al2O3-GO. https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-t002.jpg Table 2. Comparison of contact angle, porosity, and viscosity of four membranes. https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-g003.jpg Figure 3. (a) Pure water flux, (b) BSA removal, and (c) Permeation flux after 120 min for PSF, 0.25 https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-t003.jpg Table 3. Water FRR percentage of membranes. https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-t004.jpg Table 4. Fouling of nanocomposite membranes with different percentages. https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-t005.jpg Table 5. The constant values for the parameters in Figures 4a–c and the evaluation of the correlatio https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-g004.jpg Figure 4. (a) The BSA removal percentage based on HNT/Al2O3-GO concentration and time, (b) the flux https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-g005.jpg Figure 5. The Pearson correlation among the contributing factors to (a) BSA removal percentage and ( https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-g006.jpg Figure 6. The predictions for BSA removal percentage using (a) ANN, (b) RF, and (c) SVR methods. https://www.frontiersin.org/files/Articles/1644091/fenvs-13-1644091-HTML/image_m/fenvs-13-1644091-g007.jpg Figure 7. The predictions for permeation flux using (a) ANN, (b) RF, and (c) SVR methods. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1772128/full https://www.frontiersin.org/files/Articles/1772128/fendo-17-1772128-HTML/image_m/fendo-17-1772128-g001.jpg Figure 1. Flowchart for patient selection. https://www.frontiersin.org/files/Articles/1772128/fendo-17-1772128-HTML/image_m/fendo-17-1772128-t001.jpg Table 1. The general characteristics of patients in the non-recurrence and recurrence groups. https://www.frontiersin.org/files/Articles/1772128/fendo-17-1772128-HTML/image_m/fendo-17-1772128-t002.jpg Table 2. The recurrence rates and clinical characteristics of DKA in different HRR groups. https://www.frontiersin.org/files/Articles/1772128/fendo-17-1772128-HTML/image_m/fendo-17-1772128-t003.jpg Table 3. The impact of HRR on recurrence. https://www.frontiersin.org/files/Articles/1772128/fendo-17-1772128-HTML/image_m/fendo-17-1772128-g002.jpg Figure 2. The association between HRR and recurrence of DKA. https://www.frontiersin.org/files/Articles/1772128/fendo-17-1772128-HTML/image_m/fendo-17-1772128-g003.jpg Figure 3. The association between HRR and recurrence of DKA. https://www.frontiersin.org/files/Articles/1772128/fendo-17-1772128-HTML/image_m/fendo-17-1772128-g004.jpg Figure 4. The association between HRR and recurrence of DKA. https://www.frontiersin.org/files/Articles/1772128/fendo-17-1772128-HTML/image_m/fendo-17-1772128-g005.jpg Figure 5. Value of HRR ratio in predicting DKA recurrence. https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2026.1780833/full https://www.frontiersin.org/files/Articles/1780833/fclim-08-1780833-HTML/image_m/fclim-08-1780833-g001.jpg Figure 1. Conceptual framework comparing technocratic planning approaches with locally embedded know https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1770997/full https://www.frontiersin.org/files/Articles/1770997/fmicb-17-1770997-HTML/image_m/fmicb-17-1770997-g001.jpg Figure 1. Contribution of T6SSSPI-20 and T6SSSPI-21 to interbacterial competition by S. arizonae RSK https://www.frontiersin.org/files/Articles/1770997/fmicb-17-1770997-HTML/image_m/fmicb-17-1770997-t001.jpg Table 1. Predicted T6SS effector and cognate immunity protein encoded in SPI-20 and SPI-21 of S. ent https://www.frontiersin.org/files/Articles/1770997/fmicb-17-1770997-HTML/image_m/fmicb-17-1770997-g002.jpg Figure 2. SARI_02727 contributes to antibacterial activity in S. arizonae RSK2980. (A) AlphaFold3 mo https://www.frontiersin.org/files/Articles/1770997/fmicb-17-1770997-HTML/image_m/fmicb-17-1770997-g003.jpg Figure 3. Contribution of the predicted C-terminal S-type pyocin domain of SARI_02603 to antibacteri https://www.frontiersin.org/files/Articles/1770997/fmicb-17-1770997-HTML/image_m/fmicb-17-1770997-g004.jpg Figure 4. The SPI-21 T6SS gene cluster encodes a new putative antibacterial T6SS effector protein. ( https://www.frontiersin.org/files/Articles/1770997/fmicb-17-1770997-HTML/image_m/fmicb-17-1770997-t002.jpg Table 2. Bacterial strains and plasmids used in this study. https://www.frontiersin.org/files/Articles/1770997/fmicb-17-1770997-HTML/image_m/fmicb-17-1770997-t003.jpg Table 3. Primers used in this study. https://www.frontiersin.org/journals/antibiotics/articles/10.3389/frabi.2026.1773630/full https://www.frontiersin.org/files/Articles/1773630/frabi-05-1773630-HTML-r1/image_m/frabi-05-1773630-g001.jpg Figure 1. Effect of TRG on antibiotic tolerant biofilm of (a) P. aeruginosa and (b) S. aureus using https://www.frontiersin.org/files/Articles/1773630/frabi-05-1773630-HTML-r1/image_m/frabi-05-1773630-g002.jpg Figure 2. Effect of TRG on total biofilm of (a) P. aeruginosa and (b) S. aureus using porcine skin e https://www.frontiersin.org/files/Articles/1773630/frabi-05-1773630-HTML-r1/image_m/frabi-05-1773630-g003.jpg Figure 3. Effect of TRG on antibiotic tolerant biofilm of (a) P. aeruginosa and (b) S. aureus using https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1781360/full https://www.frontiersin.org/files/Articles/1781360/fmars-13-1781360-HTML/image_m/fmars-13-1781360-g001.jpg Figure 1. Analytical framework of constituent elements of the policy for strengthening marine strate https://www.frontiersin.org/files/Articles/1781360/fmars-13-1781360-HTML/image_m/fmars-13-1781360-t001.jpg Table 1. Comparative analysis between the policy for strengthening marine strategic science and tech https://www.frontiersin.org/files/Articles/1781360/fmars-13-1781360-HTML/image_m/fmars-13-1781360-t002.jpg Table 2. Policy practices of strengthening marine strategic science and technology capabilities. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1661217/full https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t001.jpg Table 1. Basic information of participants (n = 10). https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-g001.jpg Figure 1. Experimental flowchart. https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-g002.jpg Figure 2. Electrode placement locations. https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-g003.jpg Figure 3. Pectoralis major muscle. https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-g004.jpg Figure 4. Anterior deltoid muscle. https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-g005.jpg Figure 5. Triceps brachii muscle. https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t002.jpg Table 2. Specific indicators included in the bench press test with different tempos and loads. https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t003.jpg Table 3. Changes in MVIC% of the right pectoralis major muscle during bench press training with diff https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t004.jpg Table 4. Changes in MVIC% of the right anterior deltoid muscle during bench press training with diff https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t005.jpg Table 5. Changes in MVIC% of the right triceps brachii muscle during bench press training with diffe https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t006.jpg Table 6. Changes in MDF of the right pectoralis major muscle during bench press training with differ https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t007.jpg Table 7. Changes in MDF of the right anterior deltoid muscle during bench press training with differ https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t008.jpg Table 8. Changes in MDF of the right triceps brachii muscle during bench press training with differe https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t009.jpg Table 9. Changes in MV during bench press training under different rhythms and loads (n = 10). https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t010.jpg Table 10. Changes in PV during bench press training under different rhythms and loads (n = 10). https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t011.jpg Table 11. Changes in peak velocity loss rate during bench press training under different rhythms and https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t012.jpg Table 12. Changes in MP during bench press training under different rhythms and loads. https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t013.jpg Table 13. Changes in PP during bench press training under different rhythms and loads. https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t014.jpg Table 14. Changes in TUT during bench press training under different rhythms and loads. https://www.frontiersin.org/files/Articles/1661217/fphys-16-1661217-HTML/image_m/fphys-16-1661217-t015.jpg Table 15. Changes in blood lactate before and after bench press training under different rhythms and https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1788616/full https://www.frontiersin.org/files/Articles/1788616/fpsyg-17-1788616-HTML/image_m/fpsyg-17-1788616-t001.jpg Table 1. Descriptive statistics and correlation matrix. https://www.frontiersin.org/files/Articles/1788616/fpsyg-17-1788616-HTML/image_m/fpsyg-17-1788616-g001.jpg Figure 1. Correlation heatmap of key study variables. https://www.frontiersin.org/files/Articles/1788616/fpsyg-17-1788616-HTML/image_m/fpsyg-17-1788616-t002.jpg Table 2. Hierarchical regression analysis results. https://www.frontiersin.org/files/Articles/1788616/fpsyg-17-1788616-HTML/image_m/fpsyg-17-1788616-g002.jpg Figure 2. Path diagram of the mediation model. https://www.frontiersin.org/files/Articles/1788616/fpsyg-17-1788616-HTML/image_m/fpsyg-17-1788616-t003.jpg Table 3. Bootstrap analysis of mediation effects. https://www.frontiersin.org/files/Articles/1788616/fpsyg-17-1788616-HTML/image_m/fpsyg-17-1788616-g003.jpg Figure 3. Simple slope analysis plot. Although the interaction effect was not statistically signific https://www.frontiersin.org/files/Articles/1788616/fpsyg-17-1788616-HTML/image_m/fpsyg-17-1788616-t004.jpg Table 4. Conditional indirect effects at different levels of parent–child relationship. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1789248/full https://www.frontiersin.org/files/Articles/1789248/fimmu-17-1789248-HTML/image_m/fimmu-17-1789248-g001.jpg Figure 1. Trial profile stratified by priming vaccine (ChAdOx1-S, BBIBP-CorV, and Gam-COVID-Vac) and https://www.frontiersin.org/files/Articles/1789248/fimmu-17-1789248-HTML/image_m/fimmu-17-1789248-g002.jpg Figure 2. Anti-spike IgG by study visit, study arm, and priming vaccine. Horizontal black lines with https://www.frontiersin.org/files/Articles/1789248/fimmu-17-1789248-HTML/image_m/fimmu-17-1789248-g003.jpg Figure 3. IFN-γ concentrations for Ag1 (A) and Ag2 (B) by study visit, study arm, and priming vaccin https://www.frontiersin.org/files/Articles/1789248/fimmu-17-1789248-HTML/image_m/fimmu-17-1789248-g004.jpg Figure 4. SARS-CoV-2 sVNT inhibition (%) against Wuhan-Hu-1 by study visit, study arm, and priming v https://www.frontiersin.org/files/Articles/1789248/fimmu-17-1789248-HTML/image_m/fimmu-17-1789248-g005.jpg Figure 5. SARS-CoV-2 sVNT inhibition (%) against Omicron BA.1 by study visit, study arm, and priming https://www.frontiersin.org/files/Articles/1789248/fimmu-17-1789248-HTML/image_m/fimmu-17-1789248-g006.jpg Figure 6. Change in individual binding antibody titres between baseline–28 days, 28 days–6 months, 6 https://www.frontiersin.org/files/Articles/1789248/fimmu-17-1789248-HTML/image_m/fimmu-17-1789248-t001.jpg Table 1. Fold change of ≥1.2 in anti-spike IgG levels without documented SARS-CoV-2 infection, and d https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2026.1667072/full https://www.frontiersin.org/files/Articles/1667072/fenrg-14-1667072-HTML/image_m/fenrg-14-1667072-g001.jpg Figure 1. Schematic showing major pathways for net-zero hydrogen production from various energy sour https://www.frontiersin.org/files/Articles/1667072/fenrg-14-1667072-HTML/image_m/fenrg-14-1667072-g002.jpg Figure 2. Schematic diagrams summarising the major current [(A): AWE, (B) PEM] and emerging [(C) AEM https://www.frontiersin.org/files/Articles/1667072/fenrg-14-1667072-HTML/image_m/fenrg-14-1667072-t001.jpg Table 1. List of the current and emerging electrolyser systems, together with their main parameters https://www.frontiersin.org/files/Articles/1667072/fenrg-14-1667072-HTML/image_m/fenrg-14-1667072-g003.jpg Figure 3. The enthalpy of reaction as a function of the material-specific entropic potential for two https://www.frontiersin.org/files/Articles/1667072/fenrg-14-1667072-HTML/image_m/fenrg-14-1667072-g004.jpg Figure 4. Comparison between hydrogen and petroleum systems [Unmodified image from Jackson et al. (2 https://www.frontiersin.org/files/Articles/1667072/fenrg-14-1667072-HTML/image_m/fenrg-14-1667072-t002.jpg Table 2. Comparison of key parameters for each hydrogen production technology. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1765995/full https://www.frontiersin.org/files/Articles/1765995/fbioe-14-1765995-HTML/image_m/fbioe-14-1765995-g001.jpg Figure 1. Scheme of the generated expression vectors: (A) vectors encoding ND4opt with one of the MT https://www.frontiersin.org/files/Articles/1765995/fbioe-14-1765995-HTML/image_m/fbioe-14-1765995-g002.jpg Figure 2. (A) Schematic representation of the generation of the HEK-293 LHON cell line with the MT-N https://www.frontiersin.org/files/Articles/1765995/fbioe-14-1765995-HTML/image_m/fbioe-14-1765995-g003.jpg Figure 3. Values of ROS, hydrogen peroxide, calcium ions, and ΔΨm levels in HEK-293 (HEK) and HEK-29 https://www.frontiersin.org/files/Articles/1765995/fbioe-14-1765995-HTML/image_m/fbioe-14-1765995-g004.jpg Figure 4. Effects of delivery of the functionally active ND4opt gene with various MTS into HEK-293 ( https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1624485/full https://www.frontiersin.org/files/Articles/1624485/frai-08-1624485-HTML/image_m/frai-08-1624485-g001.jpg Figure 1. Schematic flowchart of AI-driven objective assessment for ADHD. https://www.frontiersin.org/files/Articles/1624485/frai-08-1624485-HTML/image_m/frai-08-1624485-g002.jpg Figure 2. Classification framework of multimodal data fusion strategies for AI in ADHD assessment. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1781447/full https://www.frontiersin.org/files/Articles/1781447/fimmu-17-1781447-HTML/image_m/fimmu-17-1781447-g001.jpg Figure 1. AEC2-centered pathogenic cascade from viral entry and innate sensing to epithelial dysfunc https://www.frontiersin.org/files/Articles/1781447/fimmu-17-1781447-HTML/image_m/fimmu-17-1781447-g002.jpg Figure 2. Convergent AEC2 hub processes targeted across viruses: immune evasion, stress-network cros https://www.frontiersin.org/files/Articles/1781447/fimmu-17-1781447-HTML/image_m/fimmu-17-1781447-g003.jpg Figure 3. Virus-specific antagonists converge on core AEC2 functional modules and produce shared def https://www.frontiersin.org/files/Articles/1781447/fimmu-17-1781447-HTML/image_m/fimmu-17-1781447-g004.jpg Figure 4. Working model of PANoptosis-like inflammatory lytic injury in AEC2s driven by convergent i https://www.frontiersin.org/files/Articles/1781447/fimmu-17-1781447-HTML/image_m/fimmu-17-1781447-t001.jpg Table 1. Convergent molecular pathogenic mechanisms of IAV, SARS-CoV-2, and RSV in human AEC2s. https://www.frontiersin.org/files/Articles/1781447/fimmu-17-1781447-HTML/image_m/fimmu-17-1781447-g005.jpg Figure 5. AEC2-centered disease-progression axis and host-directed treatment strategy library for co https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1760183/full https://www.frontiersin.org/files/Articles/1760183/feduc-10-1760183-HTML/image_m/feduc-10-1760183-g001.jpg Figure 1. PRISMA Flow chart. https://www.frontiersin.org/files/Articles/1760183/feduc-10-1760183-HTML/image_m/feduc-10-1760183-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1760183/feduc-10-1760183-HTML/image_m/feduc-10-1760183-t002.jpg Table 2. Characteristics of outcomes assessed. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1782914/full https://www.frontiersin.org/files/Articles/1782914/fphys-17-1782914-HTML/image_m/fphys-17-1782914-t001.jpg Table 1. Gene target and TaqMan®primer/probe assays. https://www.frontiersin.org/files/Articles/1782914/fphys-17-1782914-HTML/image_m/fphys-17-1782914-g001.jpg Figure 1. Overexpression of SDC4 in SL-29 chicken fibroblast cells. (A) Chicken embryonic SL-29 fibr https://www.frontiersin.org/files/Articles/1782914/fphys-17-1782914-HTML/image_m/fphys-17-1782914-g002.jpg Figure 2. Effect of SDC4 overexpression on fibrosis markers in chicken fibroblasts SL-29. Protein ex https://www.frontiersin.org/files/Articles/1782914/fphys-17-1782914-HTML/image_m/fphys-17-1782914-g003.jpg Figure 3. Effect of SDC4 overexpression on signaling pathways chicken fibroblasts. Protein expressio https://www.frontiersin.org/files/Articles/1782914/fphys-17-1782914-HTML/image_m/fphys-17-1782914-g004.jpg Figure 4. Effect of blocking peptides on SDC4 shedding. (A) Alignment of human and chicken SDC4 prot https://www.frontiersin.org/files/Articles/1782914/fphys-17-1782914-HTML/image_m/fphys-17-1782914-g005.jpg Figure 5. Effect of blocking peptides on the gene expression of the various SDCs. Effect of BP1-5 on https://www.frontiersin.org/files/Articles/1782914/fphys-17-1782914-HTML/image_m/fphys-17-1782914-g006.jpg Figure 6. Effect of blocking peptides on the gene expression of fibrosis markers. Effect of BP1-5 on https://www.frontiersin.org/files/Articles/1782914/fphys-17-1782914-HTML/image_m/fphys-17-1782914-g007.jpg Figure 7. Effect of TGF-β1 on different gene and protein expression levels. Chicken fibroblasts were https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1630907/full https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g001.jpg Figure 1. Process FMEA example of a window lift motor. https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g002.jpg Figure 2. Framework for quality assurance and control in DS projects adapted from Oliveira and Brito https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g003.jpg Figure 3. Corrective maintenance MTBF, MTTA, and MTTR KPI's workflow. https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g004.jpg Figure 4. AI prescriptive model application in corrective maintenance process. https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g005.jpg Figure 5. Phi_k correlation result between parameters and FMEA columns. https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g006.jpg Figure 6. Represents the value of the loss over the seasons and the value of the accuracy. (a) Sc 1 https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g007.jpg Figure 7. Results of class balancing using SMOTE. (a) Sc 1 loss and accuracy values in MLP with SMOT https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g008.jpg Figure 8. Results of the CNN model. (a) Sc 1 loss an accuracy values in CNN. (b) Sc 1 loss an accura https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g009.jpg Figure 9. Sc 1 loss an accuracy values in CNN with Keras auto-tuner and SMOTE. https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g010.jpg Figure 10. Results of the FCN model with and without the Keras auto-tuner. (a) Sc 1 loss an accuracy https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g011.jpg Figure 11. MLP test results, with and without the Keras auto-tuner. (a) Sc 2 loss an accuracy values https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g012.jpg Figure 12. Results of the CNN model test. (a) Sc 2 loss an accuracy values in CNN. (b) Sc 2 loss an https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g013.jpg Figure 13. Sc 2 loss an accuracy values in multi-head CNN with Keras auto-tuner. https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-g014.jpg Figure 14. Results of the FCN model with and without the Keras auto-tuner. (a) Sc 2 loss an accuracy https://www.frontiersin.org/files/Articles/1630907/frai-08-1630907-HTML/image_m/frai-08-1630907-t001.jpg Table 1. Best results scenario comparation. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1734436/full https://www.frontiersin.org/files/Articles/1734436/fpsyt-17-1734436-HTML/image_m/fpsyt-17-1734436-t001.jpg Table 1. Sample characteristics. https://www.frontiersin.org/files/Articles/1734436/fpsyt-17-1734436-HTML/image_m/fpsyt-17-1734436-g001.jpg Figure 1. Flowchart of study sample. https://www.frontiersin.org/files/Articles/1734436/fpsyt-17-1734436-HTML/image_m/fpsyt-17-1734436-t002.jpg Table 2. Group differences in behavioral and trait impulsivity, with multiple comparisons correction https://www.frontiersin.org/files/Articles/1734436/fpsyt-17-1734436-HTML/image_m/fpsyt-17-1734436-g002.jpg Figure 2. Whole brain correlates of inhibitory control across all participants. Activation maps disp https://www.frontiersin.org/files/Articles/1734436/fpsyt-17-1734436-HTML/image_m/fpsyt-17-1734436-t003.jpg Table 3. Inhibitory Control Brain Activity. https://www.frontiersin.org/files/Articles/1734436/fpsyt-17-1734436-HTML/image_m/fpsyt-17-1734436-g003.jpg Figure 3. Group differences in neural activation. AN and AE youth differed in inhibitory control BOL https://www.frontiersin.org/files/Articles/1734436/fpsyt-17-1734436-HTML/image_m/fpsyt-17-1734436-g004.jpg Figure 4. Brain-behavior correlation. Correlations with (lack) of planning in the left fusiform gyru https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1689529/full https://www.frontiersin.org/files/Articles/1689529/fmed-12-1689529-HTML-r1/image_m/fmed-12-1689529-t001.jpg Table 1. Participants' demographic and clinical information. https://www.frontiersin.org/files/Articles/1689529/fmed-12-1689529-HTML-r1/image_m/fmed-12-1689529-t002.jpg Table 2. Participants' clinical findings. https://www.frontiersin.org/files/Articles/1689529/fmed-12-1689529-HTML-r1/image_m/fmed-12-1689529-g001.jpg Figure 1. Correlations between MCHr and iron-related biomarkers in ESKD patients. (A) Ferritin (n =1 https://www.frontiersin.org/files/Articles/1689529/fmed-12-1689529-HTML-r1/image_m/fmed-12-1689529-g002.jpg Figure 2. Receiver-operating characteristic (ROC) curve showing the diagnostic performance of MCHr f https://www.frontiersin.org/files/Articles/1689529/fmed-12-1689529-HTML-r1/image_m/fmed-12-1689529-g003.jpg Figure 3. ROC analysis of MCHr in diagnosing IDA in ESKD patients with ferritin < 200 ng/mL. The cut https://www.frontiersin.org/files/Articles/1689529/fmed-12-1689529-HTML-r1/image_m/fmed-12-1689529-g004.jpg Figure 4. ROC analysis of MCHr in diagnosing IDA in ESKD patients with TSAT < 20%. The cut-off value https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1723343/full https://www.frontiersin.org/files/Articles/1723343/fimmu-16-1723343-HTML/image_m/fimmu-16-1723343-g001.jpg Figure 1. Structural diversity of antibodies across different species. This diagram illustrates the https://www.frontiersin.org/files/Articles/1723343/fimmu-16-1723343-HTML/image_m/fimmu-16-1723343-t001.jpg Table 1. Summary of avian IgY antibodies developed against respiratory viruses, highlighting their p https://www.frontiersin.org/files/Articles/1723343/fimmu-16-1723343-HTML/image_m/fimmu-16-1723343-t002.jpg Table 2. Summary of camelid single-domain antibodies (nanobodies/VHHs) targeting epitopes of respira https://www.frontiersin.org/files/Articles/1723343/fimmu-16-1723343-HTML/image_m/fimmu-16-1723343-t003.jpg Table 3. Summary of shark-derived variable new antigen receptors (VNARs) with neutralizing activity https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1710212/full https://www.frontiersin.org/files/Articles/1710212/fpls-17-1710212-HTML-r1/image_m/fpls-17-1710212-t001.jpg Table 1. List of drought tolerant and susceptible mung bean genotypes evaluated in study. https://www.frontiersin.org/files/Articles/1710212/fpls-17-1710212-HTML-r1/image_m/fpls-17-1710212-t002.jpg Table 2. List of gene primers used for relative expression analysis. https://www.frontiersin.org/files/Articles/1710212/fpls-17-1710212-HTML-r1/image_m/fpls-17-1710212-g001.jpg Figure 1. Physiological, osmoprotectant, and antioxidant enzyme responses of drought tolerant and su https://www.frontiersin.org/files/Articles/1710212/fpls-17-1710212-HTML-r1/image_m/fpls-17-1710212-g002.jpg Figure 2. Principal component analysis (PCA) biplots showing the relationship between physiological, https://www.frontiersin.org/files/Articles/1710212/fpls-17-1710212-HTML-r1/image_m/fpls-17-1710212-g003.jpg Figure 3. Pairwise correlation analysis among physiological, osmoprotectant and antioxidant enzymes https://www.frontiersin.org/files/Articles/1710212/fpls-17-1710212-HTML-r1/image_m/fpls-17-1710212-g004.jpg Figure 4. Principal component analysis (PCA) biplot illustrating the dispersion of ellipses of seven https://www.frontiersin.org/files/Articles/1710212/fpls-17-1710212-HTML-r1/image_m/fpls-17-1710212-g005.jpg Figure 5. Principal component analysis (PCA) biplot indicating separation of control and drought tre https://www.frontiersin.org/files/Articles/1710212/fpls-17-1710212-HTML-r1/image_m/fpls-17-1710212-g006.jpg Figure 6. Hierarchical cluster heatmaps of physiological, osmoprotectant, and antioxidant enzymes tr https://www.frontiersin.org/files/Articles/1710212/fpls-17-1710212-HTML-r1/image_m/fpls-17-1710212-g007.jpg Figure 7. Relative expression patterns of drought-responsive genes in drought tolerant and susceptib https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1767578/full https://www.frontiersin.org/files/Articles/1767578/fonc-16-1767578-HTML/image_m/fonc-16-1767578-t001.jpg Table 1. Patient characteristics of the studied interim cohort. https://www.frontiersin.org/files/Articles/1767578/fonc-16-1767578-HTML/image_m/fonc-16-1767578-t002.jpg Table 2. Number (%) of performed surgical technique per center (anonymous). https://www.frontiersin.org/files/Articles/1767578/fonc-16-1767578-HTML/image_m/fonc-16-1767578-t003.jpg Table 3. Distribution across TP vs RP of patients receiving a 3DPZ model (χ², p = 0.489). https://www.frontiersin.org/files/Articles/1767578/fonc-16-1767578-HTML/image_m/fonc-16-1767578-t004.jpg Table 4. Distribution across TP vs RP of performed clamping strategy (SC vs MAC) (χ², p = 0.932). https://www.frontiersin.org/files/Articles/1767578/fonc-16-1767578-HTML/image_m/fonc-16-1767578-t005.jpg Table 5. Distribution across TP vs RP for planning and performing as planned as SC strategy (primary https://www.frontiersin.org/files/Articles/1767578/fonc-16-1767578-HTML/image_m/fonc-16-1767578-g001.jpg Figure 1. Boxplot displaying total operative time (min) (TP vs RP). Boxes represent the interquartil https://www.frontiersin.org/files/Articles/1767578/fonc-16-1767578-HTML/image_m/fonc-16-1767578-t006.jpg Table 6. Summary of reported intraoperative complications during TP or RP RAPN. https://www.frontiersin.org/files/Articles/1767578/fonc-16-1767578-HTML/image_m/fonc-16-1767578-t007.jpg Table 7. Summary of reported early postoperative complications during TP or RP RAPN (and associated https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1710194/full https://www.frontiersin.org/files/Articles/1710194/fspor-07-1710194-HTML/image_m/fspor-07-1710194-g001.jpg Figure 1. Annual training volume across age stages. https://www.frontiersin.org/files/Articles/1710194/fspor-07-1710194-HTML/image_m/fspor-07-1710194-t001.jpg Table 1. Quotes made by the father about the player's training during childhood and youth. https://www.frontiersin.org/files/Articles/1710194/fspor-07-1710194-HTML/image_m/fspor-07-1710194-t002.jpg Table 2. Typical training week examples across age stages. https://www.frontiersin.org/files/Articles/1710194/fspor-07-1710194-HTML/image_m/fspor-07-1710194-g002.jpg Figure 2. The main stages of the father's training philosophy. https://www.frontiersin.org/files/Articles/1710194/fspor-07-1710194-HTML/image_m/fspor-07-1710194-t003.jpg Table 3. Key athlete characteristics contributing to successful talent development. https://www.frontiersin.org/files/Articles/1710194/fspor-07-1710194-HTML/image_m/fspor-07-1710194-t004.jpg Table 4. Key environmental factors contributing to successful talent development. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1694925/full https://www.frontiersin.org/files/Articles/1694925/fspor-07-1694925-HTML-r3/image_m/fspor-07-1694925-g001.jpg Figure 1. PRISMA-ScR flowchart. https://www.frontiersin.org/files/Articles/1694925/fspor-07-1694925-HTML-r3/image_m/fspor-07-1694925-t001.jpg Table 1. Data extracted from performance’s category publications. https://www.frontiersin.org/files/Articles/1694925/fspor-07-1694925-HTML-r3/image_m/fspor-07-1694925-t002.jpg Table 2. Physiology and biomarkers—extracted data. https://www.frontiersin.org/files/Articles/1694925/fspor-07-1694925-HTML-r3/image_m/fspor-07-1694925-t003.jpg Table 3. Nutrition and body composition—extracted data. https://www.frontiersin.org/files/Articles/1694925/fspor-07-1694925-HTML-r3/image_m/fspor-07-1694925-t004.jpg Table 4. Injuries—extracted data. https://www.frontiersin.org/files/Articles/1694925/fspor-07-1694925-HTML-r3/image_m/fspor-07-1694925-t005.jpg Table 5. Summary of included studies by domain. https://www.frontiersin.org/files/Articles/1694925/fspor-07-1694925-HTML-r3/image_m/fspor-07-1694925-g002.jpg Figure 2. Race distances and countries represented in the included studies in this review. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1655916/full https://www.frontiersin.org/files/Articles/1655916/fspor-07-1655916-HTML/image_m/fspor-07-1655916-g001.jpg Figure 1. Annual training hours as a function of age. *Reduced handball activity due to COVID-19. https://www.frontiersin.org/files/Articles/1655916/fspor-07-1655916-HTML/image_m/fspor-07-1655916-g002.jpg Figure 2. Accumulated training hours as a function of age. https://www.frontiersin.org/files/Articles/1655916/fspor-07-1655916-HTML/image_m/fspor-07-1655916-t001.jpg Table 1. Key athlete characteristics underlying the successful talent development process. https://www.frontiersin.org/files/Articles/1655916/fspor-07-1655916-HTML/image_m/fspor-07-1655916-t002.jpg Table 2. Best physical test scores obtained by the investigated player. https://www.frontiersin.org/files/Articles/1655916/fspor-07-1655916-HTML/image_m/fspor-07-1655916-t003.jpg Table 3. Typical in-season training plans for two consecutive weeks across the player's three upper- https://www.frontiersin.org/files/Articles/1655916/fspor-07-1655916-HTML/image_m/fspor-07-1655916-g003.jpg Figure 3. Annual game appearances according to age. *Reduced game activity due to COVID-19. https://www.frontiersin.org/files/Articles/1655916/fspor-07-1655916-HTML/image_m/fspor-07-1655916-g004.jpg Figure 4. Goals per game according to age. https://www.frontiersin.org/files/Articles/1655916/fspor-07-1655916-HTML/image_m/fspor-07-1655916-g005.jpg Figure 5. Relative distribution of win-draw-lass across seasons. National junior league matches are https://www.frontiersin.org/files/Articles/1655916/fspor-07-1655916-HTML/image_m/fspor-07-1655916-t004.jpg Table 4. Key environmental success factors. https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2026.1734386/full https://www.frontiersin.org/files/Articles/1734386/fpain-07-1734386-HTML/image_m/fpain-07-1734386-t001.jpg Table 1. Sociodemographic characteristics of study participants (n = 16). https://www.frontiersin.org/files/Articles/1734386/fpain-07-1734386-HTML/image_m/fpain-07-1734386-t002.jpg Table 2. Characteristics of participants’ virtual reality training and usage (n = 16). https://www.frontiersin.org/files/Articles/1734386/fpain-07-1734386-HTML/image_m/fpain-07-1734386-g001.jpg Figure 1. LTC staff's responses (n = 16) to the modified VR-Use questionnaire with statements target https://www.frontiersin.org/files/Articles/1734386/fpain-07-1734386-HTML/image_m/fpain-07-1734386-g002.jpg Figure 2. LTC staff's responses (n = 16) to the VR-Use questionnaire with statements targeting socia https://www.frontiersin.org/files/Articles/1734386/fpain-07-1734386-HTML/image_m/fpain-07-1734386-g003.jpg Figure 3. LTC staff's responses (n = 16) to the modified VR-Use questionnaire with statements target https://www.frontiersin.org/files/Articles/1734386/fpain-07-1734386-HTML/image_m/fpain-07-1734386-g004.jpg Figure 4. LTC staff's responses (n = 16) to the modified VR-Use questionnaire with statements target https://www.frontiersin.org/files/Articles/1734386/fpain-07-1734386-HTML/image_m/fpain-07-1734386-t003.jpg Table 3. Qualitative themes, categories, subcategories, and representative participant quotes. https://www.frontiersin.org/files/Articles/1734386/fpain-07-1734386-HTML/image_m/fpain-07-1734386-t004.jpg Table 4. Joint display of integrated qualitative and quantitative findings on VR adoption in long-te https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1638134/full https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-g001.jpg Figure 1. Patients enrollment flowchart. Flowchart of patient enrollment and grouping: This diagram https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-t001.jpg Table 1. Clinical and demographic information. https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-t002.jpg Table 2. Results of univariate logistic regression. https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-t003.jpg Table 3. Results of multifactorial logistic regression. https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-g002.jpg Figure 2. Construction and evaluation of a prediction model for the severity of coronary artery lesi https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-g003.jpg Figure 3. DCA curve of the prediction model for the severity of coronary artery lesions in elderly p https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-t004.jpg Table 4. Results of univariate COX regression. https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-t005.jpg Table 5. Results of multivariate COX regression. https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-g004.jpg Figure 4. Construction and evaluation of the all-cause mortality prediction model for elderly patien https://www.frontiersin.org/files/Articles/1638134/fcvm-12-1638134-HTML/image_m/fcvm-12-1638134-g005.jpg Figure 5. DCA curve of the all-cause mortality prediction model for elderly patients with coronary h https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1675467/full https://www.frontiersin.org/files/Articles/1675467/fimmu-16-1675467-HTML/image_m/fimmu-16-1675467-t001.jpg Table 1. Baseline characteristics of the study subjects. https://www.frontiersin.org/files/Articles/1675467/fimmu-16-1675467-HTML/image_m/fimmu-16-1675467-g001.jpg Figure 1. Transcriptomic analysis and WGCNA reveal a key gene module associated with HCM. (A) Volcan https://www.frontiersin.org/files/Articles/1675467/fimmu-16-1675467-HTML/image_m/fimmu-16-1675467-g002.jpg Figure 2. Functional enrichment analysis of overlapping genes from DEGs and WGCNA blue module. (A) G https://www.frontiersin.org/files/Articles/1675467/fimmu-16-1675467-HTML/image_m/fimmu-16-1675467-g003.jpg Figure 3. Machine learning-based identification of key diagnostic genes for hypertrophic cardiomyopa https://www.frontiersin.org/files/Articles/1675467/fimmu-16-1675467-HTML/image_m/fimmu-16-1675467-g004.jpg Figure 4. Functional enrichment analysis of four machine learning-derived HCM marker genes. (A–D) Ge https://www.frontiersin.org/files/Articles/1675467/fimmu-16-1675467-HTML/image_m/fimmu-16-1675467-g005.jpg Figure 5. Clinical validation and diagnostic evaluation of key HCM-related genes. (A) Boxplot compar https://www.frontiersin.org/files/Articles/1675467/fimmu-16-1675467-HTML/image_m/fimmu-16-1675467-g006.jpg Figure 6. Single-cell transcriptomic analysis reveals cell-type–specific expression patterns of key https://www.frontiersin.org/files/Articles/1675467/fimmu-16-1675467-HTML/image_m/fimmu-16-1675467-g007.jpg Figure 7. Cytokine expression landscape and MEIS3-associated signaling interactions in MSCs/fibrobla https://www.frontiersin.org/files/Articles/1675467/fimmu-16-1675467-HTML/image_m/fimmu-16-1675467-g008.jpg Figure 8. Construction of MEIS3-related ceRNA regulatory network and immune cell infiltration landsc https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1681054/full https://www.frontiersin.org/files/Articles/1681054/fonc-15-1681054-HTML/image_m/fonc-15-1681054-t001.jpg Table 1. Primers used for qPCR. https://www.frontiersin.org/files/Articles/1681054/fonc-15-1681054-HTML/image_m/fonc-15-1681054-g001.jpg Figure 1. Analysis of NET and SDHB expression by qPCR and Western blot. (A) The qPCR analysis of NET https://www.frontiersin.org/files/Articles/1681054/fonc-15-1681054-HTML/image_m/fonc-15-1681054-g002.jpg Figure 2. Cell uptake and blocking assays of 131I-MIBG. (A) Radioactive uptake of 131I-MIBG and free https://www.frontiersin.org/files/Articles/1681054/fonc-15-1681054-HTML/image_m/fonc-15-1681054-g003.jpg Figure 3. Effects of 131I-MIBG and fluzoparib on PC12-NET cells proliferation. (A, B) Survival curve https://www.frontiersin.org/files/Articles/1681054/fonc-15-1681054-HTML/image_m/fonc-15-1681054-g004.jpg Figure 4. Effects of 131I-MIBG and fluzoparib treatments on the cell cycles phases in PC12-NET cells https://www.frontiersin.org/files/Articles/1681054/fonc-15-1681054-HTML/image_m/fonc-15-1681054-g005.jpg Figure 5. Apoptosis of PC12-NET cells after treatment. (A) Quantitative analysis of apoptosis rates https://www.frontiersin.org/files/Articles/1681054/fonc-15-1681054-HTML/image_m/fonc-15-1681054-g006.jpg Figure 6. Effects of ¹³¹I-MIBG and fluzoparib on proliferation and cell cycle in PC12-NET-SDHB cells https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1552566/full https://www.frontiersin.org/files/Articles/1552566/fmed-12-1552566-HTML-r1/image_m/fmed-12-1552566-g001.jpg Figure 1. Flowchart of the collection process. https://www.frontiersin.org/files/Articles/1552566/fmed-12-1552566-HTML-r1/image_m/fmed-12-1552566-t001.jpg Table 1. Baseline data. https://www.frontiersin.org/files/Articles/1552566/fmed-12-1552566-HTML-r1/image_m/fmed-12-1552566-t002.jpg Table 2. Pre- and post-treatment baseline data for different groups. https://www.frontiersin.org/files/Articles/1552566/fmed-12-1552566-HTML-r1/image_m/fmed-12-1552566-t003.jpg Table 3. Efficacy. https://www.frontiersin.org/files/Articles/1552566/fmed-12-1552566-HTML-r1/image_m/fmed-12-1552566-t004.jpg Table 4. Safety. https://www.frontiersin.org/files/Articles/1552566/fmed-12-1552566-HTML-r1/image_m/fmed-12-1552566-g002.jpg Figure 2. Statistics on the concomitant toxic effects of treatment. Symptoms with * are those where https://www.frontiersin.org/files/Articles/1552566/fmed-12-1552566-HTML-r1/image_m/fmed-12-1552566-g003.jpg Figure 3. Comparison of the efficacy rates across treatment cycles for different GM-CSF delivery met https://www.frontiersin.org/files/Articles/1552566/fmed-12-1552566-HTML-r1/image_m/fmed-12-1552566-g004.jpg Figure 4. Effects of GM-CSF and anti-GM-CSF autoantibodies on the body and PAP formation. Anti-GM-CS https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1611958/full https://www.frontiersin.org/files/Articles/1611958/fnagi-17-1611958-HTML/image_m/fnagi-17-1611958-g001.jpg Figure 1. Expression levels of DHODH, MFN1, MFN2, GPX4 and FSP1 in PD model of mice. (A) Statistical https://www.frontiersin.org/files/Articles/1611958/fnagi-17-1611958-HTML/image_m/fnagi-17-1611958-g002.jpg Figure 2. Up-regulation of MFN2 expression increased cell viability and inhibited apoptosis. (A) The https://www.frontiersin.org/files/Articles/1611958/fnagi-17-1611958-HTML/image_m/fnagi-17-1611958-g003.jpg Figure 3. The expression levels of inflammatory and oxidative stress factors in each group were dete https://www.frontiersin.org/files/Articles/1611958/fnagi-17-1611958-HTML/image_m/fnagi-17-1611958-g004.jpg Figure 4. The effects of MFN2 on the expression levels of DHODH, MFN1, GPX4 and FSP1 were detected b https://www.frontiersin.org/files/Articles/1611958/fnagi-17-1611958-HTML/image_m/fnagi-17-1611958-g005.jpg Figure 5. Overexpression of MFN2 improved motor, cognitive and neurological damage in PD mice. (A) S https://www.frontiersin.org/files/Articles/1611958/fnagi-17-1611958-HTML/image_m/fnagi-17-1611958-g006.jpg Figure 6. Morphological observation of subcellular structure under electron microscope. Control, Nor https://www.frontiersin.org/files/Articles/1611958/fnagi-17-1611958-HTML/image_m/fnagi-17-1611958-g007.jpg Figure 7. The expression levels of inflammation and oxidative stress factors in brain tissue were de https://www.frontiersin.org/files/Articles/1611958/fnagi-17-1611958-HTML/image_m/fnagi-17-1611958-g008.jpg Figure 8. The effect of overexpression of MFN2 on the expression levels of DHODH, MFN1, GPX4 and FSP https://www.frontiersin.org/journals/virtual-reality/articles/10.3389/frvir.2025.1697190/full https://www.frontiersin.org/files/Articles/1697190/frvir-06-1697190-HTML/image_m/frvir-06-1697190-t001.jpg Table 1. Outcome measures timeline. DASS 21: depression anxiety stress scale 21; MSQoL 2.1: migraine https://www.frontiersin.org/files/Articles/1697190/frvir-06-1697190-HTML/image_m/frvir-06-1697190-g001.jpg Figure 1. Participant One, Daily Diary Data across three study phases showing level (overall phase m https://www.frontiersin.org/files/Articles/1697190/frvir-06-1697190-HTML/image_m/frvir-06-1697190-g002.jpg Figure 2. Participant Two, Daily Diary Data across three study phases showing level (overall phase m https://www.frontiersin.org/files/Articles/1697190/frvir-06-1697190-HTML/image_m/frvir-06-1697190-t002.jpg Table 2. Mean VAS scores for each phase (A1, B, A2) for Participant One and Participant Two, showing https://www.frontiersin.org/files/Articles/1697190/frvir-06-1697190-HTML/image_m/frvir-06-1697190-t003.jpg Table 3. Pre- and post-intervention secondary outcome measures for participant one and participant t https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1678863/full https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t001.jpg Table 1. Application of the PICO method. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t002.jpg Table 2. Research questions and objectives. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t003.jpg Table 3. Search strings per research question. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t004.jpg Table 4. Keywords, synonyms, and related research questions. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t005.jpg Table 5. Relevant data extraction fields. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t006.jpg Table 6. Distribution of articles by research question. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-g001.jpg Figure 1. PRISMA 2020 flow diagram for study selection. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t007.jpg Table 7. Distribution of selected articles by year and research question (2022–2025). https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t008.jpg Table 8. Frequency of AI models used in cervical cytology studies. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-g002.jpg Figure 2. Hierarchical taxonomy of AI models reported in cervical cytology studies. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t009.jpg Table 9. Distribution of datasets used in AI studies for cervical cytology. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-g003.jpg Figure 3. Dataset taxonomy in cervical cytology AI research: source type, dataset name, and image mo https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t010.jpg Table 10. Frequency and statistical values of performance metrics reported in the reviewed studies. https://www.frontiersin.org/files/Articles/1678863/fdata-08-1678863-HTML/image_m/fdata-08-1678863-t011.jpg Table 11. Comparison of metrics by architecture type. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1721105/full https://www.frontiersin.org/files/Articles/1721105/fmed-12-1721105-HTML/image_m/fmed-12-1721105-t001.jpg Table 1. Characteristics of 197 patients with hidradenitis suppurativa enrolled in the study. https://www.frontiersin.org/files/Articles/1721105/fmed-12-1721105-HTML/image_m/fmed-12-1721105-t002.jpg Table 2. Comparison of metabolic indicators among patients with different groups. https://www.frontiersin.org/files/Articles/1721105/fmed-12-1721105-HTML/image_m/fmed-12-1721105-g001.jpg Figure 1. The correlation heatmap between clinical and laboratory variables displays only correlatio https://www.frontiersin.org/files/Articles/1721105/fmed-12-1721105-HTML/image_m/fmed-12-1721105-g002.jpg Figure 2. The network diagram between clinical and laboratory variables displays only correlations w https://www.frontiersin.org/files/Articles/1721105/fmed-12-1721105-HTML/image_m/fmed-12-1721105-g003.jpg Figure 3. Forest plots of univariable logistic regression analyses identifying significant predictor https://www.frontiersin.org/files/Articles/1721105/fmed-12-1721105-HTML/image_m/fmed-12-1721105-g004.jpg Figure 4. Receiver operating characteristic (ROC) analysis demonstrating the predictive performance https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1731230/full https://www.frontiersin.org/files/Articles/1731230/fmed-13-1731230-HTML-r1/image_m/fmed-13-1731230-g001.jpg Figure 1. (A) Ultrasound on admission day showing a large cystic mass in the right adnexal region, a https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1547844/full https://www.frontiersin.org/files/Articles/1547844/fnut-12-1547844-HTML-r1/image_m/fnut-12-1547844-t001.jpg Table 1. Characteristics of study population (N = 11,615), NHANES, United States, 2011–2018†. https://www.frontiersin.org/files/Articles/1547844/fnut-12-1547844-HTML-r1/image_m/fnut-12-1547844-t002.jpg Table 2. Associations between different folate forms and obesity in middle-aged participants†. https://www.frontiersin.org/files/Articles/1547844/fnut-12-1547844-HTML-r1/image_m/fnut-12-1547844-t003.jpg Table 3. Associations between different folate forms and obesity in older participants†. https://www.frontiersin.org/files/Articles/1547844/fnut-12-1547844-HTML-r1/image_m/fnut-12-1547844-g001.jpg Figure 1. The association between RBC folate and obesity in middle-aged participants in subgroups. A https://www.frontiersin.org/files/Articles/1547844/fnut-12-1547844-HTML-r1/image_m/fnut-12-1547844-g002.jpg Figure 2. The association between RBC folate and obesity in older participants in subgroups. Adjuste https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1710349/full https://www.frontiersin.org/files/Articles/1710349/fdgth-08-1710349-HTML-r1/image_m/fdgth-08-1710349-g001.jpg Figure 1. Schematic overview of the resilient minds project, including the resilient minds longitudi https://www.frontiersin.org/files/Articles/1710349/fdgth-08-1710349-HTML-r1/image_m/fdgth-08-1710349-g002.jpg Figure 2. Screen shots from the ReMind app. https://www.frontiersin.org/files/Articles/1710349/fdgth-08-1710349-HTML-r1/image_m/fdgth-08-1710349-g003.jpg Figure 3. 24 month schedule for the resilient minds cohort study. https://www.frontiersin.org/files/Articles/1710349/fdgth-08-1710349-HTML-r1/image_m/fdgth-08-1710349-t001.jpg Table 1. Summary of topics included in the ReMind app surveys. https://www.frontiersin.org/files/Articles/1710349/fdgth-08-1710349-HTML-r1/image_m/fdgth-08-1710349-t002.jpg Table 2. Cognitive and sensory tasks administered within the resilient minds research app. https://www.frontiersin.org/files/Articles/1710349/fdgth-08-1710349-HTML-r1/image_m/fdgth-08-1710349-t003.jpg Table 3. Data collected during brain health sub-study lab sessions. https://www.frontiersin.org/files/Articles/1710349/fdgth-08-1710349-HTML-r1/image_m/fdgth-08-1710349-t004.jpg Table 4. Demographics and selected descriptive statistics for the ReMind cohort according to age-gro https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1747863/full https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-g001.jpg Figure 1. Weed image samples in the Deepweeds dataset (a) Chinese apple, (b) lantana, (c) parkinsoni https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-g002.jpg Figure 2. Architecture of TinyWeedNet showing the stem, multi-scale convolution (MSC) block with fou https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-t001.jpg Table 1. TinyWeedNet layer schedule (input size 224×224). https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-g003.jpg Figure 3. Tiny machine learning workflow. https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-t002.jpg Table 2. Technical specifications of STM32H7B3I-EVAL. https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-t003.jpg Table 3. Comparison of classification performance and model complexity. https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-g004.jpg Figure 4. Training accuracy curves of different CNN models over 100 epochs. https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-t004.jpg Table 4. Hyperparameter sensitivity analysis and On-MCU performance across combined configurations: https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-t005.jpg Table 5. Ablation study of the proposed TinyWeedNet on model architecture and MCU deployment perform https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-g005.jpg Figure 5. Trade-off between F1-score and energy consumption per inference for different TinyWeedNet https://www.frontiersin.org/files/Articles/1747863/fpls-17-1747863-HTML/image_m/fpls-17-1747863-t006.jpg Table 6. TinyWeedNet robustness evaluation on the DeepWeeds test set under controlled domain shifts. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1765705/full https://www.frontiersin.org/files/Articles/1765705/fpsyg-17-1765705-HTML/image_m/fpsyg-17-1765705-g001.jpg Figure 1. The hypothesized multilevel model of developmental feedback and creativity. *p < 0.01,**p https://www.frontiersin.org/files/Articles/1765705/fpsyg-17-1765705-HTML/image_m/fpsyg-17-1765705-t001.jpg Table 1. HLM results: the effects of developmental feedback on employee creativity. https://www.frontiersin.org/files/Articles/1765705/fpsyg-17-1765705-HTML/image_m/fpsyg-17-1765705-t002.jpg Table 2. Descriptive statistics and correlations of studied variables. https://www.frontiersin.org/files/Articles/1765705/fpsyg-17-1765705-HTML/image_m/fpsyg-17-1765705-t003.jpg Table 3. Comparison of measurement models based on confirmatory factor analyses. https://www.frontiersin.org/files/Articles/1765705/fpsyg-17-1765705-HTML/image_m/fpsyg-17-1765705-t004.jpg Table 4. HLM results: the effects of developmental feedback on team reflexivity and problem identifi https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1624815/full https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-t001.jpg Table 1. Primers for the CGB5 and GAPDH genes. https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-g001.jpg Figure 1. Illustrates CGB5 mRNA expression levels across various cancer types. (A) Illustrates the m https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-g002.jpg Figure 2. The protein expression level of CGB5 detected by IHC in pan-cancer extracted from HPA. (A) https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-g003.jpg Figure 3. CGB5 gene mutations in diverse cancers. (A) Utilizing cBioPortal, the frequency of alterat https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-g004.jpg Figure 4. CGB5 gene mutation in pan-cancer. (A) Mutation site of CGB5. (B) Methylation difference of https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-g005.jpg Figure 5. Correlation between CGB5 expression and OS in pan-cancer analysis. (A) The influence of CG https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-g006.jpg Figure 6. Immune infiltration of CGB5 in pan-cancer with estimate algorithm. (A) ESTIMATEScore, (B) https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-g007.jpg Figure 7. The association between CGB5 expression and pan-cancer immune subtypes. (A) UCEC, (B) TGCT https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-g008.jpg Figure 8. The key role of CGB5 in GC. (A) The expression of CGB5 is significantly higher in GC cell https://www.frontiersin.org/files/Articles/1624815/fmed-12-1624815-HTML/image_m/fmed-12-1624815-g009.jpg Figure 9. Functional enrichment analysis of co-expressed CGB5 genes in GC. (A) GO enrichment analysi https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1799261/full https://www.frontiersin.org/files/Articles/1799261/fendo-17-1799261-HTML/image_m/fendo-17-1799261-g001.jpg Figure 1. Senescence–SASP–insulin resistance axis in type 2 diabetes mellitus. Chronic metabolic str https://www.frontiersin.org/files/Articles/1799261/fendo-17-1799261-HTML/image_m/fendo-17-1799261-t001.jpg Table 1. Comparative tissue-specific patterns of cellular senescence and senolytic responsiveness in https://www.frontiersin.org/files/Articles/1799261/fendo-17-1799261-HTML/image_m/fendo-17-1799261-g002.jpg Figure 2. Senolytic drug classes and targeted senescent cell anti-apoptotic pathways (SCAPs). Senesc https://www.frontiersin.org/files/Articles/1799261/fendo-17-1799261-HTML/image_m/fendo-17-1799261-t002.jpg Table 2. Comparative targeting of senescent cell anti-apoptotic pathways by senolytic agents. https://www.frontiersin.org/files/Articles/1799261/fendo-17-1799261-HTML/image_m/fendo-17-1799261-t003.jpg Table 3. Summary of clinical and translational studies evaluating senolytic therapies in metabolic d https://www.frontiersin.org/files/Articles/1799261/fendo-17-1799261-HTML/image_m/fendo-17-1799261-t004.jpg Table 4. Senescence-associated molecular pathways linking inflammation and metabolic dysfunction. https://www.frontiersin.org/files/Articles/1799261/fendo-17-1799261-HTML/image_m/fendo-17-1799261-t005.jpg Table 5. Registered clinical trials investigating senolytic therapies in metabolic and age-related c https://www.frontiersin.org/files/Articles/1799261/fendo-17-1799261-HTML/image_m/fendo-17-1799261-t006.jpg Table 6. Integrated mechanistic–strategic framework linking senescence pathways to therapeutic targe https://www.frontiersin.org/files/Articles/1799261/fendo-17-1799261-HTML/image_m/fendo-17-1799261-t007.jpg Table 7. Translational, regulatory, and risk–benefit considerations for senolytic therapies in metab https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1699212/full https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g001.jpg Figure 1. Schematic diagram showing the discovery and likely applications of the transporters. https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-t001.jpg Table 1. Examples of role of membrane transporters in plants, highlighting their functions in nutrie https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g002.jpg Figure 2. Schematic diagram presenting types of phytohormones discussed in this review and their maj https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g003.jpg Figure 3. This figure illustrates different types of phytohormones and their primary synthesis sites https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g004.jpg Figure 4. This diagram represents the bioactive compound forms of various phytohormones and their ch https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g005.jpg Figure 5. Diagram presenting details of phytohormones auxin, production location, physiological effe https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g006.jpg Figure 6. A schematic diagram presenting molecular mechanism of polar auxin transport in plant cells https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g007.jpg Figure 7. Key functions and agricultural applications of cytokinins, particularly the trans-zeatin ( https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g008.jpg Figure 8. Schematic representation of molecular mechanism of cytokinin transport in plants. This sch https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g009.jpg Figure 9. Types of membrane transport proteins in the plasma membrane of a plant cell: The diagram i https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g010.jpg Figure 10. Transporters and channels in plant cell membranes and vacuoles: The diagram shows various https://www.frontiersin.org/files/Articles/1699212/fpls-16-1699212-HTML/image_m/fpls-16-1699212-g011.jpg Figure 11. Schematic representation molecular transport mechanisms of selected phytohormones in plan https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1768816/full https://www.frontiersin.org/files/Articles/1768816/fendo-17-1768816-HTML/image_m/fendo-17-1768816-g001.jpg Figure 1. Flow chart of participant selection. https://www.frontiersin.org/files/Articles/1768816/fendo-17-1768816-HTML/image_m/fendo-17-1768816-t001.jpg Table 1. Baseline characteristics of patients by different risk groups according to DKD risk score. https://www.frontiersin.org/files/Articles/1768816/fendo-17-1768816-HTML/image_m/fendo-17-1768816-g002.jpg Figure 2. Incidences of (A) DKD development, and (B) UACR increment ≥ 40%, and (C) macroalbuminuria, https://www.frontiersin.org/files/Articles/1768816/fendo-17-1768816-HTML/image_m/fendo-17-1768816-t002.jpg Table 2. Incidences and relative risks of kidney outcomes by different risk groups according to DKD https://www.frontiersin.org/files/Articles/1768816/fendo-17-1768816-HTML/image_m/fendo-17-1768816-g003.jpg Figure 3. Longitudinal trajectories of UACR and eGFR. (A) Mean UACR trajectory;and (B) Mean eGFR tra https://www.frontiersin.org/files/Articles/1768816/fendo-17-1768816-HTML/image_m/fendo-17-1768816-t003.jpg Table 3. Incidences and hazard ratios of major adverse cardiovascular events and heart failure acros https://www.frontiersin.org/files/Articles/1768816/fendo-17-1768816-HTML/image_m/fendo-17-1768816-g004.jpg Figure 4. Kaplan–Meier curves of (A) major adverse cardiovascular events (MACEs) and; (B) heart fail https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1795557/full https://www.frontiersin.org/files/Articles/1795557/fmed-13-1795557-HTML/image_m/fmed-13-1795557-g001.jpg Figure 1. Axial abdominal CT image showing a large area of low-density shadow in the left upper abdo https://www.frontiersin.org/files/Articles/1795557/fmed-13-1795557-HTML/image_m/fmed-13-1795557-g002.jpg Figure 2. Abdominal CT coronal image: The red arrow points to the spleen area in the left upper abdo https://www.frontiersin.org/files/Articles/1795557/fmed-13-1795557-HTML/image_m/fmed-13-1795557-g003.jpg Figure 3. Pathological examination of the left kidney biopsy specimen: Within the fibrous stroma, nu https://www.frontiersin.org/files/Articles/1795557/fmed-13-1795557-HTML/image_m/fmed-13-1795557-g004.jpg Figure 4. Immunohistochemistry showing CKPan positivity (+). https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1789457/full https://www.frontiersin.org/files/Articles/1789457/fmed-13-1789457-HTML/image_m/fmed-13-1789457-g001.jpg Figure 1. Main mechanisms of ventilator-induced lung injury. https://www.frontiersin.org/files/Articles/1789457/fmed-13-1789457-HTML/image_m/fmed-13-1789457-g002.jpg Figure 2. Ventilator-induced lung injury prevention strategies. VT, tidal volume; Pplat, plateau pre https://www.frontiersin.org/files/Articles/1789457/fmed-13-1789457-HTML/image_m/fmed-13-1789457-t001.jpg Table 1. Bedside monitoring indicators for the respiratory drive and drive. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1642262/full https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-t001.jpg Table 1. Inclusion criteria. https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-g001.jpg Figure 1. PRISMA search flow diagram https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-t002.jpg Table 2. Characteristics of the included nine studies. https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-t003.jpg Table 3. Study quality on the PEDro Scale of nine studies. https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-g002.jpg Figure 2. Risk of bias graph for the studies. https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-g003.jpg Figure 3. Forest plot showing the pooled effect size of RMT on MIP across RMT and control groups. https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-g004.jpg Figure 4. Forest plot showing the pooled effect size of RMT on MEP across RMT and control groups. https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-g005.jpg Figure 5. Forest plot showing the pooled effect size of RMT on respiratory muscle endurance across R https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-g006.jpg Figure 6. Forest plot showing the pooled effect size of RMT on FEV1 across RMT and control groups. https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-g007.jpg Figure 7. Forest plot showing the pooled effect size of RMT on PEF across RMT and control groups. https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-g008.jpg Figure 8. Forest plot showing the pooled effect size of RMT on FVC across RMT and control groups. https://www.frontiersin.org/files/Articles/1642262/fphys-16-1642262-HTML/image_m/fphys-16-1642262-g009.jpg Figure 9. Forest plot showing the pooled effect size of RMT on exercise capacity across RMT and cont https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1667400/full https://www.frontiersin.org/files/Articles/1667400/fneur-16-1667400-HTML/image_m/fneur-16-1667400-t001.jpg Table 1. Inclusion criteria. https://www.frontiersin.org/files/Articles/1667400/fneur-16-1667400-HTML/image_m/fneur-16-1667400-g001.jpg Figure 1. PRISMA search flow diagram. https://www.frontiersin.org/files/Articles/1667400/fneur-16-1667400-HTML/image_m/fneur-16-1667400-t002.jpg Table 2. Characteristics of the included 7 studies. https://www.frontiersin.org/files/Articles/1667400/fneur-16-1667400-HTML/image_m/fneur-16-1667400-t003.jpg Table 3. RMT parameters of the included 7 studies. https://www.frontiersin.org/files/Articles/1667400/fneur-16-1667400-HTML/image_m/fneur-16-1667400-t004.jpg Table 4. Quality score on the PEDro scale of two RCTs. https://www.frontiersin.org/files/Articles/1667400/fneur-16-1667400-HTML/image_m/fneur-16-1667400-t005.jpg Table 5. Quality score on the JBI critical appraisal checklist of one quasi-experimental study. https://www.frontiersin.org/files/Articles/1667400/fneur-16-1667400-HTML/image_m/fneur-16-1667400-t006.jpg Table 6. Quality score on the Newcastle-Ottawa Scale of one case–control study and three cohort stud https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1671629/full https://www.frontiersin.org/files/Articles/1671629/fphys-16-1671629-HTML/image_m/fphys-16-1671629-t001.jpg Table 1. Summary of clinical evidence on MMR for gastrointestinal function recovery in postoperative https://www.frontiersin.org/files/Articles/1671629/fphys-16-1671629-HTML/image_m/fphys-16-1671629-g001.jpg Figure 1. The proposed mechanisms of postoperative gastrointestinal dysfunction and how multimodal r https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1737740/full https://www.frontiersin.org/files/Articles/1737740/fnagi-18-1737740-HTML/image_m/fnagi-18-1737740-g001.jpg Figure 1. PRISMA search flow diagram. https://www.frontiersin.org/files/Articles/1737740/fnagi-18-1737740-HTML/image_m/fnagi-18-1737740-t001.jpg Table 1. Participant characteristics across the included studies. https://www.frontiersin.org/files/Articles/1737740/fnagi-18-1737740-HTML/image_m/fnagi-18-1737740-t002.jpg Table 2. Summary of intervention protocols and outcome results. https://www.frontiersin.org/files/Articles/1737740/fnagi-18-1737740-HTML/image_m/fnagi-18-1737740-t003.jpg Table 3. PEDro scale scores for the included studies. https://www.frontiersin.org/files/Articles/1737740/fnagi-18-1737740-HTML/image_m/fnagi-18-1737740-g002.jpg Figure 2. Risk of bias graph for included studies. https://www.frontiersin.org/files/Articles/1737740/fnagi-18-1737740-HTML/image_m/fnagi-18-1737740-g003.jpg Figure 3. Forest plot for the pooled ES of BCI-based rehabilitation on upper limb function in early https://www.frontiersin.org/files/Articles/1737740/fnagi-18-1737740-HTML/image_m/fnagi-18-1737740-t004.jpg Table 4. Results of subgroup analysis for upper limb function. https://www.frontiersin.org/files/Articles/1737740/fnagi-18-1737740-HTML/image_m/fnagi-18-1737740-g004.jpg Figure 4. Forest plot for the pooled ES of BCI-based rehabilitation on activities of daily living in https://www.frontiersin.org/files/Articles/1737740/fnagi-18-1737740-HTML/image_m/fnagi-18-1737740-t005.jpg Table 5. Results of subgroup analysis for activities of daily living. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1703778/full https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g001.jpg Figure 1. The workflow of the study. https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g002.jpg Figure 2. Nucleotide metabolism is increased in tumor epithelial cells: (A) Volcano plot displaying https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g003.jpg Figure 3. Pseudotime and cell communication analyses.​​ (A) Pseudotime analysis of epithelial cells. https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g004.jpg Figure 4. Systematic analysis of ligand-receptor interactions and cellular differentiation states ac https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g005.jpg Figure 5. Spatial characterization of nucleotide metabolism heterogeneity in breast cancer tissues. https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g006.jpg Figure 6. Spatial organization and cell-cell communication networks in the tumor microenvironment. ( https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g007.jpg Figure 7. Development and validation of a prognostic model based on multi-algorithm integration and https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g008.jpg Figure 8. The transcriptome features of patients with various NMRS in BC. (A) Ridge plot showing the https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g009.jpg Figure 9. Genetic alterations associated with NMRS between low- and high NMRS groups. (A) Boxplot sh https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g010.jpg Figure 10. The immune landscape associated with NMRS in BC. (A) The immune score, the ESTIMATE score https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g011.jpg Figure 11. The relationship between the NMRS and immunotherapy response. (A) Heatmap showing the dif https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g012.jpg Figure 12. Predicting therapy response with NMRS and validation of the core genes. (A) Estimated IC5 https://www.frontiersin.org/files/Articles/1703778/fonc-15-1703778-HTML/image_m/fonc-15-1703778-g013.jpg Figure 13. DCTPP1 expression and functional validation in BC (A) DCTPP1 mRNA expression was higher i https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1704475/full https://www.frontiersin.org/files/Articles/1704475/fphar-16-1704475-HTML/image_m/fphar-16-1704475-g001.jpg Figure 1. Structure and functional regions of MAPs. https://www.frontiersin.org/files/Articles/1704475/fphar-16-1704475-HTML/image_m/fphar-16-1704475-g002.jpg Figure 2. The pharmacological effects of MAPs. (A) MAPs can inhibit the occurrence of skin inflammat https://www.frontiersin.org/files/Articles/1704475/fphar-16-1704475-HTML/image_m/fphar-16-1704475-t001.jpg Table 1. A comparison of the similarities and differences between natural and recombinant mussel adh https://www.frontiersin.org/files/Articles/1704475/fphar-16-1704475-HTML/image_m/fphar-16-1704475-t002.jpg Table 2. A comparison of MAPs and other natural biomaterials. https://www.frontiersin.org/files/Articles/1704475/fphar-16-1704475-HTML/image_m/fphar-16-1704475-g003.jpg Figure 3. Recombinant MAPs and their applications in the biomedical field. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1714696/full https://www.frontiersin.org/files/Articles/1714696/fphar-16-1714696-HTML-r1/image_m/fphar-16-1714696-t001.jpg Table 1. Common genetic mutations in gliomas and their effect. https://www.frontiersin.org/files/Articles/1714696/fphar-16-1714696-HTML-r1/image_m/fphar-16-1714696-g001.jpg Figure 1. The mechanism of FGFR3-TACC3 fusion protein in glioma occurrence. https://www.frontiersin.org/files/Articles/1714696/fphar-16-1714696-HTML-r1/image_m/fphar-16-1714696-g002.jpg Figure 2. Mechanism of action of FGFR inhibitors. https://www.frontiersin.org/files/Articles/1714696/fphar-16-1714696-HTML-r1/image_m/fphar-16-1714696-t002.jpg Table 2. Effects of FGFR inhibitors and progress in clinical research. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1728048/full https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-t001.jpg Table 1. Primer sequences for target and reference genes. https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-g001.jpg Figure 1. The inhibitory effect of linalool on E. coli biofilm: (A) E. coli D5; (B) E. coli ATCC2592 https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-g002.jpg Figure 2. Scanning electron microscopy of E. coli biofilm treated by linalool for 24 h: (A) 0 μL/mL https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-g003.jpg Figure 3. Effects of linalool on motilities of E. coli D5: (A) Swarming motility; (B) Swimming motil https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-g004.jpg Figure 4. Scanning electron microscopy observation of the effect of linalool on bacterial flagella a https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-g005.jpg Figure 5. The effect of linalool on the relative expression levels of flagella and fimbriae mRNA dur https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-g006.jpg Figure 6. Effect of linalool on adhesion of E. coli D5 to endometrial epithelial cells of dairy cows https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-g007.jpg Figure 7. The inhibiting effect of linalool on E. coli D5 adhered on endometrial epithelial cells of https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-g008.jpg Figure 8. Inhibitory effect of linalool on the adhesion of E. coli D5 in rat uterus in vivo (n = 6) https://www.frontiersin.org/files/Articles/1728048/fvets-12-1728048-HTML-r1/image_m/fvets-12-1728048-g009.jpg Figure 9. Scanning electron microscope image of E. coli in uterus of rat: (A) Blank control group; ( https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1616371/full https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g001.jpg Figure 1. Structure and principle diagram. 1. The air outlet 2. Flexible air duct 3. Eight-outlet ai https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g002.jpg Figure 2. Structure diagram of the air outlet. 1. Deflector plates 2. Cylindrical section 3. A layou https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g003.jpg Figure 3. Three-dimensional model of the flow field. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g004.jpg Figure 4. Fluid computational domain grid diagram. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g005.jpg Figure 5. Schematic diagram of the flat fan atomizer model. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-t001.jpg Table 1. Key parameter settings of the flat fan atomizer model. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g006.jpg Figure 6. Schematic diagram of the liquid film thickness collection surface. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-t002.jpg Table 2. Simulation test scheme table. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g007.jpg Figure 7. Schematic diagram of the opening degree and interval of the air outlet. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-t003.jpg Table 3. Simulation test scheme table. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g008.jpg Figure 8. Schematic diagram of the monitoring planes for particle diameter distribution. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g009.jpg Figure 9. Schematic diagram of droplet particle size measurement test. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-t004.jpg Table 4. Simulation results at different test levels. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g010.jpg Figure 10. Contours of liquid film thickness for the considered cases: (A) 0.03,60,0.6; (B) 0.04,60, https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-t005.jpg Table 5. Comprehensive analysis and optimization suggestions. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-t006.jpg Table 6. Simulation results at different test levels. https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g011.jpg Figure 11. Simulation results at each test level: (A) 70,500; (B) 80,500; (C) 90,500; (D) 70,600; (E https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g012.jpg Figure 12. Contours of particle size distribution of droplets under the influence of airflow field f https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-g013.jpg Figure 13. Particle size distribution curves of each detection surface for three considered values o https://www.frontiersin.org/files/Articles/1616371/fpls-16-1616371-HTML-r1/image_m/fpls-16-1616371-t007.jpg Table 7. The results of droplet particle size measurement test. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1564032/full https://www.frontiersin.org/files/Articles/1564032/fpsyg-16-1564032-HTML-r3/image_m/fpsyg-16-1564032-t001.jpg Table 1. Keywords Boolean search strategies by database. https://www.frontiersin.org/files/Articles/1564032/fpsyg-16-1564032-HTML-r3/image_m/fpsyg-16-1564032-g001.jpg Figure 1. PRISMA search process of study inclusion and exclusion based on database searches and gray https://www.frontiersin.org/files/Articles/1564032/fpsyg-16-1564032-HTML-r3/image_m/fpsyg-16-1564032-t002.jpg Table 2. Geographic distribution of identified articles. https://www.frontiersin.org/files/Articles/1564032/fpsyg-16-1564032-HTML-r3/image_m/fpsyg-16-1564032-t003.jpg Table 3. Conditions screened. https://www.frontiersin.org/files/Articles/1564032/fpsyg-16-1564032-HTML-r3/image_m/fpsyg-16-1564032-t004.jpg Table 4. Disciplinary field of articles. https://www.frontiersin.org/files/Articles/1564032/fpsyg-16-1564032-HTML-r3/image_m/fpsyg-16-1564032-t005.jpg Table 5. Type of articles. https://www.frontiersin.org/files/Articles/1564032/fpsyg-16-1564032-HTML-r3/image_m/fpsyg-16-1564032-t006.jpg Table 6. Core categories identified from the data analysis (The numbers in parenthesis refer to the https://www.frontiersin.org/files/Articles/1564032/fpsyg-16-1564032-HTML-r3/image_m/fpsyg-16-1564032-t007.jpg Table 7. Empirical axes from results and corresponding conceptual themes in the discussion. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1766623/full https://www.frontiersin.org/files/Articles/1766623/fvets-13-1766623-HTML/image_m/fvets-13-1766623-g001.jpg Figure 1. Gross appearance of two cases of multiple intraductal papillary adenomas of the bovine udd https://www.frontiersin.org/files/Articles/1766623/fvets-13-1766623-HTML/image_m/fvets-13-1766623-g002.jpg Figure 2. Histopathology and immunohistochemistry of two cases of multiple intraductal papillary ade https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1714569/full https://www.frontiersin.org/files/Articles/1714569/fpsyt-16-1714569-HTML/image_m/fpsyt-16-1714569-g001.jpg Figure 1. Integrated ERβ/GPER1 mechanistic model in TRS. https://www.frontiersin.org/files/Articles/1714569/fpsyt-16-1714569-HTML/image_m/fpsyt-16-1714569-t001.jpg Table 1. Summary of estrogen-linked mechanisms relevant to TRS. https://www.frontiersin.org/files/Articles/1714569/fpsyt-16-1714569-HTML/image_m/fpsyt-16-1714569-t002.jpg Table 2. Summary of clinical evidence for estrogenic interventions in schizophrenia. https://www.frontiersin.org/files/Articles/1714569/fpsyt-16-1714569-HTML/image_m/fpsyt-16-1714569-t003.jpg Table 3. Comparative profiles of SERMs relevant to schizophrenia. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1667402/full https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-g001.jpg Figure 1. The flowchart of study selection. https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-t001.jpg Table 1. General characteristics of included publications. https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-g002.jpg Figure 2. Quality assessment diagram and risk of bias summary. https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-t002.jpg Table 2. Diagnostic performance analysis of eight core CSF biomarkers. https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-g003.jpg Figure 3. Forest plot of sensitivity for core CSF biomarkers. (A) Aβ42, (B) t-tau, (C) p-tau181, (D) https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-g004.jpg Figure 4. Forest plot of specificity for core CSF biomarkers. (A) Aβ42, (B) t-tau, (C) p-tau181, (D) https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-g005.jpg Figure 5. Forest plot of LR+ for core CSF biomarkers. (A) Aβ42, (B) t-tau, (C) p-tau181, (D) p-tau21 https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-g006.jpg Figure 6. Forest plot of LR− for core CSF biomarkers. (A) Aβ42, (B) t-tau, (C) p-tau181, (D) p-tau21 https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-g007.jpg Figure 7. Forest plot of DOR for core CSF biomarkers. (A) Aβ42, (B) t-tau, (C) p-tau181, (D) p-tau21 https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-g008.jpg Figure 8. SROC curve analysis of core CSF biomarkers. (A) Aβ42, (B) t-tau, (C) p-tau181, (D) p-tau21 https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-t003.jpg Table 3. Heterogeneity analysist results induced by threshold effects. https://www.frontiersin.org/files/Articles/1667402/fneur-16-1667402-HTML-r1/image_m/fneur-16-1667402-g009.jpg Figure 9. Deeks funnel chart. (A) Aβ42, (B) t-tau, (C) p-tau181, (D) p-tau217, (E) p-tau231, (F) Aβ4 https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1760407/full https://www.frontiersin.org/files/Articles/1760407/fcvm-13-1760407-HTML-r1/image_m/fcvm-13-1760407-g001.jpg Figure 1. The flowchart of patient enrollment. https://www.frontiersin.org/files/Articles/1760407/fcvm-13-1760407-HTML-r1/image_m/fcvm-13-1760407-t001.jpg Table 1. Baseline clinical and procedural characteristics. https://www.frontiersin.org/files/Articles/1760407/fcvm-13-1760407-HTML-r1/image_m/fcvm-13-1760407-t002.jpg Table 2. Quantitative coronary angiographic data. https://www.frontiersin.org/files/Articles/1760407/fcvm-13-1760407-HTML-r1/image_m/fcvm-13-1760407-t003.jpg Table 3. Pre-stent implantation OCT findings from the LAD to the LM. https://www.frontiersin.org/files/Articles/1760407/fcvm-13-1760407-HTML-r1/image_m/fcvm-13-1760407-g002.jpg Figure 2. (A) the calcification arc was significantly larger in the LCX-OS group than in the no LCX- https://www.frontiersin.org/files/Articles/1760407/fcvm-13-1760407-HTML-r1/image_m/fcvm-13-1760407-g003.jpg Figure 3. Angiographic findings before single-stent implantation exhibited no stenosis at LCX-OS; ho https://www.frontiersin.org/files/Articles/1760407/fcvm-13-1760407-HTML-r1/image_m/fcvm-13-1760407-t004.jpg Table 4. Multivariate logistic regression analysis predictors for LCX-OS compromised. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1687970/full https://www.frontiersin.org/files/Articles/1687970/fsurg-12-1687970-HTML/image_m/fsurg-12-1687970-g001.jpg Figure 1. (a–d) Preoperative x-ray and magnetic resonance imaging (MRI) showing chronic rupture and https://www.frontiersin.org/files/Articles/1687970/fsurg-12-1687970-HTML/image_m/fsurg-12-1687970-g002.jpg Figure 2. (a,b) Intraoperative exploration showing intact gastrocnemius aponeurosis and achilles ten https://www.frontiersin.org/files/Articles/1687970/fsurg-12-1687970-HTML/image_m/fsurg-12-1687970-t001.jpg Table 1. Postoperative rehabilitation timeline after Achilles tendon reconstruction. https://www.frontiersin.org/files/Articles/1687970/fsurg-12-1687970-HTML/image_m/fsurg-12-1687970-g003.jpg Figure 3. (a,b) At 6 months postoperatively, the patient demonstrated satisfactory recovery of ankle https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1783682/full https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-g001.jpg Figure 1. Policy evolution https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-2026-1783682-g002.jpg Figure 2. Carbon emission reduction mechanism for the construction of national forest cities. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t001.jpg Table 1. Carbon emission calculation scope. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t002.jpg Table 2. Cities implementing forest city construction and the start time of construction. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t003.jpg Table 3. Descriptive statistics results. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t004.jpg Table 4. Baseline regression results. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-g003.jpg Figure 3. Parallel trends test. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t005.jpg Table 5. Time dynamic effect. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-g004.jpg Figure 4. Placebo test results. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-g005.jpg Figure 5. Bacon decomposition results. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t006.jpg Table 6. Bacon decomposition results. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t007.jpg Table 7. Robustness test results. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t008.jpg Table 8. Indicator system of mechanism variables. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t009.jpg Table 9. Mechanism test. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t010.jpg Table 10. Green production. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t011.jpg Table 11. Green living. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t012.jpg Table 12. Green innovation. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t013.jpg Table 13. Heterogeneity analysis results of geographic factors. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t014.jpg Table 14. Heterogeneity analysis results of urban form factors. https://www.frontiersin.org/files/Articles/1783682/fenvs-14-1783682-HTML/image_m/fenvs-14-1783682-t015.jpg Table 15. Moderating effect analysis results. https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2026.1715723/full https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g001.jpg Figure 1. Illustration of the four tasks. Each panel shows the extreme body positions between which https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g002.jpg Figure 2. Illustration of the calculation method to obtain phasic EMG components. Electromyographic https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g003.jpg Figure 3. Organization of the experiment. Timeline of the experiment with two blocks of walking at 2 https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g004.jpg Figure 4. Illustration of the tasks. (A) Unconstrained walking condition: the participant walks at a https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g005.jpg Figure 5. Mean ± SD movement durations (s) for fast movements performed in all tasks and both groups https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g006.jpg Figure 6. Mean (±SE) integrated phasic EMGs recorded for both groups (n = 20 for younger and n = 24 https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g007.jpg Figure 7. Negativity index computed for (A) arm and whole-body movements in both groups (WB: Whole-B https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g008.jpg Figure 8. Center-of-mass analyses. Linear relationships between EMG negativity (Vastus Lateralis and https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g009.jpg Figure 9. Evolution of center of mass main parameters (A) total displacement and (B) peak velocity; https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g010.jpg Figure 10. Feet Kinematics. Feet spacing is presented across walking conditions for the two groups. https://www.frontiersin.org/files/Articles/1715723/fragi-07-1715723-HTML/image_m/fragi-07-1715723-g011.jpg Figure 11. Effort Perception & Net Metabolic Power. Quantification of effort perception (A) and Net https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1721612/full https://www.frontiersin.org/files/Articles/1721612/fimmu-16-1721612-HTML/image_m/fimmu-16-1721612-g001.jpg Figure 1. Volcano plots of RNA-sequencing data at 2, 5, and 29 hours post-amputation. (A) Experiment https://www.frontiersin.org/files/Articles/1721612/fimmu-16-1721612-HTML/image_m/fimmu-16-1721612-g002.jpg Figure 2. Correlation of fold changes between RT-qPCR and RNA-seq data. Bar plots comparing expressi https://www.frontiersin.org/files/Articles/1721612/fimmu-16-1721612-HTML/image_m/fimmu-16-1721612-g003.jpg Figure 3. Transcriptome profiling of macrophages at 2 hpA in zebrafish larvae. (A). Heat-map of diff https://www.frontiersin.org/files/Articles/1721612/fimmu-16-1721612-HTML/image_m/fimmu-16-1721612-g004.jpg Figure 4. Correlation of gene expression fold changes between early wound healing and early Salmonel https://www.frontiersin.org/files/Articles/1721612/fimmu-16-1721612-HTML/image_m/fimmu-16-1721612-g005.jpg Figure 5. Transcriptome profiling of macrophages at 5 hpA in zebrafish larvae. (A) Heat-map of diffe https://www.frontiersin.org/files/Articles/1721612/fimmu-16-1721612-HTML/image_m/fimmu-16-1721612-g006.jpg Figure 6. Correlation of gene expression fold changes between 2hpA and 5hpA. The scatter plot depict https://www.frontiersin.org/files/Articles/1721612/fimmu-16-1721612-HTML/image_m/fimmu-16-1721612-g007.jpg Figure 7. Diagram representing macrophage reprogramming during zebrafish wound healing. Macrophage p https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1790478/full https://www.frontiersin.org/files/Articles/1790478/fphar-17-1790478-HTML/image_m/fphar-17-1790478-g003.jpg Figure 3. ASIV attenuated I/R-induced autophagosome accumulation. In I/R induced mice hearts after v https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1768464/full https://www.frontiersin.org/files/Articles/1768464/feduc-11-1768464-HTML/image_m/feduc-11-1768464-g001.jpg Figure 1. Barriers affecting teachers’ use of satellite imagery in lessons. https://www.frontiersin.org/files/Articles/1768464/feduc-11-1768464-HTML/image_m/feduc-11-1768464-t001.jpg Table 1. Correlation coefficient between non-use of satellite imagery and teachers’ age groups. https://www.frontiersin.org/files/Articles/1768464/feduc-11-1768464-HTML/image_m/feduc-11-1768464-g002.jpg Figure 2. Structural-content model for teaching remote sensing (RS) materials in school geography. https://www.frontiersin.org/files/Articles/1768464/feduc-11-1768464-HTML/image_m/feduc-11-1768464-t002.jpg Table 2. Results of the analysis of responses to control tasks obtained during the diagnostic experi https://www.frontiersin.org/files/Articles/1768464/feduc-11-1768464-HTML/image_m/feduc-11-1768464-g003.jpg Figure 3. Analysis results of the control task responses from the diagnostic stage of the research. https://www.frontiersin.org/files/Articles/1768464/feduc-11-1768464-HTML/image_m/feduc-11-1768464-t003.jpg Table 3. Results of the formative stage of the pedagogical experiment. https://www.frontiersin.org/files/Articles/1768464/feduc-11-1768464-HTML/image_m/feduc-11-1768464-g004.jpg Figure 4. Final results of the formative stage of the research. https://www.frontiersin.org/files/Articles/1768464/feduc-11-1768464-HTML/image_m/feduc-11-1768464-g005.jpg Figure 5. Final results of the evaluative experiment. https://www.frontiersin.org/files/Articles/1768464/feduc-11-1768464-HTML/image_m/feduc-11-1768464-t004.jpg Table 4. The process of conducting lessons for the experimental group. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1771479/full https://www.frontiersin.org/files/Articles/1771479/fmars-13-1771479-HTML-r2/image_m/fmars-13-1771479-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1771479/fmars-13-1771479-HTML-r2/image_m/fmars-13-1771479-g001.jpg Figure 1. Geographic distribution of Italian marine facilities (fixed stations, platforms, moorings, https://www.frontiersin.org/files/Articles/1771479/fmars-13-1771479-HTML-r2/image_m/fmars-13-1771479-t001.jpg Table 1. Conceptual overview of EVs, detailing the focus, purpose, primary frameworks, applications, https://www.frontiersin.org/files/Articles/1771479/fmars-13-1771479-HTML-r2/image_m/fmars-13-1771479-t002.jpg Table 2. Overview of the RIs involved in the ITINERIS project, including scope, facility types, and https://www.frontiersin.org/files/Articles/1771479/fmars-13-1771479-HTML-r2/image_m/fmars-13-1771479-g002.jpg Figure 2. Distribution of EVs by type EOVs, ECVs, EBVs, and relative subgroups/classes (Annex 3) bas https://www.frontiersin.org/files/Articles/1771479/fmars-13-1771479-HTML-r2/image_m/fmars-13-1771479-t003.jpg Table 3. Detailed numerical count of EVs per facility subgroups per RI. https://www.frontiersin.org/files/Articles/1771479/fmars-13-1771479-HTML-r2/image_m/fmars-13-1771479-g003.jpg Figure 3. Distribution of EOVs, ECVs, and EBVs, and relative subgroups/classes, within each of the e https://www.frontiersin.org/files/Articles/1771479/fmars-13-1771479-HTML-r2/image_m/fmars-13-1771479-g004.jpg Figure 4. Distribution of CORE (blue, red, green) and SUPPORTING (light blue, orange) EVs per facili https://www.frontiersin.org/files/Articles/1771479/fmars-13-1771479-HTML-r2/image_m/fmars-13-1771479-g005.jpg Figure 5. Strategic roadmap for enhancing Italy’s marine EVs observation system. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1757637/full https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g001.jpg Figure 1. Overall pipeline representation. https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g002.jpg Figure 2. Overview of the computer vision (CV) algorithm. https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g003.jpg Figure 3. Overview of the model comparison stage where 3 foundation models are benchmarked in a zero https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-t001.jpg Table 1. Summary of foundation models evaluated in a zero-shot setting. https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g004.jpg Figure 4. Representation of the centerline-guided prompting strategy, with ilustration of positive ( https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g005.jpg Figure 5. Representation of the model assessment stage where the best-performing model is fine-tuned https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-t002.jpg Table 2. Summary of training hyperparameters. https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g006.jpg Figure 6. Representation of the quantitative analysis stage where clinically relevant anatomical met https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g007.jpg Figure 7. Comparison of the overlap metrics across the three models for each patient, with mean and https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g008.jpg Figure 8. Comparison of the boundary metrics across the three models for each patient, with mean and https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g009.jpg Figure 9. Comparison of the ITpC across the three models for each patient, with mean and standard de https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-t003.jpg Table 3. Zero-shot inference comparison of SAM2, MedSAM2, and nnInteractive. https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-t004.jpg Table 4. Comparison of nnInteractive performance before and after fine-tuning. https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g010.jpg Figure 10. Comparison of overlap metrics across the two models for each patient, with mean and stand https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g011.jpg Figure 11. Comparison of boundary metrics across the two models for each patient, with mean and stan https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g012.jpg Figure 12. Comparison of ITpC across the two models for each patient, with mean and standard deviati https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g013.jpg Figure 13. Example of perforator segmentation results before (green) and after (blue) fine-tuning, f https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g014.jpg Figure 14. Distributions of absolute errors in intramuscular path length measurements using (A) cent https://www.frontiersin.org/files/Articles/1757637/fmed-13-1757637-HTML/image_m/fmed-13-1757637-g015.jpg Figure 15. Distributions of absolute errors in perforator–umbilicus distance measurements along (A) https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1713181/full https://www.frontiersin.org/files/Articles/1713181/fonc-15-1713181-HTML/image_m/fonc-15-1713181-g001.jpg Figure 1. EGFR expression in rat 101.8 glioblastoma tissues (immunohistochemistry). (a) Staining of https://www.frontiersin.org/files/Articles/1713181/fonc-15-1713181-HTML/image_m/fonc-15-1713181-g002.jpg Figure 2. Comparison of the contrast enhancement volume on MRI on the time of [18F]-Gol1 and [18F]-G https://www.frontiersin.org/files/Articles/1713181/fonc-15-1713181-HTML/image_m/fonc-15-1713181-g003.jpg Figure 3. Comparison of tracers uptake (a) [18F]FB-Gol1; (b) [18F]FDG; (c) [18F]FB-GR20. The upper r https://www.frontiersin.org/files/Articles/1713181/fonc-15-1713181-HTML/image_m/fonc-15-1713181-g004.jpg Figure 4. Characteristics of aptamer tracers uptake. (a) Averaged over all animals time activity cur https://www.frontiersin.org/files/Articles/1713181/fonc-15-1713181-HTML/image_m/fonc-15-1713181-g005.jpg Figure 5. Formation of the intratumoral necrosis. Contrast enhanced T1-MRI combined with (a) [18F]FB https://www.frontiersin.org/files/Articles/1713181/fonc-15-1713181-HTML/image_m/fonc-15-1713181-g006.jpg Figure 6. Unconjugated label uptake. T1 –MRI image with contrast enhancement and (a) [18F]-fluoroben https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1717812/full https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-g001.jpg Figure 1. The pathogenesis of AD: the roles of epithelial barrier, innate and adaptive immunity, maj https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t001.jpg Table 1. Genome-wide association studies of atopic dermatitis (AD). https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-g002.jpg Figure 2. Areas in which causal inferences with AD have been identified through MR studies. Created https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t002.jpg Table 2. Published MR studies investigating causal associations between AD and psychiatric or neurol https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t003.jpg Table 3. Published MR studies investigating causal associations between AD and cardiovascular, metab https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t004.jpg Table 4. Published MR studies investigating causal associations between AD and different types of ca https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t005.jpg Table 5. Published MR studies investigating causal associations between AD and different types of im https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t006.jpg Table 6. Published MR studies investigating causal associations between AD and ocular diseases. (AD, https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t007.jpg Table 7. Members of the gut or skin microbiota which have been causally associated with AD through M https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t008.jpg Table 8. Published MR studies investigating causal associations between AD and different types of ex https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t009.jpg Table 9. Summary of the most important findings regarding causal inferences between AD and different https://www.frontiersin.org/files/Articles/1717812/fimmu-17-1717812-HTML/image_m/fimmu-17-1717812-t010.jpg Table 10. Summary of the most important findings regarding potential drug targets for treating and p https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1761668/full https://www.frontiersin.org/files/Articles/1761668/fimmu-17-1761668-HTML/image_m/fimmu-17-1761668-t001.jpg Table 1. Functional and phenotypic overview of mouse and human colonic γδ T cell subsets. https://www.frontiersin.org/files/Articles/1761668/fimmu-17-1761668-HTML/image_m/fimmu-17-1761668-g001.jpg Figure 1. Distinct γδ T-cell subsets in colorectal cancer (CRC) in mice and humans. (Left) – Mouse C https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1697986/full https://www.frontiersin.org/files/Articles/1697986/fphar-16-1697986-HTML/image_m/fphar-16-1697986-g006.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1697986/fphar-16-1697986-HTML/image_m/fphar-16-1697986-g001.jpg Figure 1. Anatomical and Pathophysiological Features of Ocular Tuberculosis. A cross-sectional schem https://www.frontiersin.org/files/Articles/1697986/fphar-16-1697986-HTML/image_m/fphar-16-1697986-g002.jpg Figure 2. Natural Compounds and Mechanisms of Action. Molecular schematic and mechanistic illustrati https://www.frontiersin.org/files/Articles/1697986/fphar-16-1697986-HTML/image_m/fphar-16-1697986-t001.jpg Table 1. Natural compounds for ocular TB. https://www.frontiersin.org/files/Articles/1697986/fphar-16-1697986-HTML/image_m/fphar-16-1697986-t002.jpg Table 2. Synergistic natural compound and Anti-TB formulations in ocular TB. https://www.frontiersin.org/files/Articles/1697986/fphar-16-1697986-HTML/image_m/fphar-16-1697986-g003.jpg Figure 3. Advanced Ocular Drug Delivery Systems. Infographic schematic illustrating innovative ocula https://www.frontiersin.org/files/Articles/1697986/fphar-16-1697986-HTML/image_m/fphar-16-1697986-g004.jpg Figure 4. Scaffold Optimization and Synthetic Strategies. Flowchart schematic illustrating the progr https://www.frontiersin.org/files/Articles/1697986/fphar-16-1697986-HTML/image_m/fphar-16-1697986-g005.jpg Figure 5. Preclinical evaluation pipeline. Stepwise schematic outlining the progression from in vitr https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1724061/full https://www.frontiersin.org/files/Articles/1724061/fimmu-17-1724061-HTML/image_m/fimmu-17-1724061-g001.jpg Figure 1. Prognostic impact of ALI and TL on survival in ESCC patients receiving neoadjuvant immunoc https://www.frontiersin.org/files/Articles/1724061/fimmu-17-1724061-HTML/image_m/fimmu-17-1724061-t001.jpg Table 1. Baseline clinicopathologic characteristics and laboratory findings across the three ALI–TL https://www.frontiersin.org/files/Articles/1724061/fimmu-17-1724061-HTML/image_m/fimmu-17-1724061-t002.jpg Table 2. Postoperative pathological findings and perioperative outcomes among the three ALI–TL risk https://www.frontiersin.org/files/Articles/1724061/fimmu-17-1724061-HTML/image_m/fimmu-17-1724061-t003.jpg Table 3. Univariable and multivariable logistic regression analyses of factors associated with major https://www.frontiersin.org/files/Articles/1724061/fimmu-17-1724061-HTML/image_m/fimmu-17-1724061-t004.jpg Table 4. Univariable and multivariable Cox proportional hazards analyses for overall survival and di https://www.frontiersin.org/files/Articles/1724061/fimmu-17-1724061-HTML/image_m/fimmu-17-1724061-g002.jpg Figure 2. Development and validation of a prognostic model integrating ALI and TL. (A) Nomogram pred https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1738602/full https://www.frontiersin.org/files/Articles/1738602/fbioe-13-1738602-HTML-r1/image_m/fbioe-13-1738602-g009.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1738602/fbioe-13-1738602-HTML-r1/image_m/fbioe-13-1738602-g001.jpg Figure 1. Schematic representation of the evaluation protocol for AC-EPD DNase I coatings. Discs wer https://www.frontiersin.org/files/Articles/1738602/fbioe-13-1738602-HTML-r1/image_m/fbioe-13-1738602-g002.jpg Figure 2. Surface wettability before and after ultrasonication. Water contact angles before and afte https://www.frontiersin.org/files/Articles/1738602/fbioe-13-1738602-HTML-r1/image_m/fbioe-13-1738602-g003.jpg Figure 3. PCA results of ToF-SIMS spectra for control and DNase-coated samples after 2 h of sonicati https://www.frontiersin.org/files/Articles/1738602/fbioe-13-1738602-HTML-r1/image_m/fbioe-13-1738602-g004.jpg Figure 4. PCA results of ToF-SIMS spectra for DNase-coated samples after 2 h of sonication. Scores p https://www.frontiersin.org/files/Articles/1738602/fbioe-13-1738602-HTML-r1/image_m/fbioe-13-1738602-g005.jpg Figure 5. PCA results of ToF-SIMS spectra for DNase-coated samples before and after 2 h of sonicatio https://www.frontiersin.org/files/Articles/1738602/fbioe-13-1738602-HTML-r1/image_m/fbioe-13-1738602-g006.jpg Figure 6. Cytotoxicity testing for human oral keratinocytes in contact with DNase-coated samples. Ce https://www.frontiersin.org/files/Articles/1738602/fbioe-13-1738602-HTML-r1/image_m/fbioe-13-1738602-g007.jpg Figure 7. Representative CLSM images of human oral keratinocytes on DNase-coated samples after 24 h https://www.frontiersin.org/files/Articles/1738602/fbioe-13-1738602-HTML-r1/image_m/fbioe-13-1738602-g008.jpg Figure 8. Representative SEM images of human oral keratinocytes (HOKs) on DNase-coated samples after https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1571686/full https://www.frontiersin.org/files/Articles/1571686/feduc-10-1571686-HTML/image_m/feduc-10-1571686-g001.jpg Figure 1. Racial equity in education (Collins and Whitney, 2021). https://www.frontiersin.org/files/Articles/1571686/feduc-10-1571686-HTML/image_m/feduc-10-1571686-g002.jpg Figure 2. Professional development model. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1644183/full https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g001.jpg Figure 1. Conceptual framework of the PTRM integrating lactate, stiffness, and HR recovery for inter https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g002.jpg Figure 2. Procedural flow of stratified volleyball load, HR monitoring, and recovery under superviso https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g003.jpg Figure 3. (A) Domain-specific composite recovery index, (B) Marker variability radar plot, (C) HR mo https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g004.jpg Figure 4. (a) Age and maturity offset; (b) Resting HR and lactate; and (c) TQR and DOMS. https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g005.jpg Figure 5. (A) Lactate concentration; (B) Muscle hardness; (C) Heart rate metrics; (D) Recovery varia https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g006.jpg Figure 6. (A) TQR scores; (B) DOMS by sex; and (C) TQR–discrepancy regression. https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g007.jpg Figure 7. (a) TQR vs. Lactate; (b) TQR vs. HRR; (c) VAS vs. HRR; (d) VAS vs. Lactate. https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g008.jpg Figure 8. (A) Interaction effects over time; (B) Pre-post TQR by group; (C) Lactate at T3; (D) Sorte https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g009.jpg Figure 9. (A) Recovery means after outlier exclusion; (B) HRR correction; (C) Sex-stratified markers https://www.frontiersin.org/files/Articles/1644183/fphys-16-1644183-HTML-r1/image_m/fphys-16-1644183-g010.jpg Figure 10. (A) Adverse event frequency by group; (B) Protocol deviation heatmap; (C) Severity trend; https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1771917/full https://www.frontiersin.org/files/Articles/1771917/fpubh-14-1771917-HTML-r1/image_m/fpubh-14-1771917-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1765652/full https://www.frontiersin.org/files/Articles/1765652/fsurg-13-1765652-HTML/image_m/fsurg-13-1765652-g001.jpg Figure 1. Flowchart of the in silico workflow to create and validate 3D-printed patient-specific cas https://www.frontiersin.org/files/Articles/1765652/fsurg-13-1765652-HTML/image_m/fsurg-13-1765652-t001.jpg Table 1. Summary of the material properties, including bone and soft tissues, the different cast mat https://www.frontiersin.org/files/Articles/1765652/fsurg-13-1765652-HTML/image_m/fsurg-13-1765652-g002.jpg Figure 2. Maximum von Mises stress values in the casts for different 3D-printing technologies. Cast https://www.frontiersin.org/files/Articles/1765652/fsurg-13-1765652-HTML/image_m/fsurg-13-1765652-g003.jpg Figure 3. Maximum von Mises stress values in the fracture surfaces for different 3D-printing technol https://www.frontiersin.org/files/Articles/1765652/fsurg-13-1765652-HTML/image_m/fsurg-13-1765652-g004.jpg Figure 4. Maximum displacement of the fracture surfaces for different 3D-printing technologies. Frac https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1718763/full https://www.frontiersin.org/files/Articles/1718763/fpsyg-16-1718763-HTML-r1/image_m/fpsyg-16-1718763-g001.jpg Figure 1. The theoretical model tested. The covariates (sex, age, education level, religious beliefs https://www.frontiersin.org/files/Articles/1718763/fpsyg-16-1718763-HTML-r1/image_m/fpsyg-16-1718763-t001.jpg Table 1. Correlations between demographics and other variables. https://www.frontiersin.org/files/Articles/1718763/fpsyg-16-1718763-HTML-r1/image_m/fpsyg-16-1718763-t002.jpg Table 2. Correlations between psychological variables, BOC-19 scores, and vaccination. https://www.frontiersin.org/files/Articles/1718763/fpsyg-16-1718763-HTML-r1/image_m/fpsyg-16-1718763-t003.jpg Table 3. SEM estimated coefficients for model on vaccine evaluation. https://www.frontiersin.org/files/Articles/1718763/fpsyg-16-1718763-HTML-r1/image_m/fpsyg-16-1718763-t004.jpg Table 4. SEM estimated coefficients for model on vaccine behavior. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1638750/full https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-t001.jpg Table 1. Development of Kodo millet-chickpea products. https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-g001.jpg Figure 1. Organoleptic evaluation of Kodo millet–chickpea products. https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-g002.jpg Figure 2. Consumer acceptability of Kodo millet–chickpea crispy crunch and nutrimilletvita (100 g, d https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-g003.jpg Figure 3. Methodology of the study. https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-t002.jpg Table 2. Nutritional composition of Kodo millet–chickpea products (g/100 g, dry weight basis). https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-t003.jpg Table 3. Amino acid composition of Kodo millet–chickpea products (g/100 g, dry weight basis). https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-g004.jpg Figure 4. In vitro protein digestibility of Kodo millet–chickpea products (100 g, dry weight basis). https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-g005.jpg Figure 5. Protein digestibility corrected amino acid score (PDCAAS) of Kodo millet–chickpea products https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-t004.jpg Table 4. Sensory scores of crispy crunch prepared from kodo millet-chickpea at an interval of 15 day https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-t005.jpg Table 5. Sensory scores of nutrimilletvita prepared from kodo millet–chickpea at an interval of 15 d https://www.frontiersin.org/files/Articles/1638750/fnut-12-1638750-HTML-r1/image_m/fnut-12-1638750-g006.jpg Figure 6. Nutritional composition of Kodo millet-chickpea product (per serving basis). https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1743922/full https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-g001.jpg Figure 1. Flowchart of participants in this study. TB, tuberculosis; AIDS, Acquired Immune Deficienc https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-t001.jpg Table 1. Baseline demographics of participants. https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-g002.jpg Figure 2. ROC curve analysis of biomarkers between ATB and non-TB. ROC, receiver operating character https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-t002.jpg Table 2. Comparison of AUC values between logNLR and other biomarkers. https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-t003.jpg Table 3. Factors influencing the detection results of participants. https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-g003.jpg Figure 3. RCS curve of the relationship between logNLR and ATB infection. The OR is represented by t https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-t004.jpg Table 4. Breakpoint analyses of the logNLR and ATB infection relationship. https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-g004.jpg Figure 4. Stratified analyses for the association between logNLR and ATB infection. P for interactio https://www.frontiersin.org/files/Articles/1743922/fcimb-16-1743922-HTML/image_m/fcimb-16-1743922-g005.jpg Figure 5. Interaction effects between identified factors on ATB infection risk. (A) Schematic diagra https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1770036/full https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g011.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g001.jpg Figure 1. Endogenous and environmental sources of ROS/RNS in asthmatic airways. Endogenous ROS/RNS p https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g002.jpg Figure 2. Mechanisms by which oxidative stress contributes to airway inflammation and mesenchymal re https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g003.jpg Figure 3. Oxidative stress-induced EMT contributes to airway mesenchymal reprogramming in asthma and https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g004.jpg Figure 4. Oxidative stress exacerbates airway smooth muscle dysfunction, contributing to airway mese https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g005.jpg Figure 5. Oxidative stress impairs the antioxidant enzyme system in asthma. Excessive production of https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g006.jpg Figure 6. Protective role of the Nrf2/HO-1 pathway in alleviating oxidative stress and airway mesenc https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g007.jpg Figure 7. Role of oxidative stress in activating the NF-κB signaling pathway and its impact on airwa https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g008.jpg Figure 8. Role of oxidative stress in activating the MAPK signaling pathway and its impact on early https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g009.jpg Figure 9. The PI3K/AKT signaling pathway synergistically enhances the role of oxidative stress in ai https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-g010.jpg Figure 10. 2D structures of metabolites originating from botanical drugs. Figure created with BioRen https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-t001.jpg Table 1. The mechanisms of metabolites originating from botanical drugs in modulating oxidative stre https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-t002.jpg Table 2. Effects of traditional Chinese botanical drug formulations on oxidative stress and asthma-a https://www.frontiersin.org/files/Articles/1770036/fphar-17-1770036-HTML/image_m/fphar-17-1770036-t003.jpg Table 3. Botanical composition (scientific names) of traditional Chinese botanical drug formulations https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2026.1780775/full https://www.frontiersin.org/files/Articles/1780775/fnbeh-20-1780775-HTML/image_m/fnbeh-20-1780775-g001.jpg Figure 1. Signal changes in the bilateral medial thalami (left) and mammillary bodies (right) on MRI https://www.frontiersin.org/files/Articles/1780775/fnbeh-20-1780775-HTML/image_m/fnbeh-20-1780775-g002.jpg Figure 2. Timeline of the patient’s clinical course. Image created with BioRender.com under an acade https://www.frontiersin.org/files/Articles/1780775/fnbeh-20-1780775-HTML/image_m/fnbeh-20-1780775-g003.jpg Figure 3. Citric acid cycle diagram. Image created with Microsoft Word under an academic license. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1717266/full https://www.frontiersin.org/files/Articles/1717266/fspor-08-1717266-HTML/image_m/fspor-08-1717266-t001.jpg Table 1. Profile of validators. https://www.frontiersin.org/files/Articles/1717266/fspor-08-1717266-HTML/image_m/fspor-08-1717266-t002.jpg Table 2. Results of Aiken's V and corrected CVC for each item of the interview guide. https://www.frontiersin.org/files/Articles/1717266/fspor-08-1717266-HTML/image_m/fspor-08-1717266-t003.jpg Table 3. Main qualitative contributions from experts in the validation of the interview guide. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1739149/full https://www.frontiersin.org/files/Articles/1739149/fmicb-17-1739149-HTML/image_m/fmicb-17-1739149-t001.jpg Table 1. Minimum inhibitory concentration (MIC) test concentration range. https://www.frontiersin.org/files/Articles/1739149/fmicb-17-1739149-HTML/image_m/fmicb-17-1739149-t002.jpg Table 2. Optimal PK levels for anti-TB drugs. https://www.frontiersin.org/files/Articles/1739149/fmicb-17-1739149-HTML/image_m/fmicb-17-1739149-t003.jpg Table 3. PD cut-offs. https://www.frontiersin.org/files/Articles/1739149/fmicb-17-1739149-HTML/image_m/fmicb-17-1739149-g001.jpg Figure 1. Minimum inhibitory concentration (MIC) distribution and ECOFF of 2nd line anti-TB drugs fo https://www.frontiersin.org/files/Articles/1739149/fmicb-17-1739149-HTML/image_m/fmicb-17-1739149-g002.jpg Figure 2. Minimum inhibitory concentration (MIC) distribution of 2nd-line anti-TB drugs in the Mtb c https://www.frontiersin.org/files/Articles/1739149/fmicb-17-1739149-HTML/image_m/fmicb-17-1739149-t004.jpg Table 4. Accession number of strains whose sequences were deposited in NCBI. https://www.frontiersin.org/files/Articles/1739149/fmicb-17-1739149-HTML/image_m/fmicb-17-1739149-g003.jpg Figure 3. Therapeutic and sub-therapeutic populations among sensitive strains for 2nd line drugs. Th https://www.frontiersin.org/files/Articles/1739149/fmicb-17-1739149-HTML/image_m/fmicb-17-1739149-g004.jpg Figure 4. Therapeutic and sub-therapeutic populations among resistant strains for 2nd line drugs. Th https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1712976/full https://www.frontiersin.org/files/Articles/1712976/fpsyg-17-1712976-HTML/image_m/fpsyg-17-1712976-g001.jpg Figure 1. Constraints associated with the talent identification and development process. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1761378/full https://www.frontiersin.org/files/Articles/1761378/fvets-13-1761378-HTML-r2/image_m/fvets-13-1761378-t001.jpg Table 1. Ingredients (% as fed basis) and chemical composition (g/100 g) of the diet. https://www.frontiersin.org/files/Articles/1761378/fvets-13-1761378-HTML-r2/image_m/fvets-13-1761378-t002.jpg Table 2. Growth performance parameters and carcass characteristics observed in the control group and https://www.frontiersin.org/files/Articles/1761378/fvets-13-1761378-HTML-r2/image_m/fvets-13-1761378-t003.jpg Table 3. Backfat thickness (cm) observed at the slaughtering. https://www.frontiersin.org/files/Articles/1761378/fvets-13-1761378-HTML-r2/image_m/fvets-13-1761378-t004.jpg Table 4. Numbers of pubertal and prepubertal gilts at the final stage in each treatment group, as id https://www.frontiersin.org/files/Articles/1761378/fvets-13-1761378-HTML-r2/image_m/fvets-13-1761378-t005.jpg Table 5. Blood serum parameters of pigs from control and polyphenol-supplemented groups. https://www.frontiersin.org/files/Articles/1761378/fvets-13-1761378-HTML-r2/image_m/fvets-13-1761378-g001.jpg Figure 1. Linear discriminant analysis (LDA) diagram representing differentiations of blood serum pa https://www.frontiersin.org/files/Articles/1761378/fvets-13-1761378-HTML-r2/image_m/fvets-13-1761378-g002.jpg Figure 2. DPPH, CUPRAC, and ABTS assays in liver and muscle among the three experimental groups: C ( https://www.frontiersin.org/files/Articles/1761378/fvets-13-1761378-HTML-r2/image_m/fvets-13-1761378-t006.jpg Table 6. Proximate composition analyses of meat (sternocleidomastoid) from pigs fed control or polyp https://www.frontiersin.org/files/Articles/1761378/fvets-13-1761378-HTML-r2/image_m/fvets-13-1761378-t007.jpg Table 7. Quality traits of meat and subcutaneous fat of pigs fed control or polyphenol-supplemented https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1749366/full https://www.frontiersin.org/files/Articles/1749366/fimmu-17-1749366-HTML-r1/image_m/fimmu-17-1749366-g001.jpg Figure 1. Study design. C-C and C-X-C chemokine surface marker expression profiles in human monocyte https://www.frontiersin.org/files/Articles/1749366/fimmu-17-1749366-HTML-r1/image_m/fimmu-17-1749366-g002.jpg Figure 2. Chemokine receptor expression patterns in three major monocyte populations.(A) t-SNE plot https://www.frontiersin.org/files/Articles/1749366/fimmu-17-1749366-HTML-r1/image_m/fimmu-17-1749366-g003.jpg Figure 3. Chemokine receptor expression patterns across age in monocyte subclusters in low CAD risk https://www.frontiersin.org/files/Articles/1749366/fimmu-17-1749366-HTML-r1/image_m/fimmu-17-1749366-g004.jpg Figure 4. Monocyte subset–specific chemokine receptor expression patterns across CAD and age.(A) Smo https://www.frontiersin.org/files/Articles/1749366/fimmu-17-1749366-HTML-r1/image_m/fimmu-17-1749366-g005.jpg Figure 5. Correlation of chemokine receptor expression with clinical parameters and CAD severity acr https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1729412/full https://www.frontiersin.org/files/Articles/1729412/fmed-13-1729412-HTML/image_m/fmed-13-1729412-g001.jpg Figure 1. Study algorithm. https://www.frontiersin.org/files/Articles/1729412/fmed-13-1729412-HTML/image_m/fmed-13-1729412-t001.jpg Table 1. Baseline sociodemographic characteristics. https://www.frontiersin.org/files/Articles/1729412/fmed-13-1729412-HTML/image_m/fmed-13-1729412-t002.jpg Table 2. Radiological characteristics. https://www.frontiersin.org/files/Articles/1729412/fmed-13-1729412-HTML/image_m/fmed-13-1729412-g002.jpg Figure 2. (A,B) Heat map of radiological characteristics of ovarian tumours according to Euclidean d https://www.frontiersin.org/files/Articles/1729412/fmed-13-1729412-HTML/image_m/fmed-13-1729412-t003.jpg Table 3. Performance metrics for the ML algorithms. https://www.frontiersin.org/files/Articles/1729412/fmed-13-1729412-HTML/image_m/fmed-13-1729412-g003.jpg Figure 3. ROC curve of ML models used on testing dataset. https://www.frontiersin.org/journals/human-dynamics/articles/10.3389/fhumd.2026.1769412/full https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-g001.jpg Figure 1. Conceptual model. Influencer credibility directly influences purchase intention and consci https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t001.jpg Table 1. Cronbach’s alpha coefficient for dimension. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t002.jpg Table 2. Sociodemographic characteristics of participants. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-g002.jpg Figure 2. Social networks most used by participants. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-g003.jpg Figure 3. Social networks most suitable for influencers. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t003.jpg Table 3. Credibility means by sociodemographic variables. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t004.jpg Table 4. Credibility by age groups. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t005.jpg Table 5. Credibility by marital status. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t006.jpg Table 6. Followers (item 1) by sociodemographic variables. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t007.jpg Table 7. Followers by marital status. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t008.jpg Table 8. Followers (item 2) by sociodemographic variables. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t009.jpg Table 9. Product evaluation scores by sociodemographic variables. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t010.jpg Table 10. Recognition of need and search for information by sociodemographic variables. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t011.jpg Table 11. Regularity of publications by sociodemographic variables. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-g004.jpg Figure 4. Credibility of digital influencers and more conscious consumption practices. This figure p https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t012.jpg Table 12. More conscious consumption practices by sociodemographic variables. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t013.jpg Table 13. Purchase decision by sociodemographic variables. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t014.jpg Table 14. Purchasing decisions by marital status. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-t015.jpg Table 15. Purchasing decisions and age. https://www.frontiersin.org/files/Articles/1769412/fhumd-08-1769412-HTML/image_m/fhumd-08-1769412-g005.jpg Figure 5. Mean purchase decision by age. This figure presents the mean purchase decision by age grou https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1776988/full https://www.frontiersin.org/files/Articles/1776988/fneur-17-1776988-HTML/image_m/fneur-17-1776988-t001.jpg Table 1. Key baseline characteristics and outcomes. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1766696/full https://www.frontiersin.org/files/Articles/1766696/fneur-17-1766696-HTML/image_m/fneur-17-1766696-g001.jpg Figure 1. Flow diagram for study selection. This diagram illustrates the identification, screening, https://www.frontiersin.org/files/Articles/1766696/fneur-17-1766696-HTML/image_m/fneur-17-1766696-t001.jpg Table 1. Journal characteristics. https://www.frontiersin.org/files/Articles/1766696/fneur-17-1766696-HTML/image_m/fneur-17-1766696-t002.jpg Table 2. General journal guidelines. https://www.frontiersin.org/files/Articles/1766696/fneur-17-1766696-HTML/image_m/fneur-17-1766696-t003.jpg Table 3. AI guidelines. https://www.frontiersin.org/files/Articles/1766696/fneur-17-1766696-HTML/image_m/fneur-17-1766696-t004.jpg Table 4. AI guidelines in journals mentioning AI. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1609693/full https://www.frontiersin.org/files/Articles/1609693/fpls-16-1609693-HTML-r1/image_m/fpls-16-1609693-t001.jpg Table 1. Fungal species analyzed in this study. https://www.frontiersin.org/files/Articles/1609693/fpls-16-1609693-HTML-r1/image_m/fpls-16-1609693-g001.jpg Figure 1. Mean radius of pathogen colonies when co-plated with one of the six Trichoderma isolates. https://www.frontiersin.org/files/Articles/1609693/fpls-16-1609693-HTML-r1/image_m/fpls-16-1609693-g002.jpg Figure 2. Mean percent reduction of pathogen colonies when grown on spent media from one of the six https://www.frontiersin.org/files/Articles/1609693/fpls-16-1609693-HTML-r1/image_m/fpls-16-1609693-g003.jpg Figure 3. (A) Mean developing external canker length of Diplodia seriata or Neofusicoccum parvum inf https://www.frontiersin.org/files/Articles/1609693/fpls-16-1609693-HTML-r1/image_m/fpls-16-1609693-g004.jpg Figure 4. Mean Ct values related to Xylella fastidiosa titers (with greater Ct values representing l https://www.frontiersin.org/files/Articles/1609693/fpls-16-1609693-HTML-r1/image_m/fpls-16-1609693-g005.jpg Figure 5. (A) Mean percent recovery of fungal pathogens in plants treated by mock inoculation, fungi https://www.frontiersin.org/files/Articles/1609693/fpls-16-1609693-HTML-r1/image_m/fpls-16-1609693-g006.jpg Figure 6. (A) Mean percent recovery of fungal pathogens in plants treated by mock inoculation, fungi https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1764896/full https://www.frontiersin.org/files/Articles/1764896/fcell-14-1764896-HTML/image_m/fcell-14-1764896-g001.jpg Figure 1. Single-cell transcriptomic profiling hemidesmosome-driven epithelial repair in PDT. (A) UM https://www.frontiersin.org/files/Articles/1764896/fcell-14-1764896-HTML/image_m/fcell-14-1764896-g002.jpg Figure 2. LAMB3–ITGA6 axis drives hemidesmosome formation in the wound-reparative epithelial traject https://www.frontiersin.org/files/Articles/1764896/fcell-14-1764896-HTML/image_m/fcell-14-1764896-g003.jpg Figure 3. LAMB3–ITGA6 axis in keratinization model of human oral keratinocytes. (A) Sample-to-sample https://www.frontiersin.org/files/Articles/1764896/fcell-14-1764896-HTML/image_m/fcell-14-1764896-g004.jpg Figure 4. The expression pattern of LAMB3–ITGA6 axis in patients with periodontal disease. (A) Weste https://www.frontiersin.org/files/Articles/1764896/fcell-14-1764896-HTML/image_m/fcell-14-1764896-g005.jpg Figure 5. LAMB3 promotes epithelial healing of the skin wound in mice (A) Schematic of the full-thic https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1711697/full https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-t001.jpg Table 1. Search strategy for the scientific databases selected for the systematic review. https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-t002.jpg Table 2. Eligibility criteria of the studies for inclusion in the systematic review. https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-g001.jpg Figure 1. Scientific production by country according to the terms associated with the phenological s https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-g002.jpg Figure 2. Number of studies retrieved according to the period of time in which they were published. https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-g003.jpg Figure 3. Network map based on the co-occurrence of terms in the title and summary associated with t https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-g004.jpg Figure 4. PRISMA flowchart for selecting studies on environmental factors associated with coffee flo https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-t003.jpg Table 3. Summary of key findings on the environmental triggers of coffee flowering. https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-g005.jpg Figure 5. PRISMA flowchart of the selection of studies on endogenous factors associated with floweri https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-t004.jpg Table 4. Functions of phytohormones and environmental factors that trigger the phenological stages o https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-t005.jpg Table 5. Summary of the key findings on the endogenous triggers of coffee flowering. https://www.frontiersin.org/files/Articles/1711697/fsufs-09-1711697-HTML-r1/image_m/fsufs-09-1711697-g006.jpg Figure 6. Response of coffee plants under two scenarios of water availability: water stress (left) a https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1776028/full https://www.frontiersin.org/files/Articles/1776028/fphar-17-1776028-HTML/image_m/fphar-17-1776028-g001.jpg Figure 1. Dynamic CT evaluation of mediastinal mass and pulmonary lesions. (A) at diagnosis. (B) aft https://www.frontiersin.org/files/Articles/1776028/fphar-17-1776028-HTML/image_m/fphar-17-1776028-g002.jpg Figure 2. Dynamic imaging evaluation of renal lesions and cerebral lesions. (A,D) at diagnosis. (B,E https://www.frontiersin.org/files/Articles/1776028/fphar-17-1776028-HTML/image_m/fphar-17-1776028-g003.jpg Figure 3. Pathological findings at diagnosis (A–C) and after six cycles of NACT (D,E). (A) H&E stain https://www.frontiersin.org/files/Articles/1776028/fphar-17-1776028-HTML/image_m/fphar-17-1776028-g004.jpg Figure 4. Timeline of the treatment and β-hCG trends in this case. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1607138/full https://www.frontiersin.org/files/Articles/1607138/fimmu-16-1607138-HTML/image_m/fimmu-16-1607138-t001.jpg Table 1. Patient characteristics of those treated with blinatumomab. https://www.frontiersin.org/files/Articles/1607138/fimmu-16-1607138-HTML/image_m/fimmu-16-1607138-g001.jpg Figure 1. T-cell activation and B-cell depletion following blinatumomab therapy. (A, D) The absolute https://www.frontiersin.org/files/Articles/1607138/fimmu-16-1607138-HTML/image_m/fimmu-16-1607138-g002.jpg Figure 2. T-cell activation and B-cell depletion in R/R+MRDpos and CR-MRDneg groups. (A, B), Greater https://www.frontiersin.org/files/Articles/1607138/fimmu-16-1607138-HTML/image_m/fimmu-16-1607138-g003.jpg Figure 3. Cytokine dynamics during blinatumomab therapy. (A-D) IL-10, IL-5, IFN-γ, and IL-2 increase https://www.frontiersin.org/files/Articles/1607138/fimmu-16-1607138-HTML/image_m/fimmu-16-1607138-g004.jpg Figure 4. Survival outcomes of ALL patients receiving blinatumomab. (A) EFS in patients with and wit https://www.frontiersin.org/files/Articles/1607138/fimmu-16-1607138-HTML/image_m/fimmu-16-1607138-t002.jpg Table 2. Factors for blinatumomab treatment response in B-cell ALL. https://www.frontiersin.org/files/Articles/1607138/fimmu-16-1607138-HTML/image_m/fimmu-16-1607138-t003.jpg Table 3. Adverse effects in ALL patients receiving blinatumomab. https://www.frontiersin.org/files/Articles/1607138/fimmu-16-1607138-HTML/image_m/fimmu-16-1607138-t004.jpg Table 4. Cytokine levels in serum and cerebrospinal fluid during ICANS episodes. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1707551/full https://www.frontiersin.org/files/Articles/1707551/fpubh-13-1707551-HTML-r2/image_m/fpubh-13-1707551-g001.jpg Figure 1. The trends of emerging risk factors for CKM Syndrome in Indonesia. (A) Prevalence of metab https://www.frontiersin.org/files/Articles/1707551/fpubh-13-1707551-HTML-r2/image_m/fpubh-13-1707551-g002.jpg Figure 2. Framework for the management of CKM syndrome in Indonesia. The first step is to screen the https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1734891/full https://www.frontiersin.org/files/Articles/1734891/fpubh-14-1734891-HTML-r1/image_m/fpubh-14-1734891-t001.jpg Table 1. Comparison of latent profiles of KAP among HD nurses with different demographic characteris https://www.frontiersin.org/files/Articles/1734891/fpubh-14-1734891-HTML-r1/image_m/fpubh-14-1734891-t002.jpg Table 2. Score of questionnaire on KAP of hospital infection prevention and control among hemodialys https://www.frontiersin.org/files/Articles/1734891/fpubh-14-1734891-HTML-r1/image_m/fpubh-14-1734891-t003.jpg Table 3. The top 3 items with the lowest scores in each dimension of hospital infection and control https://www.frontiersin.org/files/Articles/1734891/fpubh-14-1734891-HTML-r1/image_m/fpubh-14-1734891-t004.jpg Table 4. Latent profile analysis of KAP with fit indices. https://www.frontiersin.org/files/Articles/1734891/fpubh-14-1734891-HTML-r1/image_m/fpubh-14-1734891-g001.jpg Figure 1. Distribution of characteristics among the two latent profiles of KAP. https://www.frontiersin.org/files/Articles/1734891/fpubh-14-1734891-HTML-r1/image_m/fpubh-14-1734891-t005.jpg Table 5. Profiles differences in the KAP and the results of post hoc analysis. https://www.frontiersin.org/files/Articles/1734891/fpubh-14-1734891-HTML-r1/image_m/fpubh-14-1734891-t006.jpg Table 6. Logistic regression of different KAP profiles. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1707176/full https://www.frontiersin.org/files/Articles/1707176/fcell-14-1707176-HTML/image_m/fcell-14-1707176-g001.jpg Figure 1. The pathological and cytological characteristics of OPLL. The pathological changes of OPLL https://www.frontiersin.org/files/Articles/1707176/fcell-14-1707176-HTML/image_m/fcell-14-1707176-g002.jpg Figure 2. Imaging classification of OPLL. Sagittal CT scan images provided by Dr. Lai Jinquan, Shenz https://www.frontiersin.org/files/Articles/1707176/fcell-14-1707176-HTML/image_m/fcell-14-1707176-g003.jpg Figure 3. Key molecules and signaling pathways of angiogenesis in OPLL. The development of OPLL is a https://www.frontiersin.org/files/Articles/1707176/fcell-14-1707176-HTML/image_m/fcell-14-1707176-t001.jpg Table 1. Novel intervention strategies for OPLL based on potential therapies for ectopic ossificatio https://www.frontiersin.org/files/Articles/1707176/fcell-14-1707176-HTML/image_m/fcell-14-1707176-t002.jpg Table 2. OPLL intervention strategies targeting angiogenesis. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1716498/full https://www.frontiersin.org/files/Articles/1716498/fpubh-13-1716498-HTML/image_m/fpubh-13-1716498-g001.jpg Figure 1. Whole genome sequencing (WGS) in SeqAfrica by regional hubs (2020–2023). (A) A chloropleth https://www.frontiersin.org/files/Articles/1716498/fpubh-13-1716498-HTML/image_m/fpubh-13-1716498-g002.jpg Figure 2. Online training reach and participant engagement across SeqAfrica modules 1–3. (A) A cholo https://www.frontiersin.org/files/Articles/1716498/fpubh-13-1716498-HTML/image_m/fpubh-13-1716498-g003.jpg Figure 3. Overview of whole genome sequencing services and process. R-WGS: Regional sequencing hubs. https://www.frontiersin.org/files/Articles/1716498/fpubh-13-1716498-HTML/image_m/fpubh-13-1716498-g004.jpg Figure 4. Overview of bacterial genomes generated by regional hubs in phase 1 (2019–2023). (A) Barpl https://www.frontiersin.org/files/Articles/1716498/fpubh-13-1716498-HTML/image_m/fpubh-13-1716498-t001.jpg Table 1. Insights from phase 1 (2019–2023) and how they have shaped the design and priorities of pha https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1746603/full https://www.frontiersin.org/files/Articles/1746603/fbioe-14-1746603-HTML/image_m/fbioe-14-1746603-g001.jpg Figure 1. (A) SEM image of PCL-ECd nanofibers; (B) Fiber diameter distribution image and (C) Fluores https://www.frontiersin.org/files/Articles/1746603/fbioe-14-1746603-HTML/image_m/fbioe-14-1746603-g002.jpg Figure 2. (A) Cell proliferation of HUVECs co-cultured with P-0% E, P-10% E, P-20% E, P-30% E or P-4 https://www.frontiersin.org/files/Articles/1746603/fbioe-14-1746603-HTML/image_m/fbioe-14-1746603-g003.jpg Figure 3. (A) SEM image of P-E/H, P-E, P-H, and PCL nanofibers; (B) Fiber diameter distribution imag https://www.frontiersin.org/files/Articles/1746603/fbioe-14-1746603-HTML/image_m/fbioe-14-1746603-g004.jpg Figure 4. (A) Stress-strain curve, (B) Ultimate stress and (C) Elongation at break of P-E/H; (D) Sus https://www.frontiersin.org/files/Articles/1746603/fbioe-14-1746603-HTML/image_m/fbioe-14-1746603-g005.jpg Figure 5. (A) Fluorescence images of HUVECs co-cultured with P-E/H for 3 days (Blue: DAPI for staini https://www.frontiersin.org/files/Articles/1746603/fbioe-14-1746603-HTML/image_m/fbioe-14-1746603-g006.jpg Figure 6. (A) Hemolysis rate, (B) Dynamic coagulation curve and (C) Plasma recalcification curve of https://www.frontiersin.org/files/Articles/1746603/fbioe-14-1746603-HTML/image_m/fbioe-14-1746603-g007.jpg Figure 7. (A) Digital images of P-E/H vascular scaffolds with different diameters at 1, 2, 3, 4, 5 a https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1765370/full https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g001.jpg Figure 1. The bionic porous structure design process. (A) Regular spheres; (B) Random points; (C) Vo https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g002.jpg Figure 2. Schematic diagram of ε physical meaning and corresponding Voronoi scaffold models. (A) See https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-t001.jpg Table 1. Design parameters of bionic porous structures. https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g003.jpg Figure 3. Mechanical finite element results of bionic porous structures. (A) Mise stress cloud diagr https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-t002.jpg Table 2. Porosity deviation of the design model and the printed sample. https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g004.jpg Figure 4. Compression experiment results of bionic porous scaffolds. (A) Stress–strain curve of diff https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g005.jpg Figure 5. Fatigue test results of bionic porous structures. (A) Number of cycles. (B) Stress failure https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g006.jpg Figure 6. Fatigue test results of bionic porous structures. (A–C) Total fatigue strain with a design https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g007.jpg Figure 7. Normal P-P plot of standardized residuals. https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g008.jpg Figure 8. Fluid simulation results of bionic porous structures. (A) Velocity cloud diagram. (B) Pres https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g009.jpg Figure 9. Fluid finite element analysis results of bionic porous structures. (A) Permeability change https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g010.jpg Figure 10. Cell test results. (A) Fluorescent staining results. (B) SEM analysis of porous structure https://www.frontiersin.org/files/Articles/1765370/fbioe-14-1765370-HTML/image_m/fbioe-14-1765370-g011.jpg Figure 11. Proliferation of MC3T3-E1 cells on scaffolds: variation of OD value with irregularities. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1661680/full https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-g001.jpg Figure 1. Brief illustration of challenges. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-g002.jpg Figure 2. Overall illustration of the proposed VM-CAGSeg network. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-g003.jpg Figure 3. Details of the proposed (A) VSASS block and its sub-block (B) MVSA, (C) KASS and (D) SS2D https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-g004.jpg Figure 4. Details of the proposed (A) CSIF block and its sub-block (B) MPM and (C) MIA block. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-t001.jpg Table 1. Summary of the comparative results. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-g005.jpg Figure 5. Qualitative evaluation of low-contrast cases. GT denotes the ground truth. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-g006.jpg Figure 6. Qualitative evaluation of complex anatomy cases. GT denotes the ground truth. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-g007.jpg Figure 7. Qualitative evaluation of fuzzy vascular boundary cases. GT denotes the ground truth. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-t002.jpg Table 2. Ablation study on the different components of VM-CAGSeg. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-g008.jpg Figure 8. Heatmap of the ablation study. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-t003.jpg Table 3. Ablation study on the different feed-forward network implementations in VM-CAGSeg. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-t004.jpg Table 4. Ablation study on the module placement strategy of MVSA. https://www.frontiersin.org/files/Articles/1661680/fmed-12-1661680-HTML/image_m/fmed-12-1661680-g009.jpg Figure 9. Qualitative evaluation of the DRIVE dataset. GT denotes the ground truth. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1722103/full https://www.frontiersin.org/files/Articles/1722103/fimmu-17-1722103-HTML/image_m/fimmu-17-1722103-g001.jpg Figure 1. Annual number and phase distribution of PsA drug trial initiations (1999–2025). https://www.frontiersin.org/files/Articles/1722103/fimmu-17-1722103-HTML/image_m/fimmu-17-1722103-g002.jpg Figure 2. Operational status and disclosed outcomes of PsA clinical trials. https://www.frontiersin.org/files/Articles/1722103/fimmu-17-1722103-HTML/image_m/fimmu-17-1722103-g003.jpg Figure 3. Country participation and trial center type (single vs multicenter) in PsA clinical trials https://www.frontiersin.org/files/Articles/1722103/fimmu-17-1722103-HTML/image_m/fimmu-17-1722103-g004.jpg Figure 4. Distribution of funding sources for PsA clinical trials. https://www.frontiersin.org/files/Articles/1722103/fimmu-17-1722103-HTML/image_m/fimmu-17-1722103-g005.jpg Figure 5. Most frequently investigated drugs in PsA clinical trials. https://www.frontiersin.org/files/Articles/1722103/fimmu-17-1722103-HTML/image_m/fimmu-17-1722103-g006.jpg Figure 6. Distribution of molecular targets in PsA clinical trials. TNF, tumor necrosis factor; JAK1 https://www.frontiersin.org/files/Articles/1722103/fimmu-17-1722103-HTML/image_m/fimmu-17-1722103-g007.jpg Figure 7. Primary endpoints utilized in PsA clinical trials. Cmax, maximum plasma concentration; AUC https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1770941/full https://www.frontiersin.org/files/Articles/1770941/fpls-17-1770941-HTML/image_m/fpls-17-1770941-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1770941/fpls-17-1770941-HTML/image_m/fpls-17-1770941-t001.jpg Table 1. Classification and distinctive properties of saline, sodic, and saline-sodic soils, modifie https://www.frontiersin.org/files/Articles/1770941/fpls-17-1770941-HTML/image_m/fpls-17-1770941-g001.jpg Figure 1. The figure illustrates the combined influence of horizontal, vertical, and temporal hetero https://www.frontiersin.org/files/Articles/1770941/fpls-17-1770941-HTML/image_m/fpls-17-1770941-g002.jpg Figure 2. Illustrative diagram showing the effects of soil heterogeneity on physical, chemical, and https://www.frontiersin.org/files/Articles/1770941/fpls-17-1770941-HTML/image_m/fpls-17-1770941-g003.jpg Figure 3. Schematic representation of the impacts of heterogenous sodic and saline-sodic conditions https://www.frontiersin.org/files/Articles/1770941/fpls-17-1770941-HTML/image_m/fpls-17-1770941-g004.jpg Figure 4. The figure illustrates the mechanisms by which compost ameliorates soil conditions and het https://www.frontiersin.org/files/Articles/1770941/fpls-17-1770941-HTML/image_m/fpls-17-1770941-g005.jpg Figure 5. Reclamation strategies of saline-sodic soil heterogeneity through calcium-based amendments https://www.frontiersin.org/files/Articles/1770941/fpls-17-1770941-HTML/image_m/fpls-17-1770941-t002.jpg Table 2. Soil amendments for amelioration and mitigation of heterogeneity in sodic and saline-sodic https://www.frontiersin.org/files/Articles/1770941/fpls-17-1770941-HTML/image_m/fpls-17-1770941-t003.jpg Table 3. Advantages and limitations of strategies for mitigating heterogeneity in saline-sodic soils https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1728485/full https://www.frontiersin.org/files/Articles/1728485/fphar-17-1728485-HTML-r1/image_m/fphar-17-1728485-g001.jpg Figure 1. Study flow. https://www.frontiersin.org/files/Articles/1728485/fphar-17-1728485-HTML-r1/image_m/fphar-17-1728485-t001.jpg Table 1. Features of included studies. https://www.frontiersin.org/files/Articles/1728485/fphar-17-1728485-HTML-r1/image_m/fphar-17-1728485-g002.jpg Figure 2. Effect of febuxostat on the UA level in renal transplant recipients with hyperuricemia. https://www.frontiersin.org/files/Articles/1728485/fphar-17-1728485-HTML-r1/image_m/fphar-17-1728485-g003.jpg Figure 3. Effect of febuxostat on renal function in renal transplant recipients with hyperuricemia. https://www.frontiersin.org/files/Articles/1728485/fphar-17-1728485-HTML-r1/image_m/fphar-17-1728485-g004.jpg Figure 4. Effects of febuxostat on other biochemical indices in renal transplant recipients with hyp https://www.frontiersin.org/files/Articles/1728485/fphar-17-1728485-HTML-r1/image_m/fphar-17-1728485-g005.jpg Figure 5. Sensitivity analysis. Sensitivity analysis of UA (A), Cr (B), eGFR (C), WBC (D), Hb (E), A https://www.frontiersin.org/files/Articles/1728485/fphar-17-1728485-HTML-r1/image_m/fphar-17-1728485-t002.jpg Table 2. Quality assessment of cohort studies via the NOS. https://www.frontiersin.org/files/Articles/1728485/fphar-17-1728485-HTML-r1/image_m/fphar-17-1728485-t003.jpg Table 3. Quality assessment of single-arm studies via MINORS. https://www.frontiersin.org/files/Articles/1728485/fphar-17-1728485-HTML-r1/image_m/fphar-17-1728485-t004.jpg Table 4. Publication bias assessment. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1671389/full https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-g001.jpg Figure 1. Spatial distribution of the multi-year four seasons of averaged MHW properties in the NSCS https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-g002.jpg Figure 2. Statistical characteristics of winter MHWs in the blue box region of the NSCS during 1982− https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-t001.jpg Table 1. Summary of the MHW features during 1982–2021. https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-g003.jpg Figure 3. Time series of averaged SST in the blue box region of the NSCS from January 2015 to Decemb https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-g004.jpg Figure 4. Distribution of the composite climatological wind vectors (white arrows; m·s−1) and wind s https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-g005.jpg Figure 5. The time series of wind anomalies (a) and SSTA (b) for 4 November 2015–8 February 2016. Th https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-g006.jpg Figure 6. Composites of sea surface height (SSH) and surface geostrophic currents (black arrows) dur https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-g007.jpg Figure 7. The evolution of all the terms in the MLT equation in the blue box region of the NSCS duri https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-g008.jpg Figure 8. The time series of δSST (a) and δvelocity profiles (b) for 4 November 2015 (2016)–8 Februa https://www.frontiersin.org/files/Articles/1671389/fmars-12-1671389-HTML/image_m/fmars-12-1671389-g009.jpg Figure 9. Relationships between δSST and δWind speed (a), between δSST and δWind curl (b), between δ https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1705474/full https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-t001.jpg Table 1. Treatment details. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-g001.jpg Figure 1. Weekly average maximum, minimum air temperature, rainfall, evaporation, morning and evenin https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-t002.jpg Table 2. Effect of different tillage practices on growth parameters of potato. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-g002.jpg Figure 2. Effect of different tillage practices on grade-wise number of potatoes less than 25 g (GWP https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-g003.jpg Figure 3. Effect of different tillage practices on grade-wise yield of potatoes; tuber yield of grad https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-t003.jpg Table 3. Effect of different tillage practices on yield and harvest index of potato. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-g004.jpg Figure 4. Effect of different tillage practices on haulm and tuber nitrogen (a), phosphorus (b), and https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-t004.jpg Table 4. Effect of different tillage practices on nutrient uptake of potato. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-t005.jpg Table 5. Effect of different tillage practices on quality parameters of potato. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-t006.jpg Table 6. Effect of different tillage practices on soil properties and available nutrients. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-t007.jpg Table 7. Economics and net returns of different treatments based on marketable yield. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-g005.jpg Figure 5. Correlogram of morphological traits with tuber yield. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-g006.jpg Figure 6. Correlogram of different quality traits of potato. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-g007.jpg Figure 7. Bi-plot from PCA based on morphological traits of potato. https://www.frontiersin.org/files/Articles/1705474/fsufs-09-1705474-HTML/image_m/fsufs-09-1705474-g008.jpg Figure 8. Bi-plot from PCA based on qualitative traits of potato. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1704456/full https://www.frontiersin.org/files/Articles/1704456/fnut-12-1704456-HTML-r2/image_m/fnut-12-1704456-g001.jpg Figure 1. Potential mechanisms underlying the relationship between sarcopenia and LUTDs: This schema https://www.frontiersin.org/files/Articles/1704456/fnut-12-1704456-HTML-r2/image_m/fnut-12-1704456-t001.jpg Table 1. Potential mechanisms and supporting evidence underlying the relationship between sarcopenia https://www.frontiersin.org/files/Articles/1704456/fnut-12-1704456-HTML-r2/image_m/fnut-12-1704456-g002.jpg Figure 2. Targeted sarcopenia therapy for potential treatment methods of LUTDs: This schematic outli https://www.frontiersin.org/files/Articles/1704456/fnut-12-1704456-HTML-r2/image_m/fnut-12-1704456-t002.jpg Table 2. Intervention strategies and clinical implications. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1692721/full https://www.frontiersin.org/files/Articles/1692721/fpsyt-16-1692721-HTML/image_m/fpsyt-16-1692721-g001.jpg Figure 1. The hypothesis model diagram of the mediating effect. https://www.frontiersin.org/files/Articles/1692721/fpsyt-16-1692721-HTML/image_m/fpsyt-16-1692721-g002.jpg Figure 2. Study selection flow diagram. https://www.frontiersin.org/files/Articles/1692721/fpsyt-16-1692721-HTML/image_m/fpsyt-16-1692721-t001.jpg Table 1. Standardised Factor Loadings for the Three NSSI Motives Dimensions. https://www.frontiersin.org/files/Articles/1692721/fpsyt-16-1692721-HTML/image_m/fpsyt-16-1692721-t002.jpg Table 2. Kendall’s tau-b correlation analysis of our study variables. https://www.frontiersin.org/files/Articles/1692721/fpsyt-16-1692721-HTML/image_m/fpsyt-16-1692721-t003.jpg Table 3. Results of Causal mediation analysis for the effect of motor impulsivity on NSSI frequency https://www.frontiersin.org/files/Articles/1692721/fpsyt-16-1692721-HTML/image_m/fpsyt-16-1692721-g003.jpg Figure 3. Indirect (ACME) and total effects of motor impulsivity on NSSI frequency categories via em https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1606957/full https://www.frontiersin.org/files/Articles/1606957/fpsyt-16-1606957-HTML-r1/image_m/fpsyt-16-1606957-g001.jpg Figure 1. How access programs support perinatal mental healthcare. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1509350/full https://www.frontiersin.org/files/Articles/1509350/fpsyt-16-1509350-HTML-r1/image_m/fpsyt-16-1509350-t001.jpg Table 1. Characteristics of participants. https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2025.1622895/full https://www.frontiersin.org/files/Articles/1622895/fgwh-06-1622895-HTML/image_m/fgwh-06-1622895-t001.jpg Table 1. Longitudinal patterns of sleep stages during pregnancy. https://www.frontiersin.org/files/Articles/1622895/fgwh-06-1622895-HTML/image_m/fgwh-06-1622895-t002.jpg Table 2. Daily sleep stages associated with heart rate. https://www.frontiersin.org/files/Articles/1622895/fgwh-06-1622895-HTML/image_m/fgwh-06-1622895-t003.jpg Table 3. Daily sleep stages associated with heart rate variability. https://www.frontiersin.org/files/Articles/1622895/fgwh-06-1622895-HTML/image_m/fgwh-06-1622895-g001.jpg Figure 1. (A) Sleep cycles in gestational week 15. (B) Sleep cycles in gestational week 38 (the same https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1728061/full https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g001.jpg Figure 1. Architecture of a hybrid intelligent system. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g002.jpg Figure 2. Age distribution histogram. (a) Dataset 583 rows (b) Dataset 30 K rows. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g003.jpg Figure 3. Liver disease prevalence by segment. (a) Dataset 583 rows (b) Dataset 30 K rows. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-t001.jpg Table 1. Large dataset (30 K) performance. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-t002.jpg Table 2. Segment-specific performance metrics on the smaller dataset, 583 rows. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g004.jpg Figure 4. Performance of hybrid models by demographic segment (30 K Rows Dataset). https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g005.jpg Figure 5. Performance of hybrid models by demographic segment (30 K Rows Dataset). https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g006.jpg Figure 6. 5-Fold cross-validation across demographic segments’. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g007.jpg Figure 7. Model accuracy comparison across different demographic segments (30 K Dataset). https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g008.jpg Figure 8. Model accuracy comparison across different demographic segments (583 Dataset). https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g009.jpg Figure 9. Overall performance metrics. 583 Dataset 30 K dataset. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-t003.jpg Table 3. Optimal model architectures for different demographic segments 30 K - Dataset. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-t004.jpg Table 4. Optimal model architectures for different demographic segments 583 - Rows. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g010.jpg Figure 10. Feature importance across demographic segments (30 K Dataset). https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g011.jpg Figure 11. Feature importance across demographic segments (583 Dataset). https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-t005.jpg Table 5. Segment-specific performance metrics on the key biomarkers to monitor 30 K. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-t006.jpg Table 6. Segment-specific performance metrics on the key biomarkers to monitor 583-rows. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-t007.jpg Table 7. Overall performance of the unified model (without demographic-aware segmentation). https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-t008.jpg Table 8. Comparison with recent state-of-the-art liver disease prediction models. https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g012.jpg Figure 12. SHAP summary plot (30 K Dataset). https://www.frontiersin.org/files/Articles/1728061/fmed-12-1728061-HTML/image_m/fmed-12-1728061-g013.jpg Figure 13. SHAP summary plot (ILPD Dataset). https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1737113/full https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-g001.jpg Figure 1. Mode of action of venlafaxine. https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-g002.jpg Figure 2. The flow diagram of selecting and analyzing venlafaxine-related ADEs from FAERS database. https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-t001.jpg Table 1. Demographic and clinical characteristics of adverse event reports for venlafaxine in the FA https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-g003.jpg Figure 3. Onset time distribution of venlafaxine-related adverse events (AEs). https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-t002.jpg Table 2. The top 30 PTs ranked by the frequency of positive signals. https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-t003.jpg Table 3. Top 30 PTs ranked by the intensity of positive signals. https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-t004.jpg Table 4. Signal strength of reports of venlafaxine at the preferred term level in the FAERS database https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-t005.jpg Table 5. Signal strength of venlafaxine at the preferred term class level in the FAERS database grou https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-t006.jpg Table 6. Characteristics between death outcomes and non-death outcomes. https://www.frontiersin.org/files/Articles/1737113/fphar-17-1737113-HTML-r1/image_m/fphar-17-1737113-g004.jpg Figure 4. Functional analysis of venlafaxine targeted genes. Functional enrichment analyses of venla https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1778993/full https://www.frontiersin.org/files/Articles/1778993/fphar-17-1778993-HTML/image_m/fphar-17-1778993-g001.jpg Figure 1. Single-cell transcriptomic data. Footnotes: (A) Screening of highly variable genes. (B) Sc https://www.frontiersin.org/files/Articles/1778993/fphar-17-1778993-HTML/image_m/fphar-17-1778993-g002.jpg Figure 2. Cell type identification of single-cell RNA sequencing data. Footnotes: (A) UMAP visualiza https://www.frontiersin.org/files/Articles/1778993/fphar-17-1778993-HTML/image_m/fphar-17-1778993-g003.jpg Figure 3. Expression distribution of KIAA1429. Footnotes: (A) Expression distribution of KIAA1429 in https://www.frontiersin.org/files/Articles/1778993/fphar-17-1778993-HTML/image_m/fphar-17-1778993-g004.jpg Figure 4. The manifestations of the skin on the back of mice. https://www.frontiersin.org/files/Articles/1778993/fphar-17-1778993-HTML/image_m/fphar-17-1778993-g005.jpg Figure 5. Relevant indicators for developing a model of mouse photoaging. Footnotes: (A) Effects of https://www.frontiersin.org/files/Articles/1778993/fphar-17-1778993-HTML/image_m/fphar-17-1778993-g006.jpg Figure 6. Determination of collagen fibers. Footnotes: (A) Effects of Vicenin-2 on UV-induced skin i https://www.frontiersin.org/files/Articles/1778993/fphar-17-1778993-HTML/image_m/fphar-17-1778993-g007.jpg Figure 7. Vicenin-2 promotes the polarization of macrophages towards M2 by down-regulating the NF-κB https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1714907/full https://www.frontiersin.org/files/Articles/1714907/fphar-16-1714907-HTML-r1/image_m/fphar-16-1714907-g007.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1714907/fphar-16-1714907-HTML-r1/image_m/fphar-16-1714907-g001.jpg Figure 1. The hydrogels prepared by self-assembly method, namely, natural product photosensitive hyd https://www.frontiersin.org/files/Articles/1714907/fphar-16-1714907-HTML-r1/image_m/fphar-16-1714907-g002.jpg Figure 2. The water gels prepared by the crosslinking agent method have excellent mechanical propert https://www.frontiersin.org/files/Articles/1714907/fphar-16-1714907-HTML-r1/image_m/fphar-16-1714907-g003.jpg Figure 3. The design principle of hydrogels. (A) Synthesis of the PAS/ICG-Cu@ZOL hydrogel dressing ( https://www.frontiersin.org/files/Articles/1714907/fphar-16-1714907-HTML-r1/image_m/fphar-16-1714907-g004.jpg Figure 4. The principle of free radical polymerization. (A) TPO@Tw initiates AM polymerization, foll https://www.frontiersin.org/files/Articles/1714907/fphar-16-1714907-HTML-r1/image_m/fphar-16-1714907-g005.jpg Figure 5. Schematic diagram of the photoisomerization principle under different reaction conditions. https://www.frontiersin.org/files/Articles/1714907/fphar-16-1714907-HTML-r1/image_m/fphar-16-1714907-t001.jpg Table 1. Biomedical applications driven by PDT. https://www.frontiersin.org/files/Articles/1714907/fphar-16-1714907-HTML-r1/image_m/fphar-16-1714907-g006.jpg Figure 6. The process by which a hydrogel changes from liquid to solid state after being exposed to https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1788254/full https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-g001.jpg Figure 1. Flow chart of the study. https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-t001.jpg Table 1. The characteristics of the whole cohort. https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-g002.jpg Figure 2. Pearson correlation heatmap of candidate variables in the training cohort. https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-g003.jpg Figure 3. Selection of clinical features. (A) Recursive elimination of random forest features. (B) C https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-g004.jpg Figure 4. Summary plot of machine learning performance evaluation (test set). (A) ROC curve. (B) DCA https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-t002.jpg Table 2. Performance comparisons of seven machine learning methods. https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-g005.jpg Figure 5. Nomogram for predicting re-intubation risk within 48 h after extubation in mechanically ve https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-g006.jpg Figure 6. Dynamic nomogram for predicting re-intubation risk within 48 h after extubation in mechani https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-g007.jpg Figure 7. (A) The results of the calibration curve analysis in the training set. (B) The results of https://www.frontiersin.org/files/Articles/1788254/fmed-13-1788254-HTML-r1/image_m/fmed-13-1788254-g008.jpg Figure 8. Confusion matrix of the logistic regression model: (A) in the training set and (B) in the https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1738139/full https://www.frontiersin.org/files/Articles/1738139/fcvm-12-1738139-HTML/image_m/fcvm-12-1738139-g001.jpg Figure 1. Evolution of clinical and emerging approaches to VTE RAM. Timeline illustrating the develo https://www.frontiersin.org/files/Articles/1738139/fcvm-12-1738139-HTML/image_m/fcvm-12-1738139-t001.jpg Table 1. Padua prediction score. https://www.frontiersin.org/files/Articles/1738139/fcvm-12-1738139-HTML/image_m/fcvm-12-1738139-t002.jpg Table 2. The IMPROVE predictive score, the IMPROVE associative score, the IMPROVEDD score. https://www.frontiersin.org/files/Articles/1738139/fcvm-12-1738139-HTML/image_m/fcvm-12-1738139-t003.jpg Table 3. Geneva scores. https://www.frontiersin.org/files/Articles/1738139/fcvm-12-1738139-HTML/image_m/fcvm-12-1738139-t004.jpg Table 4. The wells PE score. https://www.frontiersin.org/files/Articles/1738139/fcvm-12-1738139-HTML/image_m/fcvm-12-1738139-t005.jpg Table 5. Comparison of VTE risk assessment tools. https://www.frontiersin.org/files/Articles/1738139/fcvm-12-1738139-HTML/image_m/fcvm-12-1738139-t006.jpg Table 6. Khorana score for prediction of VTE in cancer patients. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1744372/full https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-g001.jpg Figure 1. Day 1 clinical image of a pediatric flame burn patient (patient 3, group 2), prior to hemo https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-g002.jpg Figure 2. Day 1 clinical image of a pediatric flame burn patient (Patient 3, Group 2), after hemoglo https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-g003.jpg Figure 3. Clinical images on Day 5 post-treatment, showing wound healing progression after hemoglobi https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-g004.jpg Figure 4. Clinical images on Day 7 post-treatment, showing wound healing progression after hemoglobi https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-t001.jpg Table 1. Comparison of demographic and clinical characteristics of patients according to treatment m https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-t002.jpg Table 2. Comparison of individuals’ parameters before discharge by treatment method with burn percen https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-t003.jpg Table 3. Univariate generalized linear model results for total number of dressing changes, day of di https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-t004.jpg Table 4. Comparison of post-discharge clinical parameters by treatment method. https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-t005.jpg Table 5. Comparison of individuals’ parameters after discharge by treatment method in case of burn p https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-g005.jpg Figure 5. Clinical image on Day 30 of the same patient, demonstrating advanced epithelialization. https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-g006.jpg Figure 6. Six-month follow-up image of the same patient, showing the final healing status and scar m https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-t006.jpg Table 6. Univariable and multivariable firth penalized logistic regression results for post-treatmen https://www.frontiersin.org/files/Articles/1744372/fped-14-1744372-HTML/image_m/fped-14-1744372-t007.jpg Table 7. Intention-to-treat analysis of dressing frequency, discharge day, and epithelialization tim https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1744408/full https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g001.jpg Figure 1. Gene expression of protein kinase B isoform 1 (AKT1) across multiple cancer types (GEPIA2) https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g002.jpg Figure 2. Comparison of AKT1 expression between normal and tumor tissues using TNMplot. Boxplot illu https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g003.jpg Figure 3. Gene expression of protein kinase B isoform 2 (AKT2) across multiple cancer types (GEPIA2) https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g004.jpg Figure 4. Comparison of AKT2 expression between normal and tumor tissues using TNMplot. TNMplot-base https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g005.jpg Figure 5. (A,B) Pan-cancer genomic alteration frequencies for AKT1 (A) and AKT2 (B). Bar plots summa https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g006.jpg Figure 6. (A,B) Amino acid–level mutation distribution of AKT1 (A) and AKT2 (B). Lollipop plots depi https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g007.jpg Figure 7. (A–D) Correlation of AKT1 and AKT2 mRNA expression with mutation and copy-number alteratio https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g008.jpg Figure 8. (A,B) Alteration map of the PI3K–AKT–mTOR pathway across cancer types. Heatmap summarizing https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g009.jpg Figure 9. (A–F) Co-alteration and gene-group comparison analysis for AKT1 and AKT2. Plots highlighti https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g010.jpg Figure 10. (A,B) Overall survival analysis for altered vs. unaltered AKT1 (A) and AKT2 (B). Kaplan–M https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g011.jpg Figure 11. (A–H) Cancer-type–specific survival associations of AKT1 and AKT2 expression. Kaplan–Meie https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-t001.jpg Table 1. Binding energies (kcal/mol) of Pithecellobium dulce phytochemicals against AKT1 predicted b https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-t002.jpg Table 2. Binding energies (kcal/mol) of Pithecellobium dulce phytochemicals against AKT2 predicted b https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g012.jpg Figure 12. Molecular docking poses of the top phytochemicals with AKT1 and AKT2. Panels (A–D) show b https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-t003.jpg Table 3. Key molecular interactions and MM-GBSA binding energies for oleanolic acid, pitheduloside I https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-t004.jpg Table 4. Physicochemical and ADME (absorption, distribution, metabolism, excretion) properties of se https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-t005.jpg Table 5. Quantum-chemical descriptors (total energy and dipole moment) of top-scoring P. dulce phyto https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g013.jpg Figure 13. Frontier molecular orbitals (FMOs) of top-scoring Pithecellobium dulce phytochemicals. Hi https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g014.jpg Figure 14. (A–H) Molecular dynamics (MD) simulation analysis of AKT1 and AKT2 complexes. (A,B) Root- https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g015.jpg Figure 15. (A–D) Protein–ligand interaction frequency maps from MD simulations. (A,B) Contact maps s https://www.frontiersin.org/files/Articles/1744408/fphar-16-1744408-HTML/image_m/fphar-16-1744408-g016.jpg Figure 16. (A–D) Histogram of protein–ligand contact persistence. (A,B) Quantitative representation https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1745087/full https://www.frontiersin.org/files/Articles/1745087/fphar-16-1745087-HTML/image_m/fphar-16-1745087-g001.jpg Figure 1. (A) Expression of POLR2A across various cancers (source: GEPIA2 analyzer). (B) Expression https://www.frontiersin.org/files/Articles/1745087/fphar-16-1745087-HTML/image_m/fphar-16-1745087-g002.jpg Figure 2. Diagram describing co-deletions in POLR2A and TP53 genes (Created with BioRender.com). https://www.frontiersin.org/files/Articles/1745087/fphar-16-1745087-HTML/image_m/fphar-16-1745087-t001.jpg Table 1. List of circRNAs derived from POLR2A deposited in the circAtlas database. https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2025.1746426/full https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-g001.jpg Figure 1. Flowchart of literature screening. https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-t001.jpg Table 1. Literature feature table. https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-t002.jpg Table 2. Literature feature table. https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-g002.jpg Figure 2. Analysis of literature quality and bias. https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-g003.jpg Figure 3. Summary of literature quality and bias analysis. https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-g004.jpg Figure 4. Meta-analysis of recurrence rates of ALA-PDT in combination with different treatment modal https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-g005.jpg Figure 5. Meta-analysis of recurrence rates with ALA-PDT alone. M–H, Mantel–Haenszel; CI, confidence https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-g006.jpg Figure 6. Meta-analysis of the effectiveness of ALA-PDT in combination with different treatment moda https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-g007.jpg Figure 7. Meta-analysis of the effectiveness of ALA-PDT alone. M–H, Mantel–Haenszel; CI, confidence https://www.frontiersin.org/files/Articles/1746426/frph-07-1746426-HTML/image_m/frph-07-1746426-g008.jpg Figure 8. Funnel plot for bias analysis. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1761266/full https://www.frontiersin.org/files/Articles/1761266/fonc-16-1761266-HTML/image_m/fonc-16-1761266-g001.jpg Figure 1. The dual-role master switch of HLA-G in immune regulation. This schematic illustrates the https://www.frontiersin.org/files/Articles/1761266/fonc-16-1761266-HTML/image_m/fonc-16-1761266-g002.jpg Figure 2. Schematic representation of the seven HLA-G protein isoforms. This figure illustrates the https://www.frontiersin.org/files/Articles/1761266/fonc-16-1761266-HTML/image_m/fonc-16-1761266-t001.jpg Table 1. The seven isoforms of HLA-G. https://www.frontiersin.org/files/Articles/1761266/fonc-16-1761266-HTML/image_m/fonc-16-1761266-g003.jpg Figure 3. Multi-layered regulatory network of HLA-G expression.The expression of HLA-G is tightly co https://www.frontiersin.org/files/Articles/1761266/fonc-16-1761266-HTML/image_m/fonc-16-1761266-g004.jpg Figure 4. The molecular mechanisms of HLA-G-mediated immune suppression. The diagram illustrates the https://www.frontiersin.org/files/Articles/1761266/fonc-16-1761266-HTML/image_m/fonc-16-1761266-t002.jpg Table 2. Key receptors of the HLA-G axis. https://www.frontiersin.org/files/Articles/1761266/fonc-16-1761266-HTML/image_m/fonc-16-1761266-t003.jpg Table 3. Summary of HLA-G expression and prognostic value in solid tumors. https://www.frontiersin.org/files/Articles/1761266/fonc-16-1761266-HTML/image_m/fonc-16-1761266-t004.jpg Table 4. Clinical development of therapeutics targeting the HLA-G/ILT axis. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1517981/full https://www.frontiersin.org/files/Articles/1517981/fmed-12-1517981-HTML-r2/image_m/fmed-12-1517981-g001.jpg Figure 1. Statistical trends in ChatGPT’s online engagement. (A) Time to reach 100 million users of https://www.frontiersin.org/files/Articles/1517981/fmed-12-1517981-HTML-r2/image_m/fmed-12-1517981-g002.jpg Figure 2. Applications of ChatGPT in medical education. https://www.frontiersin.org/files/Articles/1517981/fmed-12-1517981-HTML-r2/image_m/fmed-12-1517981-g003.jpg Figure 3. Traditional education vs. ChatGPT enhanced education medical filed. https://www.frontiersin.org/files/Articles/1517981/fmed-12-1517981-HTML-r2/image_m/fmed-12-1517981-t001.jpg Table 1. The using ChatGPT for curriculum design. https://www.frontiersin.org/files/Articles/1517981/fmed-12-1517981-HTML-r2/image_m/fmed-12-1517981-g004.jpg Figure 4. Challenges and solutions of ChatGPT in medical mentorship. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1594702/full https://www.frontiersin.org/files/Articles/1594702/fmed-12-1594702-HTML/image_m/fmed-12-1594702-gr0001.jpg Graphical Abstract. Erastin-induced cell death pathways in endometriosis. https://www.frontiersin.org/files/Articles/1594702/fmed-12-1594702-HTML/image_m/fmed-12-1594702-g001.jpg Figure 1. PRISMA 2020 flow diagram of study identification and selection in the PubMed and WoSCC sea https://www.frontiersin.org/files/Articles/1594702/fmed-12-1594702-HTML/image_m/fmed-12-1594702-g002.jpg Figure 2. The mechanism of ferroptosis in EMS tissue. The iron-rich microenvironment of endometrioti https://www.frontiersin.org/files/Articles/1594702/fmed-12-1594702-HTML/image_m/fmed-12-1594702-g003.jpg Figure 3. The positive feedback loop of necroptosis induced by Erastin synergizing with ferroptosis. https://www.frontiersin.org/files/Articles/1594702/fmed-12-1594702-HTML/image_m/fmed-12-1594702-g004.jpg Figure 4. The multi-pathway regulated cell death network induced by Erastin in endometriosis. This n https://www.frontiersin.org/files/Articles/1594702/fmed-12-1594702-HTML/image_m/fmed-12-1594702-g005.jpg Figure 5. The chemical structures of Erastin and its representative analogs/derivatives. https://www.frontiersin.org/files/Articles/1594702/fmed-12-1594702-HTML/image_m/fmed-12-1594702-t001.jpg Table 1. Pharmacological characteristics and advantages compared to Erastin for representative analo https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1604740/full https://www.frontiersin.org/files/Articles/1604740/fimmu-16-1604740-HTML-r1/image_m/fimmu-16-1604740-g001.jpg Figure 1. The HCC treatment process of the patient. https://www.frontiersin.org/files/Articles/1604740/fimmu-16-1604740-HTML-r1/image_m/fimmu-16-1604740-t001.jpg Table 1. Auxiliary examinations of patients on admission. https://www.frontiersin.org/files/Articles/1604740/fimmu-16-1604740-HTML-r1/image_m/fimmu-16-1604740-t002.jpg Table 2. Auxiliary examinations during disease progression. https://www.frontiersin.org/files/Articles/1604740/fimmu-16-1604740-HTML-r1/image_m/fimmu-16-1604740-g002.jpg Figure 2. Summary of the characteristics of the patient's disease progression. https://www.frontiersin.org/files/Articles/1604740/fimmu-16-1604740-HTML-r1/image_m/fimmu-16-1604740-t003.jpg Table 3. Differential points between adrenal crisis caused by ICIs and septic shock. https://www.frontiersin.org/files/Articles/1604740/fimmu-16-1604740-HTML-r1/image_m/fimmu-16-1604740-g003.jpg Figure 3. MRI changes of the pituitary gland before and after the occurrence of adrenal crisis in th https://www.frontiersin.org/files/Articles/1604740/fimmu-16-1604740-HTML-r1/image_m/fimmu-16-1604740-g004.jpg Figure 4. MRI changes of the adrenal glands before and after the occurrence of adrenal crisis in the https://www.frontiersin.org/files/Articles/1604740/fimmu-16-1604740-HTML-r1/image_m/fimmu-16-1604740-t004.jpg Table 4. Case information. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1631011/full https://www.frontiersin.org/files/Articles/1631011/fmed-12-1631011-HTML/image_m/fmed-12-1631011-g001.jpg Figure 1. Patient screening and enrollment flowchart. https://www.frontiersin.org/files/Articles/1631011/fmed-12-1631011-HTML/image_m/fmed-12-1631011-g002.jpg Figure 2. Process diagram for model construction. https://www.frontiersin.org/files/Articles/1631011/fmed-12-1631011-HTML/image_m/fmed-12-1631011-t001.jpg Table 1. Characteristics of patients in the study. https://www.frontiersin.org/files/Articles/1631011/fmed-12-1631011-HTML/image_m/fmed-12-1631011-g003.jpg Figure 3. Evaluation of ML models. (A) ROC curves for all ML models. (B) Decision curves for all ML https://www.frontiersin.org/files/Articles/1631011/fmed-12-1631011-HTML/image_m/fmed-12-1631011-t002.jpg Table 2. Detailed parameters of each machine learning mode. https://www.frontiersin.org/files/Articles/1631011/fmed-12-1631011-HTML/image_m/fmed-12-1631011-g004.jpg Figure 4. Confusion matrix for different models. https://www.frontiersin.org/files/Articles/1631011/fmed-12-1631011-HTML/image_m/fmed-12-1631011-g005.jpg Figure 5. SHAP interpretability analysis. (A) Interpretable and analyzable swarm maps. (B) Contribut https://www.frontiersin.org/files/Articles/1631011/fmed-12-1631011-HTML/image_m/fmed-12-1631011-t003.jpg Table 3. Detailed analysis of important features. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1639487/full https://www.frontiersin.org/files/Articles/1639487/fimmu-16-1639487-HTML/image_m/fimmu-16-1639487-g001.jpg Figure 1. Mechanism of ICIs. ICIs block the proteins like PD - 1、CTLA4 and LAG - 3, produced by canc https://www.frontiersin.org/files/Articles/1639487/fimmu-16-1639487-HTML/image_m/fimmu-16-1639487-t001.jpg Table 1. Recent advances in various immune checkpoint inhibitors. https://www.frontiersin.org/files/Articles/1639487/fimmu-16-1639487-HTML/image_m/fimmu-16-1639487-g002.jpg Figure 2. Mechanism of CAR-T、TCR-T and TILs. CAR-T, Chimeric Antigen Receptor T-Cell Immunotherapy; https://www.frontiersin.org/files/Articles/1639487/fimmu-16-1639487-HTML/image_m/fimmu-16-1639487-t002.jpg Table 2. Advantages and disadvantages of various ACT modalities. https://www.frontiersin.org/files/Articles/1639487/fimmu-16-1639487-HTML/image_m/fimmu-16-1639487-t003.jpg Table 3. Recent advances in tumor vaccine research. https://www.frontiersin.org/files/Articles/1639487/fimmu-16-1639487-HTML/image_m/fimmu-16-1639487-g003.jpg Figure 3. Emerging biomarkers for predicting efficacy of immunotherapy in gastric cancer. TIME, tumo https://www.frontiersin.org/files/Articles/1639487/fimmu-16-1639487-HTML/image_m/fimmu-16-1639487-t004.jpg Table 4. Characteristics of predictive biomarkers for immunotherapy efficacy in gastric cancer. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1642828/full https://www.frontiersin.org/files/Articles/1642828/fonc-15-1642828-HTML/image_m/fonc-15-1642828-g001.jpg Figure 1. Flowchart of the study population selection. https://www.frontiersin.org/files/Articles/1642828/fonc-15-1642828-HTML/image_m/fonc-15-1642828-g002.jpg Figure 2. Overall workflow of study. (a) Tumors were manually delineated around the entire tumor out https://www.frontiersin.org/files/Articles/1642828/fonc-15-1642828-HTML/image_m/fonc-15-1642828-t001.jpg Table 1. Characteristics of the patients in the cohorts. https://www.frontiersin.org/files/Articles/1642828/fonc-15-1642828-HTML/image_m/fonc-15-1642828-g003.jpg Figure 3. A 53-year-old man with HCC was treated with DEB-TACE. (a–e) MR examination revealed that t https://www.frontiersin.org/files/Articles/1642828/fonc-15-1642828-HTML/image_m/fonc-15-1642828-g004.jpg Figure 4. A 41-year-old man with HCC was treated with DEB-TACE. (a–e) MR examination revealed that t https://www.frontiersin.org/files/Articles/1642828/fonc-15-1642828-HTML/image_m/fonc-15-1642828-g005.jpg Figure 5. Receiver operating characteristic (ROC) curves of different models. ROC curves of AP, DP, https://www.frontiersin.org/files/Articles/1642828/fonc-15-1642828-HTML/image_m/fonc-15-1642828-t002.jpg Table 2. Performances of the models. https://www.frontiersin.org/files/Articles/1642828/fonc-15-1642828-HTML/image_m/fonc-15-1642828-t003.jpg Table 3. Related factors for EPR prediction in HCC. https://www.frontiersin.org/files/Articles/1642828/fonc-15-1642828-HTML/image_m/fonc-15-1642828-g006.jpg Figure 6. Deep learning radiomics and handcrafted nomogram (DLRRN) and their performance. (a) DLRRN https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1654431/full https://www.frontiersin.org/files/Articles/1654431/fonc-15-1654431-HTML/image_m/fonc-15-1654431-g001.jpg Figure 1. Pre-treatment appearance. https://www.frontiersin.org/files/Articles/1654431/fonc-15-1654431-HTML/image_m/fonc-15-1654431-g002.jpg Figure 2. Pathological section. https://www.frontiersin.org/files/Articles/1654431/fonc-15-1654431-HTML/image_m/fonc-15-1654431-g003.jpg Figure 3. Pathological section. https://www.frontiersin.org/files/Articles/1654431/fonc-15-1654431-HTML/image_m/fonc-15-1654431-g004.jpg Figure 4. Post-treatment appearance after two treatment cycles. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1683982/full https://www.frontiersin.org/files/Articles/1683982/fonc-15-1683982-HTML-r1/image_m/fonc-15-1683982-g001.jpg Figure 1. Cohort flow diagram. https://www.frontiersin.org/files/Articles/1683982/fonc-15-1683982-HTML-r1/image_m/fonc-15-1683982-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1683982/fonc-15-1683982-HTML-r1/image_m/fonc-15-1683982-t002.jpg Table 2. Liver attenuation and liver enzymes before and after treatment. https://www.frontiersin.org/files/Articles/1683982/fonc-15-1683982-HTML-r1/image_m/fonc-15-1683982-g002.jpg Figure 2. Distribution of change in liver attenuation following immunotherapy treatment. https://www.frontiersin.org/files/Articles/1683982/fonc-15-1683982-HTML-r1/image_m/fonc-15-1683982-t003.jpg Table 3. Linear mixed-effect model for liver attenuation and liver enzyme outcomes (n=164). https://www.frontiersin.org/files/Articles/1683982/fonc-15-1683982-HTML-r1/image_m/fonc-15-1683982-t004.jpg Table 4. Post-treatment liver abnormalities according to CTCAE v5.0 guidelines. https://www.frontiersin.org/files/Articles/1683982/fonc-15-1683982-HTML-r1/image_m/fonc-15-1683982-g003.jpg Figure 3. Contrast of estimated marginal means (EMMs) for liver attenuation outcome. Estimates and 9 https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1674814/full https://www.frontiersin.org/files/Articles/1674814/fonc-15-1674814-HTML/image_m/fonc-15-1674814-t001.jpg Table 1. Mechanisms of H. pylori in promoting gastric cancer via virulence factors. https://www.frontiersin.org/files/Articles/1674814/fonc-15-1674814-HTML/image_m/fonc-15-1674814-t002.jpg Table 2. Application of PD-1/PD-L1 inhibitors in gastric cancer. https://www.frontiersin.org/files/Articles/1674814/fonc-15-1674814-HTML/image_m/fonc-15-1674814-t003.jpg Table 3. The impact of H. pylori infection on the efficacy of immunotherapy in gastric cancer. https://www.frontiersin.org/files/Articles/1674814/fonc-15-1674814-HTML/image_m/fonc-15-1674814-t004.jpg Table 4. The mechanisms of H. pylori on gastric cancer immunotherapy. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1683704/full https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g001.jpg Figure 1. Study flow chart. The study investigates the molecular mechanisms of liver regeneration in https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-t001.jpg Table 1. Primer sequences for q-PCR assay. https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g002.jpg Figure 2. Gene expression profiling and normalization. (A) PCA of combined datasets prior to batch c https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g003.jpg Figure 3. Intersection of DEGs with lactylation modification genes. (A) A Venn diagram shows the int https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g004.jpg Figure 4. Lactylation modification gene expression analysis. (A) The volcano plot illustrates differ https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g005.jpg Figure 5. Functional enrichment analysis of DEGs. (A–C) GO enrichment analysis presents biological p https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g006.jpg Figure 6. Feature selection for key genes using machine learning (A) LASSO regression selects 13 key https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g007.jpg Figure 7. Immune cell infiltration analysis in partial hepatectomy samples. (A) Bar plot depicts imm https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g008.jpg Figure 8. Correlation between core genes and immune cell infiltration. A bubble chart presents the c https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g009.jpg Figure 9. Co-expression network analysis of core genes. A heatmap illustrates co-expression patterns https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g010.jpg Figure 10. GSEA of core genes. The top 20 reactome pathways enriched for each core gene are shown, w https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g011.jpg Figure 11. Regulatory network of core genes. (A) Interaction network displays the relationship betwe https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g012.jpg Figure 12. Expression and prognostic analysis of core genes in liver cancer. (A–E) Kaplan–Meier surv https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g013.jpg Figure 13. qqPCR-based expression profiling of core genes in LIHC. (A–E) qPCR validation reveals sig https://www.frontiersin.org/files/Articles/1683704/fonc-15-1683704-HTML-r1/image_m/fonc-15-1683704-g014.jpg Figure 14. Expression levels of the core gene in normal and tumor liver tissues. (A) This figure pre https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1652271/full https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-g001.jpg Figure 1. Trends in annual publication outputs in the field of KD in liver health from 2013 to 2024. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-t001.jpg Table 1. Most relevant countries by corresponding authors of KD on liver health. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-g002.jpg Figure 2. The map of countries and institutions in the field of KD in liver health from 2013 to 2024 https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-t002.jpg Table 2. Top 10 most relevant affiliations of KD on liver health. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-t003.jpg Table 3. Top 10 journals with the most cited journals. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-g003.jpg Figure 3. The journal with the largest number of articles published and the journal with the largest https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-t004.jpg Table 4. Top 10 journals with the most published articles. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-g004.jpg Figure 4. Co-cited journals involved in the field of the field of KD in liver health. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-t005.jpg Table 5. Top 10 documents authors related to KD in liver health. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-t006.jpg Table 6. Top 10 citations authors related to KD in liver health. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-g005.jpg Figure 5. The map of co-authorship in the field of the field of KD in liver health from 2013 to 2024 https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-t007.jpg Table 7. Top 25 cited references related to KD in liver health. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-g006.jpg Figure 6. Top 25 references with the strongest citation bursts on the field of KD in liver health. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-t008.jpg Table 8. Top 10 keywords related to KD in liver health. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-g007.jpg Figure 7. Keywords co-occurrence map of publications on the field of KD in liver health. https://www.frontiersin.org/files/Articles/1652271/fnut-12-1652271-HTML-r1/image_m/fnut-12-1652271-g008.jpg Figure 8. Trend topics on the field of KD in liver health research. https://www.frontiersin.org/journals/oncology-reviews/articles/10.3389/or.2025.1697252/full https://www.frontiersin.org/files/Articles/1697252/or-19-1697252-HTML/image_m/or-19-1697252-g001.jpg Figure 1. Division of the area of the tongue into the root, center, side, and tip. The root is the a https://www.frontiersin.org/files/Articles/1697252/or-19-1697252-HTML/image_m/or-19-1697252-t001.jpg Table 1. Baseline characteristics of the participants. https://www.frontiersin.org/files/Articles/1697252/or-19-1697252-HTML/image_m/or-19-1697252-t002.jpg Table 2. Tongue color and tongue coating ratio at baseline. https://www.frontiersin.org/files/Articles/1697252/or-19-1697252-HTML/image_m/or-19-1697252-g002.jpg Figure 2. Individual and mean changes in CIE a* values of the tongue. (a) Body and (b) side regions. https://www.frontiersin.org/files/Articles/1697252/or-19-1697252-HTML/image_m/or-19-1697252-t003.jpg Table 3. Summary of change over time for tongue variables that were significantly different from bas https://www.frontiersin.org/files/Articles/1697252/or-19-1697252-HTML/image_m/or-19-1697252-g003.jpg Figure 3. Changes in CIE L* values of the tongue. (a) Body, (b) fur, (c) root, and (d) center region https://www.frontiersin.org/files/Articles/1697252/or-19-1697252-HTML/image_m/or-19-1697252-t004.jpg Table 4. Comparison of tongue variable changes between responders and nonresponders during early ICI https://www.frontiersin.org/files/Articles/1697252/or-19-1697252-HTML/image_m/or-19-1697252-t005.jpg Table 5. Associations of changes in tongue lightness parameters with PFS and OS. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1676337/full https://www.frontiersin.org/files/Articles/1676337/fcvm-12-1676337-HTML/image_m/fcvm-12-1676337-g001.jpg Figure 1. Preoperative CT analysis of the A-valve complex. Annulus perimeter was 63.4 mm, annulus ar https://www.frontiersin.org/files/Articles/1676337/fcvm-12-1676337-HTML/image_m/fcvm-12-1676337-g002.jpg Figure 2. The upper images show the position of the Safari XS in the LV. The lower images show the c https://www.frontiersin.org/files/Articles/1676337/fcvm-12-1676337-HTML/image_m/fcvm-12-1676337-g003.jpg Figure 3. The color doppler echocardiography visualized a cavity and abnormal blood flow inflow with https://www.frontiersin.org/files/Articles/1676337/fcvm-12-1676337-HTML/image_m/fcvm-12-1676337-g004.jpg Figure 4. Time course of the morphology of the pseudoaneurysm. CT scan taken 3 months after repair s https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1751646/full https://www.frontiersin.org/files/Articles/1751646/fimmu-17-1751646-HTML-r1/image_m/fimmu-17-1751646-g001.jpg Figure 1. Overview of experimental design, sampling and analyses. Sixteen domestic pigs (DP) and six https://www.frontiersin.org/files/Articles/1751646/fimmu-17-1751646-HTML-r1/image_m/fimmu-17-1751646-g002.jpg Figure 2. Leukocyte populations dynamics in domestic pigs and wild boar infected with ASFV genotype https://www.frontiersin.org/files/Articles/1751646/fimmu-17-1751646-HTML-r1/image_m/fimmu-17-1751646-g003.jpg Figure 3. Evolution of serum levels of pro-inflammatory cytokines in domestic pigs and wild boar inf https://www.frontiersin.org/files/Articles/1751646/fimmu-17-1751646-HTML-r1/image_m/fimmu-17-1751646-g004.jpg Figure 4. Evolution of serum levels of cytokines involved in Th-1 immune response in domestic pigs a https://www.frontiersin.org/files/Articles/1751646/fimmu-17-1751646-HTML-r1/image_m/fimmu-17-1751646-g005.jpg Figure 5. Evolution of serum levels of anti-inflammatory mediators in domestic pigs and wild boar in https://www.frontiersin.org/files/Articles/1751646/fimmu-17-1751646-HTML-r1/image_m/fimmu-17-1751646-g006.jpg Figure 6. Evolution of C-reactive proteins in serum levels of domestic pigs and wild boar infected w https://www.frontiersin.org/files/Articles/1751646/fimmu-17-1751646-HTML-r1/image_m/fimmu-17-1751646-g007.jpg Figure 7. Evolution of serum concentrations of biochemical analytes indicative of liver function in https://www.frontiersin.org/files/Articles/1751646/fimmu-17-1751646-HTML-r1/image_m/fimmu-17-1751646-g008.jpg Figure 8. Evolution of serum concentrations of biochemical analytes indicative of renal function in https://www.frontiersin.org/files/Articles/1751646/fimmu-17-1751646-HTML-r1/image_m/fimmu-17-1751646-g009.jpg Figure 9. Evolution of serum concentrations of glucose, total proteins and albumin in domestic pigs https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2026.1781286/full https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-g001.jpg Figure 1. Research model. https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t001.jpg Table 1. Sample information. https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t002.jpg Table 2. Demographic information. https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t003.jpg Table 3. Questionnaire items. https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-g002.jpg Figure 2. Topic mining visualization: citizen discussion of livestreams and “cloud supervisors”. https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-g003.jpg Figure 3. Topic mining visualization example: citizens’ discussion of anthropomorphism and nicknames https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-g004.jpg Figure 4. Topic mining visualization example of citizen discussion on chart beating and idolization. https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t004.jpg Table 4. Results of linear regression analysis of public trust and attitudes toward fandom-style str https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t005.jpg Table 5. Results of linear regression analysis of public acceptance and attitudes toward fandom-styl https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t006.jpg Table 6. Results of linear regression analysis of public cognition and attitudes toward fandom-style https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t007.jpg Table 7. Results of regression analysis of attitudes towards fandom-style strategies under normal ci https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t008.jpg Table 8. Results of regression analysis of attitudes towards fandom-style strategies under special c https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t009.jpg Table 9. T-test of difference between attitudes toward fandom-style strategies in specific situation https://www.frontiersin.org/files/Articles/1781286/fcomm-11-1781286-HTML/image_m/fcomm-11-1781286-t010.jpg Table 10. T-test of difference in attitudes toward fandom-style strategies between those familiar/un https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2025.1661101/full https://www.frontiersin.org/files/Articles/1661101/fcomm-10-1661101-HTML/image_m/fcomm-10-1661101-t001.jpg Table 1. Basic information of respondents. https://www.frontiersin.org/files/Articles/1661101/fcomm-10-1661101-HTML/image_m/fcomm-10-1661101-t002.jpg Table 2. Example of coding categorization scheme. https://www.frontiersin.org/files/Articles/1661101/fcomm-10-1661101-HTML/image_m/fcomm-10-1661101-t003.jpg Table 3. Thematic classification of initial codes from ethnography and semi-structured interviews. https://www.frontiersin.org/files/Articles/1661101/fcomm-10-1661101-HTML/image_m/fcomm-10-1661101-t004.jpg Table 4. Monetization models and their dialectical impact on creative labor. https://www.frontiersin.org/files/Articles/1661101/fcomm-10-1661101-HTML/image_m/fcomm-10-1661101-g001.jpg Figure 1. Payback per 100,000 views on different social media platforms. https://www.frontiersin.org/files/Articles/1661101/fcomm-10-1661101-HTML/image_m/fcomm-10-1661101-g002.jpg Figure 2. Number of followers required for a piece to earn $1,000 per unit on different platforms. https://www.frontiersin.org/files/Articles/1661101/fcomm-10-1661101-HTML/image_m/fcomm-10-1661101-g003.jpg Figure 3. A dimensional model of the value of young bloggers’ informal practices within social media https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1748943/full https://www.frontiersin.org/files/Articles/1748943/fcvm-13-1748943-HTML/image_m/fcvm-13-1748943-t001.jpg Table 1. Published studies on the association between glycemic parameters and MACE. https://www.frontiersin.org/files/Articles/1748943/fcvm-13-1748943-HTML/image_m/fcvm-13-1748943-t002.jpg Table 2. Published studies on the association between glycemic parameters and NOAF. https://www.frontiersin.org/files/Articles/1748943/fcvm-13-1748943-HTML/image_m/fcvm-13-1748943-g001.jpg Figure 1. Mechanisms linking SHG-induced inflammation to AF following AMI. SHG rapidly elevates pro- https://www.frontiersin.org/files/Articles/1748943/fcvm-13-1748943-HTML/image_m/fcvm-13-1748943-g002.jpg Figure 2. Mechanisms linking CaMKII activation to NOAF. SHG, oxidative stress, and neurohormonal act https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1727828/full https://www.frontiersin.org/files/Articles/1727828/fneur-17-1727828-HTML-r1/image_m/fneur-17-1727828-g001.jpg Figure 1. (A) Setup of the questionnaire addressing arousal and valence evoked by observing gait per https://www.frontiersin.org/files/Articles/1727828/fneur-17-1727828-HTML-r1/image_m/fneur-17-1727828-t001.jpg Table 1. Demographics of both groups and clinical characteristics of PD participants. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1678497/full https://www.frontiersin.org/files/Articles/1678497/fimmu-16-1678497-HTML/image_m/fimmu-16-1678497-t001.jpg Table 1. Clinical features of children with antibodies against intracellular antigens. https://www.frontiersin.org/files/Articles/1678497/fimmu-16-1678497-HTML/image_m/fimmu-16-1678497-g001.jpg Figure 1. (A) The profile of the included patients with antibody-mediated autoimmune encephalitis. ( https://www.frontiersin.org/files/Articles/1678497/fimmu-16-1678497-HTML/image_m/fimmu-16-1678497-g002.jpg Figure 2. The legend shows the antibody detection results of the serum and CSF of the patients. https://www.frontiersin.org/files/Articles/1678497/fimmu-16-1678497-HTML/image_m/fimmu-16-1678497-g003.jpg Figure 3. (A) Patient onsets of illness. (B) Patient clinical manifestations of illness. https://www.frontiersin.org/files/Articles/1678497/fimmu-16-1678497-HTML/image_m/fimmu-16-1678497-t002.jpg Table 2. Results of cerebrospinal fluid and brain injury related tests. https://www.frontiersin.org/files/Articles/1678497/fimmu-16-1678497-HTML/image_m/fimmu-16-1678497-g004.jpg Figure 4. Brain MRI changes in patients with autoimmune encephalitis associated with antibodies agai https://www.frontiersin.org/files/Articles/1678497/fimmu-16-1678497-HTML/image_m/fimmu-16-1678497-t003.jpg Table 3. Detailed MRI results. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1789134/full https://www.frontiersin.org/files/Articles/1789134/fpsyg-17-1789134-HTML/image_m/fpsyg-17-1789134-t001.jpg Table 1. Assessment of lower order components. https://www.frontiersin.org/files/Articles/1789134/fpsyg-17-1789134-HTML/image_m/fpsyg-17-1789134-t002.jpg Table 2. The Fornell-Larcker discriminant validity. https://www.frontiersin.org/files/Articles/1789134/fpsyg-17-1789134-HTML/image_m/fpsyg-17-1789134-t003.jpg Table 3. Heterotrait–monotrait ratio (HTMT) matrix. https://www.frontiersin.org/files/Articles/1789134/fpsyg-17-1789134-HTML/image_m/fpsyg-17-1789134-t004.jpg Table 4. Descriptive statistics and correlations. https://www.frontiersin.org/files/Articles/1789134/fpsyg-17-1789134-HTML/image_m/fpsyg-17-1789134-t005.jpg Table 5. Path coefficients and significance levels (N = 322). https://www.frontiersin.org/files/Articles/1789134/fpsyg-17-1789134-HTML/image_m/fpsyg-17-1789134-t006.jpg Table 6. Result of Q2 level assessment. https://www.frontiersin.org/files/Articles/1789134/fpsyg-17-1789134-HTML/image_m/fpsyg-17-1789134-g001.jpg Figure 1. Structural model with path coefficients and significance levels. https://www.frontiersin.org/files/Articles/1789134/fpsyg-17-1789134-HTML/image_m/fpsyg-17-1789134-g002.jpg Figure 2. Three-factor statistical verification model. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1626700/full https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-g001.jpg Figure 1. Observation of disease symptoms of P. aphanidermatum on ginger plants in the field; (A) Wh https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-g002.jpg Figure 2. Cell death assay by Evans blue staining. (A) control leaf; (B) microscopic view; (C) P. ap https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-g003.jpg Figure 3. H2O2 accumulation in ginger leaves visualized by 3′3-diaminobenzidine (DAB) staining. (A) https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-g004.jpg Figure 4. Effect on chlorophyll content in ginger plants infected with P. aphanidermatum and their e https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-g005.jpg Figure 5. Ascorbate peroxidase (APx) activity (a) and superoxide dismutase (SOD) activity (b) in gin https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-g006.jpg Figure 6. Catalase (a) and glutathione reductase (b) activities in ginger plants at different time i https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-g007.jpg Figure 7. Hydrogen peroxide (H₂O₂) production (a) and malondialdehyde (MDA) content (b) in ginger pl https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-g008.jpg Figure 8. Phenylalanine ammonia-lyase (PAL) activity (a) and polyphenol oxidase (PPO) activity (b) i https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-g009.jpg Figure 9. Mass spectrum and compound structure of major compounds present in the potential fraction https://www.frontiersin.org/files/Articles/1626700/fmicb-16-1626700-HTML/image_m/fmicb-16-1626700-t001.jpg Table 1. Chemical profile of the potential fraction 1of fungal crude extract isolated from P. aphani https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1697649/full https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g001.jpg Figure 1. Study area. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-t001.jpg Table 1. Landscape pattern index. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g002.jpg Figure 2. Research process flowchart. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g003.jpg Figure 3. Spatio-temporal patterns of surface temperature: YRD, 2000–2024. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g004.jpg Figure 4. Spatio-temporal variations of LST Differences: YRD, 2000–2024. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g005.jpg Figure 5. Spatio-temporal pattern evolution of surface temperature levels: YRD, 2000–2024. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g006.jpg Figure 6. Spatio-temporal pattern evolution of blue-green space: YRD, 2000–2024. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g007.jpg Figure 7. Mean values of landscape pattern indices: YRD, 2000–2024. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g008.jpg Figure 8. Standard deviation of landscape pattern index: YRD, 2000–2024. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-t002.jpg Table 2. Comparison of the goodness-of-fit of the GWR, TWR, and GTWR models. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g009.jpg Figure 9. GTWR index: YRD 2000. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g010.jpg Figure 10. GTWR index: YRD 2024. https://www.frontiersin.org/files/Articles/1697649/fenvs-13-1697649-HTML/image_m/fenvs-13-1697649-g011.jpg Figure 11. Areas where changes in blue-green space area have a significant impact on surface tempera https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2026.1762054/full https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g001.jpg Figure 1. Research flowchart. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g002.jpg Figure 2. Study area. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-t001.jpg Table 1. Criteria for LST level classification. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-t002.jpg Table 2. Landscape pattern index. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g003.jpg Figure 3. Spatiotemporal evolution of surface temperature: YEB 2000–2024. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g004.jpg Figure 4. Spatiotemporal evolution of surface temperature levels: YEB 2000–2024. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g005.jpg Figure 5. Evolution of the spatiotemporal pattern of BGS: YEB 2000–2024S. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g006.jpg Figure 6. Provincial landscape pattern index of the YEB, 2000–2024. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g007.jpg Figure 7. Standard deviation and meaning of the landscape pattern index of cities in the YEB, 2000–2 https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-t003.jpg Table 3. Global Moran index of surface temperature and landscape pattern indices. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g008.jpg Figure 8. Local indigo index of surface temperature and landscape pattern indices. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-t004.jpg Table 4. A comparison of the good fitness of the TWR, GWR and GTWR models. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g009.jpg Figure 9. GTWR: YEB 2000. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g010.jpg Figure 10. GTWR: YEB 2024. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-g011.jpg Figure 11. Areas where changes in BGS significantly affect LST. https://www.frontiersin.org/files/Articles/1762054/ffgc-09-1762054-HTML/image_m/ffgc-09-1762054-t005.jpg Appendix Table 1. List of abbreviations. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1791333/full https://www.frontiersin.org/files/Articles/1791333/fspor-08-1791333-HTML-r1/image_m/fspor-08-1791333-g001.jpg Figure 1. Theoretical model diagram. https://www.frontiersin.org/files/Articles/1791333/fspor-08-1791333-HTML-r1/image_m/fspor-08-1791333-t001.jpg Table 1. Descriptive statistics and correlation analysis of variables (n = 526). https://www.frontiersin.org/files/Articles/1791333/fspor-08-1791333-HTML-r1/image_m/fspor-08-1791333-t002.jpg Table 2. Mediation effect tests. https://www.frontiersin.org/files/Articles/1791333/fspor-08-1791333-HTML-r1/image_m/fspor-08-1791333-t003.jpg Table 3. Decomposition of total, direct, and indirect effects. https://www.frontiersin.org/files/Articles/1791333/fspor-08-1791333-HTML-r1/image_m/fspor-08-1791333-t004.jpg Table 4. Moderated mediation results. https://www.frontiersin.org/files/Articles/1791333/fspor-08-1791333-HTML-r1/image_m/fspor-08-1791333-g002.jpg Figure 2. Moderated mediation effect model. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1785996/full https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-g001.jpg Figure 1. Literature screening process. https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-t001.jpg Table 1. Characteristics of the included literature. https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-g002.jpg Figure 2. Radar chart of scores for each item of AMSTAR-2. Fully filled (1) indicates full complianc https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-g003.jpg Figure 3. Cartesian heatmap of the scores of each item in PRISMA 2020. https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-t002.jpg Table 2. Evidence quality assessment. https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-g004.jpg Figure 4. Meta-analysis of 24-h average systolic blood pressure. https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-g005.jpg Figure 5. Meta-analysis of 24-h average nighttime systolic blood pressure. https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-g006.jpg Figure 6. Meta-analysis of 24-h average nighttime diastolic blood pressure. https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-g007.jpg Figure 7. Meta-analysis of office systolic blood pressure. https://www.frontiersin.org/files/Articles/1785996/fmed-13-1785996-HTML/image_m/fmed-13-1785996-g008.jpg Figure 8. Meta-analysis of office diastolic blood pressure. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1647762/full https://www.frontiersin.org/files/Articles/1647762/fvets-12-1647762-HTML/image_m/fvets-12-1647762-g001.jpg Figure 1. Alpha diversity of the buffalo rumen microbiome fed additive. https://www.frontiersin.org/files/Articles/1647762/fvets-12-1647762-HTML/image_m/fvets-12-1647762-g002.jpg Figure 2. Abundance of bacteria at the phylum level in the buffalo rumen microbiome fed additive. https://www.frontiersin.org/files/Articles/1647762/fvets-12-1647762-HTML/image_m/fvets-12-1647762-g003.jpg Figure 3. LDA score at the species level. https://www.frontiersin.org/files/Articles/1647762/fvets-12-1647762-HTML/image_m/fvets-12-1647762-g004.jpg Figure 4. LDA score of control and treatment group. https://www.frontiersin.org/files/Articles/1647762/fvets-12-1647762-HTML/image_m/fvets-12-1647762-g005.jpg Figure 5. Major KEGG metabolism pathways for control and treatment group. https://www.frontiersin.org/files/Articles/1647762/fvets-12-1647762-HTML/image_m/fvets-12-1647762-g006.jpg Figure 6. Genus CAZymes. https://www.frontiersin.org/files/Articles/1647762/fvets-12-1647762-HTML/image_m/fvets-12-1647762-g007.jpg Figure 7. Phylum CAZymes. https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2026.1773515/full https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-g001.jpg Figure 1. Clinical and demographic description of the study samples. (A) Distribution of patients by https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-t001.jpg Table 1. Description of the performed exercises. https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-g002.jpg Figure 2. Covariate effects on ArmeoSpring game performance after FDR correction. Each point represe https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-g003.jpg Figure 3. Nested pie chart showing the distribution of patient pathologies across different games. T https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-g004.jpg Figure 4. Bar plot showing the mean improvement slopes per therapy day for each game, stratified by https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-g005.jpg Figure 5. Correlation matrices of performance and clinical variables by pathology for the Roll The B https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-g006.jpg Figure 6. Correlation matrices of performance and clinical variables by pathology for the Goalkeeper https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-g007.jpg Figure 7. Correlation matrices of performance and clinical variables by pathology for the Balloons e https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-g008.jpg Figure 8. Correlation matrices of performance and clinical variables by pathology for the Pirate Adv https://www.frontiersin.org/files/Articles/1773515/frobt-13-1773515-HTML/image_m/frobt-13-1773515-g009.jpg Figure 9. Correlation matrices of performance and clinical variables by pathology for the Fly High E https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1719436/full https://www.frontiersin.org/files/Articles/1719436/fspor-07-1719436-HTML/image_m/fspor-07-1719436-t001.jpg Table 1. Basic information about athletes. https://www.frontiersin.org/files/Articles/1719436/fspor-07-1719436-HTML/image_m/fspor-07-1719436-t002.jpg Table 2. Results of CMJ, CMJAM, and SJ jumps (cm) measured by OptoJump and My Jump Lab. https://www.frontiersin.org/files/Articles/1719436/fspor-07-1719436-HTML/image_m/fspor-07-1719436-g001.jpg Figure 1. Testing process. https://www.frontiersin.org/files/Articles/1719436/fspor-07-1719436-HTML/image_m/fspor-07-1719436-t003.jpg Table 3. OptoJump and My Jump Lab (Operator 1 and Operator 2) retest reliability, as well as reliabi https://www.frontiersin.org/files/Articles/1719436/fspor-07-1719436-HTML/image_m/fspor-07-1719436-t004.jpg Table 4. Mauchly's test of sphericity. https://www.frontiersin.org/files/Articles/1719436/fspor-07-1719436-HTML/image_m/fspor-07-1719436-t005.jpg Table 5. Results of the mixed-design repeated measures ANOVA. https://www.frontiersin.org/files/Articles/1719436/fspor-07-1719436-HTML/image_m/fspor-07-1719436-t006.jpg Table 6. Results of validity tests of My Jump Lab and OptoJump for measuring three jump heights. https://www.frontiersin.org/files/Articles/1719436/fspor-07-1719436-HTML/image_m/fspor-07-1719436-g002.jpg Figure 2. Graphical representation of Bland-Altman consistency analysis of CMJ, CMJAM, and SJ result https://www.frontiersin.org/files/Articles/1719436/fspor-07-1719436-HTML/image_m/fspor-07-1719436-g003.jpg Figure 3. Graphical representation of ordinary least products regression (OLP) for validity comparis https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1798127/full https://www.frontiersin.org/files/Articles/1798127/fcimb-16-1798127-HTML/image_m/fcimb-16-1798127-g001.jpg Figure 1. Schematic workflow of the duplex ddPCR assay for simultaneous detection of CMV and EBV. Th https://www.frontiersin.org/files/Articles/1798127/fcimb-16-1798127-HTML/image_m/fcimb-16-1798127-g002.jpg Figure 2. Optimization of reaction conditions for the duplex ddPCR assay. One-dimensional fluorescen https://www.frontiersin.org/files/Articles/1798127/fcimb-16-1798127-HTML/image_m/fcimb-16-1798127-g003.jpg Figure 3. Comparison of linearity and dynamic range between duplex ddPCR and qPCR assays. Serial dil https://www.frontiersin.org/files/Articles/1798127/fcimb-16-1798127-HTML/image_m/fcimb-16-1798127-g004.jpg Figure 4. Probit regression analysis for determining the Limit of Detection (LOD). The detection pro https://www.frontiersin.org/files/Articles/1798127/fcimb-16-1798127-HTML/image_m/fcimb-16-1798127-g005.jpg Figure 5. Assessment of competitive inhibition between CMV and EBV targets in the duplex ddPCR syste https://www.frontiersin.org/files/Articles/1798127/fcimb-16-1798127-HTML/image_m/fcimb-16-1798127-t001.jpg Table 1. List of non-target pathogens and clinical samples used for analytical specificity evaluatio https://www.frontiersin.org/files/Articles/1798127/fcimb-16-1798127-HTML/image_m/fcimb-16-1798127-g006.jpg Figure 6. Evaluation of the analytical specificity of the duplex ddPCR assay. The specificity of the https://www.frontiersin.org/files/Articles/1798127/fcimb-16-1798127-HTML/image_m/fcimb-16-1798127-t002.jpg Table 2. Evaluation of the intra-assay and inter-assay precision of the duplex ddPCR assay. https://www.frontiersin.org/files/Articles/1798127/fcimb-16-1798127-HTML/image_m/fcimb-16-1798127-g007.jpg Figure 7. Clinical performance evaluation and method comparison using 117 clinical plasma samples. T https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1802398/full https://www.frontiersin.org/files/Articles/1802398/fpls-17-1802398-HTML/image_m/fpls-17-1802398-g001.jpg Figure 1. Correlation heatmap of germination and vigor traits under salt stress. (A) Germination Ind https://www.frontiersin.org/files/Articles/1802398/fpls-17-1802398-HTML/image_m/fpls-17-1802398-g002.jpg Figure 2. Comparative analysis of germination traits under control and salt stress. Distribution of https://www.frontiersin.org/files/Articles/1802398/fpls-17-1802398-HTML/image_m/fpls-17-1802398-g003.jpg Figure 3. Correlation of phenotypic traits under control and salt stress conditions. (A) Germination https://www.frontiersin.org/files/Articles/1802398/fpls-17-1802398-HTML/image_m/fpls-17-1802398-g004.jpg Figure 4. Heatmap of trait response patterns under salt stress (log2R). Colors indicate trait preser https://www.frontiersin.org/files/Articles/1802398/fpls-17-1802398-HTML/image_m/fpls-17-1802398-g005.jpg Figure 5. Bootstrap evaluation of salt tolerance ranking stability. The plot illustrates the hit rat https://www.frontiersin.org/files/Articles/1802398/fpls-17-1802398-HTML/image_m/fpls-17-1802398-g006.jpg Figure 6. Canonical correlation analysis of phenotypic spatial consistency between CK and salt-stres https://www.frontiersin.org/files/Articles/1802398/fpls-17-1802398-HTML/image_m/fpls-17-1802398-g007.jpg Figure 7. Expression patterns of eight salt stress-related genes in salt-tolerant and salt-sensitive https://www.frontiersin.org/files/Articles/1802398/fpls-17-1802398-HTML/image_m/fpls-17-1802398-g008.jpg Figure 8. Physiological responses and root ion homeostasis of salt-tolerant accession (GZ196) and a https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1801309/full https://www.frontiersin.org/files/Articles/1801309/fimmu-17-1801309-HTML/image_m/fimmu-17-1801309-g001.jpg Figure 1. Flow chart of literature screening. https://www.frontiersin.org/files/Articles/1801309/fimmu-17-1801309-HTML/image_m/fimmu-17-1801309-g002.jpg Figure 2. Forest plots summarizing the effects of exercise on inflammatory markers and adiponectin i https://www.frontiersin.org/files/Articles/1801309/fimmu-17-1801309-HTML/image_m/fimmu-17-1801309-g003.jpg Figure 3. Funnel plots for publication bias assessment. The analysis includes (A) C-reactive protein https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1669815/full https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g001.jpg Figure 1. Participant flow diagram. https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g002.jpg Figure 2. Logistic regression analysis for risk factors in AF patients. https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g003.jpg Figure 3. Logistic regression analysis for risk factors in AF patients. https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g004.jpg Figure 4. Sensitivity analysis multivariate logistic regression models with varying covariate sets ( https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g005.jpg Figure 5. Sensitivity analysis multivariate logistic regression models with varying covariate sets ( https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g006.jpg Figure 6. Sensitivity analysis multivariate logistic regression models with varying covariate sets ( https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g007.jpg Figure 7. Sensitivity analysis multivariate logistic regression models with varying covariate sets ( https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g008.jpg Figure 8. Sensitivity analysis multivariate logistic regression models with varying covariate sets ( https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g009.jpg Figure 9. Sensitivity analysis multivariate logistic regression models with varying covariate sets ( https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-t001.jpg Table 1. Demographical characteristics and clinical data of the NVAF group. https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-t002.jpg Table 2. ALDH2 polymorphism in AF patients and controls. https://www.frontiersin.org/files/Articles/1669815/fphys-16-1669815-HTML/image_m/fphys-16-1669815-g010.jpg Figure 10. Sensitivity analysis multivariate logistic regression models with varying covariate sets https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1786768/full https://www.frontiersin.org/files/Articles/1786768/fpsyt-17-1786768-HTML-r1/image_m/fpsyt-17-1786768-t001.jpg Table 1. Characteristics of the sample. https://www.frontiersin.org/files/Articles/1786768/fpsyt-17-1786768-HTML-r1/image_m/fpsyt-17-1786768-g001.jpg Figure 1. Network analysis of PTSD symptom clusters and rumination. Reexp - Reexperience, Avoid – Av https://www.frontiersin.org/files/Articles/1786768/fpsyt-17-1786768-HTML-r1/image_m/fpsyt-17-1786768-g002.jpg Figure 2. Network analysis of PTSD symptom clusters, rumination, and level of anxiety and depression https://www.frontiersin.org/files/Articles/1786768/fpsyt-17-1786768-HTML-r1/image_m/fpsyt-17-1786768-g003.jpg Figure 3. Network analysis of PTSD symptoms and rumination at the item-level, adjusted for depressio https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1688807/full https://www.frontiersin.org/files/Articles/1688807/fspor-08-1688807-HTML/image_m/fspor-08-1688807-g001.jpg Figure 1. Model of wushu short video value perception determinants. https://www.frontiersin.org/files/Articles/1688807/fspor-08-1688807-HTML/image_m/fspor-08-1688807-t001.jpg Table 1. Definitions, and measurement of the feature variables. https://www.frontiersin.org/files/Articles/1688807/fspor-08-1688807-HTML/image_m/fspor-08-1688807-g002.jpg Figure 2. Determination of optimal number of topics. https://www.frontiersin.org/files/Articles/1688807/fspor-08-1688807-HTML/image_m/fspor-08-1688807-g003.jpg Figure 3. Dimensions of audience perceived value in wushu short videos. https://www.frontiersin.org/files/Articles/1688807/fspor-08-1688807-HTML/image_m/fspor-08-1688807-g004.jpg Figure 4. Positive sentiment ratio distribution across three value dimensions. https://www.frontiersin.org/files/Articles/1688807/fspor-08-1688807-HTML/image_m/fspor-08-1688807-g005.jpg Figure 5. Feature importance ranking. https://www.frontiersin.org/files/Articles/1688807/fspor-08-1688807-HTML/image_m/fspor-08-1688807-g006.jpg Figure 6. Summary of SHAP values for wushu short video value perception determinants. (a) Impact of https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1744194/full https://www.frontiersin.org/files/Articles/1744194/fmolb-13-1744194-HTML-r1/image_m/fmolb-13-1744194-t001.jpg Table 1. Demographic, clinical, and biochemical features of 133 patients included in the study. Nume https://www.frontiersin.org/files/Articles/1744194/fmolb-13-1744194-HTML-r1/image_m/fmolb-13-1744194-g001.jpg Figure 1. Receiver Operating Characteristic (ROC) curve of neurofilament light chain proteins (NfL) https://www.frontiersin.org/files/Articles/1744194/fmolb-13-1744194-HTML-r1/image_m/fmolb-13-1744194-g002.jpg Figure 2. Scatter plot showing the relationship between serum neurofilament light chain (NfL, pg/mL) https://www.frontiersin.org/files/Articles/1744194/fmolb-13-1744194-HTML-r1/image_m/fmolb-13-1744194-g003.jpg Figure 3. Boxplot shows serum neurofilament light chain (NfL, pg/mL) concentrations according to Wes https://www.frontiersin.org/files/Articles/1744194/fmolb-13-1744194-HTML-r1/image_m/fmolb-13-1744194-g004.jpg Figure 4. Scatter plot showing the relationship between serum neurofilament light chain (NfL, pg/mL) https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2025.1734456/full https://www.frontiersin.org/files/Articles/1734456/frph-07-1734456-HTML/image_m/frph-07-1734456-g001.jpg Figure 1. PRISMA flow diagram: study selection process. https://www.frontiersin.org/files/Articles/1734456/frph-07-1734456-HTML/image_m/frph-07-1734456-t001.jpg Table 1. Process of theme development. https://www.frontiersin.org/files/Articles/1734456/frph-07-1734456-HTML/image_m/frph-07-1734456-t002.jpg Table 2. Concept 1: care-seeking and care experience. https://www.frontiersin.org/files/Articles/1734456/frph-07-1734456-HTML/image_m/frph-07-1734456-t003.jpg Table 3. Concept 2: digital health. https://www.frontiersin.org/files/Articles/1734456/frph-07-1734456-HTML/image_m/frph-07-1734456-t004.jpg Table 4. Concept 3: vaccination. https://www.frontiersin.org/files/Articles/1734456/frph-07-1734456-HTML/image_m/frph-07-1734456-t005.jpg Table 5. Concept 4: ethical future of maternity care. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1752778/full https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-g001.jpg Figure 1. The technology roadmap of the system. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-g002.jpg Figure 2. An example of SPO-T and Recall. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-g003.jpg Figure 3. Work flow of SELF-RAG (Q, question; A, answer; Y, yes; N, no. Diamond shapes indicate the https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-t001.jpg Table 1. Accuracy evaluation results for the proposed model and the baseline. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-g004.jpg Figure 4. Model scores compared to the human scores. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-g005.jpg Figure 5. Radar plot for model scores in different subjects and manual evaluation. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-t002.jpg Table 2. Recall evaluation results for RAG model with and without SPO-T. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-t003.jpg Table 3. Model evaluation results by expert for the proposed model and the baseline. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-g006.jpg Figure 6. Sankey diagram showing the changes in ratings for GPT-4 vs. TOSRR. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-t004.jpg Table 4. Consistency analysis of expert ratings based on Kendall’s W. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-t005.jpg Table 5. RAGAs evaluation results for the proposed model and the baseline. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-t006.jpg Table 6. Descriptive statistics, hypothesis testing, and effect Size analysis between groups. https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-g007.jpg Figure 7. Distribution of student feedback from the pilot study regarding (a) interest in TCM learni https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-t007.jpg Table 7. Computational efficiency comparison (Mean ± SD). https://www.frontiersin.org/files/Articles/1752778/fmed-13-1752778-HTML-r2/image_m/fmed-13-1752778-g008.jpg Figure 8. Example of a factual question from TMC MLE. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1784632/full https://www.frontiersin.org/files/Articles/1784632/fenvs-14-1784632-HTML/image_m/fenvs-14-1784632-g001.jpg Figure 1. Overview of the anaerobic digestion process illustrating the four sequential biochemical s https://www.frontiersin.org/files/Articles/1784632/fenvs-14-1784632-HTML/image_m/fenvs-14-1784632-g002.jpg Figure 2. Nanoparticle–microbe interactions governing electron transfer, enzyme activity, and microb https://www.frontiersin.org/files/Articles/1784632/fenvs-14-1784632-HTML/image_m/fenvs-14-1784632-g003.jpg Figure 3. Advantages of integrating nanomaterials in anaerobic digestion processes, highlighting enh https://www.frontiersin.org/files/Articles/1784632/fenvs-14-1784632-HTML/image_m/fenvs-14-1784632-t001.jpg Table 1. Quantitative comparison of nanomaterial-assisted methane enhancement and process stability https://www.frontiersin.org/files/Articles/1784632/fenvs-14-1784632-HTML/image_m/fenvs-14-1784632-t002.jpg Table 2. Quantitative evaluation of nanobiotechnological strategies for process stability improvemen https://www.frontiersin.org/files/Articles/1784632/fenvs-14-1784632-HTML/image_m/fenvs-14-1784632-g004.jpg Figure 4. Integration of nanomaterials with bioaugmentation, pretreatment, co-digestion, and bio-ele https://www.frontiersin.org/files/Articles/1784632/fenvs-14-1784632-HTML/image_m/fenvs-14-1784632-g005.jpg Figure 5. Annual distribution of publications on nanobiotechnology-enabled anaerobic digestion over https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1656161/full https://www.frontiersin.org/files/Articles/1656161/fdgth-08-1656161-HTML/image_m/fdgth-08-1656161-t001.jpg Table 1. Characteristics of the study groups. https://www.frontiersin.org/files/Articles/1656161/fdgth-08-1656161-HTML/image_m/fdgth-08-1656161-g001.jpg Figure 1. Principal Component Analysis (PCA) of MODY datasets. (A) PCA of the GCK-MODY dataset and ( https://www.frontiersin.org/files/Articles/1656161/fdgth-08-1656161-HTML/image_m/fdgth-08-1656161-t002.jpg Table 2. Average ROC AUC scores for the classification models. https://www.frontiersin.org/files/Articles/1656161/fdgth-08-1656161-HTML/image_m/fdgth-08-1656161-g002.jpg Figure 2. SHAP decision plots illustrating feature contributions to model predictions. (A) Gaussian https://www.frontiersin.org/journals/human-dynamics/articles/10.3389/fhumd.2026.1740411/full https://www.frontiersin.org/files/Articles/1740411/fhumd-08-1740411-HTML/image_m/fhumd-08-1740411-g001.jpg Figure 1. Informal settlements in Greater Valparaíso and location of Manuel Bustos and Felipe Camiro https://www.frontiersin.org/files/Articles/1740411/fhumd-08-1740411-HTML/image_m/fhumd-08-1740411-t001.jpg Table 1. Overview of data collection methods and study participants. https://www.frontiersin.org/files/Articles/1740411/fhumd-08-1740411-HTML/image_m/fhumd-08-1740411-g002.jpg Figure 2. Aerial view of the informal settlement Felipe Camiroaga, Viña del Mar. Source: Authors. https://www.frontiersin.org/files/Articles/1740411/fhumd-08-1740411-HTML/image_m/fhumd-08-1740411-g003.jpg Figure 3. Construction typology of housing in the settlement Felipe Camiroaga, Viña del Mar. Source: https://www.frontiersin.org/files/Articles/1740411/fhumd-08-1740411-HTML/image_m/fhumd-08-1740411-g004.jpg Figure 4. Public space built by the community in the settlement Felipe Camiroaga, Viña del Mar, Chil https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1685628/full https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t001.jpg Table 1. Comparative analysis of Key blockchain-based EHR systems. https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t002.jpg Table 2. Key smart contracts and their functions in the PolyMed ecosystem. https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g001.jpg Figure 1. The overall system architecture of PolyMed. This diagram provides a holistic view of the p https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g002.jpg Figure 2. System workflow sequence diagram. This diagram illustrates a typical user interaction flow https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-i001.jpg Algorithm 1 Auth Layer Workflow: MetaMask & Anon-Aadhaar Integration https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-i002.jpg Algorithm 2 IoT-Blockchain-Based Health Monitoring and Alerting Workflow https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-i003.jpg Algorithm 3 Secure EHR Management Workflow Logic https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-i004.jpg Algorithm 4 Cross-Chain Healthcare Flow Integrating ZKP Authentication & AI-Driven Operations https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-i005.jpg Algorithm 5 Governance and Identity Management Primitives https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-i006.jpg Algorithm 6 DeFi Microloan Workflow Logic https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t003.jpg Table 3. Standardized system configuration used for All performance evaluation benchmarks. https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t004.jpg Table 4. Average transaction latency and Gas consumption for core system operations, measured on the https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g003.jpg Figure 3. Latency composition analysis by operation. This chart dissects the total average latency f https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g004.jpg Figure 4. Transaction throughput vs. Concurrent Users. The system demonstrates linear scaling up to https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t005.jpg Table 5. System resilience under Various simulated fault conditions, showing high success rates and https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g005.jpg Figure 5. Blockchain operation latency trends (100 consecutive transactions). This line graph illust https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t006.jpg Table 6. Optimized hyperparameters for the LightGBM model. https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t007.jpg Table 7. Ai model performance comparison (mean ± Std. Dev.) from 10-fold Cross-Validation. https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g006.jpg Figure 6. Aggregated confusion matrices for (left) the proposed LGBM model and (right) the logistic https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g007.jpg Figure 7. Comparison of AUC score distributions from 10-Fold cross-validation. This plot visualizes https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g008.jpg Figure 8. Comparative ROC curves (averaged over 10 folds). This curve illustrates a model's ability https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g009.jpg Figure 9. Comparative precision-recall curves (averaged over 10 folds). This curve is particularly i https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t008.jpg Table 8. Contingency table for mcNemar's test, showing prediction agreement and disagreement between https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t009.jpg Table 9. STRIDE threat analysis and mitigation strategies for the PolyMed system. https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t010.jpg Table 10. Cryptographic primitives and their applications in PolyMed. https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g010.jpg Figure 10. Residual risk heatmap. This heatmap visualizes the residual risk for each STRIDE category https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t011.jpg Table 11. Regulatory compliance feature matrix for PolyMed. https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g011.jpg Figure 11. Venn diagram of overlapping compliance features. This diagram visually illustrates the co https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t012.jpg Table 12. User perception scores on PolyMed's usability heuristics (1–5 scale, N = 20). All scores w https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g012.jpg Figure 12. Polymed usability evaluation (N = 20). The radar chart visualizes the mean scores for key https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g013.jpg Figure 13. Polymed user interface mockups evaluated in the usability study. (Top) The main patient d https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t013.jpg Table 13. Gas Fee breakdown for core operations (rate: |75/POL, April 2024), highlighting the Low co https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g014.jpg Figure 14. Relative Gas cost Per patient visit by operation (INR). This chart illustrates the breakd https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-g015.jpg Figure 15. Cross-Country comparison of EHR interaction costs (per patient visit). This chart visuali https://www.frontiersin.org/files/Articles/1685628/fdgth-07-1685628-HTML-r1/image_m/fdgth-07-1685628-t014.jpg Table 14. Comparison of estimated EHR interaction costs across different systems and regions. https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2025.1625558/full https://www.frontiersin.org/files/Articles/1625558/frhs-05-1625558-HTML/image_m/frhs-05-1625558-g001.jpg Figure 1. Screening, referral, and enrollment study flow diagram for the fresh funds for moms progra https://www.frontiersin.org/files/Articles/1625558/frhs-05-1625558-HTML/image_m/frhs-05-1625558-t001.jpg Table 1. Demographics of n = 14 participants that completed the “fresh funds for moms” online GPx pr https://www.frontiersin.org/files/Articles/1625558/frhs-05-1625558-HTML/image_m/frhs-05-1625558-t002.jpg Table 2. Process measures for a GPx program delivered through a healthcare referral system. https://www.frontiersin.org/files/Articles/1625558/frhs-05-1625558-HTML/image_m/frhs-05-1625558-g002.jpg Figure 2. Percentage of fresh fund dollars spent on each food category by participant each month. https://www.frontiersin.org/files/Articles/1625558/frhs-05-1625558-HTML/image_m/frhs-05-1625558-t003.jpg Table 3. Biometric outcome measures among participants (n = 14) in the fresh funds for moms GPx prog https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1751821/full https://www.frontiersin.org/files/Articles/1751821/fendo-17-1751821-HTML/image_m/fendo-17-1751821-t001.jpg Table 1. Demographic, clinical, and laboratory characteristics of all recruited T2DM patients. https://www.frontiersin.org/files/Articles/1751821/fendo-17-1751821-HTML/image_m/fendo-17-1751821-t002.jpg Table 2. The univariable and multivariable logistic regression analysis for STDR in T2DM. https://www.frontiersin.org/files/Articles/1751821/fendo-17-1751821-HTML/image_m/fendo-17-1751821-g001.jpg Figure 1. Risk factors predicting STDR in T2DM. (A) Univariable logistic regression analysis identif https://www.frontiersin.org/files/Articles/1751821/fendo-17-1751821-HTML/image_m/fendo-17-1751821-t003.jpg Table 3. STDR incidence across cystatin C strata in T2DM. https://www.frontiersin.org/files/Articles/1751821/fendo-17-1751821-HTML/image_m/fendo-17-1751821-g002.jpg Figure 2. Predictive performance of cystatin C and IBIL, alone and in combination, for STDR in T2DM. https://www.frontiersin.org/files/Articles/1751821/fendo-17-1751821-HTML/image_m/fendo-17-1751821-t004.jpg Table 4. Trend in STDR risk across cystatin C strata in T2DM. https://www.frontiersin.org/files/Articles/1751821/fendo-17-1751821-HTML/image_m/fendo-17-1751821-t005.jpg Table 5. Predictive performance of cystatin C and IBIL, alone and in combination. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1776706/full https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-g001.jpg Figure 1. The temperature and precipitation conditions in 2023 and 2024: (a) 2023 and (b) 2024. https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-g002.jpg Figure 2. Nitrogen absorption of cotton population under different nitrogen application rates. (a–d) https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-g003.jpg Figure 3. SPAD values and nitrogen contents of cotton inverted four leaves under different nitrogen https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-g004.jpg Figure 4. Model construction and validation of the cotton critical nitrogen concentration dilution c https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-g005.jpg Figure 5. Dynamic changes in nitrogen nutrient index of cotton at various growth stages. (a–d) The c https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-g006.jpg Figure 6. Seed cotton yield, yield stability, and sustainability index under different nitrogen appl https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-t001.jpg Table 1. Composition factors of cotton yield under different nitrogen application rates. https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-g007.jpg Figure 7. Nitrogen fertilizer use efficiency of cotton under different nitrogen application rates. ( https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-t002.jpg Table 2. Cotton fiber quality under different nitrogen application rates. https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-g008.jpg Figure 8. The relationship between the nitrogen nutrition index and relative yield of cotton under d https://www.frontiersin.org/files/Articles/1776706/fpls-17-1776706-HTML/image_m/fpls-17-1776706-g009.jpg Figure 9. Thermal map of the correlation between NNI, leaf SPAD values, nitrogen concentration, nitr https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1661117/full https://www.frontiersin.org/files/Articles/1661117/fimmu-16-1661117-HTML/image_m/fimmu-16-1661117-t001.jpg Table 1. Key laboratory and imaging results at critical clinical stages of the present case (RA with https://www.frontiersin.org/files/Articles/1661117/fimmu-16-1661117-HTML/image_m/fimmu-16-1661117-g001.jpg Figure 1. Monthly timeline of key laboratory markers and therapies in anti-GBM disease secondary to https://www.frontiersin.org/files/Articles/1661117/fimmu-16-1661117-HTML/image_m/fimmu-16-1661117-g002.jpg Figure 2. PRISMA flow of study selection for RA complicated by anti-GBM disease. A database search ( https://www.frontiersin.org/files/Articles/1661117/fimmu-16-1661117-HTML/image_m/fimmu-16-1661117-t002.jpg Table 2. Clinical characteristics, diagnosis, treatment, and prognosis of patients with rheumatoid a https://www.frontiersin.org/journals/stroke/articles/10.3389/fstro.2026.1738822/full https://www.frontiersin.org/files/Articles/1738822/fstro-05-1738822-HTML-r1/image_m/fstro-05-1738822-t001.jpg Table 1. Principal component analysis of the 24 neighborhood characteristicsa. https://www.frontiersin.org/files/Articles/1738822/fstro-05-1738822-HTML-r1/image_m/fstro-05-1738822-t002.jpg Table 2. Death or readmission within 90-days post-stroke hospitalization. https://www.frontiersin.org/files/Articles/1738822/fstro-05-1738822-HTML-r1/image_m/fstro-05-1738822-t003.jpg Table 3. Logistic regression analysis of each factor with death or readmission within 90-days post-d https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1703966/full https://www.frontiersin.org/files/Articles/1703966/fmed-13-1703966-HTML/image_m/fmed-13-1703966-t001.jpg Table 1. Key clinical studies linking OSA and pulmonary nodules. https://www.frontiersin.org/files/Articles/1703966/fmed-13-1703966-HTML/image_m/fmed-13-1703966-g001.jpg Figure 1. Mechanism pathway map of OSA promoting the occurrence and development of pulmonary nodules https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1612749/full https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-g001.jpg Figure 1. Video screening procedure. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-g002.jpg Figure 2. Proportion of different video sources. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-g003.jpg Figure 3. Statistics of videos released in different years. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-t001.jpg Table 1. General characteristics of the videos. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-t002.jpg Table 2. Descriptive statistics of TikTok videos in different categories. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-t003.jpg Table 3. Quality assessment results of different video sources. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-g004.jpg Figure 4. Video quality in different years. (A) DISCERN scores; (B) JAMA scores; (C) GQS scores. Err https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-t004.jpg Table 4. Quality assessment results of different video formats. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-g005.jpg Figure 5. Results of the quality assessment of different video contents. (A) DISCERN Section 1 (Info https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-g006.jpg Figure 6. The 5-level scores of DISCERN. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-t005.jpg Table 5. Results of the difference analysis of different video sources. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-t006.jpg Table 6. Results of the difference analysis of different video formats. https://www.frontiersin.org/files/Articles/1612749/fdgth-07-1612749-HTML/image_m/fdgth-07-1612749-t007.jpg Table 7. Correlation analysis of video variables. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1677653/full https://www.frontiersin.org/files/Articles/1677653/fendo-16-1677653-HTML/image_m/fendo-16-1677653-g000.jpg Graphical Abstract. Pre-DM, prediabetes mellitus; T2DM, type 2 diabetes mellitus; AIP, atherogenic i https://www.frontiersin.org/files/Articles/1677653/fendo-16-1677653-HTML/image_m/fendo-16-1677653-g001.jpg Figure 1. Study flowchart. NG, normal glucose; Pre-DM, prediabetes mellitus; T2DM, type 2 diabetes m https://www.frontiersin.org/files/Articles/1677653/fendo-16-1677653-HTML/image_m/fendo-16-1677653-t001.jpg Table 1. General characteristics of study populations based on the glycation levels. https://www.frontiersin.org/files/Articles/1677653/fendo-16-1677653-HTML/image_m/fendo-16-1677653-t002.jpg Table 2. Analysis of the association of various lipid indices with Pre-DM and T2DM. https://www.frontiersin.org/files/Articles/1677653/fendo-16-1677653-HTML/image_m/fendo-16-1677653-t003.jpg Table 3. Association of AIP with Pre-DM and T2DM. https://www.frontiersin.org/files/Articles/1677653/fendo-16-1677653-HTML/image_m/fendo-16-1677653-t004.jpg Table 4. Association of AIP with pre-DM and T2DM among patients of varying genders and ages. https://www.frontiersin.org/files/Articles/1677653/fendo-16-1677653-HTML/image_m/fendo-16-1677653-g002.jpg Figure 2. Relationship of AIP with Pre-DM and T2DM in patients with IS of different genders and ages https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2025.1701086/full https://www.frontiersin.org/files/Articles/1701086/fmats-12-1701086-HTML/image_m/fmats-12-1701086-g001.jpg Figure 1. Green polymer nanocomposites components. https://www.frontiersin.org/files/Articles/1701086/fmats-12-1701086-HTML/image_m/fmats-12-1701086-g002.jpg Figure 2. Structure of nanoclay. https://www.frontiersin.org/files/Articles/1701086/fmats-12-1701086-HTML/image_m/fmats-12-1701086-g003.jpg Figure 3. Fabrication techniques of GPNCs. https://www.frontiersin.org/files/Articles/1701086/fmats-12-1701086-HTML/image_m/fmats-12-1701086-g004.jpg Figure 4. (a) Elastic modulus (E), (b) tensile strength (TS), and (c) elongation at break (EB) of ea https://www.frontiersin.org/files/Articles/1701086/fmats-12-1701086-HTML/image_m/fmats-12-1701086-g005.jpg Figure 5. DSC curves of composite and nanocomposites (Krystyjan et al., 2021). https://www.frontiersin.org/files/Articles/1701086/fmats-12-1701086-HTML/image_m/fmats-12-1701086-g006.jpg Figure 6. (A) Antibacterial activity of against B. subtilis, S. aureus, and E. coli. (B) In vitro an https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1650194/full https://www.frontiersin.org/files/Articles/1650194/fimmu-16-1650194-HTML/image_m/fimmu-16-1650194-g001.jpg Figure 1. The structure of FoxO3a protein. (A) The sequence and functional domains of FoxO3a. The pr https://www.frontiersin.org/files/Articles/1650194/fimmu-16-1650194-HTML/image_m/fimmu-16-1650194-t001.jpg Table 1. Biological functions of FoxO3a in chondrocytes. https://www.frontiersin.org/files/Articles/1650194/fimmu-16-1650194-HTML/image_m/fimmu-16-1650194-t002.jpg Table 2. Regulatory mechanisms of FoxO3a in chondrocytes. https://www.frontiersin.org/files/Articles/1650194/fimmu-16-1650194-HTML/image_m/fimmu-16-1650194-t003.jpg Table 3. Role of FoxO3a in subchondral bone, synovium, and meniscus. https://www.frontiersin.org/files/Articles/1650194/fimmu-16-1650194-HTML/image_m/fimmu-16-1650194-t004.jpg Table 4. Applications of FoxO3a in OA diagnosis. https://www.frontiersin.org/files/Articles/1650194/fimmu-16-1650194-HTML/image_m/fimmu-16-1650194-t005.jpg Table 5. Applications of targeting FoxO3a in OA treatment. https://www.frontiersin.org/files/Articles/1650194/fimmu-16-1650194-HTML/image_m/fimmu-16-1650194-t006.jpg Table 6. Limitations of FoxO3a-targeted therapeutic approaches. https://www.frontiersin.org/files/Articles/1650194/fimmu-16-1650194-HTML/image_m/fimmu-16-1650194-g002.jpg Figure 2. The regulation and biological functions of FoxO3a in chondrocytes. Various stressors, incl https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1785040/full https://www.frontiersin.org/files/Articles/1785040/fphys-17-1785040-HTML/image_m/fphys-17-1785040-t001.jpg Table 1. Biodex isokinetic exercise protocol under different AOP conditions. https://www.frontiersin.org/files/Articles/1785040/fphys-17-1785040-HTML/image_m/fphys-17-1785040-g001.jpg Figure 1. Schematic of surface electromyography (sEMG) and blood lactate (BLa) collection. https://www.frontiersin.org/files/Articles/1785040/fphys-17-1785040-HTML/image_m/fphys-17-1785040-g002.jpg Figure 2. Venous blood collection. https://www.frontiersin.org/files/Articles/1785040/fphys-17-1785040-HTML/image_m/fphys-17-1785040-t002.jpg Table 2. Lower-limb muscle %EMGmax under different AOP conditions with RM-ANOVA main-effect statisti https://www.frontiersin.org/files/Articles/1785040/fphys-17-1785040-HTML/image_m/fphys-17-1785040-t003.jpg Table 3. Blood lactate (mmol·L-1) dynamic changes (Mean ± SD, n = 12). https://www.frontiersin.org/files/Articles/1785040/fphys-17-1785040-HTML/image_m/fphys-17-1785040-t004.jpg Table 4. RPE scores (Borg 6–20 scale) post-exercise (Greenhouse–Geisser corrected) (Mean ± SD, n = 1 https://www.frontiersin.org/files/Articles/1785040/fphys-17-1785040-HTML/image_m/fphys-17-1785040-t005.jpg Table 5. Descriptive statistics for IL-6 and TST (mean ± SD) with RM-ANOVA p-values for Time, Pressu https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1627460/full https://www.frontiersin.org/files/Articles/1627460/fcvm-12-1627460-HTML/image_m/fcvm-12-1627460-g001.jpg Figure 1. Representative example of acquisition of the frontal (A) and lateral (B) images in a male https://www.frontiersin.org/files/Articles/1627460/fcvm-12-1627460-HTML/image_m/fcvm-12-1627460-t001.jpg Table 1. Body surface area (BSA) predictive equations for comparison with 3-dimensional optical imag https://www.frontiersin.org/files/Articles/1627460/fcvm-12-1627460-HTML/image_m/fcvm-12-1627460-t002.jpg Table 2. Median (1st–3rd quartile) values of body surface area (BSA) estimates obtained in the sampl https://www.frontiersin.org/files/Articles/1627460/fcvm-12-1627460-HTML/image_m/fcvm-12-1627460-g002.jpg Figure 2. Top panels: correlations between 3-dimentional optical imaging (3DOI)-derived body surface https://www.frontiersin.org/files/Articles/1627460/fcvm-12-1627460-HTML/image_m/fcvm-12-1627460-g003.jpg Figure 3. Violin plots of the body surface area (BSA) estimates obtained with the 3-dimentional opti https://www.frontiersin.org/files/Articles/1627460/fcvm-12-1627460-HTML/image_m/fcvm-12-1627460-g004.jpg Figure 4. Relative bias and relative standard deviation (SD) of the differences between the 3-diment https://www.frontiersin.org/files/Articles/1627460/fcvm-12-1627460-HTML/image_m/fcvm-12-1627460-g005.jpg Figure 5. Violin plots of the body surface area (BSA) estimates obtained with ten predictive equatio https://www.frontiersin.org/files/Articles/1627460/fcvm-12-1627460-HTML/image_m/fcvm-12-1627460-t003.jpg Table 3. Median (1st–3rd quartile) values of absolute and relative (normalized to body surface area— https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1780811/full https://www.frontiersin.org/files/Articles/1780811/fped-14-1780811-HTML/image_m/fped-14-1780811-g001.jpg Figure 1. Flow diagram of the study selection process for the review. https://www.frontiersin.org/files/Articles/1780811/fped-14-1780811-HTML/image_m/fped-14-1780811-t001.jpg Table 1. Perioperative optimization strategies in children with obesity and enhanced recovery after https://www.frontiersin.org/files/Articles/1780811/fped-14-1780811-HTML/image_m/fped-14-1780811-t002.jpg Table 2. Summary of studies reporting outcomes of abdominal surgery in children with obesity. https://www.frontiersin.org/files/Articles/1780811/fped-14-1780811-HTML/image_m/fped-14-1780811-t003.jpg Table 3. Pre and post-operative strategies in children with obesity undergoing abdominal surgery. https://www.frontiersin.org/files/Articles/1780811/fped-14-1780811-HTML/image_m/fped-14-1780811-t004.jpg Table 4. Risks of surgical complications and preventive enhanced recovery after surgery (ERAS)- stra https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2025.1620861/full https://www.frontiersin.org/files/Articles/1620861/froh-06-1620861-HTML/image_m/froh-06-1620861-g001.jpg Figure 1. Flow diagram of the process of trial inclusion/exclusion. https://www.frontiersin.org/files/Articles/1620861/froh-06-1620861-HTML/image_m/froh-06-1620861-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1620861/froh-06-1620861-HTML/image_m/froh-06-1620861-t002.jpg Table 2. Clinical parameters of periodontitis. https://www.frontiersin.org/files/Articles/1620861/froh-06-1620861-HTML/image_m/froh-06-1620861-t003.jpg Table 2.1. Clinical parameters of periodontitis. https://www.frontiersin.org/files/Articles/1620861/froh-06-1620861-HTML/image_m/froh-06-1620861-t004.jpg Table 3. Linear regression model—dependent variable radiographic boneloss during OP. https://www.frontiersin.org/files/Articles/1620861/froh-06-1620861-HTML/image_m/froh-06-1620861-t005.jpg Table 4. Poisson regression model—dependent variable toothloss during OP. https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2026.1768387/full https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-t001.jpg Table 1. Index Properties of the soil. https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-g001.jpg Figure 1. Particle size distribution of the soil. https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-t002.jpg Table 2. Oxide composition of the lateritic soil and WHA. https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-g002.jpg Figure 2. Compaction behaviour of the natural soil. https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-g003.jpg Figure 3. Performance of the stabilized soil using plasticity chart. https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-g004.jpg Figure 4. Compaction characteristics of the stabilized soil. https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-g005.jpg Figure 5. UCS behaviour of the stabilized soil. https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-g006.jpg Figure 6. Micrograph of the natural and stabilized soil. https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-t003.jpg Table 3. Energy dispersive X-ray analysis of the natural and 6% Lime +2% WHA stabilized soil. https://www.frontiersin.org/files/Articles/1768387/fbuil-12-1768387-HTML-r1/image_m/fbuil-12-1768387-g007.jpg Figure 7. EDS spectra of 6L+2WHA + Soil mixture. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1778010/full https://www.frontiersin.org/files/Articles/1778010/fpsyg-17-1778010-HTML/image_m/fpsyg-17-1778010-g001.jpg Figure 1. Conceptual framework adapted from Lyubomirsky and Layous’s positive-activity model (Lyubom https://www.frontiersin.org/files/Articles/1778010/fpsyg-17-1778010-HTML/image_m/fpsyg-17-1778010-g002.jpg Figure 2. Participant flow diagram for the randomized controlled trial. This diagram shows the numbe https://www.frontiersin.org/files/Articles/1778010/fpsyg-17-1778010-HTML/image_m/fpsyg-17-1778010-t001.jpg Table 1. Baseline sociodemographic characteristics and optimism scores (LOT-R) for the intervention https://www.frontiersin.org/files/Articles/1778010/fpsyg-17-1778010-HTML/image_m/fpsyg-17-1778010-t002.jpg Table 2. Short-term intervention effects during the early post-intervention period (n = 28). https://www.frontiersin.org/files/Articles/1778010/fpsyg-17-1778010-HTML/image_m/fpsyg-17-1778010-t003.jpg Table 3. Mixed-effects model estimates for long-term effects on optimism (LOT-R). https://www.frontiersin.org/files/Articles/1778010/fpsyg-17-1778010-HTML/image_m/fpsyg-17-1778010-t004.jpg Table 4. Mixed-effects model interaction estimates (month × intervention) for psychological outcomes https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1794319/full https://www.frontiersin.org/files/Articles/1794319/fnut-13-1794319-HTML-r1/image_m/fnut-13-1794319-t001.jpg Table 1. Baseline demographic and clinical characteristics of patients with lcSSc and controls. https://www.frontiersin.org/files/Articles/1794319/fnut-13-1794319-HTML-r1/image_m/fnut-13-1794319-t002.jpg Table 2. Exploratory comparison of lipid parameters between patients with lcSSc and controls. https://www.frontiersin.org/files/Articles/1794319/fnut-13-1794319-HTML-r1/image_m/fnut-13-1794319-t003.jpg Table 3. Exploratory correlations of lipid parameters with parameters of endothelial dysfunction in https://www.frontiersin.org/files/Articles/1794319/fnut-13-1794319-HTML-r1/image_m/fnut-13-1794319-t004.jpg Table 4. Exploratory associations of lipid parameters with vascular and clinical events in lcSSc. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1633930/full https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-g001.jpg Figure 1. Flow chart of patient inclusion. https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-t001.jpg Table 1. Characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-g002.jpg Figure 2. The OS (A) and PFS (B) of the entire cohort; the OS (C) and PFS (D) of the matched cohort. https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-t002.jpg Table 2. Univariate and multivariate Cox regression analyses for OS for ESCC patients before propens https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-t003.jpg Table 3. Univariate and multivariate Cox regression analyses for PFS for ESCC patients before propen https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-t004.jpg Table 4. Univariate and multivariate Cox regression analyses for OS and PFS for ESCC patients after https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-g003.jpg Figure 3. Forest plots show factors associated with OS (A) and PFS (B) of thoracic segment esophagea https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-g004.jpg Figure 4. The OS (A) and PFS (B) in patients with distant organ involvement in the entire cohort; th https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-g005.jpg Figure 5. The OS (A) and PFS (B) of stage III patients within the entire cohort; the OS (C) and PFS https://www.frontiersin.org/files/Articles/1633930/fimmu-16-1633930-HTML/image_m/fimmu-16-1633930-t005.jpg Table 5. Acute toxicities in the 334 patients of the matched cohort. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1728912/full https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-g001.jpg Figure 1. Flow chart. https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-t001.jpg Table 1. Baseline Characteristics. https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-t002.jpg Table 2. Sequence of RT and ICI. https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-t003.jpg Table 3. Changes in hematologic parameters before, during, and after RT, and the impact of concurren https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-g002.jpg Figure 2. Dynamic hematologic changes during radiotherapy with or without concurrent ICI. https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-g003.jpg Figure 3. Overall survival (OS) and progression-free survival (PFS) of the entire cohort and the imp https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-g004.jpg Figure 4. Effects of treatment modalities on overall survival (OS) and progression-free survival (PF https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-g005.jpg Figure 5. Prognostic impact of hematologic parameters on overall survival (OS) and progression-free https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-t004.jpg Table 4. Univariate and multivariate analyses of overall survival and progression-free survival. https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-t005.jpg Table 5. The toxic effects between Con (C)RT-ICI group and Int (C)RT-ICI group. https://www.frontiersin.org/files/Articles/1728912/fimmu-17-1728912-HTML-r1/image_m/fimmu-17-1728912-t006.jpg Table 6. Failure patterns. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1632835/full https://www.frontiersin.org/files/Articles/1632835/fphar-16-1632835-HTML/image_m/fphar-16-1632835-t001.jpg Table 1. Case characteristics and patient demographics. https://www.frontiersin.org/files/Articles/1632835/fphar-16-1632835-HTML/image_m/fphar-16-1632835-t002.jpg Table 2. Mitragynine specific information. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1653752/full https://www.frontiersin.org/files/Articles/1653752/fpsyg-16-1653752-HTML/image_m/fpsyg-16-1653752-t001.jpg Table 1. Direct effects within the path analysis. https://www.frontiersin.org/files/Articles/1653752/fpsyg-16-1653752-HTML/image_m/fpsyg-16-1653752-t002.jpg Table 2. Mediating effects within the model. https://www.frontiersin.org/files/Articles/1653752/fpsyg-16-1653752-HTML/image_m/fpsyg-16-1653752-t003.jpg Table 3. Path analysis based on scale totals. https://www.frontiersin.org/files/Articles/1653752/fpsyg-16-1653752-HTML/image_m/fpsyg-16-1653752-g001.jpg Figure 1. Paths and magnitude of significant values based on the dimensions in the model. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1650810/full https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-g001.jpg Figure 1. Consort diagram for Ph-like ALL selection, chosen clinical variables, and study workflow. https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-t001.jpg Table 1. Patient features. https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-t002.jpg Table 2. Cox proportional hazards regression analysis. https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-g002.jpg Figure 2. Variable importance rankings for 1/3/5-year predictions. (A) Variable importance in Cox pr https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-g003.jpg Figure 3. Hyperparameter tuning for machine learning models. (A) Hyperparameter tuning for RF. (B) H https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-g004.jpg Figure 4. Comparison between machine learning models and Cox proportional hazards regression model. https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-g005.jpg Figure 5. ROC comparison of predictions on the training set at different time points among various m https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-g006.jpg Figure 6. (A) Risk SHAP values for continuous variables in the RF model. (B) Mean SHAP values of var https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-g007.jpg Figure 7. Impact of D33 MRD on event-free survival in Ph-like ALL. MRD negativity is achieved post-f https://www.frontiersin.org/files/Articles/1650810/fcell-13-1650810-HTML-r1/image_m/fcell-13-1650810-g008.jpg Figure 8. Performance of AUROC in external validation dataset. https://www.frontiersin.org/journals/ophthalmology/articles/10.3389/fopht.2026.1769734/full https://www.frontiersin.org/files/Articles/1769734/fopht-06-1769734-HTML/image_m/fopht-06-1769734-t001.jpg Table 1. Demographic data and binocular status of the 400 preoperative myopic patients. https://www.frontiersin.org/files/Articles/1769734/fopht-06-1769734-HTML/image_m/fopht-06-1769734-t002.jpg Table 2. Demographic data and right eye status of the 400 participants. https://www.frontiersin.org/files/Articles/1769734/fopht-06-1769734-HTML/image_m/fopht-06-1769734-t003.jpg Table 3. Baseline and corneal endothelial cell characteristics among different axial length groups. https://www.frontiersin.org/files/Articles/1769734/fopht-06-1769734-HTML/image_m/fopht-06-1769734-t004.jpg Table 4. Baseline and corneal endothelial cell characteristics among different spherical equivalent https://www.frontiersin.org/files/Articles/1769734/fopht-06-1769734-HTML/image_m/fopht-06-1769734-t005.jpg Table 5. Baseline and corneal endothelial cell characteristics between different age groups. . https://www.frontiersin.org/files/Articles/1769734/fopht-06-1769734-HTML/image_m/fopht-06-1769734-g001.jpg Figure 1. Scatter plot showing a significant negative correlation between endothelial cell density ( https://www.frontiersin.org/files/Articles/1769734/fopht-06-1769734-HTML/image_m/fopht-06-1769734-t006.jpg Table 6. The univariate and multivariate linear regression models for corneal endothelial cell densi https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1703853/full https://www.frontiersin.org/files/Articles/1703853/fmicb-16-1703853-HTML/image_m/fmicb-16-1703853-g001.jpg Figure 1. Experimental procedure flowchart. https://www.frontiersin.org/files/Articles/1703853/fmicb-16-1703853-HTML/image_m/fmicb-16-1703853-g002.jpg Figure 2. HPLC chromatogram (at 320 nm) of WEPT (A) and the reference substance (B). https://www.frontiersin.org/files/Articles/1703853/fmicb-16-1703853-HTML/image_m/fmicb-16-1703853-g003.jpg Figure 3. WEPT improves lung function, alleviates lung injury, and reduces inflammatory factor level https://www.frontiersin.org/files/Articles/1703853/fmicb-16-1703853-HTML/image_m/fmicb-16-1703853-g004.jpg Figure 4. Network pharmacological results of Polygala tenuifolia in the treatment of COPD. (A) Inter https://www.frontiersin.org/files/Articles/1703853/fmicb-16-1703853-HTML/image_m/fmicb-16-1703853-g005.jpg Figure 5. Transcriptomics analysis results of WEPT treated mice, CS group mice and NC group mice (n  https://www.frontiersin.org/files/Articles/1703853/fmicb-16-1703853-HTML/image_m/fmicb-16-1703853-g006.jpg Figure 6. WEPT down-regulated the PI3K-Akt signaling pathway (n = 3). (A) The results of the docking https://www.frontiersin.org/files/Articles/1703853/fmicb-16-1703853-HTML/image_m/fmicb-16-1703853-g007.jpg Figure 7. WEPT modulated the abnormal gut bacterial microbiota in COPD mice. (A) The results of ɑ-di https://www.frontiersin.org/files/Articles/1703853/fmicb-16-1703853-HTML/image_m/fmicb-16-1703853-g008.jpg Figure 8. Integrative analysis reveals potential mechanisms underlying the therapeutic effects of WE https://www.frontiersin.org/files/Articles/1703853/fmicb-16-1703853-HTML/image_m/fmicb-16-1703853-g009.jpg Figure 9. Schematic diagram of WEPT treatment in the COPD model: mainly through down-regulation of t https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1742313/full https://www.frontiersin.org/files/Articles/1742313/fmed-12-1742313-HTML/image_m/fmed-12-1742313-g001.jpg Figure 1. An overview of (a) cell-based therapies for treatment of RA, including genome editing and https://www.frontiersin.org/files/Articles/1742313/fmed-12-1742313-HTML/image_m/fmed-12-1742313-g002.jpg Figure 2. Schematic of gene-targeted therapy with surgical decision-making in RA (personalized appro https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1671705/full https://www.frontiersin.org/files/Articles/1671705/fmed-13-1671705-HTML-r1/image_m/fmed-13-1671705-t001.jpg Table 1. Baseline clinical characteristics of the overall cohort (N = 174). https://www.frontiersin.org/files/Articles/1671705/fmed-13-1671705-HTML-r1/image_m/fmed-13-1671705-t002.jpg Table 2. Baseline clinical and laboratory characteristics: early death (ED) vs. non-ED (N = 174). https://www.frontiersin.org/files/Articles/1671705/fmed-13-1671705-HTML-r1/image_m/fmed-13-1671705-t003.jpg Table 3. Baseline clinical and laboratory characteristics: relapse vs. non-relapse (N = 165; excludi https://www.frontiersin.org/files/Articles/1671705/fmed-13-1671705-HTML-r1/image_m/fmed-13-1671705-t004.jpg Table 4. Baseline clinical and laboratory characteristics: second primary malignancies (SPMs) vs. no https://www.frontiersin.org/files/Articles/1671705/fmed-13-1671705-HTML-r1/image_m/fmed-13-1671705-g001.jpg Figure 1. Cumulative incidence function for SPMs. https://www.frontiersin.org/files/Articles/1671705/fmed-13-1671705-HTML-r1/image_m/fmed-13-1671705-g002.jpg Figure 2. Cumulative incidence function for relapse. https://www.frontiersin.org/files/Articles/1671705/fmed-13-1671705-HTML-r1/image_m/fmed-13-1671705-g003.jpg Figure 3. Kaplan-Meier (30-day survival). https://www.frontiersin.org/files/Articles/1671705/fmed-13-1671705-HTML-r1/image_m/fmed-13-1671705-t005.jpg Table 5. Treatment-era comparison of key outcomes (Era 0 vs. Era 1; N = 174). https://www.frontiersin.org/files/Articles/1671705/fmed-13-1671705-HTML-r1/image_m/fmed-13-1671705-g004.jpg Figure 4. IPTW covariate balance (love plot). https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1727117/full https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-t001.jpg Table 1. Main clinical and laboratory manifestations of 46 pPCL patients. https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-t002.jpg Table 2. Univariate analysis of clinical characteristics predictive of OS and TTNT. https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-t003.jpg Table 3. Multivariate analysis of clinical characteristics predictive of OS and TTNT. https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-g001.jpg Figure 1. TTNT and OS in pPCL patients stratified by: (A) Serum LDH levels; (B) 1q21+ status; (C) Pl https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-g002.jpg Figure 2. TTNT and OS in pPCL patients stratified by achievement of ≥VGPR with first-line therapy: ( https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-t004.jpg Table 4. Summary of best response according to different generations of PIs and IMiDs. https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-t005.jpg Table 5. Summary of best response according to different treatments. https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-g003.jpg Figure 3. Hematologic response rates to first-line therapy: (A) ORR; (B) ≥VGPR rate. https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-g004.jpg Figure 4. Treatment sequencing and outcomes: (A) Swimmer plot of therapy duration across sequential https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-g005.jpg Figure 5. Response kinetics and survival patterns: (A, B) Serial quantification of: M-protein levels https://www.frontiersin.org/files/Articles/1727117/fonc-15-1727117-HTML/image_m/fonc-15-1727117-t006.jpg Table 6. Clinical outcomes with Ven-based induction. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1634545/full https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-t001.jpg Table 1. Search strategy in PubMed. https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-g001.jpg Figure 1. Flow diagram of included studies. https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-t002.jpg Table 2. Basic characteristics of the included literature. https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-g002.jpg Figure 2. Summary of risk bias. https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-g003.jpg Figure 3. Network diagram for each outcome. (A) WC; (B) SBP; (C) DBP; (D) TG; (E) HDL-C; (F) FBG. 1, https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-t003.jpg Table 3. League table of all pairwise comparisons of the effects of dietary patterns on WC in MetS p https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-g004.jpg Figure 4. SUCRA plots of different outcome indicators in MetS patients treated with different dietar https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-t004.jpg Table 4. League table of all pairwise comparisons of the effects of dietary patterns on SBP in MetS https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-t005.jpg Table 5. League table of all pairwise comparisons of the effects of dietary patterns on DBP in MetS https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-t006.jpg Table 6. League table of all pairwise comparisons of the effects of dietary patterns on triglyceride https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-t007.jpg Table 7. League table of all pairwise comparisons of the effects of dietary patterns on HDL-C in Met https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-t008.jpg Table 8. League table of all pairwise comparisons of the effects of dietary patterns on FBG in MetS https://www.frontiersin.org/files/Articles/1634545/fnut-12-1634545-HTML-r1/image_m/fnut-12-1634545-g005.jpg Figure 5. Publication bias of different outcome measures. 1, WC; 2, SBP; 3, DBP; 4, TG; 5, HDL-C; 6, https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1660116/full https://www.frontiersin.org/files/Articles/1660116/fpubh-13-1660116-HTML/image_m/fpubh-13-1660116-t001.jpg Table 1. Characteristics of the participants (N = 333). https://www.frontiersin.org/files/Articles/1660116/fpubh-13-1660116-HTML/image_m/fpubh-13-1660116-t002.jpg Table 2. The top 3 items with the highest error rates/lowest scores in each dimension of ovarian res https://www.frontiersin.org/files/Articles/1660116/fpubh-13-1660116-HTML/image_m/fpubh-13-1660116-t003.jpg Table 3. Indicators of latent profile analysis for ovarian reserve function KAP. https://www.frontiersin.org/files/Articles/1660116/fpubh-13-1660116-HTML/image_m/fpubh-13-1660116-g001.jpg Figure 1. Distribution of characteristics among the three latent profiles of ovarian reserve functio https://www.frontiersin.org/files/Articles/1660116/fpubh-13-1660116-HTML/image_m/fpubh-13-1660116-t004.jpg Table 4. Univariate analysis of latent profiles for ovarian reserve function KAP [n (%)]. https://www.frontiersin.org/files/Articles/1660116/fpubh-13-1660116-HTML/image_m/fpubh-13-1660116-t005.jpg Table 5. Multivariate logistic regression analysis of influencing factors for latent profiles of ova https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1728887/full https://www.frontiersin.org/files/Articles/1728887/fped-13-1728887-HTML/image_m/fped-13-1728887-g001.jpg Figure 1. Light microscopy of renal biopsy: (A) H&E staining, (B) masson staining, and (C) PASM stai https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1696089/full https://www.frontiersin.org/files/Articles/1696089/fped-14-1696089-HTML/image_m/fped-14-1696089-t001.jpg Table 1. Cohort flow and genetic findings from 3,386 pregnancies to 9 VUS-CNV cases. https://www.frontiersin.org/files/Articles/1696089/fped-14-1696089-HTML/image_m/fped-14-1696089-t002.jpg Table 2. Comparison of CNV size distribution between pathogenic/likely pathogenic CNVs and VUS-CNVs https://www.frontiersin.org/files/Articles/1696089/fped-14-1696089-HTML/image_m/fped-14-1696089-t003.jpg Table 3. VUS analysis of 9 cases of heart malformations in fetuses. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1752087/full https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-g001.jpg Figure 1. Theoretical model of the relationships between team cohesion, basic psychological need sat https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-t001.jpg Table 1. Demographic characteristics of participants (N = 390). https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-t002.jpg Table 2. Descriptive statistics and correlation matrix of core variables. https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-t003.jpg Table 3. Factor loadings, average variance extracted (AVE), and reliability indices of the measureme https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-t004.jpg Table 4. Discriminant validity of the latent variables based on Fornell–Larcker criterion and HTMT. https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-t005.jpg Table 5. Coefficients of determination (R2) for endogenous variables. https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-g002.jpg Figure 2. Structural model with standardized path coefficients and R2 values. https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-g003.jpg Figure 3. Distributions of team cohesion (A), basic psychological need satisfaction in exercise (B), https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-t006.jpg Table 6. Structural model assessment: Path coefficients, t-values, p-values, f2. https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-t007.jpg Table 7. PLSpredict results for endogenous latent variables. https://www.frontiersin.org/files/Articles/1752087/fpsyg-17-1752087-HTML/image_m/fpsyg-17-1752087-t008.jpg Table 8. Indirect, direct, and total effects with 95% bootstrap confidence intervals. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1602976/full https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-t001.jpg Table 1. Vitamin B12 Adequate Intake [AI] across life course (EFSA NDA Panel (EFSA Panel on Dietetic https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-g001.jpg Figure 1. The flow diagram of the search. https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-t002.jpg Table 2. Characteristics of the included studies. https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-t003.jpg Table 3. Jadad scale for randomised controlled trials. https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-t004.jpg Table 4. Newcastle - Ottawa quality assessment scale for case-control studies. https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-t005.jpg Table 5. Newcastle - Ottawa quality assessment scale cohort studies. https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-g002.jpg Figure 2. Funnel plot illustrating the mean change in cobalamin levels, indicating potential publica https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-g003.jpg Figure 3. Cobalamin levels significantly increase following vitamin B12 administration. Differences https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-g004.jpg Figure 4. Differences in mean change in cobalamin levels between pathology groups were not statistic https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-g005.jpg Figure 5. Differences in mean change in cobalamin levels between age groups were not statistically s https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-g006.jpg Figure 6. Differences in mean change in cobalamin levels between dosage groups were not statisticall https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-g007.jpg Figure 7. Homocysteine levels significantly decrease following vitamin B12 administration. Differenc https://www.frontiersin.org/files/Articles/1602976/fphar-16-1602976-HTML/image_m/fphar-16-1602976-g008.jpg Figure 8. Funnel plot illustrating the mean change in homocysteine levels, indicating potential publ https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2025.1647945/full https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-t001.jpg Table 1. Collection of teeth and patient information. https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-t002.jpg Table 2. Sample numbers for various characterization and testing methods. https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-t003.jpg Table 3. Primer sequences for RT-PCR. https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-g001.jpg Figure 1. Scanning electron microscope images: (A) NC; (B) Ca/P; (C) Ca/P + F; (D) Ca/P + Zn; (E) Ca https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-t004.jpg Table 4. Relative contents of each element. https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-g002.jpg Figure 2. XRD and XPS patterns: (A,E) Ca/P + F; (B,F) Ca/P + Zn; (C,G) Ca/P + Mg; (D,H) Ca/P + Sr; ( https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-g003.jpg Figure 3. (A) Enamel without acid etching; (B) acid-etched enamel surface; (C) SEM image of acid-etc https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-g004.jpg Figure 4. (A–F) SEM images after co-culture with S. mutans (NC; Ca/P; Ca/P + F; Ca/P + Zn; Ca/P + Mg https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-t005.jpg Table 5. Ph values. https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-g005.jpg Figure 5. KEGG enrichment analysis (A) Ca/P + F; (B) Ca/P + Sr; (C) Ca/P + Mg; (D) Ca/P + Zn; (E) Ca https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-g006.jpg Figure 6. Diagram showing differences in amino acid metabolites. https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-t006.jpg Table 6. Relative contents of acidic metabolites. https://www.frontiersin.org/files/Articles/1647945/froh-06-1647945-HTML/image_m/froh-06-1647945-g007.jpg Figure 7. (A) Bacterial proliferation; (B) spaP gene expression; (C) gbpB gene expression; (D) ldh g https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2025.1709163/full https://www.frontiersin.org/files/Articles/1709163/froh-06-1709163-HTML/image_m/froh-06-1709163-g001.jpg Figure 1. Flow diagram of cohort selection and bar graphs showing distribution of age at saliva coll https://www.frontiersin.org/files/Articles/1709163/froh-06-1709163-HTML/image_m/froh-06-1709163-t001.jpg Table 1. Study group demographics for participants with information on caries or periodontal status. https://www.frontiersin.org/files/Articles/1709163/froh-06-1709163-HTML/image_m/froh-06-1709163-g002.jpg Figure 2. Overview of saliva microbiota profile. The distribution of (A) observed bacterial amplicon https://www.frontiersin.org/files/Articles/1709163/froh-06-1709163-HTML/image_m/froh-06-1709163-g003.jpg Figure 3. Results from MaAsLin3 regression of species prevalence and relative abundance in those car https://www.frontiersin.org/files/Articles/1709163/froh-06-1709163-HTML/image_m/froh-06-1709163-g004.jpg Figure 4. Heat maps for Spearman correlation coefficients between dental scores at the first availab https://www.frontiersin.org/files/Articles/1709163/froh-06-1709163-HTML/image_m/froh-06-1709163-g005.jpg Figure 5. Volcano plot illustrating associations between saliva species and DMFS (A,B) or D3 (C,D). https://www.frontiersin.org/files/Articles/1709163/froh-06-1709163-HTML/image_m/froh-06-1709163-g006.jpg Figure 6. Volcano plot illustrating associations between saliva species and having two or more 6-mm- https://www.frontiersin.org/files/Articles/1709163/froh-06-1709163-HTML/image_m/froh-06-1709163-g007.jpg Figure 7. Associations between F. alocis presence or relative abundance and periodontal status. (A) https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1809053/full https://www.frontiersin.org/files/Articles/1809053/fped-14-1809053-HTML/image_m/fped-14-1809053-g001.jpg Figure 1. Participants selection. PRO, patient reported outcomes. https://www.frontiersin.org/files/Articles/1809053/fped-14-1809053-HTML/image_m/fped-14-1809053-t001.jpg Table 1. Characteristics of study groups. https://www.frontiersin.org/files/Articles/1809053/fped-14-1809053-HTML/image_m/fped-14-1809053-t002.jpg Table 2. Baseline FODMAP, energy, and nutrients intake. https://www.frontiersin.org/files/Articles/1809053/fped-14-1809053-HTML/image_m/fped-14-1809053-t003.jpg Table 3. Effect of a low-FODMAP diet on change in abdominal pain intensity (the available case analy https://www.frontiersin.org/journals/ethology/articles/10.3389/fetho.2026.1777695/full https://www.frontiersin.org/files/Articles/1777695/fetho-05-1777695-HTML/image_m/fetho-05-1777695-g001.jpg Figure 1. (A) pen schemes for both control and treatment groups; access to each pen is marked with a https://www.frontiersin.org/files/Articles/1777695/fetho-05-1777695-HTML/image_m/fetho-05-1777695-t001.jpg Table 1. Ethogram for the direct observation of adult maras (Dolichotis patagonum) housed in zoo. https://www.frontiersin.org/files/Articles/1777695/fetho-05-1777695-HTML/image_m/fetho-05-1777695-g002.jpg Figure 2. Behavioral synchronization for mara (Dolichotis patagonum) pairs in control and treatment https://www.frontiersin.org/files/Articles/1777695/fetho-05-1777695-HTML/image_m/fetho-05-1777695-t002.jpg Table 2. Absolute frequencies of records obtained for all mara pairs from the control group in month https://www.frontiersin.org/files/Articles/1777695/fetho-05-1777695-HTML/image_m/fetho-05-1777695-t003.jpg Table 3. Absolute frequencies of records obtained for all mara pairs from the treatment group in mon https://www.frontiersin.org/files/Articles/1777695/fetho-05-1777695-HTML/image_m/fetho-05-1777695-g003.jpg Figure 3. Cumulative proportion of behaviors observed in control and treatment groups, distinguishin https://www.frontiersin.org/files/Articles/1777695/fetho-05-1777695-HTML/image_m/fetho-05-1777695-g004.jpg Figure 4. Record per hour of the sitting in alert state per male mara from the control and treatment https://www.frontiersin.org/files/Articles/1777695/fetho-05-1777695-HTML/image_m/fetho-05-1777695-g005.jpg Figure 5. Record per hour of the sitting in alert state per female mara from the control and treatme https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1784147/full https://www.frontiersin.org/files/Articles/1784147/fphar-17-1784147-HTML/image_m/fphar-17-1784147-g001.jpg Figure 1. The mechanism of action of ubiquitination on atrial fibrillation. Ubiquitination can affec https://www.frontiersin.org/files/Articles/1784147/fphar-17-1784147-HTML/image_m/fphar-17-1784147-t001.jpg Table 1. Ubiquitinating and deubiquitinating enzymes regulating key substrates in atrial fibrillatio https://www.frontiersin.org/files/Articles/1784147/fphar-17-1784147-HTML/image_m/fphar-17-1784147-t002.jpg Table 2. Small molecule drugs and their targets related to ubiquitination in atrial fibrillation. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1660937/full https://www.frontiersin.org/files/Articles/1660937/fonc-15-1660937-HTML-r1/image_m/fonc-15-1660937-g001.jpg Figure 1. Pictorial representation of the employed modified Delphi consensus process. https://www.frontiersin.org/files/Articles/1660937/fonc-15-1660937-HTML-r1/image_m/fonc-15-1660937-t001.jpg Table 1. Finalized consensus statements after the second Delphi round. https://www.frontiersin.org/files/Articles/1660937/fonc-15-1660937-HTML-r1/image_m/fonc-15-1660937-g002.jpg Figure 2. Overall distribution of consensus levels for 54 statements evaluated in Delphi Round 1 on https://www.frontiersin.org/files/Articles/1660937/fonc-15-1660937-HTML-r1/image_m/fonc-15-1660937-g003.jpg Figure 3. Consensus levels by domain for statements addressing HDMTX regimen, risk factors, supporti https://www.frontiersin.org/files/Articles/1660937/fonc-15-1660937-HTML-r1/image_m/fonc-15-1660937-g004.jpg Figure 4. Algorithm for monitoring renal function and rescue strategies after high-dose methotrexate https://www.frontiersin.org/files/Articles/1660937/fonc-15-1660937-HTML-r1/image_m/fonc-15-1660937-t002.jpg Table 2. HDMTX management as per NCCN and SIOPE guidelines and the present consensus. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1743102/full https://www.frontiersin.org/files/Articles/1743102/fphar-16-1743102-HTML/image_m/fphar-16-1743102-g001.jpg Figure 1. An overview of the Hippo signaling pathway in activated and non-activated state. https://www.frontiersin.org/files/Articles/1743102/fphar-16-1743102-HTML/image_m/fphar-16-1743102-t001.jpg Table 1. Association of various targets of Hippo signaling pathway including YAP, TAZ, MST and LATS https://www.frontiersin.org/files/Articles/1743102/fphar-16-1743102-HTML/image_m/fphar-16-1743102-t002.jpg Table 2. Effects of various plant derived anticancer compounds on cellular and molecular targets of https://www.frontiersin.org/files/Articles/1743102/fphar-16-1743102-HTML/image_m/fphar-16-1743102-g002.jpg Figure 2. Modulatory effects of plant derived anticancer compounds (apigenin, curcumin, EGCG, resver https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1703178/full https://www.frontiersin.org/files/Articles/1703178/fnut-12-1703178-HTML/image_m/fnut-12-1703178-t001.jpg Table 1. Plant-derived miRNAs with evidence of cross-kingdom regulation in mammalian models. https://www.frontiersin.org/files/Articles/1703178/fnut-12-1703178-HTML/image_m/fnut-12-1703178-t002.jpg Table 2. Native regulatory functions of dietary plant-derived miRNAs in plant biology. https://www.frontiersin.org/files/Articles/1703178/fnut-12-1703178-HTML/image_m/fnut-12-1703178-g001.jpg Figure 1. The cross-kingdom journey of dietary plant miRNAs: from absorption controversy to metaboli https://www.frontiersin.org/files/Articles/1703178/fnut-12-1703178-HTML/image_m/fnut-12-1703178-g002.jpg Figure 2. Mechanism of plant-derived miRNAs in the multi-organ regulation of glucose and lipid metab https://www.frontiersin.org/files/Articles/1703178/fnut-12-1703178-HTML/image_m/fnut-12-1703178-t003.jpg Table 3. Examples of the effects of plant-derived miRNAs in animal models of metabolic diseases. https://www.frontiersin.org/files/Articles/1703178/fnut-12-1703178-HTML/image_m/fnut-12-1703178-t004.jpg Table 4. Strategies and key technologies for optimizing dietary sources. https://www.frontiersin.org/files/Articles/1703178/fnut-12-1703178-HTML/image_m/fnut-12-1703178-t005.jpg Table 5. Delivery systems for plant miRNAs. https://www.frontiersin.org/files/Articles/1703178/fnut-12-1703178-HTML/image_m/fnut-12-1703178-t006.jpg Table 6. Major translational challenges and future research directions. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1680486/full https://www.frontiersin.org/files/Articles/1680486/fmed-12-1680486-HTML/image_m/fmed-12-1680486-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1680486/fmed-12-1680486-HTML/image_m/fmed-12-1680486-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1680486/fmed-12-1680486-HTML/image_m/fmed-12-1680486-t002.jpg Table 2. Risk of bias assessment of included studies. https://www.frontiersin.org/files/Articles/1680486/fmed-12-1680486-HTML/image_m/fmed-12-1680486-t003.jpg Table 3. Summary of meta-analysis results on ADL improvement. https://www.frontiersin.org/files/Articles/1680486/fmed-12-1680486-HTML/image_m/fmed-12-1680486-g002.jpg Figure 2. Risk of bias summary. https://www.frontiersin.org/files/Articles/1680486/fmed-12-1680486-HTML/image_m/fmed-12-1680486-g003.jpg Figure 3. Forest plot of ADL improvement. https://www.frontiersin.org/files/Articles/1680486/fmed-12-1680486-HTML/image_m/fmed-12-1680486-g004.jpg Figure 4. Funnel plot for publication bias. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1777017/full https://www.frontiersin.org/files/Articles/1777017/fpubh-14-1777017-HTML/image_m/fpubh-14-1777017-t001.jpg Table 1. Patient characteristics of COVID-19 and influenza cases. https://www.frontiersin.org/files/Articles/1777017/fpubh-14-1777017-HTML/image_m/fpubh-14-1777017-g001.jpg Figure 1. Onset time of COVID-19 and influenza cases during the study period. Time series of weekly https://www.frontiersin.org/files/Articles/1777017/fpubh-14-1777017-HTML/image_m/fpubh-14-1777017-g002.jpg Figure 2. Frequency of symptoms and symptom combinations in COVID-19 and influenza cases. (A) Freque https://www.frontiersin.org/files/Articles/1777017/fpubh-14-1777017-HTML/image_m/fpubh-14-1777017-t002.jpg Table 2. Multivariable logistic regression of factors associated with outpatient visits within 14 da https://www.frontiersin.org/files/Articles/1777017/fpubh-14-1777017-HTML/image_m/fpubh-14-1777017-t003.jpg Table 3. Multivariable Cox regression analysis of time to first all-cause outpatient visit during th https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1802643/full https://www.frontiersin.org/files/Articles/1802643/fonc-16-1802643-HTML/image_m/fonc-16-1802643-g001.jpg Figure 1. PRISMA flow diagram of study identification, screening, and inclusion. https://www.frontiersin.org/files/Articles/1802643/fonc-16-1802643-HTML/image_m/fonc-16-1802643-t001.jpg Table 1. Characteristics of the included studies. https://www.frontiersin.org/files/Articles/1802643/fonc-16-1802643-HTML/image_m/fonc-16-1802643-g002.jpg Figure 2. Summary and visualization of risk of bias assessment. (A) Risk of bias summary. “+” means https://www.frontiersin.org/files/Articles/1802643/fonc-16-1802643-HTML/image_m/fonc-16-1802643-g003.jpg Figure 3. Forest plots showing the effects of photobiomodulation on limb volume and limb circumferen https://www.frontiersin.org/files/Articles/1802643/fonc-16-1802643-HTML/image_m/fonc-16-1802643-g004.jpg Figure 4. Forest plots of the effects of photobiomodulation on grip strength and pain in patients wi https://www.frontiersin.org/files/Articles/1802643/fonc-16-1802643-HTML/image_m/fonc-16-1802643-t002.jpg Table 2. Summary of primary outcomes with GRADE evidence assessment. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1806913/full https://www.frontiersin.org/files/Articles/1806913/fpsyg-17-1806913-HTML/image_m/fpsyg-17-1806913-t001.jpg Table 1. Participant demographics. https://www.frontiersin.org/files/Articles/1806913/fpsyg-17-1806913-HTML/image_m/fpsyg-17-1806913-g001.jpg Figure 1. Workflow design process of the workshop experiment. https://www.frontiersin.org/files/Articles/1806913/fpsyg-17-1806913-HTML/image_m/fpsyg-17-1806913-g002.jpg Figure 2. Thematic analysis process. https://www.frontiersin.org/files/Articles/1806913/fpsyg-17-1806913-HTML/image_m/fpsyg-17-1806913-t002.jpg Table 2. Descriptive statistics for pre-test and post-test scores by group. https://www.frontiersin.org/files/Articles/1806913/fpsyg-17-1806913-HTML/image_m/fpsyg-17-1806913-t003.jpg Table 3. Paired comparisons between pre-test and post-test scores. https://www.frontiersin.org/files/Articles/1806913/fpsyg-17-1806913-HTML/image_m/fpsyg-17-1806913-t004.jpg Table 4. ANOVA regression results. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1744929/full https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-g001.jpg Figure 1. The process of literature search and selection. https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-g002.jpg Figure 2. (A) Annual publication trends from 2000 to 2024. (B) Subject categories distribution. (C) https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-t001.jpg Table 1. Top 10 most productive countries and affiliations in the field of TCM in NAFLD. https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-g003.jpg Figure 3. (A) Visualization map of institutional collaborations. (B) Distribution of funding agencie https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-t002.jpg Table 2. Top 10 most published authors and co-cited authors in the field of TCM in NAFLD. https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-t003.jpg Table 3. Top 10 Most Productive Journals in the Field of TCM in NAFLD. https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-g004.jpg Figure 4. (A) Most relevant journals. (B) Most cited journals. https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-t004.jpg Table 4. Top 10 Most Cited Journals in the Field of TCM in NAFLD. https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-g005.jpg Figure 5. Dual-map overlay of journals. https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-g006.jpg Figure 6. Top 25 references with the strongest citation bursts. https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-t005.jpg Table 5. Top 20 keywords in the field of TCM in NAFLD. https://www.frontiersin.org/files/Articles/1744929/fmed-13-1744929-HTML/image_m/fmed-13-1744929-g007.jpg Figure 7. (A) Visualization map of keyword co-occurrence. (B) Overlay visualization map of keyword c https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1631585/full https://www.frontiersin.org/files/Articles/1631585/fmed-12-1631585-HTML/image_m/fmed-12-1631585-t001.jpg Table 1. Demographics of patients with normal albumin (≥35 g/L) versus those with hypoalbuminemia (< https://www.frontiersin.org/files/Articles/1631585/fmed-12-1631585-HTML/image_m/fmed-12-1631585-t002.jpg Table 2. Variables that predict increased SSD postoperative hip fracture in elderly, based on univar https://www.frontiersin.org/files/Articles/1631585/fmed-12-1631585-HTML/image_m/fmed-12-1631585-t003.jpg Table 3. Variables that predict increased SSD postoperative hip fracture in elderly, based on multiv https://www.frontiersin.org/files/Articles/1631585/fmed-12-1631585-HTML/image_m/fmed-12-1631585-t004.jpg Table 4. Subgroup analysis based on sex. https://www.frontiersin.org/files/Articles/1631585/fmed-12-1631585-HTML/image_m/fmed-12-1631585-t005.jpg Table 5. Threshold analysis results. https://www.frontiersin.org/files/Articles/1631585/fmed-12-1631585-HTML/image_m/fmed-12-1631585-g001.jpg Figure 1. Albumin and SSD threshold analysis. https://www.frontiersin.org/files/Articles/1631585/fmed-12-1631585-HTML/image_m/fmed-12-1631585-g002.jpg Figure 2. Albumin and SSD threshold analysis after adjustment for covariates. https://www.frontiersin.org/files/Articles/1631585/fmed-12-1631585-HTML/image_m/fmed-12-1631585-t006.jpg Table 6. Postoperative destination of hip fracture in elderly. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1727982/full https://www.frontiersin.org/files/Articles/1727982/fenvs-13-1727982-HTML/image_m/fenvs-13-1727982-g001.jpg Figure 1. A diagram illustrating the relationship between ILG, public participation, and landscape d https://www.frontiersin.org/files/Articles/1727982/fenvs-13-1727982-HTML/image_m/fenvs-13-1727982-g002.jpg Figure 2. Selected hot springs study sites in Linyi City, Shandong, China. https://www.frontiersin.org/files/Articles/1727982/fenvs-13-1727982-HTML/image_m/fenvs-13-1727982-t001.jpg Table 1. Demographic characteristics of participants. https://www.frontiersin.org/files/Articles/1727982/fenvs-13-1727982-HTML/image_m/fenvs-13-1727982-t002.jpg Table 2. Public interview questions. https://www.frontiersin.org/files/Articles/1727982/fenvs-13-1727982-HTML/image_m/fenvs-13-1727982-g003.jpg Figure 3. QualCoder results. https://www.frontiersin.org/files/Articles/1727982/fenvs-13-1727982-HTML/image_m/fenvs-13-1727982-t003.jpg Table 3. Coding analysis of public insights into challenges facing inclusive landscape governance. https://www.frontiersin.org/files/Articles/1727982/fenvs-13-1727982-HTML/image_m/fenvs-13-1727982-g004.jpg Figure 4. Challenges facing inclusive landscape governance framework. https://www.frontiersin.org/journals/soil-science/articles/10.3389/fsoil.2025.1666961/full https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-t001.jpg Table 1. The physicochemical properties of tobacco-planted soil under different treatments. https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-g001.jpg Figure 1. Phylum-level species composition analysis. (A) Venn diagram analysis. (B) Community compos https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-t002.jpg Table 2. Bacterial α-diversity indices under different treatments. https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-g002.jpg Figure 2. Principal co-ordinate analysis at the phylum level. https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-g003.jpg Figure 3. LEfSe multi-level species difference analysis. https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-g004.jpg Figure 4. Environmental factor correlation analysis. (A) Class-level db-RDA analysis. (B) Phylum-lev https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-g005.jpg Figure 5. BugBase phenotypic prediction. https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-g006.jpg Figure 6. BugBase phenotypic group difference test. (A) The five functional traits that differ betwe https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-g007.jpg Figure 7. Tobacco leaf conditions during the prosperously growing stage under different treatments. https://www.frontiersin.org/files/Articles/1666961/fsoil-05-1666961-HTML/image_m/fsoil-05-1666961-t003.jpg Table 3. Analysis of the appearance quality of tobacco leaves. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1717189/full https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g001.jpg Figure 1. Venn diagram of fungal community. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g002.jpg Figure 2. Venn diagram of bacterial community. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-t001.jpg Table 1. Analysis of α-diversity of soil fungal microorganisms. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g003.jpg Figure 3. Rarefaction curve analysis of α-diversity of soil fungal microorganisms. (A) Microbial com https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-t002.jpg Table 2. Analysis of α-diversity of soil bacterial microorganisms. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g004.jpg Figure 4. Rarefaction curve analysis of α-diversity of soil bacterial microorganisms. (A) Microbial https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g005.jpg Figure 5. Analysis of β-diversity of soil microbial communities. (A) Principal component analysis (P https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g006.jpg Figure 6. Composition of soil fungal microorganisms at the phylum level in different samples. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g007.jpg Figure 7. Composition of soil bacterial microorganisms at the phylum level in different samples. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g008.jpg Figure 8. Composition of soil fungal microorganisms at the genus level in different samples. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g009.jpg Figure 9. Composition of soil bacterial microorganisms at the genus level in different samples. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-t003.jpg Table 3. Analysis of photosynthetic performance of tobacco plants under different treatments. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-t004.jpg Table 4. Analysis of photosynthetic activity of tobacco plants under different treatments. https://www.frontiersin.org/files/Articles/1717189/fmicb-17-1717189-HTML/image_m/fmicb-17-1717189-g010.jpg Figure 10. Phenotypic appearance of tobacco plants under different treatments. (A) 15 °C without inh https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1727692/full https://www.frontiersin.org/files/Articles/1727692/fonc-16-1727692-HTML-r1/image_m/fonc-16-1727692-g001.jpg Figure 1. Flow chart of this study. TACE, transarterial chemoembolization; pRFA, percutaneous radiof https://www.frontiersin.org/files/Articles/1727692/fonc-16-1727692-HTML-r1/image_m/fonc-16-1727692-g002.jpg Figure 2. Images of a 63-year-old male with a single hepatocellular carcinoma (HCC) nodule in segmen https://www.frontiersin.org/files/Articles/1727692/fonc-16-1727692-HTML-r1/image_m/fonc-16-1727692-t001.jpg Table 1. Baseline characteristics of the 118 patients who received TACE-pRFA as the first-line optio https://www.frontiersin.org/files/Articles/1727692/fonc-16-1727692-HTML-r1/image_m/fonc-16-1727692-t002.jpg Table 2. Tumor recurrence patterns after TACE-pRFA. https://www.frontiersin.org/files/Articles/1727692/fonc-16-1727692-HTML-r1/image_m/fonc-16-1727692-g003.jpg Figure 3. Kaplan–Meier curves for progression-free survival (PFS). (a) Kaplan–Meier curve of PFS in https://www.frontiersin.org/files/Articles/1727692/fonc-16-1727692-HTML-r1/image_m/fonc-16-1727692-t003.jpg Table 3. Univariate and multivariate analysis of factors associated with PFS. https://www.frontiersin.org/files/Articles/1727692/fonc-16-1727692-HTML-r1/image_m/fonc-16-1727692-t004.jpg Table 4. Second-line treatments. https://www.frontiersin.org/files/Articles/1727692/fonc-16-1727692-HTML-r1/image_m/fonc-16-1727692-t005.jpg Table 5. The patient’s complications requiring medication after treatment. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1669805/full https://www.frontiersin.org/files/Articles/1669805/fphar-16-1669805-HTML/image_m/fphar-16-1669805-g001.jpg Figure 1. Graphical abstract: Bile acids participate in ferroptosis and influence lipid metabolism i https://www.frontiersin.org/files/Articles/1669805/fphar-16-1669805-HTML/image_m/fphar-16-1669805-g002.jpg Figure 2. Relationship between lipid peroxidation and ferroptosis. SLC7A11 and SLC3A2 on the cell me https://www.frontiersin.org/files/Articles/1669805/fphar-16-1669805-HTML/image_m/fphar-16-1669805-t001.jpg Table 1. Summary of bile acid mechanisms in lipid peroxidation and liver disease. https://www.frontiersin.org/files/Articles/1669805/fphar-16-1669805-HTML/image_m/fphar-16-1669805-t002.jpg Table 2. TCM regulation of bile acids in NAFLD. https://www.frontiersin.org/files/Articles/1669805/fphar-16-1669805-HTML/image_m/fphar-16-1669805-t003.jpg Table 3. TCM regulation of lipid peroxidation and ferroptosis in NAFLD. https://www.frontiersin.org/files/Articles/1669805/fphar-16-1669805-HTML/image_m/fphar-16-1669805-t004.jpg Table 4. Clinical evidence of TCM targeting NAFLD. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1608987/full https://www.frontiersin.org/files/Articles/1608987/fpubh-13-1608987-HTML/image_m/fpubh-13-1608987-t001.jpg Table 1. Basic parameters of the flat-water kayak and canoe athletes gendera. https://www.frontiersin.org/files/Articles/1608987/fpubh-13-1608987-HTML/image_m/fpubh-13-1608987-g001.jpg Figure 1. Injury rate per 1,000 training hours by anatomical site and sex in flat-water kayak athlet https://www.frontiersin.org/files/Articles/1608987/fpubh-13-1608987-HTML/image_m/fpubh-13-1608987-g002.jpg Figure 2. Injury rate per 1,000 training hours by anatomical site and sex in flat-water canoe athlet https://www.frontiersin.org/files/Articles/1608987/fpubh-13-1608987-HTML/image_m/fpubh-13-1608987-g003.jpg Figure 3. Injury rate per 1,000 training hours among flat-water kayak and canoe athletes by sex. https://www.frontiersin.org/files/Articles/1608987/fpubh-13-1608987-HTML/image_m/fpubh-13-1608987-t002.jpg Table 2. Distribution of injury related to flat-water kayak and canoe athletes broken down by sex. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1703317/full https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-g001.jpg Figure 1. Theoretical mechanism diagram. https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-t001.jpg Table 1. Definition and descriptive statistics of the main variables. https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-t002.jpg Table 2. Heterogeneous results of the dependent and independent variables. https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-t003.jpg Table 3. Results of the benchmark regression. https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-t004.jpg Table 4. Results of endogeneity testing. https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-t005.jpg Table 5. Results of robustness tests. https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-t006.jpg Table 6. Estimated results of impact mechanism. https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-t007.jpg Table 7. Results of regional heterogeneity. https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-t008.jpg Table 8. Results of group heterogeneity. https://www.frontiersin.org/files/Articles/1703317/fnut-12-1703317-HTML/image_m/fnut-12-1703317-t009.jpg Table 9. Results of income heterogeneity. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1764497/full https://www.frontiersin.org/files/Articles/1764497/fimmu-17-1764497-HTML/image_m/fimmu-17-1764497-g001.jpg Figure 1. Research approach diagram. https://www.frontiersin.org/files/Articles/1764497/fimmu-17-1764497-HTML/image_m/fimmu-17-1764497-t001.jpg Table 1. Inclusion criteria. https://www.frontiersin.org/files/Articles/1764497/fimmu-17-1764497-HTML/image_m/fimmu-17-1764497-t002.jpg Table 2. Clinical data of GA patients with chronic tophi and acute joint cavity effusion. https://www.frontiersin.org/files/Articles/1764497/fimmu-17-1764497-HTML/image_m/fimmu-17-1764497-g002.jpg Figure 2. Laboratory parameters of the patients with GA. (A) CRP; (B) ESR; (C) leukocyte; (D) PLT; ( https://www.frontiersin.org/files/Articles/1764497/fimmu-17-1764497-HTML/image_m/fimmu-17-1764497-g003.jpg Figure 3. Protein quality assessment results. (A) Peptide length. The horizontal axis represents the https://www.frontiersin.org/files/Articles/1764497/fimmu-17-1764497-HTML/image_m/fimmu-17-1764497-g004.jpg Figure 4. Differential protein analyses. (A) Differential protein screening results, showing the sta https://www.frontiersin.org/files/Articles/1764497/fimmu-17-1764497-HTML/image_m/fimmu-17-1764497-g005.jpg Figure 5. GO analysis and KEGG enrichment analysis results of the differentially expressed proteins. https://www.frontiersin.org/files/Articles/1764497/fimmu-17-1764497-HTML/image_m/fimmu-17-1764497-g006.jpg Figure 6. Results of the differential protein validation experiment. (A) Target protein band. (B) St https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1769719/full https://www.frontiersin.org/files/Articles/1769719/fimmu-17-1769719-HTML-r1/image_m/fimmu-17-1769719-g001.jpg Figure 1. Schematic Overview of the Molecular mechanisms underlying ferroptosis. System Xc− facilita https://www.frontiersin.org/files/Articles/1769719/fimmu-17-1769719-HTML-r1/image_m/fimmu-17-1769719-g002.jpg Figure 2. Pathogenic mechanisms underlying MSU crystal–driven inflammation in GA. ①MSU crystals aber https://www.frontiersin.org/files/Articles/1769719/fimmu-17-1769719-HTML-r1/image_m/fimmu-17-1769719-g003.jpg Figure 3. Molecular mechanisms by which ferroptosis contributes to GA progression. MSU crystal–induc https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1794782/full https://www.frontiersin.org/files/Articles/1794782/fimmu-17-1794782-HTML/image_m/fimmu-17-1794782-g001.jpg Figure 1. Formation of MSU crystals. High concentrations of SUA aggregate from monomers to oligomers https://www.frontiersin.org/files/Articles/1794782/fimmu-17-1794782-HTML/image_m/fimmu-17-1794782-g002.jpg Figure 2. Schematic diagram of the multipathway pathogenesis of SUA. High concentrations of SUA prom https://www.frontiersin.org/files/Articles/1794782/fimmu-17-1794782-HTML/image_m/fimmu-17-1794782-g003.jpg Figure 3. Pathological mechanisms linking SUA to cardiovascular injury. Elevated soluble uric acid p https://www.frontiersin.org/files/Articles/1794782/fimmu-17-1794782-HTML/image_m/fimmu-17-1794782-g004.jpg Figure 4. SUA induces liver injury and cirrhosis via AMPK-mediated lipid accumulation and inflammati https://www.frontiersin.org/files/Articles/1794782/fimmu-17-1794782-HTML/image_m/fimmu-17-1794782-g005.jpg Figure 5. Pathological mechanisms of SUA on kidney damage. Circulating soluble uric acid is transpor https://www.frontiersin.org/files/Articles/1794782/fimmu-17-1794782-HTML/image_m/fimmu-17-1794782-g006.jpg Figure 6. Pathogenic mechanisms of MSU. MSU crystals are engulfed by synovial macrophages after ente https://www.frontiersin.org/files/Articles/1794782/fimmu-17-1794782-HTML/image_m/fimmu-17-1794782-t001.jpg Table 1. Therapeutic interventions targeting SUA and MSU–driven mechanisms in gout. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1756592/full https://www.frontiersin.org/files/Articles/1756592/fvets-13-1756592-HTML/image_m/fvets-13-1756592-g001.jpg Figure 1. Endoscopic and dorsal CT images of nasal tumors obtained during first ECT sessions and at https://www.frontiersin.org/files/Articles/1756592/fvets-13-1756592-HTML/image_m/fvets-13-1756592-g002.jpg Figure 2. The figure shows endoscopic-guided ECT for the treatment of a nasal tumor in a dog. The ar https://www.frontiersin.org/files/Articles/1756592/fvets-13-1756592-HTML/image_m/fvets-13-1756592-t001.jpg Table 1. Dimensions of the nasal carcinoma measured through head CT scans before each endoscopicguid https://www.frontiersin.org/files/Articles/1756592/fvets-13-1756592-HTML/image_m/fvets-13-1756592-g003.jpg Figure 3. The figure shows the appearance of the fronto-nasal profile in a dog before and after the https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1633892/full https://www.frontiersin.org/files/Articles/1633892/fimmu-16-1633892-HTML/image_m/fimmu-16-1633892-g001.jpg Figure 1. IL-7 upregulated the expression of molecules involved in DNA repair. (A) Heat map represen https://www.frontiersin.org/files/Articles/1633892/fimmu-16-1633892-HTML/image_m/fimmu-16-1633892-g002.jpg Figure 2. IL-7 increased the formation of double strand breaks. Pro-B cells, cultured with IL-7 (50 https://www.frontiersin.org/files/Articles/1633892/fimmu-16-1633892-HTML/image_m/fimmu-16-1633892-g003.jpg Figure 3. Evaluation of γ-H2AX foci in pro-B cells after extended IL-7 treatment. γ-H2AX foci were e https://www.frontiersin.org/files/Articles/1633892/fimmu-16-1633892-HTML/image_m/fimmu-16-1633892-g004.jpg Figure 4. IL-7 upregulated CD43 expression in pro-B cells. CD43 expression in pro-B cells treated wi https://www.frontiersin.org/files/Articles/1633892/fimmu-16-1633892-HTML/image_m/fimmu-16-1633892-g005.jpg Figure 5. Relationship between γ-H2AX and CD43 antigen expression in pro-B cells treated with IL-7. https://www.frontiersin.org/files/Articles/1633892/fimmu-16-1633892-HTML/image_m/fimmu-16-1633892-g006.jpg Figure 6. Expression of γ-H2AX in untreated and IL-7 treated pro-B cells from C57BL/6, RAG1- and RAG https://www.frontiersin.org/files/Articles/1633892/fimmu-16-1633892-HTML/image_m/fimmu-16-1633892-g007.jpg Figure 7. Evaluation of γ-H2AX foci in RAG2-deficient pro-B cells. Pro-B cells from RAG2-deficient m https://www.frontiersin.org/files/Articles/1633892/fimmu-16-1633892-HTML/image_m/fimmu-16-1633892-g008.jpg Figure 8. Effects of IL-7 treatment on irradiated pro-B cells. Pro-B cells were irradiated and cultu https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1769023/full https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g014.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g001.jpg Figure 1. Identification of potential therapeutic targets for DEHP-induced pancreatic cancer. Venn d https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g002.jpg Figure 2. Functional enrichment and pathway analysis of DEHP–pancreatic cancer intersection targets. https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g003.jpg Figure 3. Predictive performance of the multi-algorithm machine-learning framework. ROC curves were https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g004.jpg Figure 4. SHAP-based model interpretability across the eight classifiers. For each algorithm, the le https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g005.jpg Figure 5. CIBERSORT-inferred tumor immune microenvironment and core gene–immune associations. (A) St https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g006.jpg Figure 6. Differential expression validation of the six-gene signature in TCGA-PAAD and GTEx. Box pl https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g007.jpg Figure 7. Kaplan–Meier overall survival analyses stratified by gene expression in TCGA–PAAD. Patient https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g008.jpg Figure 8. Causal association between KLF5 expression and pancreatic cancer risks inferred by Mendeli https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g009.jpg Figure 9. Structural basis of the DEHP–KLF5 interaction revealed by molecular docking. (A) Ribbon/su https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g010.jpg Figure 10. Conformational dynamics of the DEHP–KLF5 complex over a 100-ns MD simulation. (A) Protein https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g011.jpg Figure 11. Single-cell profiling and network-based virtual knockout implicate KLF5-associated microe https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g012.jpg Figure 12. Experimental validation of DEHP-induced KLF5/MMP7 expression and malignant phenotypes in https://www.frontiersin.org/files/Articles/1769023/fcell-14-1769023-HTML-r1/image_m/fcell-14-1769023-g013.jpg Figure 13. Proposed AOP for DEHP-induced pancreatic cancer. This schematic summarizes an OECD-aligne https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1653134/full https://www.frontiersin.org/files/Articles/1653134/fneur-16-1653134-HTML/image_m/fneur-16-1653134-g001.jpg Figure 1. Schematic representation of the proposed visual-dopaminergic circuit involved in early MS https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1802050/full https://www.frontiersin.org/files/Articles/1802050/fonc-16-1802050-HTML-r1/image_m/fonc-16-1802050-g001.jpg Figure 1. Flow chart of the study. D, Germany; AUT, Austria; CH, Switzerland, MPN, myeloproliferativ https://www.frontiersin.org/files/Articles/1802050/fonc-16-1802050-HTML-r1/image_m/fonc-16-1802050-g002.jpg Figure 2. Symptom severity clusters (n = 633). https://www.frontiersin.org/files/Articles/1802050/fonc-16-1802050-HTML-r1/image_m/fonc-16-1802050-t001.jpg Table 1. Characteristics of patients depending on the symptom severity cluster. https://www.frontiersin.org/files/Articles/1802050/fonc-16-1802050-HTML-r1/image_m/fonc-16-1802050-t002.jpg Table 2. Multinomial logistic regression with low symptom severity cluster as the base outcome (n = https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1702689/full https://www.frontiersin.org/files/Articles/1702689/fimmu-17-1702689-HTML/image_m/fimmu-17-1702689-g001.jpg Figure 1. Overview of human and mouse liver transcriptomic profiling across ACLF progression. (A) Sc https://www.frontiersin.org/files/Articles/1702689/fimmu-17-1702689-HTML/image_m/fimmu-17-1702689-g002.jpg Figure 2. Functional classification and GO enrichment analysis of DEGs in human and mouse livers dur https://www.frontiersin.org/files/Articles/1702689/fimmu-17-1702689-HTML/image_m/fimmu-17-1702689-g003.jpg Figure 3. Z-score normalized gene expression profiles (9 clusters) and functional enrichment analysi https://www.frontiersin.org/files/Articles/1702689/fimmu-17-1702689-HTML/image_m/fimmu-17-1702689-g004.jpg Figure 4. KEGG pathway analysis of metabolism-related pathways in human and mouse liver transcriptom https://www.frontiersin.org/files/Articles/1702689/fimmu-17-1702689-HTML/image_m/fimmu-17-1702689-g005.jpg Figure 5. Metabolic Dysfunction-Associated Impairment of Mitochondrial Anaplerotic Capacity in ACLF. https://www.frontiersin.org/files/Articles/1702689/fimmu-17-1702689-HTML/image_m/fimmu-17-1702689-g006.jpg Figure 6. Immune landscape and cytokine expression profiles in human and mouse livers during ACLF pr https://www.frontiersin.org/files/Articles/1702689/fimmu-17-1702689-HTML/image_m/fimmu-17-1702689-g007.jpg Figure 7. Cross-species identification and validation of monocyte/macrophage-associated genes in ACL https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2026.1738731/full https://www.frontiersin.org/files/Articles/1738731/fncir-20-1738731-HTML-r1/image_m/fncir-20-1738731-g001.jpg Figure 1. Calcium responses in thoracic motoneurons (MNs) during tonic and rhythmic ventral root (VR https://www.frontiersin.org/files/Articles/1738731/fncir-20-1738731-HTML-r1/image_m/fncir-20-1738731-g002.jpg Figure 2. Calcium responses of thoracic SPNs during VR activity induced by whole-bath application of https://www.frontiersin.org/files/Articles/1738731/fncir-20-1738731-HTML-r1/image_m/fncir-20-1738731-g003.jpg Figure 3. Increased recruitment of thoracic SPNs during tonic and rhythmic VR activity. (A) Schemati https://www.frontiersin.org/files/Articles/1738731/fncir-20-1738731-HTML-r1/image_m/fncir-20-1738731-g004.jpg Figure 4. Thoracic SPN Ca-RI responses recorded from a variety of thoracic segmental levels in respo https://www.frontiersin.org/files/Articles/1738731/fncir-20-1738731-HTML-r1/image_m/fncir-20-1738731-g005.jpg Figure 5. Thoracic SPN Ca-RI responses recorded from a variety of thoracic segmental levels in respo https://www.frontiersin.org/files/Articles/1738731/fncir-20-1738731-HTML-r1/image_m/fncir-20-1738731-g006.jpg Figure 6. Distinct thoracic SPN Ca-RI responses are observed at different rostrocaudal levels during https://www.frontiersin.org/files/Articles/1738731/fncir-20-1738731-HTML-r1/image_m/fncir-20-1738731-g007.jpg Figure 7. 3-way ANOVA graphic showing main interactions for mean Ca-RI as the measured variable and https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1756644/full https://www.frontiersin.org/files/Articles/1756644/fnins-20-1756644-HTML-r1/image_m/fnins-20-1756644-g001.jpg Figure 1. Vagus nerve stimulation (VNS) facilitates extinction from cocaine seeking and reduces cue- https://www.frontiersin.org/files/Articles/1756644/fnins-20-1756644-HTML-r1/image_m/fnins-20-1756644-g002.jpg Figure 2. Photomicrographs of infusions sites of a retrograde AAV expressing eGFP in the infralimbic https://www.frontiersin.org/files/Articles/1756644/fnins-20-1756644-HTML-r1/image_m/fnins-20-1756644-g003.jpg Figure 3. VNS differentially modulates cFos expression following reinstatement in IL- and PL-project https://www.frontiersin.org/files/Articles/1756644/fnins-20-1756644-HTML-r1/image_m/fnins-20-1756644-g004.jpg Figure 4. VNS differentially modulates cFos expression following reinstatement in IL- and PL-project https://www.frontiersin.org/files/Articles/1756644/fnins-20-1756644-HTML-r1/image_m/fnins-20-1756644-g005.jpg Figure 5. VNS modulates cFos expression following reinstatement differentially in IL- and PL-project https://www.frontiersin.org/files/Articles/1756644/fnins-20-1756644-HTML-r1/image_m/fnins-20-1756644-g006.jpg Figure 6. VNS affects activity of parvalbumin-positive interneurons (PVI) in the mPFC during drug-se https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1778067/full https://www.frontiersin.org/files/Articles/1778067/fimmu-17-1778067-HTML/image_m/fimmu-17-1778067-g001.jpg Figure 1. Analysis of GC activation in IDLV-S vaccinated mice. (A) Schedule of immunization. Female https://www.frontiersin.org/files/Articles/1778067/fimmu-17-1778067-HTML/image_m/fimmu-17-1778067-g002.jpg Figure 2. Sex difference in IDLV-S-induced anti-Spike neutralizing antibodies (nAbs). (A) Schedule o https://www.frontiersin.org/files/Articles/1778067/fimmu-17-1778067-HTML/image_m/fimmu-17-1778067-g003.jpg Figure 3. Cross-neutralization activity elicited by IDLV-S immunization. (A) NAbs against each VoC w https://www.frontiersin.org/files/Articles/1778067/fimmu-17-1778067-HTML/image_m/fimmu-17-1778067-g004.jpg Figure 4. Anti-Spike specific T cell immunity. Splenocytes recovered 24 weeks after immunization wit https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1681047/full https://www.frontiersin.org/files/Articles/1681047/fmicb-16-1681047-HTML/image_m/fmicb-16-1681047-t001.jpg Table 1. Effectors produced by different plant pathogens during pathogenesis. https://www.frontiersin.org/files/Articles/1681047/fmicb-16-1681047-HTML/image_m/fmicb-16-1681047-g001.jpg Figure 1. Strategies for targeting host factors to manipulate pathogen effectors for durable plant d https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1723750/full https://www.frontiersin.org/files/Articles/1723750/fpsyg-16-1723750-HTML-r1/image_m/fpsyg-16-1723750-t001.jpg Table 1. Descriptive statistics of family economy indicators. https://www.frontiersin.org/files/Articles/1723750/fpsyg-16-1723750-HTML-r1/image_m/fpsyg-16-1723750-t002.jpg Table 2. Descriptive statistics for each variable of parental accompaniment. https://www.frontiersin.org/files/Articles/1723750/fpsyg-16-1723750-HTML-r1/image_m/fpsyg-16-1723750-t003.jpg Table 3. Descriptive statistics of family book collections. https://www.frontiersin.org/files/Articles/1723750/fpsyg-16-1723750-HTML-r1/image_m/fpsyg-16-1723750-t004.jpg Table 4. Correlation of dimensions. https://www.frontiersin.org/files/Articles/1723750/fpsyg-16-1723750-HTML-r1/image_m/fpsyg-16-1723750-t005.jpg Table 5. Pathways of family economy on pupils' mathematical problem-solving skills. https://www.frontiersin.org/files/Articles/1723750/fpsyg-16-1723750-HTML-r1/image_m/fpsyg-16-1723750-t006.jpg Table 6. Regression models for mathematical problem solving. https://www.frontiersin.org/files/Articles/1723750/fpsyg-16-1723750-HTML-r1/image_m/fpsyg-16-1723750-g001.jpg Figure 1. Diagram of the mediating effects model. https://www.frontiersin.org/files/Articles/1723750/fpsyg-16-1723750-HTML-r1/image_m/fpsyg-16-1723750-t007.jpg Table 7. Analysis of mediating effects. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1774451/full https://www.frontiersin.org/files/Articles/1774451/fnut-13-1774451-HTML/image_m/fnut-13-1774451-t001.jpg Table 1. Summarized MMCA framework adapted to US higher education food and dining settings to encour https://www.frontiersin.org/files/Articles/1774451/fnut-13-1774451-HTML/image_m/fnut-13-1774451-t002.jpg Table 2. Summary of inclusion and exclusion criteria and search strategy for scoping review. https://www.frontiersin.org/files/Articles/1774451/fnut-13-1774451-HTML/image_m/fnut-13-1774451-g001.jpg Figure 1. PRISMA flow diagram for systematic scoping review to identify US higher education institut https://www.frontiersin.org/files/Articles/1774451/fnut-13-1774451-HTML/image_m/fnut-13-1774451-g002.jpg Figure 2. Proportion of higher education institutions and their use of MMCA strategies to encourage https://www.frontiersin.org/files/Articles/1774451/fnut-13-1774451-HTML/image_m/fnut-13-1774451-t003.jpg Table 3. Summary of higher education institutions and their use of MMCA strategies categorized by pl https://www.frontiersin.org/files/Articles/1774451/fnut-13-1774451-HTML/image_m/fnut-13-1774451-t004.jpg Table 4. Summary of the use of MMCA strategies across 166 higher education institutions. https://www.frontiersin.org/files/Articles/1774451/fnut-13-1774451-HTML/image_m/fnut-13-1774451-g003.jpg Figure 3. Higher education institutions that used MMCA strategies to encourage customers to select p https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1741996/full https://www.frontiersin.org/files/Articles/1741996/fpubh-14-1741996-HTML-r3/image_m/fpubh-14-1741996-t001.jpg Table 1. Baseline characteristics of the respondents from the impact of COVID-19 on chronic disease https://www.frontiersin.org/files/Articles/1741996/fpubh-14-1741996-HTML-r3/image_m/fpubh-14-1741996-g001.jpg Figure 1. Ratings of health status comparing peak COVID-19 to post COVID-19. https://www.frontiersin.org/files/Articles/1741996/fpubh-14-1741996-HTML-r3/image_m/fpubh-14-1741996-g002.jpg Figure 2. Number of health conditions by reserve status. https://www.frontiersin.org/files/Articles/1741996/fpubh-14-1741996-HTML-r3/image_m/fpubh-14-1741996-t002.jpg Table 2. Median self efficacy score in managing chronic diseases by characteristic. https://www.frontiersin.org/files/Articles/1741996/fpubh-14-1741996-HTML-r3/image_m/fpubh-14-1741996-g003.jpg Figure 3. Proportion of individuals who delayed getting care during the COVID-19 peak. https://www.frontiersin.org/files/Articles/1741996/fpubh-14-1741996-HTML-r3/image_m/fpubh-14-1741996-g004.jpg Figure 4. Proportion of individuals who avoided getting care during the COVID-19 peak. https://www.frontiersin.org/files/Articles/1741996/fpubh-14-1741996-HTML-r3/image_m/fpubh-14-1741996-t003.jpg Table 3. Reasons for not receiving healthcare when individuals needed it—comparing during COVID-19 p https://www.frontiersin.org/files/Articles/1741996/fpubh-14-1741996-HTML-r3/image_m/fpubh-14-1741996-t004.jpg Table 4. Mixed models regression results of the association of self-efficacy on during COVID-19 vers https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1571468/full https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-g001.jpg Figure 1. Study flow diagram: patient screening, enrollment, and allocation. https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-t001.jpg Table 1. Comparison of baseline characteristics between included and excluded patients. https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-t002.jpg Table 2. Sensitivity analysis comparing complete-case and imputed datasets. https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-t003.jpg Table 3. Treatment ineffectiveness, clinical parameters, and other related parameters between traini https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-t004.jpg Table 4. Comparison of clinical parameters between ineffective and effective groups in the training https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-t005.jpg Table 5. Logistic regression analysis of risk factors for clinical effects. https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-g002.jpg Figure 2. Alignment diagram of clinical effect prediction model of ivab combined with Met-S in the t https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-g003.jpg Figure 3. Calibration curve in the training set (A) and the testing set (B). https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-g004.jpg Figure 4. ROC curve in the training set (A) and the testing set (B). https://www.frontiersin.org/files/Articles/1571468/fcvm-12-1571468-HTML/image_m/fcvm-12-1571468-g005.jpg Figure 5. Decision curve in the training set (A) and the testing set (B). https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1727573/full https://www.frontiersin.org/files/Articles/1727573/fmicb-17-1727573-HTML/image_m/fmicb-17-1727573-g001.jpg Figure 1. Conservation analysis of Rv0737. (A) Genomic context of the sigL-rslA operon (red) and nei https://www.frontiersin.org/files/Articles/1727573/fmicb-17-1727573-HTML/image_m/fmicb-17-1727573-g002.jpg Figure 2. (A) PCR validation of M. smegmatis overexpression strain Ms_pNIT_Rv0737. M: Marker, A: 1-3 https://www.frontiersin.org/files/Articles/1727573/fmicb-17-1727573-HTML/image_m/fmicb-17-1727573-g003.jpg Figure 3. (A) Biofilm-forming ability of M. smegmatis overexpression strains versus empty strains. ( https://www.frontiersin.org/files/Articles/1727573/fmicb-17-1727573-HTML/image_m/fmicb-17-1727573-g004.jpg Figure 4. (A,B) PCR validation of wild-type strain (left), deletion strain (right). and complemented https://www.frontiersin.org/files/Articles/1727573/fmicb-17-1727573-HTML/image_m/fmicb-17-1727573-g005.jpg Figure 5. (A) Deletion of Ms_1492 enhances isoniazid tolerance in M. smegmatis. Time-kill assays dem https://www.frontiersin.org/files/Articles/1727573/fmicb-17-1727573-HTML/image_m/fmicb-17-1727573-g006.jpg Figure 6. M. tuberculosis Rv0737 and M. smegmatis homolog Ms_1492 bind to the sigL-rslA operon promo https://www.frontiersin.org/files/Articles/1727573/fmicb-17-1727573-HTML/image_m/fmicb-17-1727573-g007.jpg Figure 7. Integrated multi-omics analysis reveals that Rv0737/Ms_1492 remodels lipid metabolism and https://www.frontiersin.org/files/Articles/1727573/fmicb-17-1727573-HTML/image_m/fmicb-17-1727573-t001.jpg Table 1. Differential lipid species identified in WT vs. ΔMs_1492/complemented strain by untargeted https://www.frontiersin.org/files/Articles/1727573/fmicb-17-1727573-HTML/image_m/fmicb-17-1727573-g008.jpg Figure 8. Diagram of the regulatory mechanism of Rv0737. Rv0737 affects bacterial growth and lipid s https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2026.1786848/full https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-g001.jpg Figure 1. Shoreline evolution of the Curonian Lagoon near Preila (1995–2024). Source: remote sensing https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-t001.jpg Table 1. Characteristics of orthophotographic maps. https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-g002.jpg Figure 2. Aerial mapping products of the Curonian spit coastline: Orthophotographic image (mosaic) a https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-g003.jpg Figure 3. Shoreline of the Curonian Lagoon near Preila in orthophotographic maps from different year https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-t002.jpg Table 2. Wind conditions at Nida meteorological station (1991–2020 climate norms): (i) average wind https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-g004.jpg Figure 4. Vectorized shoreline of the Curonian Lagoon near Preila and division of the analyzed shore https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-t003.jpg Table 3. Changes in the Curonian Lagoon shoreline near Preila (in meters, compared to 1995–1999). https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-g005.jpg Figure 5. Representative examples of shoreline change near Preila, contrasting an accumulation-domin https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-t004.jpg Table 4. Changes in the most eroded and accumulated shoreline areas near Preila (m2, compared to 199 https://www.frontiersin.org/files/Articles/1786848/frsen-07-1786848-HTML/image_m/frsen-07-1786848-g006.jpg Figure 6. Representative cartographic example of shoreline-area change in a hotspot section near Pre https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1787518/full https://www.frontiersin.org/files/Articles/1787518/fimmu-17-1787518-HTML/image_m/fimmu-17-1787518-g001.jpg Figure 1. Overall study design and workflow of the voxel-level radiomics deep learning model. (A) Da https://www.frontiersin.org/files/Articles/1787518/fimmu-17-1787518-HTML/image_m/fimmu-17-1787518-t001.jpg Table 1. Baseline clinical characteristics of the study cohorts. https://www.frontiersin.org/files/Articles/1787518/fimmu-17-1787518-HTML/image_m/fimmu-17-1787518-g002.jpg Figure 2. Performance evaluation of the models. The evaluation is presented for (A) the training set https://www.frontiersin.org/files/Articles/1787518/fimmu-17-1787518-HTML/image_m/fimmu-17-1787518-t002.jpg Table 2. Predictive performance of the voxel level radiomics model across study cohorts. https://www.frontiersin.org/files/Articles/1787518/fimmu-17-1787518-HTML/image_m/fimmu-17-1787518-g003.jpg Figure 3. Calibration curve of the models across different datasets. The calibration performance is https://www.frontiersin.org/files/Articles/1787518/fimmu-17-1787518-HTML/image_m/fimmu-17-1787518-g004.jpg Figure 4. Kaplan-Meier survival estimates risk stratification across datasets. Overall survivals are https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2026.1641380/full https://www.frontiersin.org/files/Articles/1641380/fpos-08-1641380-HTML/image_m/fpos-08-1641380-g001.jpg Figure 1. Exchange rate between PAK-Japan. Source: author’s own compilation. https://www.frontiersin.org/files/Articles/1641380/fpos-08-1641380-HTML/image_m/fpos-08-1641380-g002.jpg Figure 2. Behavior of S-curve between Pakistan and Japan commodity trade. Source: the author owns th https://www.frontiersin.org/journals/tropical-diseases/articles/10.3389/fitd.2025.1641807/full https://www.frontiersin.org/files/Articles/1641807/fitd-06-1641807-HTML/image_m/fitd-06-1641807-t001.jpg Table 1. Covariates used and their key characteristics (see Supplementary Figures 2, 3 for the panel https://www.frontiersin.org/files/Articles/1641807/fitd-06-1641807-HTML/image_m/fitd-06-1641807-g001.jpg Figure 1. Maps of environmental covariates at their original resolutions for the latest years (see T https://www.frontiersin.org/files/Articles/1641807/fitd-06-1641807-HTML/image_m/fitd-06-1641807-g002.jpg Figure 2. Moderate Resolution Imaging Spectroradiometer (MODIS)-defined land cover for 2023. https://www.frontiersin.org/files/Articles/1641807/fitd-06-1641807-HTML/image_m/fitd-06-1641807-g003.jpg Figure 3. Geographic distribution of Ae. aegypti s.l. occurrence records between 2000 and 2024 (red https://www.frontiersin.org/files/Articles/1641807/fitd-06-1641807-HTML/image_m/fitd-06-1641807-g004.jpg Figure 4. Pearson’s correlation coefficient matrix results for environmental covariate values extrac https://www.frontiersin.org/files/Articles/1641807/fitd-06-1641807-HTML/image_m/fitd-06-1641807-g005.jpg Figure 5. The receiver operator characteristic (ROC) curves used to estimate the area under the curv https://www.frontiersin.org/files/Articles/1641807/fitd-06-1641807-HTML/image_m/fitd-06-1641807-g006.jpg Figure 6. Ecological niche modelling analysis for Aedes aegypti s.l. in 2024 in Kenya. High probabil https://www.frontiersin.org/files/Articles/1641807/fitd-06-1641807-HTML/image_m/fitd-06-1641807-t002.jpg Table 2. Summary of covariate relative importance in the prediction models for Aedes aegypti s.l. “L https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1694107/full https://www.frontiersin.org/files/Articles/1694107/fvets-12-1694107-HTML/image_m/fvets-12-1694107-t001.jpg Table 1. Flock demographics. https://www.frontiersin.org/files/Articles/1694107/fvets-12-1694107-HTML/image_m/fvets-12-1694107-g001.jpg Figure 1. Respondent knowledge. (A) Respondents were asked to describe their knowledge of mineral su https://www.frontiersin.org/files/Articles/1694107/fvets-12-1694107-HTML/image_m/fvets-12-1694107-g002.jpg Figure 2. Survey responses regarding supplemental feed and mineral supplementation practices among e https://www.frontiersin.org/files/Articles/1694107/fvets-12-1694107-HTML/image_m/fvets-12-1694107-g003.jpg Figure 3. Survey responses regarding health challenges, reproductive issues, and mineral testing pra https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1769593/full https://www.frontiersin.org/files/Articles/1769593/fmars-13-1769593-HTML/image_m/fmars-13-1769593-g001.jpg Figure 1. Sampling collection of the two study species. (A) Overview of the Antarctic and South Shet https://www.frontiersin.org/files/Articles/1769593/fmars-13-1769593-HTML/image_m/fmars-13-1769593-g002.jpg Figure 2. Diplasterias sp. (A) Principal component analysis (PCA) of expression values of 52,176 tot https://www.frontiersin.org/files/Articles/1769593/fmars-13-1769593-HTML/image_m/fmars-13-1769593-g003.jpg Figure 3. Diplasterias sp. Hierarchical clustering of differentially expressed (DE) transcripts in a https://www.frontiersin.org/files/Articles/1769593/fmars-13-1769593-HTML/image_m/fmars-13-1769593-g004.jpg Figure 4. Diplasterias sp. Representative gene ontology (GO) terms that differentially expressed gen https://www.frontiersin.org/files/Articles/1769593/fmars-13-1769593-HTML/image_m/fmars-13-1769593-g005.jpg Figure 5. Spinoserolis beddardi. (A) Principal component analysis (PCA) of expression values of 46,1 https://www.frontiersin.org/files/Articles/1769593/fmars-13-1769593-HTML/image_m/fmars-13-1769593-g006.jpg Figure 6. Spinoserolis beddardi. Hierarchical clustering of differentially expressed (DE) transcript https://www.frontiersin.org/files/Articles/1769593/fmars-13-1769593-HTML/image_m/fmars-13-1769593-g007.jpg Figure 7. Spinoserolis beddardi. Representative gene ontology (GO) terms that differentially express https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1708318/full https://www.frontiersin.org/files/Articles/1708318/fonc-15-1708318-HTML-r1/image_m/fonc-15-1708318-g001.jpg Figure 1. Flow diagram of the selection process. https://www.frontiersin.org/files/Articles/1708318/fonc-15-1708318-HTML-r1/image_m/fonc-15-1708318-t001.jpg Table 1. Characteristics of the included studies in the meta-analysis. https://www.frontiersin.org/files/Articles/1708318/fonc-15-1708318-HTML-r1/image_m/fonc-15-1708318-g002.jpg Figure 2. Forest plot of the meta-analysis evaluating the effect of hyperthermic intraperitoneal che https://www.frontiersin.org/files/Articles/1708318/fonc-15-1708318-HTML-r1/image_m/fonc-15-1708318-t002.jpg Table 2. Subgroup analyses assessing the association between hyperthermic intraperitoneal chemothera https://www.frontiersin.org/files/Articles/1708318/fonc-15-1708318-HTML-r1/image_m/fonc-15-1708318-g003.jpg Figure 3. Forest plot of the meta-analysis evaluating the effect of hyperthermic intraperitoneal che https://www.frontiersin.org/files/Articles/1708318/fonc-15-1708318-HTML-r1/image_m/fonc-15-1708318-t003.jpg Table 3. Subgroup analyses assessing the association between hyperthermic intraperitoneal chemothera https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1733027/full https://www.frontiersin.org/files/Articles/1733027/fpubh-14-1733027-HTML-r1/image_m/fpubh-14-1733027-g001.jpg Figure 1. Conceptual framework. https://www.frontiersin.org/files/Articles/1733027/fpubh-14-1733027-HTML-r1/image_m/fpubh-14-1733027-t001.jpg Table 1. The framework of the intervention contents. https://www.frontiersin.org/files/Articles/1733027/fpubh-14-1733027-HTML-r1/image_m/fpubh-14-1733027-g002.jpg Figure 2. Diagram according to consolidated standards of reporting trials (CONSORT). https://www.frontiersin.org/files/Articles/1733027/fpubh-14-1733027-HTML-r1/image_m/fpubh-14-1733027-t002.jpg Table 2. Baseline assessment of adolescents (IG; n = 133) and (CG; n = 146). https://www.frontiersin.org/files/Articles/1733027/fpubh-14-1733027-HTML-r1/image_m/fpubh-14-1733027-g003.jpg Figure 3. Mean scores of BMI with SE bars (95% CI and ±2 SE); T0, baseline; T1, 2 months; T2, 5 mont https://www.frontiersin.org/files/Articles/1733027/fpubh-14-1733027-HTML-r1/image_m/fpubh-14-1733027-t003.jpg Table 3. Mean differences of BMI and HBM constructs between IG (n = 133) and CG (n = 146) at differe https://www.frontiersin.org/files/Articles/1733027/fpubh-14-1733027-HTML-r1/image_m/fpubh-14-1733027-t004.jpg Table 4. Results of two-way repeated measures analysis of variance of BMI and HBM constructs at diff https://www.frontiersin.org/files/Articles/1733027/fpubh-14-1733027-HTML-r1/image_m/fpubh-14-1733027-g004.jpg Figure 4. Mean scores of HBM constructs with SE bars (95% CI and ±2 SE); T0, baseline; T1, 2 months; https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2026.1728339/full https://www.frontiersin.org/files/Articles/1728339/fpain-07-1728339-HTML/image_m/fpain-07-1728339-g001.jpg Figure 1. The brain as inference machine. Schematic of the brain and spinal cord as inference machin https://www.frontiersin.org/files/Articles/1728339/fpain-07-1728339-HTML/image_m/fpain-07-1728339-g002.jpg Figure 2. Box 1 — key definitions in variational free energy. Core terms used in the free energy fra https://www.frontiersin.org/files/Articles/1728339/fpain-07-1728339-HTML/image_m/fpain-07-1728339-g003.jpg Figure 3. Overfitting analogy. Simple illustration of the tradeoff between accuracy and generalizabi https://www.frontiersin.org/files/Articles/1728339/fpain-07-1728339-HTML/image_m/fpain-07-1728339-g004.jpg Figure 4. Complexity barrier. Free energy landscape metaphor. The “pain model” lies in a higher ener https://www.frontiersin.org/files/Articles/1728339/fpain-07-1728339-HTML/image_m/fpain-07-1728339-g005.jpg Figure 5. Asymmetry of Kullback–Leibler divergence. Stylized representation of probability densities https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2025.1655712/full https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g001.jpg Figure 1. Timeline of experimental procedures. On the line, the hour (6 h- 24 h- 48 h) or the day of https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-t001.jpg Table 1. FIA-MS/MS acquisition parameters used for the determination of whole blood amino acids (AAs https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-t002.jpg Table 2. MS/MS operating conditions. Multiple reaction monitoring (MRM) functions and settings for d https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g002.jpg Figure 2. Onset and evolution of neuropathy and metabolic changes in aging mice. (A) Body weight in https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g003.jpg Figure 3. Insulin receptor substrate 1 expression following neuropathy. Confocal images (40 × 1.25x) https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g004.jpg Figure 4. Energy metabolism in aging mice following neuropathy. The left panel (a) illustrates the e https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g005.jpg Figure 5. Sex-dependent metabolomic and steroidomic changes in aging mice following neuropathy. Bar https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-t003.jpg Table 3. Concentration levels (ng/ml) for calibrators and QC materials of each steroid monitored in https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g006.jpg Figure 6. (A) Heatmap representation of the 406 differentially expressed proteins (DEPs) across the https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g007.jpg Figure 7. Chart representing enrichment pathway analysis in KEGG pathway database. The chart was obt https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g008.jpg Figure 8. Metascape analysis: enriched terms clusters statistically enriched terms (GO/KEGG terms, r https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g009.jpg Figure 9. Reactome expression analysis. Voronoi Plot representation of pathways enriched from the di https://www.frontiersin.org/files/Articles/1655712/fpain-06-1655712-HTML/image_m/fpain-06-1655712-g010.jpg Figure 10. Adiponectin and peroxisome proliferator-activated receptor gamma (pPARγ) variation after https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1716122/full https://www.frontiersin.org/files/Articles/1716122/fmed-12-1716122-HTML/image_m/fmed-12-1716122-t001.jpg Table 1. List of tested LLMs. https://www.frontiersin.org/files/Articles/1716122/fmed-12-1716122-HTML/image_m/fmed-12-1716122-t002.jpg Table 2. Test results (I: basic medical knowledge; II: related medical professional knowledge; III: https://www.frontiersin.org/files/Articles/1716122/fmed-12-1716122-HTML/image_m/fmed-12-1716122-g001.jpg Figure 1. Total accuracy of tested LLMs. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1755983/full https://www.frontiersin.org/files/Articles/1755983/fmed-13-1755983-HTML-r1/image_m/fmed-13-1755983-t001.jpg Table 1. List of evaluated large language models. https://www.frontiersin.org/files/Articles/1755983/fmed-13-1755983-HTML-r1/image_m/fmed-13-1755983-t002.jpg Table 2. Test results. https://www.frontiersin.org/files/Articles/1755983/fmed-13-1755983-HTML-r1/image_m/fmed-13-1755983-g001.jpg Figure 1. Overall accuracy of 12 LLMs. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1645543/full https://www.frontiersin.org/files/Articles/1645543/fneur-16-1645543-HTML/image_m/fneur-16-1645543-g001.jpg Figure 1. CFRP protocol by CONMEBOL. https://www.frontiersin.org/files/Articles/1645543/fneur-16-1645543-HTML/image_m/fneur-16-1645543-t001.jpg Table 1. Characteristics of study population. https://www.frontiersin.org/files/Articles/1645543/fneur-16-1645543-HTML/image_m/fneur-16-1645543-g002.jpg Figure 2. Concussion numbers according to period of the game. https://www.frontiersin.org/files/Articles/1645543/fneur-16-1645543-HTML/image_m/fneur-16-1645543-t002.jpg Table 2. Concussion rates per 1,000 player-hours by position (GK: goalkeeper, DEF: defenses, MID: mi https://www.frontiersin.org/files/Articles/1645543/fneur-16-1645543-HTML/image_m/fneur-16-1645543-g003.jpg Figure 3. Concussion rates per 1,000 player-hours by specific position (GK, goalkeeper; DEF, defense https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2026.1717540/full https://www.frontiersin.org/files/Articles/1717540/fpain-07-1717540-HTML/image_m/fpain-07-1717540-g001.jpg Figure 1. PRISMA diagram. The flow chart shows the process of selecting studies. https://www.frontiersin.org/files/Articles/1717540/fpain-07-1717540-HTML/image_m/fpain-07-1717540-g002.jpg Figure 2. Year of publication. The bar chart shows the distribution of the included systematic revie https://www.frontiersin.org/files/Articles/1717540/fpain-07-1717540-HTML/image_m/fpain-07-1717540-g003.jpg Figure 3. Types of exercises. The bar chart demonstrates the distribution of the included studies by https://www.frontiersin.org/files/Articles/1717540/fpain-07-1717540-HTML/image_m/fpain-07-1717540-g004.jpg Figure 4. Types of therapy outcomes. The bar chart shows the distribution of studies by therapy outc https://www.frontiersin.org/files/Articles/1717540/fpain-07-1717540-HTML/image_m/fpain-07-1717540-g005.jpg Figure 5. Pain outcomes. The bar chart ilustrates the distribution of the studies by the results of https://www.frontiersin.org/files/Articles/1717540/fpain-07-1717540-HTML/image_m/fpain-07-1717540-g006.jpg Figure 6. Physical function outcome. The bar chart shows the distribution of studies by the results https://www.frontiersin.org/files/Articles/1717540/fpain-07-1717540-HTML/image_m/fpain-07-1717540-g007.jpg Figure 7. Quality of life outcome. The bar chart demonstrates the distribution of studies by the res https://www.frontiersin.org/files/Articles/1717540/fpain-07-1717540-HTML/image_m/fpain-07-1717540-g008.jpg Figure 8. Knee biomechanics outcomes. The bar chart shows the distribution of studies by the results https://www.frontiersin.org/files/Articles/1717540/fpain-07-1717540-HTML/image_m/fpain-07-1717540-g009.jpg Figure 9. Distribution of methodological quality by reported outcome improvement. https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2025.1693068/full https://www.frontiersin.org/files/Articles/1693068/fpain-06-1693068-HTML/image_m/fpain-06-1693068-g001.jpg Figure 1. Flow diagram based on PRISMA statement (https://www.prisma-statement.org). https://www.frontiersin.org/files/Articles/1693068/fpain-06-1693068-HTML/image_m/fpain-06-1693068-t001.jpg Table 1. Characteristics of the included studies. https://www.frontiersin.org/files/Articles/1693068/fpain-06-1693068-HTML/image_m/fpain-06-1693068-t002.jpg Table 2. Methodological quality of the included studies assessed through the Joanna Briggs Institute https://www.frontiersin.org/files/Articles/1693068/fpain-06-1693068-HTML/image_m/fpain-06-1693068-t003.jpg Table 3. Summary of findings. https://www.frontiersin.org/files/Articles/1693068/fpain-06-1693068-HTML/image_m/fpain-06-1693068-g002.jpg Figure 2. Forest plot of comparison between LBP subjects and healthy people. Gait speed (cm/s). https://www.frontiersin.org/files/Articles/1693068/fpain-06-1693068-HTML/image_m/fpain-06-1693068-g003.jpg Figure 3. Forest plot of comparison between LBP subjects and healthy people (sensitivity analysis). https://www.frontiersin.org/files/Articles/1693068/fpain-06-1693068-HTML/image_m/fpain-06-1693068-g004.jpg Figure 4. Forest plot of comparison between LBP subjects and healthy people. Cadence (steps/min). https://www.frontiersin.org/files/Articles/1693068/fpain-06-1693068-HTML/image_m/fpain-06-1693068-g005.jpg Figure 5. Forest plot of comparison between LBP subjects and healthy people. Step length (cm). https://www.frontiersin.org/files/Articles/1693068/fpain-06-1693068-HTML/image_m/fpain-06-1693068-g006.jpg Figure 6. Forest plot of comparison between LBP subjects and healthy people. Stride length (cm). https://www.frontiersin.org/journals/animal-science/articles/10.3389/fanim.2026.1799523/full https://www.frontiersin.org/files/Articles/1799523/fanim-07-1799523-HTML-r1/image_m/fanim-07-1799523-t001.jpg Table 1. Description of flock characteristics used in ANOVA, comprising original qualitative and cat https://www.frontiersin.org/files/Articles/1799523/fanim-07-1799523-HTML-r1/image_m/fanim-07-1799523-t002.jpg Table 2. Descriptive statistics of microbiological quality variables, expressed as log10 CFU/mL, for https://www.frontiersin.org/files/Articles/1799523/fanim-07-1799523-HTML-r1/image_m/fanim-07-1799523-t003.jpg Table 3. Factors extracted and saturation coefficients with the original variables after varimax rot https://www.frontiersin.org/files/Articles/1799523/fanim-07-1799523-HTML-r1/image_m/fanim-07-1799523-t004.jpg Table 4. Mixed models result for effects of flock and season on the extracted factors (F1–F4; n = 24 https://www.frontiersin.org/files/Articles/1799523/fanim-07-1799523-HTML-r1/image_m/fanim-07-1799523-g001.jpg Figure 1. Least squares means and standard errors (SE) of the extracted factors (F1–F4) for differen https://www.frontiersin.org/files/Articles/1799523/fanim-07-1799523-HTML-r1/image_m/fanim-07-1799523-t005.jpg Table 5. Means and standard deviations of the extracted factors (F1-F4; n=245) according to flock ch https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1703063/full https://www.frontiersin.org/files/Articles/1703063/fmed-13-1703063-HTML-r1/image_m/fmed-13-1703063-t001.jpg Table 1. Characteristics of the participants (n = 21). https://www.frontiersin.org/files/Articles/1703063/fmed-13-1703063-HTML-r1/image_m/fmed-13-1703063-t002.jpg Table 2. The main findings catgorized in accordance with the SWOC framework. https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2026.1787659/full https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g011.jpg Graphical Abstract https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g001.jpg Figure 1. (Top right) Regional map showing the country and the study area, (bottom right) wadi catch https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g002.jpg Figure 2. Geological map of the study area and a schematic cross section in the north-south directio https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g003.jpg Figure 3. Stepwise methodology of recharge estimation with uncertainty analysis. https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g004.jpg Figure 4. Physics-informed Bayesian Neural Network (PI-BNN) architecture. https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-t001.jpg Table 1. Loss function components. https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-t002.jpg Table 2. Statistical parameters (mm) for all months from 1990 to 2023. https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g005.jpg Figure 5. (Top) Monthly recharge estimated by the PI-BNN (red line with 95% confidence interval in p https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g006.jpg Figure 6. (Top) Boxplots showing the spread of the posterior predictive recharge for each month base https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-t003.jpg Table 3. Model results showing recharge summary from PI-BNN. https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-t004.jpg Table 4. Monthly comparison between PI-BNN and BNN only. https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g007.jpg Figure 7. Monthly recharge (top) and 95% confidence intervals (bottom) for PI-BNN vs. BNN only. https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g008.jpg Figure 8. (Top) monthly recharge estimated by the LHS with 95% confidence interval. (Bottom) mean va https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g009.jpg Figure 9. (Top) Boxplots showing the spread of the posterior predictive recharge for each month base https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-t005.jpg Table 5. Model results showing recharge summary from LHS. https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-g010.jpg Figure 10. Recharge estimates of LHS method using correlated variables. https://www.frontiersin.org/files/Articles/1787659/frwa-08-1787659-HTML-r1/image_m/frwa-08-1787659-t006.jpg Table 6. Recharge rates from various studies in the region. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1772490/full https://www.frontiersin.org/files/Articles/1772490/fpsyg-17-1772490-HTML-r1/image_m/fpsyg-17-1772490-t001.jpg Table 1. Physical fitness component comparisons between high- and low-fitness groups. https://www.frontiersin.org/files/Articles/1772490/fpsyg-17-1772490-HTML-r1/image_m/fpsyg-17-1772490-t002.jpg Table 2. Descriptive statistics and independent-samples t test results for PACES scores by fitness g https://www.frontiersin.org/files/Articles/1772490/fpsyg-17-1772490-HTML-r1/image_m/fpsyg-17-1772490-g001.jpg Figure 1. Comparison of PACES scores between high- and low-fitness groups. Bars represent mean PACES https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1652682/full https://www.frontiersin.org/files/Articles/1652682/fendo-17-1652682-HTML-r1/image_m/fendo-17-1652682-g001.jpg Figure 1. Flow chart for study participants. https://www.frontiersin.org/files/Articles/1652682/fendo-17-1652682-HTML-r1/image_m/fendo-17-1652682-t001.jpg Table 1. Individual effects of BMI and the TyG index on the risk of mortality in patients with type https://www.frontiersin.org/files/Articles/1652682/fendo-17-1652682-HTML-r1/image_m/fendo-17-1652682-t002.jpg Table 2. Baseline demographic and clinical characteristics of patients with type 2 diabetes accordin https://www.frontiersin.org/files/Articles/1652682/fendo-17-1652682-HTML-r1/image_m/fendo-17-1652682-g002.jpg Figure 2. Associations of BMI and the TyG index with the risk of all-cause mortality and CCVD mortal https://www.frontiersin.org/files/Articles/1652682/fendo-17-1652682-HTML-r1/image_m/fendo-17-1652682-t003.jpg Table 3. Joint effects of BMI and TyG index on the risk of mortality in patients with type 2 diabete https://www.frontiersin.org/files/Articles/1652682/fendo-17-1652682-HTML-r1/image_m/fendo-17-1652682-g003.jpg Figure 3. Adjusted combined effects of BMI and the TyG index on the risk of all-cause and CCVD morta https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1728150/full https://www.frontiersin.org/files/Articles/1728150/fonc-16-1728150-HTML/image_m/fonc-16-1728150-g001.jpg Figure 1. Flow chart representing details of the study selection. https://www.frontiersin.org/files/Articles/1728150/fonc-16-1728150-HTML/image_m/fonc-16-1728150-t001.jpg Table 1. Characteristics of all the studies included in this meta-analysis. https://www.frontiersin.org/files/Articles/1728150/fonc-16-1728150-HTML/image_m/fonc-16-1728150-g002.jpg Figure 2. Forest plot showing the effect of NCT and NCRT on PFS (p=0.718). https://www.frontiersin.org/files/Articles/1728150/fonc-16-1728150-HTML/image_m/fonc-16-1728150-g003.jpg Figure 3. Begg’s funnel plot showing publication bias of PFS (p=0.260). https://www.frontiersin.org/files/Articles/1728150/fonc-16-1728150-HTML/image_m/fonc-16-1728150-g004.jpg Figure 4. Plot of sensitivity analysis of PFS. https://www.frontiersin.org/files/Articles/1728150/fonc-16-1728150-HTML/image_m/fonc-16-1728150-g005.jpg Figure 5. Forest plot showing the effect of NCT and NCRT on OS (p=0.603). https://www.frontiersin.org/files/Articles/1728150/fonc-16-1728150-HTML/image_m/fonc-16-1728150-g006.jpg Figure 6. Begg’s funnel plot showing publication bias of OS (p=0.452). https://www.frontiersin.org/files/Articles/1728150/fonc-16-1728150-HTML/image_m/fonc-16-1728150-g007.jpg Figure 7. Plot of sensitivity analysis of OS. https://www.frontiersin.org/files/Articles/1728150/fonc-16-1728150-HTML/image_m/fonc-16-1728150-t002.jpg Table 2. Short-term outcome analysis. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1708594/full https://www.frontiersin.org/files/Articles/1708594/fvets-12-1708594-HTML/image_m/fvets-12-1708594-t001.jpg Table 1. Composition and nutrient levels of the basal diet. https://www.frontiersin.org/files/Articles/1708594/fvets-12-1708594-HTML/image_m/fvets-12-1708594-g001.jpg Figure 1. Flow of the trial. Control group (CON), model group (MOD), ginseng polysaccharide group (G https://www.frontiersin.org/files/Articles/1708594/fvets-12-1708594-HTML/image_m/fvets-12-1708594-g002.jpg Figure 2. Construction of the DSS-induced canine IBD model. Changes in body weight (A), CIBDAI score https://www.frontiersin.org/files/Articles/1708594/fvets-12-1708594-HTML/image_m/fvets-12-1708594-g003.jpg Figure 3. Analysis of intestinal bacterial composition in DSS-induced IBD dogs. (A) Comparison of al https://www.frontiersin.org/files/Articles/1708594/fvets-12-1708594-HTML/image_m/fvets-12-1708594-g004.jpg Figure 4. Effects of ginseng polysaccharides on DSS-induced colitis in dogs. Changes in body weight https://www.frontiersin.org/files/Articles/1708594/fvets-12-1708594-HTML/image_m/fvets-12-1708594-g005.jpg Figure 5. Effects of ginseng polysaccharides on the gut microbiota in DSS-induced IBD dogs. Comparis https://www.frontiersin.org/files/Articles/1708594/fvets-12-1708594-HTML/image_m/fvets-12-1708594-g006.jpg Figure 6. Effects of ginseng polysaccharides on SCFA production in DSS-induced IBD dogs. Comparison https://www.frontiersin.org/files/Articles/1708594/fvets-12-1708594-HTML/image_m/fvets-12-1708594-g007.jpg Figure 7. Predicted functional changes in the gut microbiota based on PICRUSt analysis. (A) Heatmap https://www.frontiersin.org/files/Articles/1708594/fvets-12-1708594-HTML/image_m/fvets-12-1708594-g008.jpg Figure 8. Spearman’s correlation analysis between colitis indices and gut microbiota at the phylum ( https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1782228/full https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-g001.jpg Figure 1. Assessment of radiation knowledge and protection practices in nuclear medicine staff. (A) https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-t001.jpg Table 1. Chi-square test results assessing the association between selected demographic, occupationa https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-g002.jpg Figure 2. Distribution of opinion levels among nuclear medicine personnel (n = 20) across eight doma https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-g003.jpg Figure 3. Heatmap illustrating the distribution of nuclear medicine personnel’s responses concerning https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-g004.jpg Figure 4. Heatmap illustrates the frequency distribution of nuclear medicine personnel’s responses r https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-g005.jpg Figure 5. Heatmap represents the frequency distribution of occupational stress-related experiences a https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-t002.jpg Table 2. Summary of thematic analysis on the perspectives and training needs of nuclear medicine per https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-g006.jpg Figure 6. Environmental radiation exposure in 18F-FDG workplace. (A) Floor plan of the nuclear medic https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-g007.jpg Figure 7. Effective dose per working day (μSv) measured at different body parts among staff involved https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-t003.jpg Table 3. Exploratory linear regression models examining associations between radiation dose metrics https://www.frontiersin.org/files/Articles/1782228/fpubh-14-1782228-HTML/image_m/fpubh-14-1782228-t004.jpg Table 4. Distribution-robust analyses evaluating associations between radiation dose metrics and QWL https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1655007/full https://www.frontiersin.org/files/Articles/1655007/fcvm-12-1655007-HTML-r1/image_m/fcvm-12-1655007-i001.jpg Graphical Abstract. Created using Figdraw (https://www.figdraw.com/). https://www.frontiersin.org/files/Articles/1655007/fcvm-12-1655007-HTML-r1/image_m/fcvm-12-1655007-g001.jpg Figure 1. Proposed pathways and mechanisms linking gut flora-derived metabolites to AF development. https://www.frontiersin.org/files/Articles/1655007/fcvm-12-1655007-HTML-r1/image_m/fcvm-12-1655007-t001.jpg Table 1. Dysregulated Gut Flora-derived metabolites and their pathogenic roles in atrial fibrillatio https://www.frontiersin.org/files/Articles/1655007/fcvm-12-1655007-HTML-r1/image_m/fcvm-12-1655007-t002.jpg Table 2. Summary of gut flora interactions with traditional cardiovascular risk factors. https://www.frontiersin.org/files/Articles/1655007/fcvm-12-1655007-HTML-r1/image_m/fcvm-12-1655007-g002.jpg Figure 2. Intestinal flora leads to chronological ageing and molecular ageing. Created using Figdraw https://www.frontiersin.org/files/Articles/1655007/fcvm-12-1655007-HTML-r1/image_m/fcvm-12-1655007-t003.jpg Table 3. Main evidence on dietary interventions targeting intestinal Flora. https://www.frontiersin.org/files/Articles/1655007/fcvm-12-1655007-HTML-r1/image_m/fcvm-12-1655007-t004.jpg Table 4. Main evidence on supplementation with specific strains and exercise. https://www.frontiersin.org/files/Articles/1655007/fcvm-12-1655007-HTML-r1/image_m/fcvm-12-1655007-t005.jpg Table 5. Ongoing clinical trials investigating the role of Gut Flora in risk factors associated with https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1660249/full https://www.frontiersin.org/files/Articles/1660249/fmed-12-1660249-HTML/image_m/fmed-12-1660249-g001.jpg Figure 1. Flowchart of the study design. The study applied a multi-step Mendelian randomization (MR) https://www.frontiersin.org/files/Articles/1660249/fmed-12-1660249-HTML/image_m/fmed-12-1660249-g002.jpg Figure 2. Colocalization analysis of druggable genes associated with AD. Colocalization analysis was https://www.frontiersin.org/files/Articles/1660249/fmed-12-1660249-HTML/image_m/fmed-12-1660249-g003.jpg Figure 3. Forest plot of MR results for 12 colocalized druggable genes associated with AD. Forest pl https://www.frontiersin.org/files/Articles/1660249/fmed-12-1660249-HTML/image_m/fmed-12-1660249-g004.jpg Figure 4. Forest plot of pQTL-based associations between 6 druggable genes and AD. Forest plots pres https://www.frontiersin.org/files/Articles/1660249/fmed-12-1660249-HTML/image_m/fmed-12-1660249-t001.jpg Table 1. NMB significantly associated with AD in summary-based Mendelian randomization (SMR) analysi https://www.frontiersin.org/files/Articles/1660249/fmed-12-1660249-HTML/image_m/fmed-12-1660249-g005.jpg Figure 5. MR associations between NMB expression and multiple pruritic skin diseases. Forest plots p https://www.frontiersin.org/files/Articles/1660249/fmed-12-1660249-HTML/image_m/fmed-12-1660249-t002.jpg Table 2. The regulatory effect of drugs targeting NMB. https://www.frontiersin.org/files/Articles/1660249/fmed-12-1660249-HTML/image_m/fmed-12-1660249-g006.jpg Figure 6. Relative expression of candidate genes in AD patients and healthy controls. Reverse transc https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1788828/full https://www.frontiersin.org/files/Articles/1788828/fphys-17-1788828-HTML/image_m/fphys-17-1788828-g001.jpg Figure 1. Cigarette smoke exposure exacerbates weight loss induced by lipopolysaccharide challenge. https://www.frontiersin.org/files/Articles/1788828/fphys-17-1788828-HTML/image_m/fphys-17-1788828-g002.jpg Figure 2. Cigarette smoke exposure does not significantly affect bronchoalveolar lavage fluid cell c https://www.frontiersin.org/files/Articles/1788828/fphys-17-1788828-HTML/image_m/fphys-17-1788828-t001.jpg Table 1. mRNA expression of cytokines, chemokines, and proteases in the whole lung. https://www.frontiersin.org/files/Articles/1788828/fphys-17-1788828-HTML/image_m/fphys-17-1788828-g003.jpg Figure 3. Cigarette smoke exposure amplifies the expression of inflammatory genes induced by lipopol https://www.frontiersin.org/files/Articles/1788828/fphys-17-1788828-HTML/image_m/fphys-17-1788828-g004.jpg Figure 4. Cigarette smoke exposure exacerbates oxidative stress induced by lipopolysaccharide challe https://www.frontiersin.org/files/Articles/1788828/fphys-17-1788828-HTML/image_m/fphys-17-1788828-g005.jpg Figure 5. Cigarette smoke exposure exacerbates IκBα phosphorylation induced by lipopolysaccharide ch https://www.frontiersin.org/files/Articles/1788828/fphys-17-1788828-HTML/image_m/fphys-17-1788828-g006.jpg Figure 6. Cigarette smoke exposure enhances the expression of early remodeling-associated markers in https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1734573/full https://www.frontiersin.org/files/Articles/1734573/fmed-13-1734573-HTML-r1/image_m/fmed-13-1734573-g001.jpg Figure 1. Schematic diagrams of the three types. Type I: The gestational sac is regular, with poor b https://www.frontiersin.org/files/Articles/1734573/fmed-13-1734573-HTML-r1/image_m/fmed-13-1734573-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1734573/fmed-13-1734573-HTML-r1/image_m/fmed-13-1734573-t002.jpg Table 2. Results of stepwise linear regression analysis of risk factors related to intraoperative bl https://www.frontiersin.org/files/Articles/1734573/fmed-13-1734573-HTML-r1/image_m/fmed-13-1734573-g002.jpg Figure 2. Effect of preoperative uterine artery embolization (UAE) on intraoperative blood loss in p https://www.frontiersin.org/files/Articles/1734573/fmed-13-1734573-HTML-r1/image_m/fmed-13-1734573-g003.jpg Figure 3. A case of type III cesarean scar pregnancy (CSP). (1). Preoperative color Doppler flow ima https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1683322/full https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-t001.jpg Table 1. Inter-evaluator agreement among three evaluators. https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-g001.jpg Figure 1. Workflow for comparative study of LLMs. https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-g002.jpg Figure 2. Evaluation of LLM-generated recommendations across the accuracy dimension (4-point scale: https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-g003.jpg Figure 3. Evaluation of LLM-generated recommendations across the relevance/timeliness dimension (4-p https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-g004.jpg Figure 4. Evaluation of LLM-generated recommendations across the comprehensiveness dimension (4-poin https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-g005.jpg Figure 5. Evaluation of LLM-generated recommendations across the hallucination dimension (binary out https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-g006.jpg Figure 6. Evaluation of LLM-generated recommendations across the clinical-use readiness dimension (b https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-t002.jpg Table 2. Performance of Five AI systems across evaluation dimensions. https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-g007.jpg Figure 7. Ranking distribution of LLM-generated recommendations quality across models on 50 clinical https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-t003.jpg Table 3. Post-hoc pairwise comparisons of model rankings based on Wilcoxon signed-rank tests with Ho https://www.frontiersin.org/files/Articles/1683322/frai-08-1683322-HTML/image_m/frai-08-1683322-t004.jpg Table 4. Strengths and limitations of LLMs based on qualitative analysis. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1706317/full https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-t001.jpg Table 1. Demographics of participants considered in this study. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-g001.jpg Figure 1. Spectrograms derived from raw audio signals. (A) Healthy control group, (B) Parkinson's di https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-g002.jpg Figure 2. The proposed network model architecture. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-g003.jpg Figure 3. Structure of the DenseNet121 model. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-g004.jpg Figure 4. Structure of the MobileNetV3-Large model. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-g005.jpg Figure 5. Structure of the ShuffleNetV2 model. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-t002.jpg Table 2. Hardware configuration and model parameters. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-g006.jpg Figure 6. Confusion matrix of predicted results for a single model on the test set. (A) DenseNet121 https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-t003.jpg Table 3. Indicators for the classification of a single model. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-g007.jpg Figure 7. Classification results of PD on the test set. (A) DenseNet121+ShuffleNetV2 (B) DenseNet121 https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-t004.jpg Table 4. The classification results of feature fusion methods. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-g008.jpg Figure 8. Visualization of PD classification metrics. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-t005.jpg Table 5. Classification results of feature fusion methods on public datasets. https://www.frontiersin.org/files/Articles/1706317/fneur-16-1706317-HTML/image_m/fneur-16-1706317-t006.jpg Table 6. Comparison with other state-of-the-art models. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1693932/full https://www.frontiersin.org/files/Articles/1693932/fonc-15-1693932-HTML-r1/image_m/fonc-15-1693932-g001.jpg Figure 1. Endoscopic image from Feb 28, 2024. Colonoscopy showing the descending colon tumor from di https://www.frontiersin.org/files/Articles/1693932/fonc-15-1693932-HTML-r1/image_m/fonc-15-1693932-g002.jpg Figure 2. (Time) Pathological image of the descending colon tumor from the initial diagnosis. https://www.frontiersin.org/files/Articles/1693932/fonc-15-1693932-HTML-r1/image_m/fonc-15-1693932-g003.jpg Figure 3. Images from the first CT exam on Feb 22, 2024, with red arrow indicating enlarged lymph no https://www.frontiersin.org/files/Articles/1693932/fonc-15-1693932-HTML-r1/image_m/fonc-15-1693932-g004.jpg Figure 4. Images from the first MRI exam on Feb 24, 2024, with blue arrows marking the tumor sites. https://www.frontiersin.org/files/Articles/1693932/fonc-15-1693932-HTML-r1/image_m/fonc-15-1693932-t001.jpg Table 1. MSI Test Report. https://www.frontiersin.org/files/Articles/1693932/fonc-15-1693932-HTML-r1/image_m/fonc-15-1693932-g005.jpg Figure 5. CT exam on May 24, 2024: Red arrow marks enlarged lymph nodes, and blue arrow marks the tu https://www.frontiersin.org/files/Articles/1693932/fonc-15-1693932-HTML-r1/image_m/fonc-15-1693932-g006.jpg Figure 6. MRI exam on July 4, 2024:Blue arrows mark the tumor locations. https://www.frontiersin.org/files/Articles/1693932/fonc-15-1693932-HTML-r1/image_m/fonc-15-1693932-g007.jpg Figure 7. Preoperative colonoscopy on July 4, 2024: Blue-highlighted area indicates the tumor. https://www.frontiersin.org/files/Articles/1693932/fonc-15-1693932-HTML-r1/image_m/fonc-15-1693932-g008.jpg Figure 8. Postoperative pathological examination on July 19, 2024. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1734987/full https://www.frontiersin.org/files/Articles/1734987/fped-13-1734987-HTML-r1/image_m/fped-13-1734987-g001.jpg Figure 1. A female with a small superficial IH on the top of the head. (A) Before starting topical t https://www.frontiersin.org/files/Articles/1734987/fped-13-1734987-HTML-r1/image_m/fped-13-1734987-g002.jpg Figure 2. A male with a superficial IH in front of the right ear. (A) Before starting topical timolo https://www.frontiersin.org/files/Articles/1734987/fped-13-1734987-HTML-r1/image_m/fped-13-1734987-g003.jpg Figure 3. A female with a large superficial IH on the head. (A) Before starting topical timolol ther https://www.frontiersin.org/files/Articles/1734987/fped-13-1734987-HTML-r1/image_m/fped-13-1734987-g004.jpg Figure 4. A female with a small superficial IH in the nape. (A) Before starting topical timolol ther https://www.frontiersin.org/files/Articles/1734987/fped-13-1734987-HTML-r1/image_m/fped-13-1734987-t001.jpg Table 1. Demographic and clinical characteristics of patients and infantile hemangioma (IH). https://www.frontiersin.org/files/Articles/1734987/fped-13-1734987-HTML-r1/image_m/fped-13-1734987-t002.jpg Table 2. Infantile hemangioma (IH) patients' outcomes with sex, age and lesion size groups. https://www.frontiersin.org/files/Articles/1734987/fped-13-1734987-HTML-r1/image_m/fped-13-1734987-t003.jpg Table 3. Comparison of demographic and clinical characteristics between patients' efficacy evaluatio https://www.frontiersin.org/files/Articles/1734987/fped-13-1734987-HTML-r1/image_m/fped-13-1734987-t004.jpg Table 4. Factors predictive of efficacy evaluations evaluation (logistic regression analysis). https://www.frontiersin.org/files/Articles/1734987/fped-13-1734987-HTML-r1/image_m/fped-13-1734987-t005.jpg Table 5. Adverse events with topical timolol treatment. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1743772/full https://www.frontiersin.org/files/Articles/1743772/fped-13-1743772-HTML/image_m/fped-13-1743772-g001.jpg Figure 1. The molecular mechanisms of propranolol by inhibiting angiogenesis in infantile hemangioma https://www.frontiersin.org/files/Articles/1743772/fped-13-1743772-HTML/image_m/fped-13-1743772-t001.jpg Table 1. Multi-stage mechanisms of action of propranolol in infantile hemangioma. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1787438/full https://www.frontiersin.org/files/Articles/1787438/fpsyt-17-1787438-HTML/image_m/fpsyt-17-1787438-g001.jpg Figure 1. LASSO regression: cross-validated prediction error and coefficient shrinkage paths. (a, b) https://www.frontiersin.org/files/Articles/1787438/fpsyt-17-1787438-HTML/image_m/fpsyt-17-1787438-t001.jpg Table 1. LASSO-selected predictors (non-zero penalized coefficients). https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1620158/full https://www.frontiersin.org/files/Articles/1620158/fphar-16-1620158-HTML/image_m/fphar-16-1620158-g001.jpg Figure 1. The flowchart of the study. Participants are assessed for eligibility, with those fulling https://www.frontiersin.org/files/Articles/1620158/fphar-16-1620158-HTML/image_m/fphar-16-1620158-t001.jpg Table 1. Characteristics of patients. https://www.frontiersin.org/files/Articles/1620158/fphar-16-1620158-HTML/image_m/fphar-16-1620158-g002.jpg Figure 2. Individual responce to the stimulus of gastroscopy placement in male (A) and female (B) co https://www.frontiersin.org/files/Articles/1620158/fphar-16-1620158-HTML/image_m/fphar-16-1620158-g003.jpg Figure 3. Dose–response curve of oliceridine in painless gastroscopy for male (A) and female (B) coh https://www.frontiersin.org/files/Articles/1620158/fphar-16-1620158-HTML/image_m/fphar-16-1620158-t002.jpg Table 2. HR, SpO2 and MAP at different time points for patients. https://www.frontiersin.org/files/Articles/1620158/fphar-16-1620158-HTML/image_m/fphar-16-1620158-t003.jpg Table 3. Postoperative adverse events data. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1757935/full https://www.frontiersin.org/files/Articles/1757935/fcell-14-1757935-HTML/image_m/fcell-14-1757935-t001.jpg Table 1. Inclusion and exclusion criteria for trial selection. https://www.frontiersin.org/files/Articles/1757935/fcell-14-1757935-HTML/image_m/fcell-14-1757935-g001.jpg Figure 1. (A) Temporal distribution of clinical trials by year and phase. (B) Global distribution an https://www.frontiersin.org/files/Articles/1757935/fcell-14-1757935-HTML/image_m/fcell-14-1757935-g002.jpg Figure 2. (A) Distribution of clinical trials by primary treated drugs. (B) Comparison of oncology b https://www.frontiersin.org/files/Articles/1757935/fcell-14-1757935-HTML/image_m/fcell-14-1757935-g003.jpg Figure 3. Comparison of clinical trials by endpoint categories. https://www.frontiersin.org/files/Articles/1757935/fcell-14-1757935-HTML/image_m/fcell-14-1757935-g004.jpg Figure 4. Distribution and outcomes of clinical trials. (A) Proportion of clinical trial data catego https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1767057/full https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-g001.jpg Figure 1. The canonical biogenesis and functional targeting of miRNAs. The schematic illustrates the https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-t001.jpg Table 1. Key miRNAs and their roles in exercise-induced cardiac adaptation. https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-t002.jpg Table 2. Differential ncRNA responses by exercise modality. https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-g002.jpg Figure 2. Differential miRNA expression in response to exercise. Endurance training (aerobic activit https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-t003.jpg Table 3. Sex, age, and training status as modulators of miRNA response to exercise. https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-g003.jpg Figure 3. Circulating miRNAs and therapeutic strategies: Circulating miRNAs (e.g., miR-21, miR-210, https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-g004.jpg Figure 4. The key long non-coding RNA (lncRNA)–microRNA (miRNA) interaction networks through which e https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-t004.jpg Table 4. Long non-coding RNA–microRNA interactions in exercise-induced cardioprotection. https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-g005.jpg Figure 5. The major circRNA–miRNA regulatory networks involved in exercise-induced cardioprotection, https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-t005.jpg Table 5. Circular RNA–microRNA interactions in exercise-induced cardioprotection. https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-g006.jpg Figure 6. The central role of exercise-induced exosomal microRNAs (miRNAs) in mediating systemic and https://www.frontiersin.org/files/Articles/1767057/fcell-14-1767057-HTML/image_m/fcell-14-1767057-t006.jpg Table 6. Exosomal microRNAs: central mediators of exercise-induced cardioprotection. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1706977/full https://www.frontiersin.org/files/Articles/1706977/fcell-14-1706977-HTML/image_m/fcell-14-1706977-g001.jpg Figure 1. The life cycle and functional shift of exosomes. (A) Biogenesis and Secretion The process https://www.frontiersin.org/files/Articles/1706977/fcell-14-1706977-HTML/image_m/fcell-14-1706977-t001.jpg Table 1. Differential effects of exercise modalities on exosome profile. https://www.frontiersin.org/files/Articles/1706977/fcell-14-1706977-HTML/image_m/fcell-14-1706977-g002.jpg Figure 2. Mechanistic workflow of exercise-induced and engineered exosome therapy for target-tissue https://www.frontiersin.org/files/Articles/1706977/fcell-14-1706977-HTML/image_m/fcell-14-1706977-t002.jpg Table 2. Key exosomal miRNA cargos and their functions in muscle health. https://www.frontiersin.org/files/Articles/1706977/fcell-14-1706977-HTML/image_m/fcell-14-1706977-g003.jpg Figure 3. This illustration depicts the context-dependent signaling of exosomes within the multi-cel https://www.frontiersin.org/files/Articles/1706977/fcell-14-1706977-HTML/image_m/fcell-14-1706977-t003.jpg Table 3. Methodological and translational challenges in myo-exosome research. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1518720/full https://www.frontiersin.org/files/Articles/1518720/fsurg-12-1518720-HTML/image_m/fsurg-12-1518720-g001.jpg Figure 1. PRISMA flow diagram of study identification and selection. https://www.frontiersin.org/files/Articles/1518720/fsurg-12-1518720-HTML/image_m/fsurg-12-1518720-t001.jpg Table 1. Reported incidence of postcoarctectomy syndrome. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1673861/full https://www.frontiersin.org/files/Articles/1673861/fonc-15-1673861-HTML-r1/image_m/fonc-15-1673861-t001.jpg Table 1. Clinical and demographic characteristics of 420 patients with ovarian carcinosarcoma. https://www.frontiersin.org/files/Articles/1673861/fonc-15-1673861-HTML-r1/image_m/fonc-15-1673861-t002.jpg Table 2. Multivariable Cox regression model of 420 patients with ovarian carcinosarcoma. https://www.frontiersin.org/files/Articles/1673861/fonc-15-1673861-HTML-r1/image_m/fonc-15-1673861-g001.jpg Figure 1. Overall survival by NCDB analytic stage of patients with ovarian carcinosarcoma (N = 420, https://www.frontiersin.org/files/Articles/1673861/fonc-15-1673861-HTML-r1/image_m/fonc-15-1673861-g002.jpg Figure 2. Overall survival by primary radiation of patients with ovarian carcinosarcoma (N = 420, p https://www.frontiersin.org/files/Articles/1673861/fonc-15-1673861-HTML-r1/image_m/fonc-15-1673861-g003.jpg Figure 3. Overall survival by primary chemotherapy of patients with ovarian carcinosarcoma (N = 420, https://www.frontiersin.org/files/Articles/1673861/fonc-15-1673861-HTML-r1/image_m/fonc-15-1673861-g004.jpg Figure 4. Overall survival by Charlson-Deyo comorbidity scores of patients with ovarian carcinosarco https://www.frontiersin.org/files/Articles/1673861/fonc-15-1673861-HTML-r1/image_m/fonc-15-1673861-g005.jpg Figure 5. Overall survival by education of patients with ovarian carcinosarcoma (N = 420, p = 0.026) https://www.frontiersin.org/files/Articles/1673861/fonc-15-1673861-HTML-r1/image_m/fonc-15-1673861-g006.jpg Figure 6. Overall survival by income of patients with ovarian carcinosarcoma (N = 420, p = 0.001). https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2026.1798195/full https://www.frontiersin.org/files/Articles/1798195/fagro-08-1798195-HTML/image_m/fagro-08-1798195-t001.jpg Table 1. Summary of experimental treatments and application methods of green-synthesized nanoparticl https://www.frontiersin.org/files/Articles/1798195/fagro-08-1798195-HTML/image_m/fagro-08-1798195-g001.jpg Figure 1. Correlation matrix of morpho-agro-physiological traits in two wheat cultivars (V1: SKD-1 a https://www.frontiersin.org/files/Articles/1798195/fagro-08-1798195-HTML/image_m/fagro-08-1798195-g002.jpg Figure 2. Nickel Accumulation and Translocation in SKD-1 (V1) and Borlaug-16 Cultivars (V2). (a) Ni https://www.frontiersin.org/files/Articles/1798195/fagro-08-1798195-HTML/image_m/fagro-08-1798195-g003.jpg Figure 3. Phytochemical contents of leaves across different treatments in two wheat cultivars (V1: S https://www.frontiersin.org/files/Articles/1798195/fagro-08-1798195-HTML/image_m/fagro-08-1798195-g004.jpg Figure 4. Antioxidant potential of leaf measured across different treatments in two wheat cultivars https://www.frontiersin.org/files/Articles/1798195/fagro-08-1798195-HTML/image_m/fagro-08-1798195-g005.jpg Figure 5. Enzymatic antioxidant activities across two wheat cultivars (V1: SKD-1 and V2: Borlaug-16) https://www.frontiersin.org/files/Articles/1798195/fagro-08-1798195-HTML/image_m/fagro-08-1798195-g006.jpg Figure 6. Oxidative stress markers across two wheat cultivars (V1: SKD-1 and V2: Borlaug-16) under n https://www.frontiersin.org/files/Articles/1798195/fagro-08-1798195-HTML/image_m/fagro-08-1798195-g007.jpg Figure 7. Photosynthetic pigments across two wheat cultivars (V1: SKD-1 and V2: Borlaug-16) under ni https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1771587/full https://www.frontiersin.org/files/Articles/1771587/fonc-16-1771587-HTML/image_m/fonc-16-1771587-g001.jpg Figure 1. cfDNA and gDNA sample distinct biological compartments. (A) Reference methylation atlas co https://www.frontiersin.org/files/Articles/1771587/fonc-16-1771587-HTML/image_m/fonc-16-1771587-g002.jpg Figure 2. Technical cohort validation with matched BM samples. (A) Mutation landscape of the technic https://www.frontiersin.org/files/Articles/1771587/fonc-16-1771587-HTML/image_m/fonc-16-1771587-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1771587/fonc-16-1771587-HTML/image_m/fonc-16-1771587-g003.jpg Figure 3. Validation cohort confirms gene-specific cfDNA advantage. (A) Mutation landscape of valida https://www.frontiersin.org/files/Articles/1771587/fonc-16-1771587-HTML/image_m/fonc-16-1771587-t002.jpg Table 2. Gene-specific cfDNA advantage in the validation cohort. https://www.frontiersin.org/files/Articles/1771587/fonc-16-1771587-HTML/image_m/fonc-16-1771587-g004.jpg Figure 4. Longitudinal monitoring demonstrates complementary clinical utilities. (A) Mean VAF change https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1652825/full https://www.frontiersin.org/files/Articles/1652825/fped-13-1652825-HTML/image_m/fped-13-1652825-t001.jpg Table 1. Comparison of clinical general conditions between EBV infection group and control group. https://www.frontiersin.org/files/Articles/1652825/fped-13-1652825-HTML/image_m/fped-13-1652825-t002.jpg Table 2. Comparison of cytokine profiles between EBV-infected and control groups [M (P25–P75)] (%). https://www.frontiersin.org/files/Articles/1652825/fped-13-1652825-HTML/image_m/fped-13-1652825-t003.jpg Table 3. Comparison of immune-related indicators between EBV-infected group and control group [M (P2 https://www.frontiersin.org/files/Articles/1652825/fped-13-1652825-HTML/image_m/fped-13-1652825-g001.jpg Figure 1. Comparison of immune-related indicators between EBV-infected group and control group [M (P https://www.frontiersin.org/files/Articles/1652825/fped-13-1652825-HTML/image_m/fped-13-1652825-t004.jpg Table 4. Immune-related comparison between EBV-infected LI and NLI group [M (P25–P75)] (%). https://www.frontiersin.org/files/Articles/1652825/fped-13-1652825-HTML/image_m/fped-13-1652825-g002.jpg Figure 2. Comparison of immune-related indicators between EBV-infected LI and NLI group [M (P25-P75) https://www.frontiersin.org/files/Articles/1652825/fped-13-1652825-HTML/image_m/fped-13-1652825-t005.jpg Table 5. Results of correlation analysis of immune indicators of EBV infection. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1714229/full https://www.frontiersin.org/files/Articles/1714229/fmicb-16-1714229-HTML-r1/image_m/fmicb-16-1714229-g001.jpg Figure 1. Mean relative abundance of all detected genera (n = 1,348) on a log10 scale. The bar plot https://www.frontiersin.org/files/Articles/1714229/fmicb-16-1714229-HTML-r1/image_m/fmicb-16-1714229-t001.jpg Table 1. Characteristics of the study population by serum cotinine levels. https://www.frontiersin.org/files/Articles/1714229/fmicb-16-1714229-HTML-r1/image_m/fmicb-16-1714229-g002.jpg Figure 2. Observed ASVs (alpha diversity) by smoking status and age group. Boxplots represent the di https://www.frontiersin.org/files/Articles/1714229/fmicb-16-1714229-HTML-r1/image_m/fmicb-16-1714229-t002.jpg Table 2. Estimated β coefficients (SE) from linear regression models assessing the association betwe https://www.frontiersin.org/files/Articles/1714229/fmicb-16-1714229-HTML-r1/image_m/fmicb-16-1714229-g003.jpg Figure 3. Principal coordinates analysis (PCoA) of oral microbiome beta diversity based on Bray-Curt https://www.frontiersin.org/files/Articles/1714229/fmicb-16-1714229-HTML-r1/image_m/fmicb-16-1714229-g004.jpg Figure 4. Linear trends in the relative abundance of 29 oral bacterial genera in relation to serum c https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1644453/full https://www.frontiersin.org/files/Articles/1644453/fpsyg-16-1644453-HTML/image_m/fpsyg-16-1644453-t001.jpg Table 1. Mean comparison of parenting dimensions (Mean ± SD, n = 2392). https://www.frontiersin.org/files/Articles/1644453/fpsyg-16-1644453-HTML/image_m/fpsyg-16-1644453-t002.jpg Table 2. Distribution of parenting style, by gender (%). https://www.frontiersin.org/files/Articles/1644453/fpsyg-16-1644453-HTML/image_m/fpsyg-16-1644453-t003.jpg Table 3. Prevalence of sexual-related behaviors, by gender (n (%)). https://www.frontiersin.org/files/Articles/1644453/fpsyg-16-1644453-HTML/image_m/fpsyg-16-1644453-t004.jpg Table 4. Relationship between parenting dimensions and sexual-related behaviors, by gender (aOR(95%C https://www.frontiersin.org/files/Articles/1644453/fpsyg-16-1644453-HTML/image_m/fpsyg-16-1644453-t005.jpg Table 5. Relationship between parenting style and sexual-related behaviors, by gender (aOR(95%CI)). https://www.frontiersin.org/journals/nuclear-medicine/articles/10.3389/fnume.2026.1723650/full https://www.frontiersin.org/files/Articles/1723650/fnume-06-1723650-HTML/image_m/fnume-06-1723650-g001.jpg Figure 1. Project steps for report generation as a follow-up examination to a previous melanoma [18F https://www.frontiersin.org/files/Articles/1723650/fnume-06-1723650-HTML/image_m/fnume-06-1723650-t001.jpg Table 1. Complexity of prompts for report generation and cosine similarity between generated report https://www.frontiersin.org/files/Articles/1723650/fnume-06-1723650-HTML/image_m/fnume-06-1723650-t002.jpg Table 2. Example of a report text generated in German and translated into English for case 3, writte https://www.frontiersin.org/files/Articles/1723650/fnume-06-1723650-HTML/image_m/fnume-06-1723650-g002.jpg Figure 2. Cosine similarity on and above the main diagonal between the generated 27 report texts. https://www.frontiersin.org/files/Articles/1723650/fnume-06-1723650-HTML/image_m/fnume-06-1723650-t003.jpg Table 3. Text quality assessment by readers (questions 1–4) and the influence of case complexity (Fi https://www.frontiersin.org/files/Articles/1723650/fnume-06-1723650-HTML/image_m/fnume-06-1723650-g003.jpg Figure 3. Mean score split by reader and by author, plotted over increasing case complexity (questio https://www.frontiersin.org/files/Articles/1723650/fnume-06-1723650-HTML/image_m/fnume-06-1723650-t004.jpg Table 4. Identification of the true author (question 5, Figure 4) and the influence of true author o https://www.frontiersin.org/files/Articles/1723650/fnume-06-1723650-HTML/image_m/fnume-06-1723650-t005.jpg Table 5. IRR by Gwet and Fleiss’ kappa. https://www.frontiersin.org/files/Articles/1723650/fnume-06-1723650-HTML/image_m/fnume-06-1723650-g004.jpg Figure 4. Confusion matrix by reader, true author over predicted author (question 5). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1733845/full https://www.frontiersin.org/files/Articles/1733845/fpsyg-17-1733845-HTML-r2/image_m/fpsyg-17-1733845-g001.jpg Figure 1. Flow diagram of participant recruitment and allocation. https://www.frontiersin.org/files/Articles/1733845/fpsyg-17-1733845-HTML-r2/image_m/fpsyg-17-1733845-t001.jpg Table 1. Description of selected actions in the five platform-based training modules. https://www.frontiersin.org/files/Articles/1733845/fpsyg-17-1733845-HTML-r2/image_m/fpsyg-17-1733845-g002.jpg Figure 2. Overview of the intervention structure. https://www.frontiersin.org/files/Articles/1733845/fpsyg-17-1733845-HTML-r2/image_m/fpsyg-17-1733845-t002.jpg Table 2. Progressive difficulty levels of six intervention games. https://www.frontiersin.org/files/Articles/1733845/fpsyg-17-1733845-HTML-r2/image_m/fpsyg-17-1733845-t003.jpg Table 3. Comparison of general characteristics among four groups [M ± SD, n (%)]. https://www.frontiersin.org/files/Articles/1733845/fpsyg-17-1733845-HTML-r2/image_m/fpsyg-17-1733845-t004.jpg Table 4. Effects of different interventions on social anxiety (M ± SD). https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1781988/full https://www.frontiersin.org/files/Articles/1781988/fcimb-16-1781988-HTML/image_m/fcimb-16-1781988-g001.jpg Figure 1. Diverging view of appropriateness for indicated versus inappropriateness for non-indicated https://www.frontiersin.org/files/Articles/1781988/fcimb-16-1781988-HTML/image_m/fcimb-16-1781988-g002.jpg Figure 2. Heatmap of prescribing appropriateness by category. Dental procedures by their indication https://www.frontiersin.org/files/Articles/1781988/fcimb-16-1781988-HTML/image_m/fcimb-16-1781988-g003.jpg Figure 3. Antibiotic prescribing appropriateness: comprehensive dashboard. Antibiotic prescribing ad https://www.frontiersin.org/files/Articles/1781988/fcimb-16-1781988-HTML/image_m/fcimb-16-1781988-t001.jpg Table 1. Characteristics of dentists included in the study (n=905). https://www.frontiersin.org/files/Articles/1781988/fcimb-16-1781988-HTML/image_m/fcimb-16-1781988-t002.jpg Table 2. Number and percentage of dentists who reported appropriate prescribing of antibiotics (n = https://www.frontiersin.org/files/Articles/1781988/fcimb-16-1781988-HTML/image_m/fcimb-16-1781988-t003.jpg Table 3. Associations between dentists’ characteristics and quality of antibiotic prescribing practi https://www.frontiersin.org/files/Articles/1781988/fcimb-16-1781988-HTML/image_m/fcimb-16-1781988-t004.jpg Table 4. Predictors and factors associated with good antibiotic prescribing practice. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1767261/full https://www.frontiersin.org/files/Articles/1767261/fonc-16-1767261-HTML/image_m/fonc-16-1767261-g001.jpg Figure 1. MUC21 Expression by analyzing high-throughput dataset including Affymetrix GeneChip® Human https://www.frontiersin.org/files/Articles/1767261/fonc-16-1767261-HTML/image_m/fonc-16-1767261-g002.jpg Figure 2. MUC21 co expression gene analysis by cor.test of R language in the TCGA. (A-K), the correl https://www.frontiersin.org/files/Articles/1767261/fonc-16-1767261-HTML/image_m/fonc-16-1767261-g003.jpg Figure 3. Quantitative qRT-PCR and immunohistochemistry analysis of MUC21, KRT4, KRT13, and CRNN in https://www.frontiersin.org/files/Articles/1767261/fonc-16-1767261-HTML/image_m/fonc-16-1767261-g004.jpg Figure 4. MUC21 expression analysis in OSCC and para-OSCC via immunohistochemistry (IHC) and its rel https://www.frontiersin.org/files/Articles/1767261/fonc-16-1767261-HTML/image_m/fonc-16-1767261-g005.jpg Figure 5. Kaplan–Meier survival analyses for postoperative OSCC patients based on MUC21 expression. https://www.frontiersin.org/files/Articles/1767261/fonc-16-1767261-HTML/image_m/fonc-16-1767261-t001.jpg Table 1. Univariate and multivariate analysis of overall survival in OSCC. https://www.frontiersin.org/files/Articles/1767261/fonc-16-1767261-HTML/image_m/fonc-16-1767261-t002.jpg Table 2. Univariate and multivariate analysis of disease-free survival in OSCC. https://www.frontiersin.org/files/Articles/1767261/fonc-16-1767261-HTML/image_m/fonc-16-1767261-g006.jpg Figure 6. In vitro cell lines experiment post overexpression and knockdown of MUC21. (A) MUC21 was s https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1704007/full https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-g001.jpg Figure 1. Flow diagram of the literature selection process. Records were identified from the Web of https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-g002.jpg Figure 2. Annual and cumulative number of publications from 2014 to 2024. The blue bars represent th https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-t001.jpg Table 1. Top 10 productive countries. https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-g003.jpg Figure 3. Analysis of country and regional collaboration networks. (A) Scimago Graphica collaboratio https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-t002.jpg Table 2. Top 10 productive institutions. https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-g004.jpg Figure 4. Institutional collaboration network generated by VOSviewer. (A) Network visualization map. https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-t003.jpg Table 3. Top 10 journals. https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-g005.jpg Figure 5. The dual-map overlay of journal publishing research. The length of the horizontal axis of https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-t004.jpg Table 4. Top 10 authors. https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-g006.jpg Figure 6. Analysis of author collaboration networks. The size of each node corresponds to the author https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-t005.jpg Table 5. Top 10 highly cited literatures. https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-t006.jpg Table 6. Top 10 co-cited references. https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-g007.jpg Figure 7. Top 20 references with the strongest citation bursts. The blue bar indicates the time inte https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-t007.jpg Table 7. Top 10 Keywords. https://www.frontiersin.org/files/Articles/1704007/fonc-16-1704007-HTML/image_m/fonc-16-1704007-g008.jpg Figure 8. Analysis of keyword clusters and citation bursts. (A) CiteSpace visualization timeline vie https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1740753/full https://www.frontiersin.org/files/Articles/1740753/fpls-17-1740753-HTML/image_m/fpls-17-1740753-g001.jpg Figure 1. Experimental flowchart for LF-NMR analysis of rice seeds treated with SAEW. https://www.frontiersin.org/files/Articles/1740753/fpls-17-1740753-HTML/image_m/fpls-17-1740753-t001.jpg Table 1. Physicochemical parameters of SAEW. https://www.frontiersin.org/files/Articles/1740753/fpls-17-1740753-HTML/image_m/fpls-17-1740753-g002.jpg Figure 2. Pseudo-color diagram of the germination process of rice seeds soaked in SAEW from differen https://www.frontiersin.org/files/Articles/1740753/fpls-17-1740753-HTML/image_m/fpls-17-1740753-g003.jpg Figure 3. T2 relaxation spectra of rice seeds in seven experimental groups. (A) SAEW10; (B) SAEW20; https://www.frontiersin.org/files/Articles/1740753/fpls-17-1740753-HTML/image_m/fpls-17-1740753-g004.jpg Figure 4. SAEW on water content dynamics in germinating rice seeds (ACC). (A) Bound water (A21), (B) https://www.frontiersin.org/files/Articles/1740753/fpls-17-1740753-HTML/image_m/fpls-17-1740753-g005.jpg Figure 5. SAEW on water content dynamics in germinating rice seeds (Germination time). (A) Bound wat https://www.frontiersin.org/files/Articles/1740753/fpls-17-1740753-HTML/image_m/fpls-17-1740753-g006.jpg Figure 6. Effects of SAEW on rice seed germination parameters (ACC). (A) Comparison between rice see https://www.frontiersin.org/files/Articles/1740753/fpls-17-1740753-HTML/image_m/fpls-17-1740753-g007.jpg Figure 7. Effects of SAEW on rice seed germination parameters (Germination time). (A) Germination pe https://www.frontiersin.org/files/Articles/1740753/fpls-17-1740753-HTML/image_m/fpls-17-1740753-g008.jpg Figure 8. Diagram showing the effect of SAEW on rice seed germination. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1779140/full https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-g001.jpg Figure 1. Boruta feature-selection results for three mortality outcomes. (A) Important predictors se https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-g002.jpg Figure 2. Performance comparison of five machine learning models (Logistic Regression, XGBoost, Rand https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-g003.jpg Figure 3. Nomogram development and performance evaluation for in-hospital mortality. (A) Final nomog https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-g004.jpg Figure 4. Nomogram development and performance evaluation for 30-day mortality. (A) Final nomogram c https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-g005.jpg Figure 5. Nomogram development and performance evaluation for 365-day mortality. (A) Final nomogram https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-t001.jpg Table 1. Baseline demographic, clinical, and laboratory characteristics of patients stratified by in https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-t002.jpg Table 2. Baseline demographic, clinical, and laboratory characteristics of patients stratified by 30 https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-t003.jpg Table 3. Baseline demographic, clinical, and laboratory characteristics of patients stratified by 36 https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-t004.jpg Table 4. Predictive performance of five models (Logistic Regression, XGBoost, Random Forest, Extra T https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-t005.jpg Table 5. Predictive performance of five models (Logistic Regression, XGBoost, Random Forest, Extra T https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-t006.jpg Table 6. Predictive performance of five models (Logistic Regression, XGBoost, Random Forest, Extra T https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-t007.jpg Table 7. Pairwise comparisons of model discrimination for in-hospital mortality using DeLong’s test. https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-t008.jpg Table 8. Pairwise comparisons of model discrimination for 30-day mortality using DeLong’s test. https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-t009.jpg Table 9. Pairwise comparisons of model discrimination for 365-day mortality using DeLong’s test. https://www.frontiersin.org/files/Articles/1779140/fmed-13-1779140-HTML/image_m/fmed-13-1779140-g006.jpg Figure 6. Feature importance rankings for the best-performing models across different prediction hor https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1776812/full https://www.frontiersin.org/files/Articles/1776812/fpubh-14-1776812-HTML-r2/image_m/fpubh-14-1776812-t001.jpg Table 1. Indicators for fitting the model of potential categories of compassion fatigue in psychiatr https://www.frontiersin.org/files/Articles/1776812/fpubh-14-1776812-HTML-r2/image_m/fpubh-14-1776812-g001.jpg Figure 1. Distribution of entry scores for the three potential profiles of compassion fatigue in psy https://www.frontiersin.org/files/Articles/1776812/fpubh-14-1776812-HTML-r2/image_m/fpubh-14-1776812-t002.jpg Table 2. Univariate analysis of potential categories of compassion fatigue among psychiatric nurses. https://www.frontiersin.org/files/Articles/1776812/fpubh-14-1776812-HTML-r2/image_m/fpubh-14-1776812-t003.jpg Table 3. Logistic regression analysis results of the three potential profiles of psychiatric nurses' https://www.frontiersin.org/files/Articles/1776812/fpubh-14-1776812-HTML-r2/image_m/fpubh-14-1776812-t004.jpg Table 4. Comparison of differences in caring behavior scores of psychiatric nurses with different po https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1731492/full https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g001.jpg Figure 1. Effects of doses and treatment durations of fenofibrate on autophagy by bMECs. (A) bMECs w https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g002.jpg Figure 2. Fenofibrate restores autophagic activity and autophagic flux in bMECs infected with M. bov https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g003.jpg Figure 3. Fenofibrate induces nuclear translocation of TFE3 in bMECs infected with M. bovis. (A) bME https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g004.jpg Figure 4. Fenofibrate induces nuclear translocation of TFEB in bMECs infected with M. bovis. (A) bME https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g005.jpg Figure 5. Fenofibrate affects free and total cholesterol concentrations, cholesterol-related gene tr https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g006.jpg Figure 6. Fenofibrate affects co-localization of M. bovis, cholesterol, and LC3. (A) bMECs were divi https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g007.jpg Figure 7. Fenofibrate affects expression of autophagy markers in mammary tissue of mice infected wit https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g008.jpg Figure 8. Fenofibrate affects expression of lysosome markers in mammary tissue of mice infected with https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g009.jpg Figure 9. Fenofibrate affects cholesterol concentrations, M. bovis load, tissue morphology, and cyto https://www.frontiersin.org/files/Articles/1731492/fcimb-15-1731492-HTML/image_m/fcimb-15-1731492-g010.jpg Figure 10. Fenofibrate suppresses M. bovis infection via autophagy-mediated cholesterol regulation i https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1533515/full https://www.frontiersin.org/files/Articles/1533515/fmicb-16-1533515-HTML/image_m/fmicb-16-1533515-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1533515/fmicb-16-1533515-HTML/image_m/fmicb-16-1533515-g001.jpg Figure 1. (A) Screening results of ZEN removal strains; (B) Colony morphology of CN1 strain; (C) Gra https://www.frontiersin.org/files/Articles/1533515/fmicb-16-1533515-HTML/image_m/fmicb-16-1533515-g002.jpg Figure 2. (a) HPLC of ZEN removal. ZEN (10 μg/mL) was added to CN1 and cultured in the MRS medium fo https://www.frontiersin.org/files/Articles/1533515/fmicb-16-1533515-HTML/image_m/fmicb-16-1533515-g003.jpg Figure 3. ZEN removing ability of CN1 under different ZEN-concentrations, pH, temperature and bacter https://www.frontiersin.org/files/Articles/1533515/fmicb-16-1533515-HTML/image_m/fmicb-16-1533515-g004.jpg Figure 4. (A) Degradation effect of fermentation supernatant, cell wall and intracellular fluid of s https://www.frontiersin.org/files/Articles/1533515/fmicb-16-1533515-HTML/image_m/fmicb-16-1533515-g005.jpg Figure 5. (A) Desorption rate of cell wall ZEN complex after different treatments; (B) Changes of cr https://www.frontiersin.org/files/Articles/1533515/fmicb-16-1533515-HTML/image_m/fmicb-16-1533515-t001.jpg Table 1. ZEN recovery rate verification results. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1770366/full https://www.frontiersin.org/files/Articles/1770366/fmed-13-1770366-HTML/image_m/fmed-13-1770366-g001.jpg Figure 1. Study flow diagram. The diagram illustrates the systematic screening process from 376,933 https://www.frontiersin.org/files/Articles/1770366/fmed-13-1770366-HTML/image_m/fmed-13-1770366-g002.jpg Figure 2. Kaplan–Meier survival curves for perioperative ischemic stroke patients stratified by post https://www.frontiersin.org/files/Articles/1770366/fmed-13-1770366-HTML/image_m/fmed-13-1770366-t001.jpg Table 1. Baseline characteristics unadjusted sample and propensity score-matched sample. https://www.frontiersin.org/files/Articles/1770366/fmed-13-1770366-HTML/image_m/fmed-13-1770366-t002.jpg Table 2. Logistic regression and propensity score analysis of the association between fentanyl and p https://www.frontiersin.org/files/Articles/1770366/fmed-13-1770366-HTML/image_m/fmed-13-1770366-g003.jpg Figure 3. Propensity score histograms for the two groups. (a) Before matching: fentanyl group (n = 7 https://www.frontiersin.org/files/Articles/1770366/fmed-13-1770366-HTML/image_m/fmed-13-1770366-g004.jpg Figure 4. Subgroup analysis of the association between fentanyl use and postoperative delirium risk. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1661483/full https://www.frontiersin.org/files/Articles/1661483/fpsyt-16-1661483-HTML-r2/image_m/fpsyt-16-1661483-g001.jpg Figure 1. PRISMA flowchart (Page et al., 2021) of the search process and selection of the studies fo https://www.frontiersin.org/files/Articles/1661483/fpsyt-16-1661483-HTML-r2/image_m/fpsyt-16-1661483-t001.jpg Table 1. Eligibility criteria for screening. https://www.frontiersin.org/files/Articles/1661483/fpsyt-16-1661483-HTML-r2/image_m/fpsyt-16-1661483-t002.jpg Table 2. Summary of included studies. https://www.frontiersin.org/files/Articles/1661483/fpsyt-16-1661483-HTML-r2/image_m/fpsyt-16-1661483-t003.jpg Table 3. Example extracted data from the studies relating to the theme of ‘between care failures and https://www.frontiersin.org/files/Articles/1661483/fpsyt-16-1661483-HTML-r2/image_m/fpsyt-16-1661483-t004.jpg Table 4. Example extracted data from the studies relating to the theme of ‘a patchwork of support an https://www.frontiersin.org/files/Articles/1661483/fpsyt-16-1661483-HTML-r2/image_m/fpsyt-16-1661483-t005.jpg Table 5. Example extracted data from the studies relating to the Theme of ‘A Bereaved Mother is Stil https://www.frontiersin.org/files/Articles/1661483/fpsyt-16-1661483-HTML-r2/image_m/fpsyt-16-1661483-g002.jpg Figure 2. Illustration of the final theory. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1742479/full https://www.frontiersin.org/files/Articles/1742479/fped-14-1742479-HTML/image_m/fped-14-1742479-t001.jpg Table 1. Clinical characteristics of 4 cases of microdeletion KBGS. https://www.frontiersin.org/files/Articles/1742479/fped-14-1742479-HTML/image_m/fped-14-1742479-g001.jpg Figure 1. The typical features of 3 patients: case 1 was photographed at 8 years and 4 months. Case https://www.frontiersin.org/files/Articles/1742479/fped-14-1742479-HTML/image_m/fped-14-1742479-t002.jpg Table 2. Case 1 and case 4: growth hormone therapy and follow-Up. https://www.frontiersin.org/files/Articles/1742479/fped-14-1742479-HTML/image_m/fped-14-1742479-t003.jpg Table 3. List of genes with deletions in the 16q24 region along with ANKRD11 in 4 patients. https://www.frontiersin.org/files/Articles/1742479/fped-14-1742479-HTML/image_m/fped-14-1742479-t004.jpg Table 4. Comparative analysis of clinical features in children with KBGS caused by 16q24.3 microdele https://www.frontiersin.org/files/Articles/1742479/fped-14-1742479-HTML/image_m/fped-14-1742479-t005.jpg Table 5. Comparison between KBG patients with microdeletions involving only ANKRD11 and those with m https://www.frontiersin.org/files/Articles/1742479/fped-14-1742479-HTML/image_m/fped-14-1742479-g002.jpg Figure 2. Clinical characteristics of all children with KBGS due to 16q24.3 microdeletion. https://www.frontiersin.org/files/Articles/1742479/fped-14-1742479-HTML/image_m/fped-14-1742479-g003.jpg Figure 3. Changes in height during treatment with rhGH in case 1. https://www.frontiersin.org/files/Articles/1742479/fped-14-1742479-HTML/image_m/fped-14-1742479-g004.jpg Figure 4. Changes in height during treatment with rhGH in case 4. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1766362/full https://www.frontiersin.org/files/Articles/1766362/fmed-13-1766362-HTML/image_m/fmed-13-1766362-g001.jpg Figure 1. Injection methods and observations of methylene blue. (A) Methylene blue and 50 ml of ster https://www.frontiersin.org/files/Articles/1766362/fmed-13-1766362-HTML/image_m/fmed-13-1766362-g002.jpg Figure 2. The abundance of gut microbiota. https://www.frontiersin.org/files/Articles/1766362/fmed-13-1766362-HTML/image_m/fmed-13-1766362-t001.jpg Table 1. Dietary and defecation conditions after intestinal flora transplantation. https://www.frontiersin.org/files/Articles/1766362/fmed-13-1766362-HTML/image_m/fmed-13-1766362-g003.jpg Figure 3. Test indicators after fecal microbiota transplantation. (A) TBil. (B) AST/ALT. (C) ALB. (D https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1760943/full https://www.frontiersin.org/files/Articles/1760943/fendo-17-1760943-HTML/image_m/fendo-17-1760943-t001.jpg Table 1. A comparative overview of regulated cell death modalities in diabetic ulcer pathophysiology https://www.frontiersin.org/files/Articles/1760943/fendo-17-1760943-HTML/image_m/fendo-17-1760943-g001.jpg Figure 1. Key molecular pathways inducing ferroptosis. The graphic illustrates the core mechanisms t https://www.frontiersin.org/files/Articles/1760943/fendo-17-1760943-HTML/image_m/fendo-17-1760943-g002.jpg Figure 2. Key pathways conferring resistance to ferroptosis. This diagram summarizes the principal e https://www.frontiersin.org/files/Articles/1760943/fendo-17-1760943-HTML/image_m/fendo-17-1760943-g003.jpg Figure 3. Central signaling pathways in glycolipid metabolic disorders within the diabetic ulcer mic https://www.frontiersin.org/files/Articles/1760943/fendo-17-1760943-HTML/image_m/fendo-17-1760943-g004.jpg Figure 4. The vicious cycle linking glycolipid metabolic disorders and ferroptosis in diabetic ulcer https://www.frontiersin.org/files/Articles/1760943/fendo-17-1760943-HTML/image_m/fendo-17-1760943-t002.jpg Table 2. Key molecular hubs integrating glycolipid metabolic disorders with ferroptosis in diabetic https://www.frontiersin.org/files/Articles/1760943/fendo-17-1760943-HTML/image_m/fendo-17-1760943-t003.jpg Table 3. Representative therapeutic strategies targeting the ferroptosis-glucolipid metabolism axis https://www.frontiersin.org/files/Articles/1760943/fendo-17-1760943-HTML/image_m/fendo-17-1760943-t004.jpg Table 4. Potential implications of commonly used anti-diabetic agents and ferroptosis modulators for https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1679182/full https://www.frontiersin.org/files/Articles/1679182/fphar-16-1679182-HTML/image_m/fphar-16-1679182-t001.jpg Table 1. Clinical summary table–losartan-associated toothache. https://www.frontiersin.org/files/Articles/1679182/fphar-16-1679182-HTML/image_m/fphar-16-1679182-t002.jpg Table 2. Naranjo causality assessment. https://www.frontiersin.org/files/Articles/1679182/fphar-16-1679182-HTML/image_m/fphar-16-1679182-t003.jpg Table 3. Comparative overview of losartan adverse effects. https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2026.1782845/full https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g001.jpg Figure 1. Examples of Hypergraphs and Hypergraph Dissemination. (A) Hypergraph (B) Hypergraph dissem https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g002.jpg Figure 2. Example of S2IR dissemination model state transition. https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-t001.jpg Table 1. Comparative summary of dissemination model features from the literature. https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-t002.jpg Table 2. Parameter explanation. https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g003.jpg Figure 3. Example of Hyper-S2IR dissemination model state transition. https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g004.jpg Figure 4. Numerical simulation of the evolution process of population density over time for differen https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g005.jpg Figure 5. Heatmap of the distribution of state density of different populations over time (R0 < 1). https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g006.jpg Figure 6. The transmission trajectories (R0 < 1) under different initial infection proportions. (A) https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g007.jpg Figure 7. Numerical simulation of the evolution process of population density over time for differen https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g008.jpg Figure 8. Heatmap of the distribution of state density of different populations over time (R0 > 1). https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g009.jpg Figure 9. The transmission trajectories (R0 > 1) under different initial infection proportions. (A) https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g010.jpg Figure 10. Numerical simulation of the evolution process of population state density over time under https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g011.jpg Figure 11. Numerical simulation of the evolution process of population state density over time under https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-t003.jpg Table 3. Comparison of spreading outcomes on graph and hypergraph structures. https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g012.jpg Figure 12. Comparison of density evolution of HI1(t), HI2(t), and HR(t) with different parameters α. https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g013.jpg Figure 13. Comparison of density evolution of HI1(t), HI2(t), and HR(t) with different parameters γ. https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g014.jpg Figure 14. Comparison of density evolution of HI1(t), HI2(t), and HR(t) with different parameters ω. https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g015.jpg Figure 15. Comparison of density evolution of HI1(t), HI2(t), and HR(t) with different parameters β1 https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-g016.jpg Figure 16. Comparison of density evolution of HI1(t) and HI2(t) for different combinations of transm https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-t004.jpg Table 4. Sobol total effect indices ST for HI1(t) and HI2(t). https://www.frontiersin.org/files/Articles/1782845/fphy-14-1782845-HTML/image_m/fphy-14-1782845-t005.jpg Table 5. Morris screening. https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2026.1768439/full https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-g001.jpg Figure 1. Land use/land cover of pikrodafni river basin. https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-t001.jpg Table 1. Standardized protocol for coding of the critical points in field surveys. https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-g002.jpg Figure 2. Design hyetographs for Pikrodafni river basin for 50, 100 and 1000-year return period. https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-g003.jpg Figure 3. (A) Sub-basins of Pikrodafni river basin. (B) Schematic of river basin in HEC-HMS. https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-g004.jpg Figure 4. (A) Pre-modification DEM representation (B) and Post-modification DEM representation in th https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-g005.jpg Figure 5. Flowchart of the applied methodology (Sargentis et al., 2025c). https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-g006.jpg Figure 6. Hyetographs and hydrographs at a basin outlet under average antecedent soil moisture condi https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-t002.jpg Table 2. Flood inundation simulation scenarios using the LISFLOOD-FP hydraulic model. https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-t003.jpg Table 3. Exceedance probabilities of different flood depths for each flood inundation simulation sce https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-g007.jpg Figure 7. Flood Hazard maps of Pikrodafni river basin for 50 (A), 100 (B) and 1000 (C) years return https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-g008.jpg Figure 8. Flood Risk Map of Pikrodafni river basin including first priority critical points. https://www.frontiersin.org/files/Articles/1768439/fbuil-12-1768439-HTML/image_m/fbuil-12-1768439-g009.jpg Figure 9. Technical reports for indicative critical points along the Pikrodafni river basin. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1680015/full https://www.frontiersin.org/files/Articles/1680015/fimmu-16-1680015-HTML/image_m/fimmu-16-1680015-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1680015/fimmu-16-1680015-HTML/image_m/fimmu-16-1680015-g001.jpg Figure 1. Survival outcomes of hepatocellular carcinoma patients with or without concomitant use of https://www.frontiersin.org/files/Articles/1680015/fimmu-16-1680015-HTML/image_m/fimmu-16-1680015-g002.jpg Figure 2. Survival outcomes of hepatocellular carcinoma patients receiving antibiotics, proton pump https://www.frontiersin.org/files/Articles/1680015/fimmu-16-1680015-HTML/image_m/fimmu-16-1680015-g003.jpg Figure 3. Univariable and multivariable Cox regression analyses for overall survival of hepatocellul https://www.frontiersin.org/files/Articles/1680015/fimmu-16-1680015-HTML/image_m/fimmu-16-1680015-g004.jpg Figure 4. Univariable and multivariable Cox regression analyses for progression-free survival of HCC https://www.frontiersin.org/files/Articles/1680015/fimmu-16-1680015-HTML/image_m/fimmu-16-1680015-t002.jpg Table 2. The relationship between concomitant antibiotic use and efficacy outcomes of restricted sub https://www.frontiersin.org/files/Articles/1680015/fimmu-16-1680015-HTML/image_m/fimmu-16-1680015-g005.jpg Figure 5. Summary of the associations between each drug category and incidence of various types of t https://www.frontiersin.org/files/Articles/1680015/fimmu-16-1680015-HTML/image_m/fimmu-16-1680015-g006.jpg Figure 6. Summary of the study. The impact of concomitant medication use on survival outcomes in pat https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1593214/full https://www.frontiersin.org/files/Articles/1593214/fpubh-13-1593214-HTML-r1/image_m/fpubh-13-1593214-g001.jpg Figure 1. Flow chart of the patients included in the study. https://www.frontiersin.org/files/Articles/1593214/fpubh-13-1593214-HTML-r1/image_m/fpubh-13-1593214-t001.jpg Table 1. Characteristics of participants in NHANES 1999–2010 (weighted). https://www.frontiersin.org/files/Articles/1593214/fpubh-13-1593214-HTML-r1/image_m/fpubh-13-1593214-g002.jpg Figure 2. Scatter plots depicting the relationship between the four components of metabolic syndrome https://www.frontiersin.org/files/Articles/1593214/fpubh-13-1593214-HTML-r1/image_m/fpubh-13-1593214-t002.jpg Table 2. The associations of metabolic syndrome with PhenoAge. https://www.frontiersin.org/files/Articles/1593214/fpubh-13-1593214-HTML-r1/image_m/fpubh-13-1593214-g003.jpg Figure 3. Non-linear association between metabolic syndrome and PhenoAge. Cubic spline models adjust https://www.frontiersin.org/files/Articles/1593214/fpubh-13-1593214-HTML-r1/image_m/fpubh-13-1593214-t003.jpg Table 3. The associations of metabolic syndrome with PhenoAge in subgroups. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1730491/full https://www.frontiersin.org/files/Articles/1730491/fvets-12-1730491-HTML/image_m/fvets-12-1730491-g001.jpg Figure 1. Flow diagram showing eligibility assessment, exclusion criteria, and group allocation for https://www.frontiersin.org/files/Articles/1730491/fvets-12-1730491-HTML/image_m/fvets-12-1730491-t001.jpg Table 1. Demographics, duration of pain, medication status, diagnosis, procedure, and outcomes in 18 https://www.frontiersin.org/files/Articles/1730491/fvets-12-1730491-HTML/image_m/fvets-12-1730491-g002.jpg Figure 2. Boxplot illustrating changes in clinical pain severity scores (LSPain Scale) at baseline, https://www.frontiersin.org/files/Articles/1730491/fvets-12-1730491-HTML/image_m/fvets-12-1730491-g003.jpg Figure 3. Boxplot depicting changes in quality of life (QoL) scores at baseline, first follow-up, an https://www.frontiersin.org/files/Articles/1730491/fvets-12-1730491-HTML/image_m/fvets-12-1730491-g004.jpg Figure 4. Scatter plot with fitted regression lines showing the reduction in Canine Brief Pain Inven https://www.frontiersin.org/files/Articles/1730491/fvets-12-1730491-HTML/image_m/fvets-12-1730491-g005.jpg Figure 5. Violin plot with kernel density estimation (KDE) illustrating the distribution of clinical https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1778224/full https://www.frontiersin.org/files/Articles/1778224/fonc-16-1778224-HTML/image_m/fonc-16-1778224-t001.jpg Table 1. Patients’ characteristics. https://www.frontiersin.org/files/Articles/1778224/fonc-16-1778224-HTML/image_m/fonc-16-1778224-t002.jpg Table 2. BMI variation and weight loss across all subgroups. https://www.frontiersin.org/files/Articles/1778224/fonc-16-1778224-HTML/image_m/fonc-16-1778224-g001.jpg Figure 1. (A) BMI variation according to nutrition support (Kruskal–Wallis test). (B) BMI variation https://www.frontiersin.org/files/Articles/1778224/fonc-16-1778224-HTML/image_m/fonc-16-1778224-t003.jpg Table 3. Median BMI variation and weight loss in patients underwent allo-HSCT because of malignant d https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1689794/full https://www.frontiersin.org/files/Articles/1689794/fbioe-13-1689794-HTML-r2/image_m/fbioe-13-1689794-g001.jpg Figure 1. Flowchart. https://www.frontiersin.org/files/Articles/1689794/fbioe-13-1689794-HTML-r2/image_m/fbioe-13-1689794-g002.jpg Figure 2. Network architecture schematic (device→receiver→AP→server). https://www.frontiersin.org/files/Articles/1689794/fbioe-13-1689794-HTML-r2/image_m/fbioe-13-1689794-g003.jpg Figure 3. Latency definition and temporal diagram. https://www.frontiersin.org/files/Articles/1689794/fbioe-13-1689794-HTML-r2/image_m/fbioe-13-1689794-t001.jpg Table 1. Demographic characteristics of the study cohorts. https://www.frontiersin.org/files/Articles/1689794/fbioe-13-1689794-HTML-r2/image_m/fbioe-13-1689794-g004.jpg Figure 4. Comparison of transmission latency and signal acquisition rate across statistical hypothes https://www.frontiersin.org/files/Articles/1689794/fbioe-13-1689794-HTML-r2/image_m/fbioe-13-1689794-g005.jpg Figure 5. Comparison of Transmission latency Across Variables. p-value: Adjusted with Gender, Age, U https://www.frontiersin.org/files/Articles/1689794/fbioe-13-1689794-HTML-r2/image_m/fbioe-13-1689794-g006.jpg Figure 6. Comparison of Signal acquisition rate Across Variables. p-value: Adjusted with Gender, Age https://www.frontiersin.org/files/Articles/1689794/fbioe-13-1689794-HTML-r2/image_m/fbioe-13-1689794-g007.jpg Figure 7. Comparison of Transmission latency by Subgroup variables. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1658991/full https://www.frontiersin.org/files/Articles/1658991/fphar-16-1658991-HTML-r1/image_m/fphar-16-1658991-g001.jpg Figure 1. Collection and phytochemical profiling of Selaginella samples. (A) Geographic distribution https://www.frontiersin.org/files/Articles/1658991/fphar-16-1658991-HTML-r1/image_m/fphar-16-1658991-g002.jpg Figure 2. Effects of Selaginellae herba extracts on C. elegans lifespan and transcriptomic profiles. https://www.frontiersin.org/files/Articles/1658991/fphar-16-1658991-HTML-r1/image_m/fphar-16-1658991-t001.jpg Table 1. Lifespan extension in C. elegans treated with Selaginella extracts (S4 and S16) and amentof https://www.frontiersin.org/files/Articles/1658991/fphar-16-1658991-HTML-r1/image_m/fphar-16-1658991-g003.jpg Figure 3. Amentoflavone extend lifespan and enhance the resilience to environmental stress of C. ele https://www.frontiersin.org/files/Articles/1658991/fphar-16-1658991-HTML-r1/image_m/fphar-16-1658991-t002.jpg Table 2. Survival of C. elegans under ultraviolet (UV) stress following treatment with Selaginella e https://www.frontiersin.org/files/Articles/1658991/fphar-16-1658991-HTML-r1/image_m/fphar-16-1658991-t003.jpg Table 3. Lifespan of C. elegans under heat stress after treatment with amentoflavone and astaxanthin https://www.frontiersin.org/files/Articles/1658991/fphar-16-1658991-HTML-r1/image_m/fphar-16-1658991-t004.jpg Table 4. Lifespan of C. elegans under oxidative stress after treatment with amentoflavone and astaxa https://www.frontiersin.org/files/Articles/1658991/fphar-16-1658991-HTML-r1/image_m/fphar-16-1658991-g004.jpg Figure 4. Transcriptomes of amentoflavone-treated C. elegans. (A) Volcano plot in control vs amentof https://www.frontiersin.org/files/Articles/1658991/fphar-16-1658991-HTML-r1/image_m/fphar-16-1658991-g005.jpg Figure 5. Exploration of the antioxidant mechanism of amentoflavone from Selaginella in delaying agi https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1790077/full https://www.frontiersin.org/files/Articles/1790077/fmicb-17-1790077-HTML/image_m/fmicb-17-1790077-t001.jpg Table 1. Primers used in this study. https://www.frontiersin.org/files/Articles/1790077/fmicb-17-1790077-HTML/image_m/fmicb-17-1790077-g001.jpg Figure 1. Schematic process of this study. (A) The overview of RNA modification analysis. (B) PK-15 https://www.frontiersin.org/files/Articles/1790077/fmicb-17-1790077-HTML/image_m/fmicb-17-1790077-g002.jpg Figure 2. PRV infection largely suppresses the level of RNA modifications. (A) The heat map showed t https://www.frontiersin.org/files/Articles/1790077/fmicb-17-1790077-HTML/image_m/fmicb-17-1790077-g003.jpg Figure 3. Changes in RNA modifications by PRV infection. (A–L) Statistical analysis of the specific https://www.frontiersin.org/files/Articles/1790077/fmicb-17-1790077-HTML/image_m/fmicb-17-1790077-g004.jpg Figure 4. The levels of m1A modification are regulated by PRV infection. (A–F) PK-15 cells were infe https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1786731/full https://www.frontiersin.org/files/Articles/1786731/fendo-17-1786731-HTML/image_m/fendo-17-1786731-t001.jpg Table 1. The baseline clinical characteristics of non-diabetic adults. https://www.frontiersin.org/files/Articles/1786731/fendo-17-1786731-HTML/image_m/fendo-17-1786731-t002.jpg Table 2. The baseline clinical characteristics of T2DM patients. https://www.frontiersin.org/files/Articles/1786731/fendo-17-1786731-HTML/image_m/fendo-17-1786731-t003.jpg Table 3. Spearman’s correlation between UCPCR and HOMA-IR, Matsuda index, FCP, 2hCP, and HOMA-β. https://www.frontiersin.org/files/Articles/1786731/fendo-17-1786731-HTML/image_m/fendo-17-1786731-g001.jpg Figure 1. Forest plot of multiple linear regression analysis for UCPCR with the Matsuda index in non https://www.frontiersin.org/files/Articles/1786731/fendo-17-1786731-HTML/image_m/fendo-17-1786731-g002.jpg Figure 2. ROC curves of UCPCRs and lipid-related indices for screening insulin resistance (IR) in no https://www.frontiersin.org/files/Articles/1786731/fendo-17-1786731-HTML/image_m/fendo-17-1786731-t004.jpg Table 4. Logistic regression analysis of UCPCRs and DMC risk in patients with T2DM. https://www.frontiersin.org/files/Articles/1786731/fendo-17-1786731-HTML/image_m/fendo-17-1786731-g003.jpg Figure 3. ROC curves of 2hUCPCR and other traditional risk indicators for predicting diabetic microv https://www.frontiersin.org/files/Articles/1786731/fendo-17-1786731-HTML/image_m/fendo-17-1786731-t005.jpg Table 5. Logistic regression analysis of UCPCRs and DKD, DR and DPN risk in patients with T2DM. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1652179/full https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-g001.jpg Figure 1. Flowchart of this study. https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-t001.jpg Table 1. Primers used for qRT‐PCR. https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-g002.jpg Figure 2. Genetic alterations of DRGs in MM. (A) Distribution of DRGs on chromosomes. (B) Comprehens https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-g003.jpg Figure 3. Consensus clustering of DRGs in MM. (A–C) Consensus matrix heatmap of cohort samples defin https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-g004.jpg Figure 4. Biological properties of two subtypes of MM. (A) Differences in immune infiltration betwee https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-g005.jpg Figure 5. Establishment and verification of the disulfidptosis-related signature. (A) Construction o https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-g006.jpg Figure 6. Nomogram construction. (A) MM risk prediction nomogram. (B) Calibration curve of the nomog https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-g007.jpg Figure 7. TME cell infiltration and tumor immune in ltration analysis with disulfidptosis risk score https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-g008.jpg Figure 8. Relationships between risk groups and drug sensitivity. (A–L) Estimated IC50 values of com https://www.frontiersin.org/files/Articles/1652179/fimmu-16-1652179-HTML/image_m/fimmu-16-1652179-g009.jpg Figure 9. External experimental validation of the prognostic signature. (A) qRT-PCR was used to vali https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2025.1663892/full https://www.frontiersin.org/files/Articles/1663892/froh-06-1663892-HTML/image_m/froh-06-1663892-g001.jpg Figure 1. Cinical photographs and digital treatment plan of patient. (A) Patient's handwriting and i https://www.frontiersin.org/files/Articles/1663892/froh-06-1663892-HTML/image_m/froh-06-1663892-g002.jpg Figure 2. Digital workflow for maxillomandibular relationship transfer and prosthetic design. (A) Sc https://www.frontiersin.org/files/Articles/1663892/froh-06-1663892-HTML/image_m/froh-06-1663892-g003.jpg Figure 3. Digitally guided implant procedure. (A) CT image showing implant planning. (B) Implant sur https://www.frontiersin.org/files/Articles/1663892/froh-06-1663892-HTML/image_m/froh-06-1663892-g004.jpg Figure 4. Post-treatment evaluation of definitive prosthesis. (A) Facial photograph. (B) Intraoral v https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1731178/full https://www.frontiersin.org/files/Articles/1731178/fbinf-06-1731178-HTML-r1/image_m/fbinf-06-1731178-g001.jpg Figure 1. The flowchart for predicting essential proteins. https://www.frontiersin.org/files/Articles/1731178/fbinf-06-1731178-HTML-r1/image_m/fbinf-06-1731178-g002.jpg Figure 2. Analysis of the number of top orderings for different prediction methods (yeast data). https://www.frontiersin.org/files/Articles/1731178/fbinf-06-1731178-HTML-r1/image_m/fbinf-06-1731178-g003.jpg Figure 3. Analysis of the number of top orderings for different prediction methods (Escherichia coli https://www.frontiersin.org/files/Articles/1731178/fbinf-06-1731178-HTML-r1/image_m/fbinf-06-1731178-t001.jpg Table 1. Multi - performance evaluation analysis (yeast data). https://www.frontiersin.org/files/Articles/1731178/fbinf-06-1731178-HTML-r1/image_m/fbinf-06-1731178-t002.jpg Table 2. Multi-performance evaluation analysis (Escherichia coli data). https://www.frontiersin.org/files/Articles/1731178/fbinf-06-1731178-HTML-r1/image_m/fbinf-06-1731178-g004.jpg Figure 4. Ablation study on the yeast dataset across five evaluation metrics. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1790536/full https://www.frontiersin.org/files/Articles/1790536/fphar-17-1790536-HTML/image_m/fphar-17-1790536-g001.jpg Figure 1. Comprehensive flowchart of literature selection, evidence stratification, and mechanism. https://www.frontiersin.org/files/Articles/1790536/fphar-17-1790536-HTML/image_m/fphar-17-1790536-t001.jpg Table 1. Evidence grading criteria for causal inference in the efficacy of TCM and plant metabolites https://www.frontiersin.org/files/Articles/1790536/fphar-17-1790536-HTML/image_m/fphar-17-1790536-t002.jpg Table 2. Composition, preparation, and taxonomic validation of botanical drugs and plant metabolites https://www.frontiersin.org/files/Articles/1790536/fphar-17-1790536-HTML/image_m/fphar-17-1790536-g002.jpg Figure 2. Characteristic overview and evidence stratification included in the study (N = 25). (A) In https://www.frontiersin.org/files/Articles/1790536/fphar-17-1790536-HTML/image_m/fphar-17-1790536-t003.jpg Table 3. The characteristics of the research include: intervention type, animal model, changes in mi https://www.frontiersin.org/files/Articles/1790536/fphar-17-1790536-HTML/image_m/fphar-17-1790536-g003.jpg Figure 3. Risk of bias assessment summary. The stacked bar chart illustrates the proportion of studi https://www.frontiersin.org/files/Articles/1790536/fphar-17-1790536-HTML/image_m/fphar-17-1790536-g004.jpg Figure 4. Left (Health/Steady State): The intestinal microbiota is in a health-related state, and th https://www.frontiersin.org/files/Articles/1790536/fphar-17-1790536-HTML/image_m/fphar-17-1790536-t004.jpg Table 4. Priority list for future validation: High-Frequency candidates derived from level C evidenc https://www.frontiersin.org/files/Articles/1790536/fphar-17-1790536-HTML/image_m/fphar-17-1790536-t005.jpg Table 5. Summary of significant alterations in gut microbiota genera induced by botanical drugs and https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1768627/full https://www.frontiersin.org/files/Articles/1768627/fpsyg-17-1768627-HTML-r1/image_m/fpsyg-17-1768627-t001.jpg Table 1. Measurement properties of scale items. https://www.frontiersin.org/files/Articles/1768627/fpsyg-17-1768627-HTML-r1/image_m/fpsyg-17-1768627-t002.jpg Table 2. Means, standard deviations, correlations, and discriminant validity estimates. https://www.frontiersin.org/files/Articles/1768627/fpsyg-17-1768627-HTML-r1/image_m/fpsyg-17-1768627-g001.jpg Figure 1. Conceptual framework and research hypotheses of the study. https://www.frontiersin.org/files/Articles/1768627/fpsyg-17-1768627-HTML-r1/image_m/fpsyg-17-1768627-g002.jpg Figure 2. Result of the proposed research model. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1739103/full https://www.frontiersin.org/files/Articles/1739103/fmicb-17-1739103-HTML/image_m/fmicb-17-1739103-g001.jpg Figure 1. Schematic of the four-layer GI-MAPS platform integrating patented modules for anaerobic sa https://www.frontiersin.org/files/Articles/1739103/fmicb-17-1739103-HTML/image_m/fmicb-17-1739103-t001.jpg Table 1. Patented engineering modules of the Gi-MAPS system and their technical function roles. https://www.frontiersin.org/files/Articles/1739103/fmicb-17-1739103-HTML/image_m/fmicb-17-1739103-t002.jpg Table 2. Analytical performance benchmarks of validated patented components within Gi-MAPS. https://www.frontiersin.org/files/Articles/1739103/fmicb-17-1739103-HTML/image_m/fmicb-17-1739103-g002.jpg Figure 2. Predictive performance highlights of the GI-MAPS subsystems. (a) ROC curve for the micro-e https://www.frontiersin.org/files/Articles/1739103/fmicb-17-1739103-HTML/image_m/fmicb-17-1739103-t003.jpg Table 3. Software copyright modules and filed patents supporting the Gi-MAPS platform execution laye https://www.frontiersin.org/files/Articles/1739103/fmicb-17-1739103-HTML/image_m/fmicb-17-1739103-g003.jpg Figure 3. Computational workflow of the Gi-MAPS AI analytical core. Workflow diagram showing sequent https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1709477/full https://www.frontiersin.org/files/Articles/1709477/fnut-13-1709477-HTML/image_m/fnut-13-1709477-g001.jpg Figure 1. Magnesium ion (Mg2+) homeostasis in the body is primarily maintained by three key systems: https://www.frontiersin.org/files/Articles/1709477/fnut-13-1709477-HTML/image_m/fnut-13-1709477-g002.jpg Figure 2. The contribution of magnesium deficiency to multiple liver diseases. Magnesium deficiency https://www.frontiersin.org/files/Articles/1709477/fnut-13-1709477-HTML/image_m/fnut-13-1709477-g003.jpg Figure 3. Regulation of magnesium homeostasis through magnesium supplementation, pharmacological int https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1757255/full https://www.frontiersin.org/files/Articles/1757255/fendo-17-1757255-HTML/image_m/fendo-17-1757255-g001.jpg Figure 1. Flowchart illustrating the patient recruitment and selection process. A total of 991 patie https://www.frontiersin.org/files/Articles/1757255/fendo-17-1757255-HTML/image_m/fendo-17-1757255-t001.jpg Table 1. Demographic and clinical characteristics of all patients in the study population. https://www.frontiersin.org/files/Articles/1757255/fendo-17-1757255-HTML/image_m/fendo-17-1757255-g002.jpg Figure 2. Restricted cubic spline (RCS) analysis depicting the associations between continuous clini https://www.frontiersin.org/files/Articles/1757255/fendo-17-1757255-HTML/image_m/fendo-17-1757255-t002.jpg Table 2. Patient characteristics in training and validation cohorts. https://www.frontiersin.org/files/Articles/1757255/fendo-17-1757255-HTML/image_m/fendo-17-1757255-t003.jpg Table 3. Patient characteristics in training and validation cohorts with Negative biopsy and PCa. https://www.frontiersin.org/files/Articles/1757255/fendo-17-1757255-HTML/image_m/fendo-17-1757255-g003.jpg Figure 3. Variable selection using least absolute shrinkage and selection operator (LASSO) regressio https://www.frontiersin.org/files/Articles/1757255/fendo-17-1757255-HTML/image_m/fendo-17-1757255-t004.jpg Table 4. Multivariate logistic regression analysis for clinical features based Lasso regression anal https://www.frontiersin.org/files/Articles/1757255/fendo-17-1757255-HTML/image_m/fendo-17-1757255-g004.jpg Figure 4. Nomogram for predicting the probability of prostate cancer in patients with PSA levels bet https://www.frontiersin.org/files/Articles/1757255/fendo-17-1757255-HTML/image_m/fendo-17-1757255-g005.jpg Figure 5. Performance and validation of the nomogram in the training and validation cohorts. (A) Rec https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1730409/full https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-g001.jpg Figure 1. The participant selection process. https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-g002.jpg Figure 2. Comparison and consensus of features selected by three different methods. https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-t001.jpg Table 1. Baseline characteristics of the study participants according to the aortic arteriosclerosis https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-t002.jpg Table 2. Feature union from four feature selection methods. https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-t003.jpg Table 3. Variance inflation factor (VIF). https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-g003.jpg Figure 3. ROC curves comparing multiple machine learning models on test set. https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-g004.jpg Figure 4. Calibration curves for multiple machine learning models on test set. https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-g005.jpg Figure 5. Decision curves for multiple machine learning models on test set. https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-t004.jpg Table 4. Performance of the machine learning models for predicting aortic arteriosclerosis in test s https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-t005.jpg Table 5. Performance of the XGBoost model evaluated by 5-fold cross-validation on the training set. https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-g006.jpg Figure 6. ROC curves of six machine learning models from 5-fold cross-validation on the training set https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-g007.jpg Figure 7. Comprehensive performance evaluation of the predictive model on the external validation se https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-g008.jpg Figure 8. Model interpretation using SHAP: (A) global interpretation: beeswarm plot of SHAP value di https://www.frontiersin.org/files/Articles/1730409/fcvm-12-1730409-HTML-r1/image_m/fcvm-12-1730409-g009.jpg Figure 9. Web interface for the XGBoost prediction model. The application calculates the risk of aor https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1778601/full https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g001.jpg Figure 1. PSR surgical equipment and procedure. (A) Komai’s frame-based CT-Stereotactic system. (B) https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g002.jpg Figure 2. MR-DTI Imaging for Trigeminal Ganglion Treatment Target (TGT) (A, B) Axial and coronal ima https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-t001.jpg Table 1. Characteristics of the total TN cohort (n=30) vs. PSR-DTI surgical subgroup (n=15). https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g003.jpg Figure 3. Regional homogeneity differences in TN patients. Differential analysis between TN patients https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-t002.jpg Table 2. T-test data for abnormal brain regions in TN patients vs. HC. https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g004.jpg Figure 4. ReHo differences in PSR-DTI surgical patients. Differential analysis between the PSR-DTI g https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-t003.jpg Table 3. t-test data for abnormal brain regions in PSR-DTI patients vs. HC. https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g005.jpg Figure 5. Positive sites of potential predictive brain regions for recurrence in TN patients. Treatm https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g006.jpg Figure 6. Characteristics of potential predictive brain regions for pain recurrence in PSR-DTI patie https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-t004.jpg Table 4. Complete remission and recurrence rates for patients stratified by potential recurrence-rel https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g007.jpg Figure 7. Recurrence risk stratification based on fMRI-Defined brain regions. (A)Recurrence rates ac https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-t005.jpg Table 5. Treatment outcomes by laterality (ipsilateral vs. contralateral) for each potential recurre https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g008.jpg Figure 8. Spatial distribution of fMRI-based risk regions in surgical trigeminal neuralgia patients. https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g009.jpg Figure 9. Apparent performance of the pre-specified H-fMRI rule in the PSR-DTI derivation cohort (n= https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g010.jpg Figure 10. Schematic representation of brain region locations in healthy individuals and TN patients https://www.frontiersin.org/files/Articles/1778601/fpsyt-17-1778601-HTML-r1/image_m/fpsyt-17-1778601-g011.jpg Figure 11. Schematic of potential predictive brain regions pre- and post PSR-DTI in TN patients. (A) https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1618270/full https://www.frontiersin.org/files/Articles/1618270/fonc-15-1618270-HTML/image_m/fonc-15-1618270-g001.jpg Figure 1. Studies on the FLASH effect in rodent organs. https://www.frontiersin.org/files/Articles/1618270/fonc-15-1618270-HTML/image_m/fonc-15-1618270-g002.jpg Figure 2. Schematic of testicular structure and spermatogenesis. https://www.frontiersin.org/files/Articles/1618270/fonc-15-1618270-HTML/image_m/fonc-15-1618270-t001.jpg Table 1. Summary of studies on the effects of different radiation dose rates on the testes, spermato https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1622262/full https://www.frontiersin.org/files/Articles/1622262/fmicb-16-1622262-HTML/image_m/fmicb-16-1622262-g001.jpg Figure 1. Comparison between NCR/NCR-like peptides and more general antimicrobial peptides (defensin https://www.frontiersin.org/files/Articles/1622262/fmicb-16-1622262-HTML/image_m/fmicb-16-1622262-g002.jpg Figure 2. Phylogenetic distribution and gene characteristics of NCR and NCR-like peptides in legume https://www.frontiersin.org/files/Articles/1622262/fmicb-16-1622262-HTML/image_m/fmicb-16-1622262-g003.jpg Figure 3. Spatial mapping of the relative quantity of NCRs expressed at 4 weeks post-nodulation in M https://www.frontiersin.org/files/Articles/1622262/fmicb-16-1622262-HTML/image_m/fmicb-16-1622262-t001.jpg Table 1. NCRs essential to symbiosis. https://www.frontiersin.org/files/Articles/1622262/fmicb-16-1622262-HTML/image_m/fmicb-16-1622262-t002.jpg Table 2. NCR or NCR-like genes identified across legumes. https://www.frontiersin.org/files/Articles/1622262/fmicb-16-1622262-HTML/image_m/fmicb-16-1622262-t003.jpg Table 3. NCR-pathogen assays. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1746281/full https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-t001.jpg Table 1. Primer sequences for RT-qPCR analysis. https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g001.jpg Figure 1. Differential gene expression analysis and identification of intersection gene. (A,B) Volca https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g002.jpg Figure 2. Biological function analysis of 58 candidate genes. KEGG pathway enrichment analysis (A), https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-t002.jpg Table 2. Univariate Cox regression analysis for prognosis-related genes in patients with ESCC. https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-t003.jpg Table 3. A proportional hazards assumption test was performed on the prognosis-related genes include https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g003.jpg Figure 3. Construction and evaluation of risk model in TCGA-ESCC. (A) LASSO regression to screen the https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g004.jpg Figure 4. Validation of the risk model in the GSE53622 cohort. (A) The distribution of risk scores, https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g005.jpg Figure 5. Correlation between the risk score and clinical characteristics. The figure shows (A) A un https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-t004.jpg Table 4. Following single-factor screening, perform a proportional hazards assumption test on the ri https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g006.jpg Figure 6. Comparison of molecular mechanisms in different risk cohorts of patients and the single-ce https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g007.jpg Figure 7. Analysis of somatic mutation information in low- and high-risk groups. Waterfall plot of m https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g008.jpg Figure 8. Immune cell infiltration in low- and high-risk groups. (A,B) Comparison of 28 immune cells https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g009.jpg Figure 9. Predictive values in drug sensitivity, molecular network and methylation analysis. (A) Box https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g010.jpg Figure 10. Expression analysis of prognostic genes in pan-cancer and ESCC. Figure represents (A) dif https://www.frontiersin.org/files/Articles/1746281/fmed-13-1746281-HTML-r3/image_m/fmed-13-1746281-g011.jpg Figure 11. Gene expression in high- and low-risk groups, and validation of chemotherapy, radiotherap https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1784190/full https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-g001.jpg Figure 1. Research framework. https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-t001.jpg Table 1. Guideline for semi-structured interview. https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-t002.jpg Table 2. Questionnaire of auditory and olfactory environments. https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-g002.jpg Figure 2. Experimental process flow diagram. https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-t003.jpg Table 3. Coding frame of auditory and olfactory environment perceptions. https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-t004.jpg Table 4. Rotated factor matrix of auditory environment perception. https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-g003.jpg Figure 3. Factor loading plot of auditory environment perception. (a) Dimension 1 versus Dimension 2 https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-g004.jpg Figure 4. Distributions of factor analysis for auditory environment perception. (a) Dimension 1 vers https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-t005.jpg Table 5. Rotated factor matrix of olfactory environment perception. https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-g005.jpg Figure 5. Factor loading plot of olfactory environment perception. (a) Dimension 1 versus Dimension https://www.frontiersin.org/files/Articles/1784190/fpsyg-17-1784190-HTML/image_m/fpsyg-17-1784190-g006.jpg Figure 6. Distributions of factor analysis for olfactory environment perception. (a) Dimension 1 ver https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2026.1641073/full https://www.frontiersin.org/files/Articles/1641073/fgwh-07-1641073-HTML/image_m/fgwh-07-1641073-t001.jpg Table 1. Global and regional prevalence rates of CLD among WCBA, age-standardized prevalence rates, https://www.frontiersin.org/files/Articles/1641073/fgwh-07-1641073-HTML/image_m/fgwh-07-1641073-g001.jpg Figure 1. AS prevalence rates and ND of prevalence for CLD among WCBA in 204 countries and regions i https://www.frontiersin.org/files/Articles/1641073/fgwh-07-1641073-HTML/image_m/fgwh-07-1641073-g002.jpg Figure 2. Local drifts and temporal changes in age distribution of CLD prevalence among WCBA from 19 https://www.frontiersin.org/files/Articles/1641073/fgwh-07-1641073-HTML/image_m/fgwh-07-1641073-g003.jpg Figure 3. Composition of HBV, HCV, ALD, NAFLD, and other CLD among WCBA in different SDIRs from 1992 https://www.frontiersin.org/files/Articles/1641073/fgwh-07-1641073-HTML/image_m/fgwh-07-1641073-g004.jpg Figure 4. Effects of age, period, and birth cohort on CLD prevalence among WCBA across SDI quintiles https://www.frontiersin.org/files/Articles/1641073/fgwh-07-1641073-HTML/image_m/fgwh-07-1641073-g005.jpg Figure 5. The effects of age, period, and birth cohort on prevalence of CLD among WCBA in the typica https://www.frontiersin.org/files/Articles/1641073/fgwh-07-1641073-HTML/image_m/fgwh-07-1641073-g006.jpg . https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1762693/full https://www.frontiersin.org/files/Articles/1762693/fsufs-10-1762693-HTML-r1/image_m/fsufs-10-1762693-t001.jpg Table 1. Key tensions in agroecology transition research are also found in other food system transit https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1768846/full https://www.frontiersin.org/files/Articles/1768846/fendo-17-1768846-HTML/image_m/fendo-17-1768846-g001.jpg Figure 1. Patient selection flowchart. A total of 62 ISS patients and 37 post-surgical CP patients w https://www.frontiersin.org/files/Articles/1768846/fendo-17-1768846-HTML/image_m/fendo-17-1768846-t001.jpg Table 1. Baseline characteristics of the participants in two groups. https://www.frontiersin.org/files/Articles/1768846/fendo-17-1768846-HTML/image_m/fendo-17-1768846-g002.jpg Figure 2. Changes in height standard deviation score (HtSDS) over time in CP and ISS groups. Box plo https://www.frontiersin.org/files/Articles/1768846/fendo-17-1768846-HTML/image_m/fendo-17-1768846-g003.jpg Figure 3. Longitudinal comparison of HtSDS between CP and ISS groups.Box plots show HtSDS at baselin https://www.frontiersin.org/files/Articles/1768846/fendo-17-1768846-HTML/image_m/fendo-17-1768846-t002.jpg Table 2. Growth velocity and GH dosage in CP and ISS groups. https://www.frontiersin.org/files/Articles/1768846/fendo-17-1768846-HTML/image_m/fendo-17-1768846-g004.jpg Figure 4. Growth velocity during rhGH treatment in ISS and CP groups. Bar plots show annual growth v https://www.frontiersin.org/files/Articles/1768846/fendo-17-1768846-HTML/image_m/fendo-17-1768846-g005.jpg Figure 5. Annual HRT dose during rhGH treatment in ISS and CP groups. Bar plots show the annual HRT https://www.frontiersin.org/files/Articles/1768846/fendo-17-1768846-HTML/image_m/fendo-17-1768846-g006.jpg Figure 6. BA/CA ratio at baseline and after 3 years of rhGH treatment. Bar plots show BA/CA ratios f https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1740965/full https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g001.jpg Figure 1. The visual effects of various data augmentation methods. (a–e) represent the type of each https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g002.jpg Figure 2. YOLOv7 network structure after changing the backbone to EfficientNetV2-S. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g003.jpg Figure 3. Structure of the MBConv module. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g004.jpg Figure 4. Structure of the DWConv module. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g005.jpg Figure 5. The flowchart of the SE algorithm. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g006.jpg Figure 6. The diagram of the SPPCSPC structure involves convolutional operations followed by four ty https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-t001.jpg Table 1. The configuration of the deep learning environment and training hyperparameters. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g007.jpg Figure 7. Comparison chart of loss functions. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g008.jpg Figure 8. Comparison of training loss and validation loss curves across epochs. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-t002.jpg Table 2. Ablation study of different improvement strategies. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-t003.jpg Table 3. The performance comparison of target detection algorithms. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g009.jpg Figure 9. Comparison of mAP convergence curves between YOLOv7 and YOLOv7+. https://www.frontiersin.org/files/Articles/1740965/fpls-17-1740965-HTML/image_m/fpls-17-1740965-g010.jpg Figure 10. The comparison of results: (a) YOLOv5 recognition image; (b) YOLOv7 recognition image; (c https://www.frontiersin.org/journals/neuroanatomy/articles/10.3389/fnana.2026.1778296/full https://www.frontiersin.org/files/Articles/1778296/fnana-20-1778296-HTML/image_m/fnana-20-1778296-g001.jpg Figure 1. Parameter Selection interface used to define image processing settings. The interface prom https://www.frontiersin.org/files/Articles/1778296/fnana-20-1778296-HTML/image_m/fnana-20-1778296-g002.jpg Figure 2. Selection of in-focus slices and automated nucleus detection. The macro first displays eac https://www.frontiersin.org/files/Articles/1778296/fnana-20-1778296-HTML/image_m/fnana-20-1778296-g003.jpg Figure 3. Consolidated worksheet after metadata extraction and intensity per area calculation. Each https://www.frontiersin.org/files/Articles/1778296/fnana-20-1778296-HTML/image_m/fnana-20-1778296-g004.jpg Figure 4. Construction of a PivotTable to organize optical density measurements across animals, regi https://www.frontiersin.org/files/Articles/1778296/fnana-20-1778296-HTML/image_m/fnana-20-1778296-g005.jpg Figure 5. Example of a StarDist output table used for nuclei quantification. The StarDist results fi https://www.frontiersin.org/files/Articles/1778296/fnana-20-1778296-HTML/image_m/fnana-20-1778296-t001.jpg Table 1. Comparison between manual and acro-based immunofluorescence quantification workflows. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1657655/full https://www.frontiersin.org/files/Articles/1657655/fmed-12-1657655-HTML/image_m/fmed-12-1657655-t001.jpg Table 1. Baseline characteristics of late-onset GBS sepsis infants. https://www.frontiersin.org/files/Articles/1657655/fmed-12-1657655-HTML/image_m/fmed-12-1657655-t002.jpg Table 2. Clinical manifestation in late-onset GBS sepsis infants. https://www.frontiersin.org/files/Articles/1657655/fmed-12-1657655-HTML/image_m/fmed-12-1657655-t003.jpg Table 3. Laboratory tests and critical illness scores in late-onset GBS sepsis infants. https://www.frontiersin.org/files/Articles/1657655/fmed-12-1657655-HTML/image_m/fmed-12-1657655-t004.jpg Table 4. Multivariate logistic regression analysis of complications in late-onset GBS sepsis infants https://www.frontiersin.org/files/Articles/1657655/fmed-12-1657655-HTML/image_m/fmed-12-1657655-g001.jpg Figure 1. The ROC curve for predicting complications in infants with late-onset GBS sepsis. The ROC https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1672255/full https://www.frontiersin.org/files/Articles/1672255/fped-13-1672255-HTML/image_m/fped-13-1672255-t001.jpg Table 1. Comparison of clinical characteristics and laboratory indicators between the cKD group and https://www.frontiersin.org/files/Articles/1672255/fped-13-1672255-HTML/image_m/fped-13-1672255-t002.jpg Table 2. Comparison of clinical characteristics and laboratory parameters between IVIG-responders an https://www.frontiersin.org/files/Articles/1672255/fped-13-1672255-HTML/image_m/fped-13-1672255-g001.jpg Figure 1. ROC curves for predicting IVIG resistance in infants with KD using NLR, PLR, and SIII. IVI https://www.frontiersin.org/files/Articles/1672255/fped-13-1672255-HTML/image_m/fped-13-1672255-t003.jpg Table 3. ROC curves for predicting IVIG resistance in infants with KD using NLR, PLR, and SIII. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1704622/full https://www.frontiersin.org/files/Articles/1704622/fped-14-1704622-HTML/image_m/fped-14-1704622-g001.jpg Figure 1. The study flowchart. LOS: late-onset sepsis; NPM: neonatal purulent meningitis. https://www.frontiersin.org/files/Articles/1704622/fped-14-1704622-HTML/image_m/fped-14-1704622-t001.jpg Table 1. Comparison of clinical basic data between NPM group and Non-NPM group. https://www.frontiersin.org/files/Articles/1704622/fped-14-1704622-HTML/image_m/fped-14-1704622-t002.jpg Table 2. Clinical characteristics, underlying primary diseases, and comorbidities in the NPM group a https://www.frontiersin.org/files/Articles/1704622/fped-14-1704622-HTML/image_m/fped-14-1704622-t003.jpg Table 3. Comparison of laboratory tests and blood culture pathogens between the NPM group and Non-NP https://www.frontiersin.org/files/Articles/1704622/fped-14-1704622-HTML/image_m/fped-14-1704622-t004.jpg Table 4. Multivariate logistic regression analysis of neonatal LOS and secondary NPM. https://www.frontiersin.org/files/Articles/1704622/fped-14-1704622-HTML/image_m/fped-14-1704622-t005.jpg Table 5. Parameters related to the prediction of secondary NPM in infants with LOS using logistic mo https://www.frontiersin.org/files/Articles/1704622/fped-14-1704622-HTML/image_m/fped-14-1704622-g002.jpg Figure 2. The ROC curve of predicting LOS in newborns with NPM based on independent risk factors. (A https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1721089/full https://www.frontiersin.org/files/Articles/1721089/fimmu-17-1721089-HTML/image_m/fimmu-17-1721089-g001.jpg Figure 1. The flowchart of study. NLE, Neonatal Lupus Erythematosus; CHB, congenital heart block; CN https://www.frontiersin.org/files/Articles/1721089/fimmu-17-1721089-HTML/image_m/fimmu-17-1721089-t001.jpg Table 1. Clinical features, EEG, and neuroimaging findings of NLE infants with seizures. https://www.frontiersin.org/files/Articles/1721089/fimmu-17-1721089-HTML/image_m/fimmu-17-1721089-t002.jpg Table 2. Baseline clinical characteristics of infants and mothers. https://www.frontiersin.org/files/Articles/1721089/fimmu-17-1721089-HTML/image_m/fimmu-17-1721089-t003.jpg Table 3. Expression of autoimmune antibodies in infants with NLE. https://www.frontiersin.org/files/Articles/1721089/fimmu-17-1721089-HTML/image_m/fimmu-17-1721089-t004.jpg Table 4. Organ involvement in infants with NLE. https://www.frontiersin.org/files/Articles/1721089/fimmu-17-1721089-HTML/image_m/fimmu-17-1721089-g002.jpg Figure 2. The distribution of organ involvement (A) and blood system involvement (B) in infants of N https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1758724/full https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-g001.jpg Figure 1. Flowchart of the method. The model and data input process are briefly described. https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-t001.jpg Table 1. Key characteristics of food production systems and dietary guidelines in selected countries https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-g002.jpg Figure 2. Composition of raw animal products from selected countries. Composition of animal products https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-t002.jpg Table 2. Protein contribution of FBDGs in four countries per capita per day, by population average. https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-g003.jpg Figure 3. Differences between animal products in the original dietary guidelines and those recommend https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-g004.jpg Figure 4. Changes in animal products compared with the original FBDGs under multiple scenarios. Comp https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-g005.jpg Figure 5. Changes in nutrients provided by animal products compared with those provided by original https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-g006.jpg Figure 6. Changes in the environment and economy compared with those of the original FBDGs under mul https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-g007.jpg Figure 7. Environmental, economic and nutritional subindicators for each option. (A) shows the avera https://www.frontiersin.org/files/Articles/1758724/fnut-13-1758724-HTML/image_m/fnut-13-1758724-g008.jpg Figure 8. Pareto frontiers of dual and triple objectives for different countries. (A) Country-specif https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1770729/full https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g001.jpg Figure 1. (a) Study area; (b) Experimental Design. (c) Different growth stages. https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-t001.jpg Table 1. Initial soil physical and chemical properties. https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-t002.jpg Table 2. Salt content and ion composition of salt crust. https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g002.jpg Figure 2. Effect of planting density on plant height of S. salsa (a) and curve fitting (b). Note Ver https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g003.jpg Figure 3. Effect of planting density on stem diameter of S. salsa (a) and curve fitting (b). Note ve https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g004.jpg Figure 4. Effect of planting density on leaf area of S. salsa (a) and curve fitting (b). Note vertic https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g005.jpg Figure 5. Effect of planting density on biomass of S. salsa (a) and curve fitting (b). Note vertical https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g006.jpg Figure 6. Effects of planting density on biomass allocation in different organs Biomass allocation a https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g007.jpg Figure 7. Effects of planting density on total dissolved salts (a) and Na+ concentration in S. salsa https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g008.jpg Figure 8. Effect of planting density on nitrogen concentration (a) and nitrogen accumulation (b). Ve https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g009.jpg Figure 9. Correlation analysis of S. salsa with different planting densities and different growth st https://www.frontiersin.org/files/Articles/1770729/fpls-17-1770729-HTML/image_m/fpls-17-1770729-g010.jpg Figure 10. Effects of planting density on the root-to-crown ratio (a) and the specific leaf area (b) https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1721782/full https://www.frontiersin.org/files/Articles/1721782/fpubh-13-1721782-HTML/image_m/fpubh-13-1721782-t001.jpg Table 1. Baseline characteristics of study participants. https://www.frontiersin.org/files/Articles/1721782/fpubh-13-1721782-HTML/image_m/fpubh-13-1721782-g001.jpg Figure 1. Adherence to guideline-concordant prevention behaviors in nephrolithiasis. (A) Baseline pr https://www.frontiersin.org/files/Articles/1721782/fpubh-13-1721782-HTML/image_m/fpubh-13-1721782-t002.jpg Table 2. Twenty-four-hours urine parameters by self-reported adherence categories. https://www.frontiersin.org/files/Articles/1721782/fpubh-13-1721782-HTML/image_m/fpubh-13-1721782-t003.jpg Table 3. Knowledge, attitude, and practice scores by demographic characteristics. https://www.frontiersin.org/files/Articles/1721782/fpubh-13-1721782-HTML/image_m/fpubh-13-1721782-t004.jpg Table 4. Perceived barriers to kidney stone prevention adherence by patient characteristics. https://www.frontiersin.org/files/Articles/1721782/fpubh-13-1721782-HTML/image_m/fpubh-13-1721782-t005.jpg Table 5. Multivariable logistic regression models for adherence behaviors. https://www.frontiersin.org/files/Articles/1721782/fpubh-13-1721782-HTML/image_m/fpubh-13-1721782-t006.jpg Table 6. Environmental and occupational factors associated with adherence behaviors. https://www.frontiersin.org/files/Articles/1721782/fpubh-13-1721782-HTML/image_m/fpubh-13-1721782-t007.jpg Table 7. Correlation matrix and pathway analysis of knowledge-attitude-practice framework. https://www.frontiersin.org/files/Articles/1721782/fpubh-13-1721782-HTML/image_m/fpubh-13-1721782-t008.jpg Table 8. Clinical and behavioral predictors of 12–months stone recurrence. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1725144/full https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-t001.jpg Table 1. Attitudes toward sexual health recovery interventions by patient characteristics. https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-t002.jpg Table 2. Health literacy-stratified analysis of barriers and facilitators to treatment adherence. https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-t003.jpg Table 3. Multivariable analysis of factors associated with optimal sexual health recovery practicesa https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-t004.jpg Table 4. Baseline characteristics of study participants by health literacy level (N = 1,615). https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-t005.jpg Table 5. Knowledge assessment scores by sociodemographic and clinical characteristics. https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-g001.jpg Figure 1. Knowledge and treatment utilization patterns by health literacy level. (A) Violin plots sh https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-t006.jpg Table 6. Sexual health recovery practices by health literacy level. https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-g002.jpg Figure 2. Clinical decision support and risk stratification framework. (A) ROC curve validating the https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-g003.jpg Figure 3. Predictive models and multidimensional analysis. (A) Forest plot of multivariable predicto https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-t007.jpg Table 7. Mediation analysis of health literacy as mediator between knowledge and practice implementa https://www.frontiersin.org/files/Articles/1725144/fpubh-14-1725144-HTML/image_m/fpubh-14-1725144-t008.jpg Table 8. Clinical risk stratification and intervention priority matrix. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1690443/full https://www.frontiersin.org/files/Articles/1690443/fimmu-16-1690443-HTML/image_m/fimmu-16-1690443-g001.jpg Figure 1. Metabolic dysregulation fuels cardiovascular injury via inflammasomes. Metabolic stress, c https://www.frontiersin.org/files/Articles/1690443/fimmu-16-1690443-HTML/image_m/fimmu-16-1690443-g002.jpg Figure 2. Immune dysregulation–inflammasome axis in cardiovascular injury. Acute and chronic infecti https://www.frontiersin.org/files/Articles/1690443/fimmu-16-1690443-HTML/image_m/fimmu-16-1690443-g003.jpg Figure 3. Cross-disease therapeutic strategies targeting the inflammasome–IL-1 axis. IL-1 blockade ( https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1623833/full https://www.frontiersin.org/files/Articles/1623833/fcvm-13-1623833-HTML-r2/image_m/fcvm-13-1623833-g001.jpg Figure 1. Annual percent change (APC) and trends in global incidence, mortality, and disability-adju https://www.frontiersin.org/files/Articles/1623833/fcvm-13-1623833-HTML-r2/image_m/fcvm-13-1623833-t001.jpg Table 1. Incidence of myocarditis in adolescents and young people between 1990 and 2021 at the globa https://www.frontiersin.org/files/Articles/1623833/fcvm-13-1623833-HTML-r2/image_m/fcvm-13-1623833-g002.jpg Figure 2. Incidence of myocarditis among adolescents and young adults in 204 countries and territori https://www.frontiersin.org/files/Articles/1623833/fcvm-13-1623833-HTML-r2/image_m/fcvm-13-1623833-g003.jpg Figure 3. Trends in incidence, mortality, and disability-adjusted life years (DALYs) of myocarditis https://www.frontiersin.org/files/Articles/1623833/fcvm-13-1623833-HTML-r2/image_m/fcvm-13-1623833-g004.jpg Figure 4. Age-specific percentages of myocarditis incidence, mortality, and disability-adjusted life https://www.frontiersin.org/files/Articles/1623833/fcvm-13-1623833-HTML-r2/image_m/fcvm-13-1623833-g005.jpg Figure 5. Association between incidence, mortality, and disability-adjusted life years (DALYs) rates https://www.frontiersin.org/files/Articles/1623833/fcvm-13-1623833-HTML-r2/image_m/fcvm-13-1623833-g006.jpg Figure 6. Frontier analysis exploring the relationship between SDI and DALYs for myocarditis among a https://www.frontiersin.org/files/Articles/1623833/fcvm-13-1623833-HTML-r2/image_m/fcvm-13-1623833-g007.jpg Figure 7. Proportion of myocarditis among mortality and DALYs risk factors for adolescents and young https://www.frontiersin.org/files/Articles/1623833/fcvm-13-1623833-HTML-r2/image_m/fcvm-13-1623833-g008.jpg Figure 8. The prediction of myocarditis for adolescents and young adults in incidence and mortality https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1748494/full https://www.frontiersin.org/files/Articles/1748494/fmed-13-1748494-HTML/image_m/fmed-13-1748494-g001.jpg Figure 1. AIMS study timeline of implementation strategies and constructs. https://www.frontiersin.org/files/Articles/1748494/fmed-13-1748494-HTML/image_m/fmed-13-1748494-t001.jpg Table 1. Results of the learning collaborative satisfaction survey (n = 19). https://www.frontiersin.org/files/Articles/1748494/fmed-13-1748494-HTML/image_m/fmed-13-1748494-t002.jpg Table 2. The identified areas for improvement and recommended modifications following the process ev https://www.frontiersin.org/files/Articles/1748494/fmed-13-1748494-HTML/image_m/fmed-13-1748494-t003.jpg Table 3. Results from the in-person meeting post-meeting survey. https://www.frontiersin.org/files/Articles/1748494/fmed-13-1748494-HTML/image_m/fmed-13-1748494-t004.jpg Table 4. Modifications made to the implementation process during the AIMS process evaluation categor https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1788744/full https://www.frontiersin.org/files/Articles/1788744/fpsyg-17-1788744-HTML/image_m/fpsyg-17-1788744-t001.jpg Table 1. Narrative interplays trajectories between hardest AMs of BC and processes of PTG. https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2026.1685357/full https://www.frontiersin.org/files/Articles/1685357/fpain-07-1685357-HTML/image_m/fpain-07-1685357-t001.jpg Table 1. Number of pain episodes by site. https://www.frontiersin.org/files/Articles/1685357/fpain-07-1685357-HTML/image_m/fpain-07-1685357-g001.jpg Figure 1. Frequencies of drugs used according to pain location and intensity. https://www.frontiersin.org/files/Articles/1685357/fpain-07-1685357-HTML/image_m/fpain-07-1685357-t002.jpg Table 2. Pharmacological effectiveness according to drug, location of pain, and intensity. https://www.frontiersin.org/files/Articles/1685357/fpain-07-1685357-HTML/image_m/fpain-07-1685357-t003.jpg Table 3. Relative frequency (%) of pain episodes with total improvement. https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2025.1650693/full https://www.frontiersin.org/files/Articles/1650693/fresc-06-1650693-HTML/image_m/fresc-06-1650693-g001.jpg Figure 1. Sampling framework for selecting districts, blocks, and villages/wards. https://www.frontiersin.org/files/Articles/1650693/fresc-06-1650693-HTML/image_m/fresc-06-1650693-g002.jpg Figure 2. Key training components for efficient and standardized survey execution. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1669276/full https://www.frontiersin.org/files/Articles/1669276/fimmu-16-1669276-HTML/image_m/fimmu-16-1669276-g001.jpg Figure 1. Flow chart of patient selection. IgAN, IgA nephropathy; NS, nephrotic syndrome; eGFR, esti https://www.frontiersin.org/files/Articles/1669276/fimmu-16-1669276-HTML/image_m/fimmu-16-1669276-t001.jpg Table 1. Comparison of clinical and pathological characteristics. https://www.frontiersin.org/files/Articles/1669276/fimmu-16-1669276-HTML/image_m/fimmu-16-1669276-g002.jpg Figure 2. Clinicopathological features of IgA nephropathy with nephrotic syndrome (NS-IgAN). (A) Tra https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1631415/full https://www.frontiersin.org/files/Articles/1631415/fonc-15-1631415-HTML/image_m/fonc-15-1631415-g001.jpg Figure 1. Alpelisib inhibits the proliferation and clonogenic potential of SK-BR-3 and BT-474 cells. https://www.frontiersin.org/files/Articles/1631415/fonc-15-1631415-HTML/image_m/fonc-15-1631415-g002.jpg Figure 2. Alpelisib inhibits stemness activity in both SK-BR-3 and BT-474 cells. (A) Effect of alpel https://www.frontiersin.org/files/Articles/1631415/fonc-15-1631415-HTML/image_m/fonc-15-1631415-g003.jpg Figure 3. Metformin synergistically enhances alpelisib-induced inhibition of proliferation and colon https://www.frontiersin.org/files/Articles/1631415/fonc-15-1631415-HTML/image_m/fonc-15-1631415-g004.jpg Figure 4. Combination of alpelisib and metformin enhances cell cycle arrest in SK-BR-3 and BT-474 ce https://www.frontiersin.org/files/Articles/1631415/fonc-15-1631415-HTML/image_m/fonc-15-1631415-g005.jpg Figure 5. Metformin enhances alpelisib-induced inhibition of stemness in SK-BR-3 and BT-474 cells. ( https://www.frontiersin.org/files/Articles/1631415/fonc-15-1631415-HTML/image_m/fonc-15-1631415-g006.jpg Figure 6. Metformin enhances alpelisib-induced inhibition of anchorage-independent growth in SK-BR-3 https://www.frontiersin.org/files/Articles/1631415/fonc-15-1631415-HTML/image_m/fonc-15-1631415-g007.jpg Figure 7. Metformin enhances alpelisib-induced inhibition of receptor tyrosine kinase signaling and https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1694322/full https://www.frontiersin.org/files/Articles/1694322/fphar-16-1694322-HTML/image_m/fphar-16-1694322-g002.jpg https://www.frontiersin.org/files/Articles/1694322/fphar-16-1694322-HTML/image_m/fphar-16-1694322-t001.jpg Table 1. AI drug repurposing tools for natural depsipeptides as breast cancer agents. (1) Drug devel https://www.frontiersin.org/files/Articles/1694322/fphar-16-1694322-HTML/image_m/fphar-16-1694322-g001.jpg Figure 1. Proposed mechanistic hypothesis of natural depsipeptides (teixobactin and clovibactin) for https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1653918/full https://www.frontiersin.org/files/Articles/1653918/fbioe-13-1653918-HTML-r1/image_m/fbioe-13-1653918-g001.jpg Figure 1. Biomechanical experimental setup: (a) Flexibility test: a coupled pulley system applying n https://www.frontiersin.org/files/Articles/1653918/fbioe-13-1653918-HTML-r1/image_m/fbioe-13-1653918-g002.jpg Figure 2. Diagram comparing “Intact Group” and “Spondylolysis Group” spine models. Both models displ https://www.frontiersin.org/files/Articles/1653918/fbioe-13-1653918-HTML-r1/image_m/fbioe-13-1653918-t001.jpg Table 1. Comparison of kinematic parameters between the intact group and the spondylolysis group. https://www.frontiersin.org/files/Articles/1653918/fbioe-13-1653918-HTML-r1/image_m/fbioe-13-1653918-g003.jpg Figure 3. Comparison of range of motion (ROM) in degrees for L4/L5 and L5/S1 segments. Graph a shows https://www.frontiersin.org/files/Articles/1653918/fbioe-13-1653918-HTML-r1/image_m/fbioe-13-1653918-g004.jpg Figure 4. Comparison of cranial adjacent segments (L4/L5) contact biomechanical parameters between t https://www.frontiersin.org/files/Articles/1653918/fbioe-13-1653918-HTML-r1/image_m/fbioe-13-1653918-t002.jpg Table 2. Comparison of contact biomechanical outcomes between the intact group and the spondylolysis https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1701028/full https://www.frontiersin.org/files/Articles/1701028/fimmu-16-1701028-HTML/image_m/fimmu-16-1701028-g001.jpg Figure 1. Flow chart of the study search and screening process. https://www.frontiersin.org/files/Articles/1701028/fimmu-16-1701028-HTML/image_m/fimmu-16-1701028-t001.jpg Table 1. Included study characteristics (N = 27). https://www.frontiersin.org/files/Articles/1701028/fimmu-16-1701028-HTML/image_m/fimmu-16-1701028-g002.jpg Figure 2. Meta-analyses on viral tropism prevalence among newly diagnosed PLWH. https://www.frontiersin.org/files/Articles/1701028/fimmu-16-1701028-HTML/image_m/fimmu-16-1701028-t002.jpg Table 2. Subgroup analysis of the prevalence of PLWH virus tropism. https://www.frontiersin.org/files/Articles/1701028/fimmu-16-1701028-HTML/image_m/fimmu-16-1701028-g003.jpg Figure 3. Characteristics of viral tropism in each subgroup. https://www.frontiersin.org/files/Articles/1701028/fimmu-16-1701028-HTML/image_m/fimmu-16-1701028-g004.jpg Figure 4. The bar plot of pooled effect sizes for CD4+ T cell counts by viral tropism (R5 vs. Non-R5 https://www.frontiersin.org/files/Articles/1701028/fimmu-16-1701028-HTML/image_m/fimmu-16-1701028-g005.jpg Figure 5. Meta-analysis of CD4+ cell count differences at diagnosis between viral tropism groups in https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2026.1774650/full https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-t001.jpg Table 1. Descriptive statistics. https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-t002.jpg Table 2. System GMM results. https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-t003.jpg Table 3. Baseline regression results (Equation 1). https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-t004.jpg Table 4. Robustness checks (Equation 1 with alternative specifications). https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-g001.jpg Figure 1. Placebo test. https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-t005.jpg Table 5. Regression results with controls for ETS (Equation 1). https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-g002.jpg Figure 2. Quantile regression. https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-t006.jpg Table 6. Mediation analysis (Equation 2, 3). https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-t007.jpg Table 7. Threshold regression—per capita GDP (Equation 4). https://www.frontiersin.org/files/Articles/1774650/frsus-07-1774650-HTML/image_m/frsus-07-1774650-t008.jpg Table 8. Threshold regression—green credit intensity (Equation 4). https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1732319/full https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g001.jpg Figure 1. VB-84922 inhibits SREBP-2 nuclear translocation. (A) U2OS cells were treated with 10 μM U1 https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g002.jpg Figure 2. VB-84922 inhibits the proteolytic processing of SREBP-1c and SREBP-2. HepG2 cells were inc https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g003.jpg Figure 3. VB-84922 inhibits SREBP-1c-dependent SCD1 and SREBP-2-dependent HMGCS1 mRNA and protein ex https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g004.jpg Figure 4. Bypassing SREBP-SCAP ER-Golgi translocation reduces VB-84922’s inhibition of LDLR-SRE-luci https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g005.jpg Figure 5. VB-84922 treatment causes SREBP-2 to accumulate within ER-like structures. (A) NMuMG cells https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g006.jpg Figure 6. VB-84922 treatment blocks SREBP-2 ER-Golgi translocation. NMuMG cells (A–D) or MCF10A cell https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g007.jpg Figure 7. SCAP ER-Golgi translocation is inhibited by VB-84922. (A) NMuMG cells expressing mNeonGree https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g008.jpg Figure 8. VB-87496 blocks lovastatin-induced HMGCS1 expression and protein levels. HepG2 cells were https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g009.jpg Figure 9. VB-87496 in vivo study plan. (A) treatment conditions for individual treatment groups. (B) https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g010.jpg Figure 10. VB-87496 blocks SREBP-1c and SREBP-2 proteolytic processing and maturation in vivo. Mice https://www.frontiersin.org/files/Articles/1732319/fphar-17-1732319-HTML/image_m/fphar-17-1732319-g011.jpg Figure 11. VB-87496 reduces SREBP-dependent gene expression in vivo. mRNA levels were determined by https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1637085/full https://www.frontiersin.org/files/Articles/1637085/fimmu-16-1637085-HTML/image_m/fimmu-16-1637085-t001.jpg Table 1. CBC for the patient with HCMV. https://www.frontiersin.org/files/Articles/1637085/fimmu-16-1637085-HTML/image_m/fimmu-16-1637085-g001.jpg Figure 1. Timeline of disease progression and treatment. HCMV, human cytomegalovirus; mNGS, metageno https://www.frontiersin.org/files/Articles/1637085/fimmu-16-1637085-HTML/image_m/fimmu-16-1637085-g002.jpg Figure 2. Technical roadmap of next-generation sequencing (NGS) workflows employed in our study. (A) https://www.frontiersin.org/files/Articles/1637085/fimmu-16-1637085-HTML/image_m/fimmu-16-1637085-t002.jpg Table 2. Clinical characteristics of 10 patients with HCMV-associated lymphocytosis. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1707408/full https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-t001.jpg Table 1. Search strategy. https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-g001.jpg Figure 1. PRISMA flowchartn. https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-t002.jpg Table 2. Basic characteristics of included studies. https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-g002.jpg Figure 2. Risk assessment of bias. https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-g003.jpg Figure 3. Forest plot of the meta-analysis on blood lipid-related indicators: (A) low-density lipopr https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-g004.jpg Figure 4. Forest plot of the meta-analysis on blood glucose-related indicators: (A) fasting blood gl https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-g005.jpg Figure 5. Forest plot of the meta-analysis on blood pressure-related indicators: (A) systolic blood https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-g006.jpg Figure 6. Forest plot of the meta-analysis on adverse events rate. https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-g007.jpg Figure 7. Trial sequential analysis of the efficacy outcomes: (A) LDL-C; (B) HDL-C; (C) TC; (D) FBG; https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-g008.jpg Figure 8. Funnel plot of publication bias: (A) LDL-C; (B) VLDL-C; (C) HDL-C; (D) TC; (E) TG; (F) FBG https://www.frontiersin.org/files/Articles/1707408/fcvm-13-1707408-HTML-r1/image_m/fcvm-13-1707408-t003.jpg Table 3. Certainty of evidence. https://www.frontiersin.org/journals/toxicology/articles/10.3389/ftox.2026.1781588/full https://www.frontiersin.org/files/Articles/1781588/ftox-08-1781588-HTML/image_m/ftox-08-1781588-t001.jpg Table 1. BBB Triculture compounds and assay results.a https://www.frontiersin.org/files/Articles/1781588/ftox-08-1781588-HTML/image_m/ftox-08-1781588-g001.jpg Figure 1. Schematic representation of the blood-brain barrier (BBB): Left, in vivo BBB structure; Mi https://www.frontiersin.org/files/Articles/1781588/ftox-08-1781588-HTML/image_m/ftox-08-1781588-g002.jpg Figure 2. Immunofluorescence characterization of the in vitro BBB triculture model. Tight junction r https://www.frontiersin.org/files/Articles/1781588/ftox-08-1781588-HTML/image_m/ftox-08-1781588-g003.jpg Figure 3. Calculated Efflux Ratios (ER). Efflux Ratios greater than one are active export into the s https://www.frontiersin.org/files/Articles/1781588/ftox-08-1781588-HTML/image_m/ftox-08-1781588-g004.jpg Figure 4. Comparison of Caco-2 Papp (black) with our BBB PappAB values (red) and PappBA values (blue https://www.frontiersin.org/files/Articles/1781588/ftox-08-1781588-HTML/image_m/ftox-08-1781588-g005.jpg Figure 5. Estimated initial uptake half-lives of compounds reported to have either beneficial or tox https://www.frontiersin.org/files/Articles/1781588/ftox-08-1781588-HTML/image_m/ftox-08-1781588-g006.jpg Figure 6. Estimated steady state brain concentrations for chronic exposure. Values are μM. Compounds https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1690414/full https://www.frontiersin.org/files/Articles/1690414/fonc-15-1690414-HTML/image_m/fonc-15-1690414-t001.jpg Table 1. Cancer driver gene variants across canine cell lines. https://www.frontiersin.org/files/Articles/1690414/fonc-15-1690414-HTML/image_m/fonc-15-1690414-g001.jpg Figure 1. Canine glioma stem-like cells (GSLCs) increased cellular respiration following hypoxia. Ox https://www.frontiersin.org/files/Articles/1690414/fonc-15-1690414-HTML/image_m/fonc-15-1690414-g002.jpg Figure 2. Hypoxia differentially augmented malignant features of canine glioma cell lines. (A) Relat https://www.frontiersin.org/files/Articles/1690414/fonc-15-1690414-HTML/image_m/fonc-15-1690414-g003.jpg Figure 3. Hypoxia-induced cytosine modifications are similar across canine glioma cell lines. (A) De https://www.frontiersin.org/files/Articles/1690414/fonc-15-1690414-HTML/image_m/fonc-15-1690414-g004.jpg Figure 4. Differentially methylated/hydroxymethylated genes were cell line specific. (A) Table displ https://www.frontiersin.org/files/Articles/1690414/fonc-15-1690414-HTML/image_m/fonc-15-1690414-g005.jpg Figure 5. Hypoxia-induced cytosine modifications changes did not correlate with molecular expression https://www.frontiersin.org/files/Articles/1690414/fonc-15-1690414-HTML/image_m/fonc-15-1690414-g006.jpg Figure 6. The hypomethylated gene signature following hypoxia in canine astrocytoma GSLCs positively https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1650375/full https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-g001.jpg Figure 1. Expression profiling and correlation analysis of NAFRGs in osteoarthritis. (A) Heatmap sho https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-t001.jpg Table 1. Correlation between the NAFRGs. https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-g002.jpg Figure 2. Functional enrichment analysis of differentially expressed genes in OA and NAFRGs. (A) Gen https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-g003.jpg Figure 3. Identification and validation of key NAFRGs associated with osteoarthritis. (A) LASSO coef https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-g004.jpg Figure 4. GSVA and GSEA-KEGG pathway analysis of GLUL and TLR3 in osteoarthritis samples. (A) GSVA a https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-t002.jpg Table 2. Correlation analysis between GLUL expression and immune cell infiltration in cartilage and https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-t003.jpg Table 3. Correlation analysis between TLR3 expression and immune cell infiltration in cartilage and https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-g005.jpg Figure 5. Immune cell infiltration analysis comparing OA patients and normal controls. (A) Correlati https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-g006.jpg Figure 6. Validation and functional analysis of TLR3 in osteoarthritis. (A) Immunohistochemical stai https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-g007.jpg Figure 7. Validation and functional analysis of GLUL in osteoarthritis in patient samples and three https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-g008.jpg Figure 8. In vivo validation of the TLR3/GLUL axis and therapeutic intervention in an OA rat model. https://www.frontiersin.org/files/Articles/1650375/fimmu-16-1650375-HTML/image_m/fimmu-16-1650375-g009.jpg Figure 9. Graphical summary of the TLR3–GLUL axis in osteoarthritis. OA joints exhibit an inflammato https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1649744/full https://www.frontiersin.org/files/Articles/1649744/fpsyg-16-1649744-HTML/image_m/fpsyg-16-1649744-t001.jpg Table 1. Rotated factor loadings, organized by location and behavioral (black) vs. cognitive (blue) https://www.frontiersin.org/files/Articles/1649744/fpsyg-16-1649744-HTML/image_m/fpsyg-16-1649744-g001.jpg Figure 1. Standardized CFA results of the spaces of engagement hypothesized structure. https://www.frontiersin.org/files/Articles/1649744/fpsyg-16-1649744-HTML/image_m/fpsyg-16-1649744-t002.jpg Table 2. Standardized path coefficients of the spaces of engagement model (n = 772). https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1646625/full https://www.frontiersin.org/files/Articles/1646625/fsurg-12-1646625-HTML/image_m/fsurg-12-1646625-g001.jpg Figure 1. CT scan: Abscess (60 × 53 × 90 mm) in hepatic segments S6/S7, with necrotic components and https://www.frontiersin.org/files/Articles/1646625/fsurg-12-1646625-HTML/image_m/fsurg-12-1646625-g002.jpg Figure 2. CT scan: Abscess in S6/S7 containing a hyperdense calcified element. https://www.frontiersin.org/files/Articles/1646625/fsurg-12-1646625-HTML/image_m/fsurg-12-1646625-g003.jpg Figure 3. CT scan: Abscess in S6/S7 with an additional hyperdense calcified element. https://www.frontiersin.org/files/Articles/1646625/fsurg-12-1646625-HTML/image_m/fsurg-12-1646625-g004.jpg Figure 4. CT scan (after 4 months): Persistent but reduced round collection in S6, showing periphera https://www.frontiersin.org/files/Articles/1646625/fsurg-12-1646625-HTML/image_m/fsurg-12-1646625-g005.jpg Figure 5. Abdominal MRI (after 4 months): Organized collection in S6 with wall thickening. The solid https://www.frontiersin.org/files/Articles/1646625/fsurg-12-1646625-HTML/image_m/fsurg-12-1646625-g006.jpg Figure 6. CT scan: Second collection located caudally within the same segment (S6). https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1732442/full https://www.frontiersin.org/files/Articles/1732442/fsurg-13-1732442-HTML-r1/image_m/fsurg-13-1732442-g001.jpg Figure 1. Hematoxylin and eosin poorly differentiated/signet ring cell adenocarcinoma. https://www.frontiersin.org/files/Articles/1732442/fsurg-13-1732442-HTML-r1/image_m/fsurg-13-1732442-g002.jpg Figure 2. Near-complete loss of membranous E-cadherin expression: protein absent in over 90% of tumo https://www.frontiersin.org/files/Articles/1732442/fsurg-13-1732442-HTML-r1/image_m/fsurg-13-1732442-g003.jpg Figure 3. A family pedigree was obtained during the genetic counseling session. https://www.frontiersin.org/files/Articles/1732442/fsurg-13-1732442-HTML-r1/image_m/fsurg-13-1732442-g004.jpg Figure 4. Prophylactic total gastrectomy with section of the stomach 3 cm above the cardia. https://www.frontiersin.org/files/Articles/1732442/fsurg-13-1732442-HTML-r1/image_m/fsurg-13-1732442-g005.jpg Figure 5. Prophylactic total gastrectomy with section of the stomach 3 cm above the first cm of the https://www.frontiersin.org/files/Articles/1732442/fsurg-13-1732442-HTML-r1/image_m/fsurg-13-1732442-t001.jpg Table 1. Cumulative risk according to NCCN 2025 guidelines. https://www.frontiersin.org/journals/electronics/articles/10.3389/felec.2025.1645594/full https://www.frontiersin.org/files/Articles/1645594/felec-06-1645594-HTML-r1/image_m/felec-06-1645594-g001.jpg Figure 1. Induced voltage is produced when the sample mounted at the bottom of the rod vibrates perp https://www.frontiersin.org/files/Articles/1645594/felec-06-1645594-HTML-r1/image_m/felec-06-1645594-g002.jpg Figure 2. Schematic representation of an AGM. (A) The Basic structure and electronics. The magnetic https://www.frontiersin.org/files/Articles/1645594/felec-06-1645594-HTML-r1/image_m/felec-06-1645594-g003.jpg Figure 3. Illustration of the magneto-optical Kerr effect principle and a representative setup of Ke https://www.frontiersin.org/files/Articles/1645594/felec-06-1645594-HTML-r1/image_m/felec-06-1645594-g004.jpg Figure 4. Principle and measurement images of MFM. (A) The measurement principle of MFM. (B) A typic https://www.frontiersin.org/files/Articles/1645594/felec-06-1645594-HTML-r1/image_m/felec-06-1645594-g005.jpg Figure 5. Schematic and characteristic response curves of a superconducting quantum interference dev https://www.frontiersin.org/files/Articles/1645594/felec-06-1645594-HTML-r1/image_m/felec-06-1645594-g006.jpg Figure 6. Principle and instrumentation of AC magnetic susceptibility measurement. (A) The Debye rel https://www.frontiersin.org/files/Articles/1645594/felec-06-1645594-HTML-r1/image_m/felec-06-1645594-g007.jpg Figure 7. Schematics of different FMR. (A) Schematic of the cavity-FMR. The waveguide serves the pur https://www.frontiersin.org/files/Articles/1645594/felec-06-1645594-HTML-r1/image_m/felec-06-1645594-t001.jpg Table 1. Summary of key performance parameters for static and dynamic magnetic measurement technique https://www.frontiersin.org/files/Articles/1645594/felec-06-1645594-HTML-r1/image_m/felec-06-1645594-t002.jpg Table 2. Overview of magnetic measurement techniques and applications in spintronics. This table out https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1692829/full https://www.frontiersin.org/files/Articles/1692829/frai-08-1692829-HTML-r1/image_m/frai-08-1692829-g001.jpg Figure 1. PRISMA flowchart. https://www.frontiersin.org/files/Articles/1692829/frai-08-1692829-HTML-r1/image_m/frai-08-1692829-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1692829/frai-08-1692829-HTML-r1/image_m/frai-08-1692829-g002.jpg Figure 2. Forest plot shows pooled AUC across all studies. https://www.frontiersin.org/files/Articles/1692829/frai-08-1692829-HTML-r1/image_m/frai-08-1692829-g003.jpg Figure 3. Forest plot for pooled sensitivity across all studies. https://www.frontiersin.org/files/Articles/1692829/frai-08-1692829-HTML-r1/image_m/frai-08-1692829-g004.jpg Figure 4. Forest plot for pooled specificity across all studies. https://www.frontiersin.org/files/Articles/1692829/frai-08-1692829-HTML-r1/image_m/frai-08-1692829-g005.jpg Figure 5. Forest plot for pooled diagnostic odds ratio (DOR) across all studies. https://www.frontiersin.org/files/Articles/1692829/frai-08-1692829-HTML-r1/image_m/frai-08-1692829-g006.jpg Figure 6. SROC plane with 95% confidence interval plot of all studies. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1712677/full https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g001.jpg Figure 1. Sample estimated across survey years. https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-t001.jpg Table 1. Descriptive statistics of study participants; weighted sample of 59,597. https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g002.jpg Figure 2. Trends in household WASH indicators in Ghana from 1993–2022. https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g003.jpg Figure 3. Predictors of improved water and sanitation in Ghana. (A) Left panel; improved water acces https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g004.jpg Figure 4. Predictors of improved hand hygiene and time to water access in Ghana. (A) Left panel; imp https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g005.jpg Figure 5. Predictors of composite household WASH in Ghana. https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g006.jpg Figure 6. Predictive model curves (AUC-ROC); (A) Improved water access, (B) Improved sanitation, (C) https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g007.jpg Figure 7. (A) Decomposition of improved water by residence (explained and unexplained components). ( https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g008.jpg Figure 8. (A) Decomposition of improved sanitation by residence (explained and unexplained component https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g009.jpg Figure 9. (A) Decomposition of improved time to water by residence (explained and unexplained compon https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g010.jpg Figure 10. (A) Decomposition of improved hand hygiene by residence (explained and unexplained compon https://www.frontiersin.org/files/Articles/1712677/fpubh-14-1712677-HTML/image_m/fpubh-14-1712677-g011.jpg Figure 11. (A) Decomposition of improved household WASH by residence (explained and unexplained comp https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1715148/full https://www.frontiersin.org/files/Articles/1715148/fmed-12-1715148-HTML/image_m/fmed-12-1715148-g001.jpg Figure 1. Flow diagram. https://www.frontiersin.org/files/Articles/1715148/fmed-12-1715148-HTML/image_m/fmed-12-1715148-g002.jpg Figure 2. (A) Flow chart of sequential trial in sufentanil + ciprofol group; (B) Flow chart of seque https://www.frontiersin.org/files/Articles/1715148/fmed-12-1715148-HTML/image_m/fmed-12-1715148-t001.jpg Table 1. Baseline characteristics of participants. https://www.frontiersin.org/files/Articles/1715148/fmed-12-1715148-HTML/image_m/fmed-12-1715148-g003.jpg Figure 3. Under BIS monitoring, the dose-response curve of intravenous injection of ciprofol for ane https://www.frontiersin.org/files/Articles/1715148/fmed-12-1715148-HTML/image_m/fmed-12-1715148-t002.jpg Table 2. Adverse events. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1637853/full https://www.frontiersin.org/files/Articles/1637853/fcvm-12-1637853-HTML/image_m/fcvm-12-1637853-g001.jpg Figure 1. Study design and flowchart of master athletes undergoing preparticipation cardiovascular s https://www.frontiersin.org/files/Articles/1637853/fcvm-12-1637853-HTML/image_m/fcvm-12-1637853-t001.jpg Table 1. Comparative summary of the 2010 ESC, 2013 Seattle, and 2017 International ECG interpretatio https://www.frontiersin.org/files/Articles/1637853/fcvm-12-1637853-HTML/image_m/fcvm-12-1637853-t002.jpg Table 2. Demographic and clinical characteristics of the athletes. https://www.frontiersin.org/files/Articles/1637853/fcvm-12-1637853-HTML/image_m/fcvm-12-1637853-t003.jpg Table 3. Patients diagnosed with abnormalities associated with sudden cardiac death. https://www.frontiersin.org/files/Articles/1637853/fcvm-12-1637853-HTML/image_m/fcvm-12-1637853-g002.jpg Figure 2. Receiver operating characteristic (ROC) curves comparing the predictive performance of thr https://www.frontiersin.org/files/Articles/1637853/fcvm-12-1637853-HTML/image_m/fcvm-12-1637853-t004.jpg Table 4. Diagnostic performance of the three ECG interpretation criteria in master athletes for path https://www.frontiersin.org/journals/virology/articles/10.3389/fviro.2026.1801380/full https://www.frontiersin.org/files/Articles/1801380/fviro-06-1801380-HTML/image_m/fviro-06-1801380-g000.jpg Graphical Abstract. Hypothetical model of TMV-associated epithelial interferon priming and potential https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1790130/full https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-g001.jpg Figure 1. Computational pipeline for cytogenetic risk prediction from bone marrow aspirate smears. T https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-g002.jpg Figure 2. Attention-based identification of morphologically diagnostic cells and validation against https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-t001.jpg Table 1. Cohort characteristics (N = 720). https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-g003.jpg Figure 3. Cohort characteristics and data overview (N = 720). (A) Cytogenetic marker prevalence show https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-g004.jpg Figure 4. ROC curves for cytogenetic marker prediction. (A) del(17p), (B) t(4;14), (C) t(11;14), (D) https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-t002.jpg Table 2. Classification performance on test set (N = 144). https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-t003.jpg Table 3. Method comparison (AUC). Bold indicates best performance. https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-g005.jpg Figure 5. Performance comparison across methods on the held-out test set (n = 144). The proposed Din https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-t004.jpg Table 4. Morphological features of high-attention cells by genetic marker. https://www.frontiersin.org/files/Articles/1790130/fonc-16-1790130-HTML/image_m/fonc-16-1790130-g006.jpg Figure 6. Attention-based explainability and morphology-genetics validation. (A) Attention heatmaps https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1692382/full https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-t001.jpg Table 1. Sociodemographic and clinical data of all participants in the study. https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g001.jpg Figure 1. Peripheral blood CD4+ T cell composition and immune aging markers in EOCRC and LOCRC parti https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g002.jpg Figure 2. Functional profiling of peripheral CD4+ Th1 and Th2 cells in EOCRC and LOCRC participants. https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g003.jpg Figure 3. Functional profiling of peripheral CD4+ Th9, Th17, and Th22 cells in EOCRC and LOCRC parti https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g004.jpg Figure 4. Peripheral blood CD8+ T cell composition and immune aging markers in EOCRC and LOCRC parti https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g005.jpg Figure 5. Peripheral blood Tγδ cell levels and expression of CD107a in EOCRC and LOCRC participants. https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g006.jpg Figure 6. Functional profiling of peripheral NKT-like cells in EOCRC and LOCRC participants. (A) Lev https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g007.jpg Figure 7. Functional profiling of peripheral NK cells in EOCRC and LOCRC participants. (A) Levels of https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g008.jpg Figure 8. Glucose uptake and GLUT-1 expression in PBMCs from EOCRC and LOCRC participants. Percentag https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g009.jpg Figure 9. Plasma cytokine levels in EOCRC and LOCRC participants. Levels of pro-inflammatory (A), an https://www.frontiersin.org/files/Articles/1692382/fimmu-16-1692382-HTML/image_m/fimmu-16-1692382-g010.jpg Figure 10. PCA visualization and random forest-based classification performance and feature importan https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1753287/full https://www.frontiersin.org/files/Articles/1753287/fpubh-14-1753287-HTML/image_m/fpubh-14-1753287-t001.jpg Table 1. Health education checklist for varicose veins of lower extremity. https://www.frontiersin.org/files/Articles/1753287/fpubh-14-1753287-HTML/image_m/fpubh-14-1753287-t002.jpg Table 2. The Compression stocking wear skill assessment sheet. https://www.frontiersin.org/files/Articles/1753287/fpubh-14-1753287-HTML/image_m/fpubh-14-1753287-g001.jpg Figure 1. Flow of participants in each group. https://www.frontiersin.org/files/Articles/1753287/fpubh-14-1753287-HTML/image_m/fpubh-14-1753287-t003.jpg Table 3. Baseline characteristics (n = 193). https://www.frontiersin.org/files/Articles/1753287/fpubh-14-1753287-HTML/image_m/fpubh-14-1753287-g002.jpg Figure 2. Chronic venous insufficiency questionnaire (CIVIQ) scores results. *p < 0.05. https://www.frontiersin.org/files/Articles/1753287/fpubh-14-1753287-HTML/image_m/fpubh-14-1753287-t004.jpg Table 4. Local complications after surgery (n = 193). https://www.frontiersin.org/files/Articles/1753287/fpubh-14-1753287-HTML/image_m/fpubh-14-1753287-g003.jpg Figure 3. The numeric rating scale (NRS) scores results. *p < 0.05. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1606453/full https://www.frontiersin.org/files/Articles/1606453/fcimb-15-1606453-HTML/image_m/fcimb-15-1606453-t001.jpg Table 1. Comparison of baseline data between two groups of children. https://www.frontiersin.org/files/Articles/1606453/fcimb-15-1606453-HTML/image_m/fcimb-15-1606453-g001.jpg Figure 1. Comparison of gut microbiota diversity between GDD and HC groups. (A) Alpha diversity as m https://www.frontiersin.org/files/Articles/1606453/fcimb-15-1606453-HTML/image_m/fcimb-15-1606453-g002.jpg Figure 2. Composition and differential abundance of dominant bacterial phyla in GDD and HC groups.(A https://www.frontiersin.org/files/Articles/1606453/fcimb-15-1606453-HTML/image_m/fcimb-15-1606453-g003.jpg Figure 3. Differential abundance analysis of gut microbiota at the genus level between GDD and HC gr https://www.frontiersin.org/files/Articles/1606453/fcimb-15-1606453-HTML/image_m/fcimb-15-1606453-g004.jpg Figure 4. Predicted functional alterations of the gut microbiome in GDD children based on KEGG pathw https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1771627/full https://www.frontiersin.org/files/Articles/1771627/fonc-16-1771627-HTML-r1/image_m/fonc-16-1771627-g001.jpg Figure 1. (A) image of the patient at 6 months of age; (B) image of severe cutaneous GVHD; (C, D) cu https://www.frontiersin.org/files/Articles/1771627/fonc-16-1771627-HTML-r1/image_m/fonc-16-1771627-g002.jpg Figure 2. MLPA analysis of the deletion identified by array CGH, highlighting the deletion interval https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1784867/full https://www.frontiersin.org/files/Articles/1784867/fpsyg-17-1784867-HTML/image_m/fpsyg-17-1784867-g001.jpg Figure 1. Flow diagram of the study methodology. https://www.frontiersin.org/files/Articles/1784867/fpsyg-17-1784867-HTML/image_m/fpsyg-17-1784867-t001.jpg Table 1. Summary of articles included in the review. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1777684/full https://www.frontiersin.org/files/Articles/1777684/fmars-13-1777684-HTML-r1/image_m/fmars-13-1777684-g001.jpg Figure 1. Sampling locations in the lower Chesapeake Bay. The map on the left shows the a large scal https://www.frontiersin.org/files/Articles/1777684/fmars-13-1777684-HTML-r1/image_m/fmars-13-1777684-t001.jpg Table 1. Site descriptions of mature living shorelines. https://www.frontiersin.org/files/Articles/1777684/fmars-13-1777684-HTML-r1/image_m/fmars-13-1777684-g002.jpg Figure 2. Areial photo of Captain Sinclair. This photo illustrates the sampling design, with transec https://www.frontiersin.org/files/Articles/1777684/fmars-13-1777684-HTML-r1/image_m/fmars-13-1777684-t002.jpg Table 2. Site conditions as a range based on the lowest and highest seasonal values observed for eac https://www.frontiersin.org/files/Articles/1777684/fmars-13-1777684-HTML-r1/image_m/fmars-13-1777684-t003.jpg Table 3. Primary GLMM results for biomass and soil characteristics. https://www.frontiersin.org/files/Articles/1777684/fmars-13-1777684-HTML-r1/image_m/fmars-13-1777684-g003.jpg Figure 3. Biomass variation across site and season. Interval plots of measured primary producer biom https://www.frontiersin.org/files/Articles/1777684/fmars-13-1777684-HTML-r1/image_m/fmars-13-1777684-g004.jpg Figure 4. Nutrient flux variation across site and season. Interval plots of denitrification and net https://www.frontiersin.org/files/Articles/1777684/fmars-13-1777684-HTML-r1/image_m/fmars-13-1777684-t004.jpg Table 4. Peak seasonal values (means ± 1 s.d.) collected from living shorelines in the current study https://www.frontiersin.org/files/Articles/1777684/fmars-13-1777684-HTML-r1/image_m/fmars-13-1777684-t005.jpg Table 5. The capacity of each sink to store or remove nutrients over the growing season in living sh https://www.frontiersin.org/journals/fungal-biology/articles/10.3389/ffunb.2026.1770745/full https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-g001.jpg Figure 1. (A) Distribution of fungal genera, identified by ITS region sequencing from olive root–ass https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-g002.jpg Figure 2. Phylogenetic tree generated by maximum likelihood method (bootstrap 1000 replicates) of TE https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-g003.jpg Figure 3. Gel-like image of rep-PCR profiles of 30 bacteria isolated from olive rhizosphere, in Tuni https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-g004.jpg Figure 4. (A) Distribution of bacterial Order, identified by 16S rRNA gene sequencing, on olive rhiz https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-g005.jpg Figure 5. Pathogenicity test on detached olive leaves and stems and tomato seedling inoculated with https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-t001.jpg Table 1. Disease severity index (DSI) and infection rates in tomato seedlings and olive leaves and s https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-t002.jpg Table 2. Activity of selected bacterial strains against Fusarium mycelial growth. https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-t003.jpg Table 3. Inhibition rate of growth (%) of MB68, MB5, MB50, MB48 and MB59 in confrontation with the B https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-g006.jpg Figure 6. In vitro interaction between Bacillus strain B6 and MB68, MB5, MB50, MB54, MB48 and MB59 i https://www.frontiersin.org/files/Articles/1770745/ffunb-07-1770745-HTML/image_m/ffunb-07-1770745-g007.jpg Figure 7. Light microscopic (x40) and SEM observations of (A) Fusarium solani. Aa. fungal morphology https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1749404/full https://www.frontiersin.org/files/Articles/1749404/fbinf-06-1749404-HTML/image_m/fbinf-06-1749404-g001.jpg Figure 1. Different stages of drug development. Created in BioRender. Allwell, E. (2025) https://Bio https://www.frontiersin.org/files/Articles/1749404/fbinf-06-1749404-HTML/image_m/fbinf-06-1749404-g002.jpg Figure 2. Computational methods in antimicrobial peptide (AMP) design. Database: Curated AMP sequenc https://www.frontiersin.org/files/Articles/1749404/fbinf-06-1749404-HTML/image_m/fbinf-06-1749404-t001.jpg Table 1. Some publicly available Antimicrobial Peptide (AMP) Databases. https://www.frontiersin.org/files/Articles/1749404/fbinf-06-1749404-HTML/image_m/fbinf-06-1749404-t002.jpg Table 2. Molecular docking tools for antibacterial peptide discovery. https://www.frontiersin.org/files/Articles/1749404/fbinf-06-1749404-HTML/image_m/fbinf-06-1749404-g003.jpg Figure 3. Molecular docking and dynamics in AMP discovery. (A) Protein retrieval or modeling and act https://www.frontiersin.org/files/Articles/1749404/fbinf-06-1749404-HTML/image_m/fbinf-06-1749404-g004.jpg Figure 4. Artificial Intelligence overview. Created in BioRender. Allwell, E. (2025) https://BioRend https://www.frontiersin.org/files/Articles/1749404/fbinf-06-1749404-HTML/image_m/fbinf-06-1749404-g005.jpg Figure 5. Artificial intelligence approaches in antimicrobial peptide Discovery. Created in BioRende https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1784581/full https://www.frontiersin.org/files/Articles/1784581/fenvs-14-1784581-HTML/image_m/fenvs-14-1784581-g001.jpg Figure 1. Estimated changes in the frequency of drying events, translated with permission from OCEau https://www.frontiersin.org/files/Articles/1784581/fenvs-14-1784581-HTML/image_m/fenvs-14-1784581-g002.jpg Figure 2. Integrated concept for catchment management, reproduced with permission from hydrosuisse. https://www.frontiersin.org/files/Articles/1784581/fenvs-14-1784581-HTML/image_m/fenvs-14-1784581-g003.jpg Figure 3. (A) Conceptual catchment system adapted with permission from (Hydrosuisse, 2025). (B) Aggr https://www.frontiersin.org/files/Articles/1784581/fenvs-14-1784581-HTML/image_m/fenvs-14-1784581-g004.jpg Figure 4. (A) Geographical situation; (B) climatic evolution of the catchment (NCCS, 2021). https://www.frontiersin.org/files/Articles/1784581/fenvs-14-1784581-HTML/image_m/fenvs-14-1784581-t001.jpg Table 1. Low water flows: Current situation and objectives. https://www.frontiersin.org/files/Articles/1784581/fenvs-14-1784581-HTML/image_m/fenvs-14-1784581-g005.jpg Figure 5. Frequency-Duration analysis of low water flows (2015–2024). https://www.frontiersin.org/files/Articles/1784581/fenvs-14-1784581-HTML/image_m/fenvs-14-1784581-g006.jpg Figure 6. Integrated global concept. Measures developed in blue/green, complementary ones in red. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1792179/full https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-g001.jpg Figure 1. Workflow of this study. https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-g002.jpg Figure 2. Showcases the ROC curves for each model’s performance on the different datasets: (A) train https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-t001.jpg Table 1. Baseline of clinical features. https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-t002.jpg Table 2. Univariable analysis of clinical features. https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-t003.jpg Table 3. Presents the patch-level accuracy and AUC scores for each model, focusing on label. https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-g003.jpg Figure 3. pinpoints the crucial areas in the final convolutional layer that play a pivotal role in C https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-g004.jpg Figure 4. t-SNE visualization of patient-level features generated by multi-instance learning after P https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-t004.jpg Table 4. Prediction performance of different signatures. https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-g005.jpg Figure 5. ROC curves of patient-level classifiers in each cohort: (A) training set; (B) validation s https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-t005.jpg Table 5. Metrics on different signature. https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-g006.jpg Figure 6. presents the data from each model measurement from each cohort and the respective ROC curv https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-g007.jpg Figure 7. Calibration curves of different signatures in the different cohort:(A) training set; (B) v https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-g008.jpg Figure 8. DeLong Test of different signatures:(A) training set; (B) validation set; (C) test 1 set; https://www.frontiersin.org/files/Articles/1792179/fimmu-17-1792179-HTML/image_m/fimmu-17-1792179-g009.jpg Figure 9. Decision curves of different signatures in the different cohort:(A) training set; (B) vali https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1758594/full https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-t001.jpg Table 1. Patient characteristics and bivariate analysis for primary outcome. https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g001.jpg Figure 1. LV scar distribution. Number represents the percentage of patients with LGE in each LV seg https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-t002.jpg Table 2. CMR characteristics and bivariate analysis for primary outcome. https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-t003.jpg Table 3. Multivariable cox proportional hazards model for the primary combined outcome (all-cause mo https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-t004.jpg Table 4. Multivariable cox proportional hazards model for all-cause mortality. https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g002.jpg Figure 2. Primary outcome according to LVEF categories. Log rank X2: 7.04, p value = 0.007. https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g003.jpg Figure 3. All-cause mortality according LVEF categories. Log rank X2: 9.38, p value = 0.002. https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g004.jpg Figure 4. LV scar extent distribution. https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g005.jpg Figure 5. Age-adjusted restricted cubic spline analysis of myocardial fibrosis burden and risk of th https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g006.jpg Figure 6. Primary outcome according subendocardial LGE. Log rank X2 = 6.77, p value 0.009. https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g007.jpg Figure 7. Primary outcome according LV scar extent. Log rank X2 = 5.25, p value = 0.021. https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g008.jpg Figure 8. All-cause mortality according to LV scar extent. Log rank X2 = 3.33, p value = 0.067. https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g009.jpg Figure 9. Primary outcome according LV scar extent and LVEF categories. Log rank X2 = 11.51, p value https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g010.jpg Figure 10. Cardiac magnetic resonance (CMR) of a 62-year-old male patient with chagas cardiomyopathy https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g011.jpg Figure 11. Cardiac magnetic resonance (CMR) of a 67-year-old female patient with chagas cardiomyopat https://www.frontiersin.org/files/Articles/1758594/fcvm-13-1758594-HTML/image_m/fcvm-13-1758594-g012.jpg Central Illustration. LV scar extent and pattern are predictors of adverse clinical outcomes in pati https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1676459/full https://www.frontiersin.org/files/Articles/1676459/fimmu-16-1676459-HTML/image_m/fimmu-16-1676459-g001.jpg Figure 1. Before treatment, erythema, erosion, and exudation were observed in the left axilla (a), l https://www.frontiersin.org/files/Articles/1676459/fimmu-16-1676459-HTML/image_m/fimmu-16-1676459-g002.jpg Figure 2. Histopathological examination of the left inguinal skin showed: local disruption of the ep https://www.frontiersin.org/files/Articles/1676459/fimmu-16-1676459-HTML/image_m/fimmu-16-1676459-t001.jpg Table 1. Summary of family history and treatment with Janus kinase inhibitors in Hailey-Hailey disea https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1688942/full https://www.frontiersin.org/files/Articles/1688942/fpsyg-16-1688942-HTML/image_m/fpsyg-16-1688942-g001.jpg Figure 1. Two example working memory trials in Experiment 1. On each trial, 4 words are studied. Aft https://www.frontiersin.org/files/Articles/1688942/fpsyg-16-1688942-HTML/image_m/fpsyg-16-1688942-g002.jpg Figure 2. Working memory accuracy (% correct) for Match probes and Mismatch probes across all 5 expe https://www.frontiersin.org/files/Articles/1688942/fpsyg-16-1688942-HTML/image_m/fpsyg-16-1688942-g003.jpg Figure 3. Response times (RTs) within working memory for Match probes and Mismatch probes across all https://www.frontiersin.org/files/Articles/1688942/fpsyg-16-1688942-HTML/image_m/fpsyg-16-1688942-t001.jpg Table 1. Mean accuracy and mean response time (RT) as a function of probe type in working memory for https://www.frontiersin.org/files/Articles/1688942/fpsyg-16-1688942-HTML/image_m/fpsyg-16-1688942-g004.jpg Figure 4. Long-term memory accuracy (% correct) for Match probes and Mismatch probes across all 5 ex https://www.frontiersin.org/files/Articles/1688942/fpsyg-16-1688942-HTML/image_m/fpsyg-16-1688942-t002.jpg Table 2. Incidental long-term memory mean accuracy as a function of probe type for each experiment, https://www.frontiersin.org/files/Articles/1688942/fpsyg-16-1688942-HTML/image_m/fpsyg-16-1688942-g005.jpg Figure 5. Two example working memory trials from Experiment 5 with a recent probes manipulation to i https://www.frontiersin.org/files/Articles/1688942/fpsyg-16-1688942-HTML/image_m/fpsyg-16-1688942-t003.jpg Table 3. Mean accuracy and mean response time (RT) for proactive interference-inducing recent probes https://www.frontiersin.org/files/Articles/1688942/fpsyg-16-1688942-HTML/image_m/fpsyg-16-1688942-g006.jpg Figure 6. Similar proactive interference for Match-Recent and Mismatch-Recent probes in Experiments https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2025.1648844/full https://www.frontiersin.org/files/Articles/1648844/fncir-19-1648844-HTML/image_m/fncir-19-1648844-g001.jpg Figure 1. Electrophysiological features of CA1 and subiculum recordings. (A) Histological verificati https://www.frontiersin.org/files/Articles/1648844/fncir-19-1648844-HTML/image_m/fncir-19-1648844-g002.jpg Figure 2. (A–C) Example SUB periodic cell: cluster isolation, waveform/ISI with burst %, and stable https://www.frontiersin.org/files/Articles/1648844/fncir-19-1648844-HTML/image_m/fncir-19-1648844-g003.jpg Figure 3. Grid and spatially periodic firing across arena size. (A–D) Example SUB neuron in 50 × 50  https://www.frontiersin.org/files/Articles/1648844/fncir-19-1648844-HTML/image_m/fncir-19-1648844-g004.jpg Figure 4. Spatial features in CA1 vs. SUB (A–E) group comparisons: spatial information per second, s https://www.frontiersin.org/files/Articles/1648844/fncir-19-1648844-HTML/image_m/fncir-19-1648844-g005.jpg Figure 5. Multisite silicon-probe recordings in subiculum. (A) Schematic of probe geometry and dorso https://www.frontiersin.org/files/Articles/1648844/fncir-19-1648844-HTML/image_m/fncir-19-1648844-g006.jpg Figure 6. Heterogeneous spatial representations in subiculum (probe cohort, 80 × 80 cm). (A–C) Examp https://www.frontiersin.org/files/Articles/1648844/fncir-19-1648844-HTML/image_m/fncir-19-1648844-g007.jpg Figure 7. Different spatial types in the sub and Proximo–distal description of subicular firing. (A– https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1645604/full https://www.frontiersin.org/files/Articles/1645604/fnut-12-1645604-HTML/image_m/fnut-12-1645604-t001.jpg Table 1. Gradient elution schedule. https://www.frontiersin.org/files/Articles/1645604/fnut-12-1645604-HTML/image_m/fnut-12-1645604-g001.jpg Figure 1. (a) Effect of frying time and temperature for sodium saccharin, (b) Chromatograms of sodiu https://www.frontiersin.org/files/Articles/1645604/fnut-12-1645604-HTML/image_m/fnut-12-1645604-g002.jpg Figure 2. (a) Spectra of sodium saccharin and o-sulfamoylbenzoic acid (b) Standard curve for sodium https://www.frontiersin.org/files/Articles/1645604/fnut-12-1645604-HTML/image_m/fnut-12-1645604-g003.jpg Figure 3. (a) Chromatograms of LOD and LOQ of sodium saccharin and o-sulfamoylbenzoic acid (b) Spike https://www.frontiersin.org/files/Articles/1645604/fnut-12-1645604-HTML/image_m/fnut-12-1645604-t002.jpg Table 2. Spiked recovery rate and precision. https://www.frontiersin.org/files/Articles/1645604/fnut-12-1645604-HTML/image_m/fnut-12-1645604-t003.jpg Table 3. Matrix stabilization experiments 1–10 h (as indexed by peak area). https://www.frontiersin.org/files/Articles/1645604/fnut-12-1645604-HTML/image_m/fnut-12-1645604-t004.jpg Table 4. Matrix effect calculation results. https://www.frontiersin.org/files/Articles/1645604/fnut-12-1645604-HTML/image_m/fnut-12-1645604-t005.jpg Table 5. Results of the actual samples. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1700876/full https://www.frontiersin.org/files/Articles/1700876/feduc-10-1700876-HTML/image_m/feduc-10-1700876-t001.jpg Table 1. The Sociotechnical-Ethical-Pedagogical (STEP) framework for AI in education. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1748543/full https://www.frontiersin.org/files/Articles/1748543/fsufs-10-1748543-HTML/image_m/fsufs-10-1748543-t001.jpg Table 1. Variable descriptions and descriptive statistics (n = 80). https://www.frontiersin.org/files/Articles/1748543/fsufs-10-1748543-HTML/image_m/fsufs-10-1748543-t002.jpg Table 2. Baseline model estimation (pooled OLS, dependent variable: FSI). https://www.frontiersin.org/files/Articles/1748543/fsufs-10-1748543-HTML/image_m/fsufs-10-1748543-t003.jpg Table 3. Summary of model fit and diagnostic statistics. https://www.frontiersin.org/files/Articles/1748543/fsufs-10-1748543-HTML/image_m/fsufs-10-1748543-g001.jpg Figure 1. Agricultural exports in 2009–2023, bil. USD. Source: FAOSTAT (FAO Statistical Database, 20 https://www.frontiersin.org/files/Articles/1748543/fsufs-10-1748543-HTML/image_m/fsufs-10-1748543-t004.jpg Table 4. Results of the FSI panel regression for BRICS countries (2009–2024, N = 80). https://www.frontiersin.org/files/Articles/1748543/fsufs-10-1748543-HTML/image_m/fsufs-10-1748543-t005.jpg Table 5. Factor contributions to FSI changes by BRICS countries (2009–2024). https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1681542/full https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-t001.jpg Table 1. Comparative summary of previous studies. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g001.jpg Figure 1. Class distribution of the HAM10000 dataset across seven diagnostic categories. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g002.jpg Figure 2. Dataset split distribution showing training (60%), validation (20%), and testing (20%) set https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g003.jpg Figure 3. Pipeline architecture of the proposed dual-task deep learning framework for skin lesion se https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g004.jpg Figure 4. Bar chart representing Specificity per class at epoch 20. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-t002.jpg Table 2. Class-wise classification metrics. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g005.jpg Figure 5. Class-wise performance metrics with Precision, Recall, and F1-Score. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g006.jpg Figure 6. Confusion matrix for all classes on the validation set. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-t003.jpg Table 3. Segmentation performance metrics. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g007.jpg Figure 7. Segmentation loss curve and dice score trend across epochs. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g008.jpg Figure 8. Predicted masks for input images across all seven lesion classes. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g009.jpg Figure 9. Grad-CAM visualization highlighting the lesion region. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-g010.jpg Figure 10. Grad-CAM highlights for all seven lesion classes. https://www.frontiersin.org/files/Articles/1681542/fmed-12-1681542-HTML-r1/image_m/fmed-12-1681542-t004.jpg Table 4. Comparative summary of recent deep learning approaches for skin lesion analysis and the pro https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1676754/full https://www.frontiersin.org/files/Articles/1676754/fendo-16-1676754-HTML/image_m/fendo-16-1676754-g001.jpg Figure 1. Experimental design for both juvenile and adult rats involved exposure to a HFD for either https://www.frontiersin.org/files/Articles/1676754/fendo-16-1676754-HTML/image_m/fendo-16-1676754-g002.jpg Figure 2. Body weight gain over time in juvenile and adult rats. The data reflect the body weight va https://www.frontiersin.org/files/Articles/1676754/fendo-16-1676754-HTML/image_m/fendo-16-1676754-g003.jpg Figure 3. Quantification of adiposity index in juveniles (A) and adults (B). BAT is depicted in pane https://www.frontiersin.org/files/Articles/1676754/fendo-16-1676754-HTML/image_m/fendo-16-1676754-t001.jpg Table 1. Energy intake of juvenile and adult rats fed a standard or high fat diet over 4 weeks. https://www.frontiersin.org/files/Articles/1676754/fendo-16-1676754-HTML/image_m/fendo-16-1676754-t002.jpg Table 2. Energy intake of juvenile and adult rats fed a standard or high fat diet over 12 weeks. https://www.frontiersin.org/files/Articles/1676754/fendo-16-1676754-HTML/image_m/fendo-16-1676754-g004.jpg Figure 4. Effects of high fat diet (HFD) consumption on components of the thyroid axis in juvenile r https://www.frontiersin.org/files/Articles/1676754/fendo-16-1676754-HTML/image_m/fendo-16-1676754-g005.jpg Figure 5. Effects of high fat diet (HFD) consumption on components of the thyroid axis in adult rats https://www.frontiersin.org/files/Articles/1676754/fendo-16-1676754-HTML/image_m/fendo-16-1676754-t003.jpg Table 3. Leptin and corticosterone serum levels. https://www.frontiersin.org/files/Articles/1676754/fendo-16-1676754-HTML/image_m/fendo-16-1676754-g006.jpg Figure 6. IL-1β mRNA and protein content. Gene expression was measured in the hypothalamic paraventr https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1754426/full https://www.frontiersin.org/files/Articles/1754426/fdgth-08-1754426-HTML-r2/image_m/fdgth-08-1754426-g001.jpg Figure 1. Vocal biomarker implementation barriers & solutions in clinical care. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1670845/full https://www.frontiersin.org/files/Articles/1670845/fphar-16-1670845-HTML-r2/image_m/fphar-16-1670845-t001.jpg Table 1. Traditional drug discovery vs Drug Repurposing (Adapted from Drug Discovery from Technology https://www.frontiersin.org/files/Articles/1670845/fphar-16-1670845-HTML-r2/image_m/fphar-16-1670845-t002.jpg Table 2. Government-sponsored collaborative drug repurposing networks. https://www.frontiersin.org/files/Articles/1670845/fphar-16-1670845-HTML-r2/image_m/fphar-16-1670845-t003.jpg Table 3. Geographical scope of policy frameworks and initiatives in drug repurposing. https://www.frontiersin.org/files/Articles/1670845/fphar-16-1670845-HTML-r2/image_m/fphar-16-1670845-g001.jpg Figure 1. Off-patent drug repurposing lifecycle. Source: Own Elaboration. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1733287/full https://www.frontiersin.org/files/Articles/1733287/fpls-16-1733287-HTML-r1/image_m/fpls-16-1733287-g001.jpg Figure 1. Geographical location of study area. https://www.frontiersin.org/files/Articles/1733287/fpls-16-1733287-HTML-r1/image_m/fpls-16-1733287-g002.jpg Figure 2. Differences in leaf morphological traits under different human disturbance gradients. ILA, https://www.frontiersin.org/files/Articles/1733287/fpls-16-1733287-HTML-r1/image_m/fpls-16-1733287-g003.jpg Figure 3. Difference of stoichiometric characteristics of leaves under different human disturbance g https://www.frontiersin.org/files/Articles/1733287/fpls-16-1733287-HTML-r1/image_m/fpls-16-1733287-g004.jpg Figure 4. Differences of root morphological characters under different interference gradients. RL, r https://www.frontiersin.org/files/Articles/1733287/fpls-16-1733287-HTML-r1/image_m/fpls-16-1733287-g005.jpg Figure 5. Difference of root stoichiometric characteristics under different interference gradients. https://www.frontiersin.org/files/Articles/1733287/fpls-16-1733287-HTML-r1/image_m/fpls-16-1733287-g006.jpg Figure 6. Response of plant functional traits to environmental factors. SW, soil moisture; ST, soil https://www.frontiersin.org/files/Articles/1733287/fpls-16-1733287-HTML-r1/image_m/fpls-16-1733287-g007.jpg Figure 7. RDA ordination of plant functional traits under mild human disturbance. (a) Leaf traits. ( https://www.frontiersin.org/files/Articles/1733287/fpls-16-1733287-HTML-r1/image_m/fpls-16-1733287-g008.jpg Figure 8. RDA ordination of plant functional traits under mild human disturbance. (a) Leaf traits. ( https://www.frontiersin.org/files/Articles/1733287/fpls-16-1733287-HTML-r1/image_m/fpls-16-1733287-g009.jpg Figure 9. RDA ordination of plant functional traits under mild human disturbance. (a) Leaf traits. ( https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1704347/full https://www.frontiersin.org/files/Articles/1704347/fphar-16-1704347-HTML-r1/image_m/fphar-16-1704347-g001.jpg Figure 1. Wild-Type KRAS Signaling and Mutant KRAS G12C Signaling. KRAS binds to guanine nucleoside https://www.frontiersin.org/files/Articles/1704347/fphar-16-1704347-HTML-r1/image_m/fphar-16-1704347-g002.jpg Figure 2. Monotherapy strategies for KRAS G12C-mutated non-small cell lung cancer. Blue box represen https://www.frontiersin.org/files/Articles/1704347/fphar-16-1704347-HTML-r1/image_m/fphar-16-1704347-t001.jpg Table 1. Clinical trials of KRAS G12C small molecule inhibitor monotherapies in NSCLC. https://www.frontiersin.org/files/Articles/1704347/fphar-16-1704347-HTML-r1/image_m/fphar-16-1704347-g003.jpg Figure 3. Resistance mechanisms to KRAS G12C inhibition. The black dashed circle highlights predomin https://www.frontiersin.org/files/Articles/1704347/fphar-16-1704347-HTML-r1/image_m/fphar-16-1704347-g004.jpg Figure 4. Combination therapy strategies for KRASG12C inhibition. The figure highlights several appr https://www.frontiersin.org/files/Articles/1704347/fphar-16-1704347-HTML-r1/image_m/fphar-16-1704347-t002.jpg Table 2. A summary of current trials assessing KRAS G12C inhibitors in combination with other Inhibi https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1656493/full https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t001.jpg Table 1. Monoterpenoids in Gentiana rhodantha Franch. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-g001.jpg Figure 1. Chemical structures of Monoterpenoids in Gentiana rhodantha Franch. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t002.jpg Table 2. Iridoids in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-g002.jpg Figure 2. Chemical structures of iridoids in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t003.jpg Table 3. Sesterterpenoid in Gentiana scabra Bunge. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-g003.jpg Figure 3. Chemical structures of sesterpenoids in Gentiana scabra Bunge. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t004.jpg Table 4. Triterpenoids in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-g004.jpg Figure 4. Chemical structures of triterpenoids in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t005.jpg Table 5. Flavonoid in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-g005.jpg Figure 5. Chemical structures of flavonoids in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t006.jpg Table 6. Lignans in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-g006.jpg Figure 6. Chemical structures of Lignans in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t007.jpg Table 7. Alkaloids in Gentiana scabra Bunge. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-g007.jpg Figure 7. Chemical structures of Alkaloids in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t008.jpg Table 8. Other constituents in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-g008.jpg Figure 8. Chemical structures of other constituents in Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t009.jpg Table 9. Anti-inflammatory and analgesic compounds in Gentianae Radix et Rhizoma: Bioactive componen https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t010.jpg Table 10. Hepatoprotective and Choleretic compounds in Gentianae Radix et Rhizoma: Bioactive compone https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t011.jpg Table 11. Antitumor component in Gentianae Radix et Rhizoma: Bioactive components and mechanisms of https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t012.jpg Table 12. Gastrointestinal-Regulating Bioactive compounds in Gentianae Radix et Rhizoma: mechanisms https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t013.jpg Table 13. Nervous system-Regulating Bioactive compounds in Gentianae Radix et Rhizoma: mechanisms of https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t014.jpg Table 14. Antioxidant component in Gentianae Radix et Rhizoma: Bioactive components and mechanisms o https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-t015.jpg Table 15. Other activities of Gentianae Radix et Rhizoma. https://www.frontiersin.org/files/Articles/1656493/fphar-16-1656493-HTML/image_m/fphar-16-1656493-g009.jpg Figure 9. Clinical applications and Mechanisms of Gentianae Radix et Rhizoma and Its compound formul https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1714686/full https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-g001.jpg Figure 1. Chemical structure of escitalopram (SCT), S-demethylcitalopram (S-DCT), and S-didemethylci https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-t001.jpg Table 1. Mass spectrometry parameters of SCT, S-DCT, S-DDCT, and dexamethasone (IS). https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-g002.jpg Figure 2. MRM chromatograms of SCT, S-DCT, S-DDCT, and dexamethasone (IS) in RLM, respectively. (A) https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-t002.jpg Table 2. Intra-day and inter-day accuracy and precision of SCT, S-DCT, and S-DDCT in RLM. https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-t003.jpg Table 3. Extraction recovery and matrix effect of SCT, S-DCT, S-DDCT, and dexamethasone (IS) in RLM https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-t004.jpg Table 4. Matrix effect of SCT, S-DCT, S-DDCT, and dexamethasone (IS) in HLM and HPM (n = 6). https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-t005.jpg Table 5. Stability of SCT, S-DCT, and S-DDCT in RLM (n = 6). https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-g003.jpg Figure 3. The Michaelis-Menten curves of escitalopram metabolism in RLM, HLM, and HPM. All data were https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-g004.jpg Figure 4. The kinetic analysis of escitalopram metabolism in RLM, HLM, and HPM. The Km (A), Vmax (B) https://www.frontiersin.org/files/Articles/1714686/fphar-16-1714686-HTML/image_m/fphar-16-1714686-g005.jpg Figure 5. The proportion of major metabolites and the remained escitalopram in RLM, HLM, and HPM. SC https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1792060/full https://www.frontiersin.org/files/Articles/1792060/fpls-17-1792060-HTML/image_m/fpls-17-1792060-g001.jpg Figure 1. Responses of NSC, SS and ST pools in the community (a, b) and grasses (c, d) to N addition https://www.frontiersin.org/files/Articles/1792060/fpls-17-1792060-HTML/image_m/fpls-17-1792060-t001.jpg Table 1. The MANOVA results for the effects of the sampling years (Y), N addition rates (N), categor https://www.frontiersin.org/files/Articles/1792060/fpls-17-1792060-HTML/image_m/fpls-17-1792060-g002.jpg Figure 2. The Quadratic-plus-plateau model showed the non-linear responses of community (a) and gras https://www.frontiersin.org/files/Articles/1792060/fpls-17-1792060-HTML/image_m/fpls-17-1792060-g003.jpg Figure 3. Relationships between NSC, SS and ST pools and N concentrations (N-limited and N-saturated https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1711350/full https://www.frontiersin.org/files/Articles/1711350/fphar-17-1711350-HTML/image_m/fphar-17-1711350-g001.jpg Figure 1. clopidogrel metabolism and CYP2C19. https://www.frontiersin.org/files/Articles/1711350/fphar-17-1711350-HTML/image_m/fphar-17-1711350-t001.jpg Table 1. Main RCTs to evaluate the selection of P2Y12 inhibitors guided by CYP2C19 genotype. https://www.frontiersin.org/files/Articles/1711350/fphar-17-1711350-HTML/image_m/fphar-17-1711350-t002.jpg Table 2. Comparison table of CYP2C19 genotype and phenotype. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1629840/full https://www.frontiersin.org/files/Articles/1629840/fped-13-1629840-HTML/image_m/fped-13-1629840-g001.jpg Figure 1. Flowchart of expanded newborn screening for IMDs and diagnosis of patients. https://www.frontiersin.org/files/Articles/1629840/fped-13-1629840-HTML/image_m/fped-13-1629840-t001.jpg Table 1. Disease spectrum of 57 children with IMDs in 161,966 newborns. https://www.frontiersin.org/files/Articles/1629840/fped-13-1629840-HTML/image_m/fped-13-1629840-g002.jpg Figure 2. (A) Distributions of inherited metabolic disease. (B) Distributions of amino acid metaboli https://www.frontiersin.org/files/Articles/1629840/fped-13-1629840-HTML/image_m/fped-13-1629840-t002.jpg Table 2. Results of MS/MS in children with IMDs (μmol/L). https://www.frontiersin.org/files/Articles/1629840/fped-13-1629840-HTML/image_m/fped-13-1629840-t003.jpg Table 3. Mutations in 57 patients with IEMs identified by expanded newborn screening. https://www.frontiersin.org/files/Articles/1629840/fped-13-1629840-HTML/image_m/fped-13-1629840-t004.jpg Table 4. Comparison of expanded newborn screening detection incidences of inherited metabolic diseas https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1796491/full https://www.frontiersin.org/files/Articles/1796491/fpsyg-17-1796491-HTML/image_m/fpsyg-17-1796491-g001.jpg Figure 1. Proportion of male preschool teachers and birth rate in China. https://www.frontiersin.org/files/Articles/1796491/fpsyg-17-1796491-HTML/image_m/fpsyg-17-1796491-t001.jpg Table 1. The coding of interview materials. https://www.frontiersin.org/files/Articles/1796491/fpsyg-17-1796491-HTML/image_m/fpsyg-17-1796491-g002.jpg Figure 2. Explanatory framework of restriction in identity construction. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1804892/full https://www.frontiersin.org/files/Articles/1804892/fpubh-14-1804892-HTML/image_m/fpubh-14-1804892-t001.jpg Table 1. The results of initial screening. https://www.frontiersin.org/files/Articles/1804892/fpubh-14-1804892-HTML/image_m/fpubh-14-1804892-t002.jpg Table 2. Results of the comprehensive evaluation for donors with reduced fragility presented herein. https://www.frontiersin.org/files/Articles/1804892/fpubh-14-1804892-HTML/image_m/fpubh-14-1804892-t003.jpg Table 3. Genetic results of 14 donors. https://www.frontiersin.org/files/Articles/1804892/fpubh-14-1804892-HTML/image_m/fpubh-14-1804892-t004.jpg Table 4. Transfusion efficacy for recipients of blood from donors with decreased osmotic fragility. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1667180/full https://www.frontiersin.org/files/Articles/1667180/fimmu-17-1667180-HTML/image_m/fimmu-17-1667180-t001.jpg Table 1. Barrier system from the gut-liver axis perspective: structure-function integration and path https://www.frontiersin.org/files/Articles/1667180/fimmu-17-1667180-HTML/image_m/fimmu-17-1667180-g001.jpg Figure 1. Immunometabolic gating along the gut–liver axis. The gut–liver axis is a bidirectional cir https://www.frontiersin.org/files/Articles/1667180/fimmu-17-1667180-HTML/image_m/fimmu-17-1667180-g002.jpg Figure 2. Gut dysbiosis–barrier dysfunction axis drives MAFLD progression via portal circulation. In https://www.frontiersin.org/files/Articles/1667180/fimmu-17-1667180-HTML/image_m/fimmu-17-1667180-g003.jpg Figure 3. Drivers of gut dysbiosis–barrier dysfunction and their contribution to MAFLD proinflammato https://www.frontiersin.org/files/Articles/1667180/fimmu-17-1667180-HTML/image_m/fimmu-17-1667180-g004.jpg Figure 4. Gut leakiness–driven MAMP signaling promotes hepatic inflammation and fibrosis. Dysbiosis https://www.frontiersin.org/files/Articles/1667180/fimmu-17-1667180-HTML/image_m/fimmu-17-1667180-g005.jpg Figure 5. Schematic overview of the gut microbiota–liver axis in MAFLD. Dysbiosis alters microbial m https://www.frontiersin.org/files/Articles/1667180/fimmu-17-1667180-HTML/image_m/fimmu-17-1667180-g006.jpg Figure 6. Microbiome-targeted interventions restore gut barrier function and attenuate liver inflamm https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1637272/full https://www.frontiersin.org/files/Articles/1637272/fmicb-16-1637272-HTML/image_m/fmicb-16-1637272-g001.jpg Figure 1. Diagram of anti-tumor effect of Berberine. Berberine, a naturally occurring alkaloid with https://www.frontiersin.org/files/Articles/1637272/fmicb-16-1637272-HTML/image_m/fmicb-16-1637272-g002.jpg Figure 2. The evolution of colorectal adenoma-carcinoma progression. Under the long-term synergistic https://www.frontiersin.org/files/Articles/1637272/fmicb-16-1637272-HTML/image_m/fmicb-16-1637272-g003.jpg Figure 3. The mechanism underlying Berberine action in stage 1. Berberine can directly kill Fusobact https://www.frontiersin.org/files/Articles/1637272/fmicb-16-1637272-HTML/image_m/fmicb-16-1637272-g004.jpg Figure 4. The mechanism underlying Berberine action in stage 2. Berberine alleviates intestinal infl https://www.frontiersin.org/files/Articles/1637272/fmicb-16-1637272-HTML/image_m/fmicb-16-1637272-g005.jpg Figure 5. The 3D spatial mechanistic network underlying Berberine's suppression of Fusobacterium nuc https://www.frontiersin.org/files/Articles/1637272/fmicb-16-1637272-HTML/image_m/fmicb-16-1637272-t001.jpg Table 1. Clinical studies related to BBR in Clinicaltrials.gov. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1677190/full https://www.frontiersin.org/files/Articles/1677190/fimmu-17-1677190-HTML/image_m/fimmu-17-1677190-g001.jpg Figure 1. Overview of BEV biogenesis. Gram-negative outer membrane vesicles (OMVs) are formed by bud https://www.frontiersin.org/files/Articles/1677190/fimmu-17-1677190-HTML/image_m/fimmu-17-1677190-g002.jpg Figure 2. MAPK and NF-κB suppression by BEV sRNAs and tRFs. Created in BioRender. Charpentier, L. (2 https://www.frontiersin.org/files/Articles/1677190/fimmu-17-1677190-HTML/image_m/fimmu-17-1677190-g003.jpg Figure 3. Overview of the effects of BEV sRNAs and tRFs on inflammation and trained immunity. Create https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1774257/full https://www.frontiersin.org/files/Articles/1774257/fsurg-13-1774257-HTML/image_m/fsurg-13-1774257-g001.jpg Figure 1. The CT examination image of the patient. (A) Shows the image before the surgery, and (B) s https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1741133/full https://www.frontiersin.org/files/Articles/1741133/fnhum-20-1741133-HTML/image_m/fnhum-20-1741133-t001.jpg Table 1. Profile of baseline neuropsychological measurements of the Uniform Data Set version 3 (UDS3 https://www.frontiersin.org/files/Articles/1741133/fnhum-20-1741133-HTML/image_m/fnhum-20-1741133-g001.jpg Figure 1. Schematics of SPECTRE framework for spatially resolving frequency-dependent EEG signals us https://www.frontiersin.org/files/Articles/1741133/fnhum-20-1741133-HTML/image_m/fnhum-20-1741133-g002.jpg Figure 2. SPECTRE estimated theta band spatiotemporal electric field activation summary mode “wsum” https://www.frontiersin.org/files/Articles/1741133/fnhum-20-1741133-HTML/image_m/fnhum-20-1741133-g003.jpg Figure 3. SPECTRE estimated theta band spatiotemporal electric field activation summary mode histogr https://www.frontiersin.org/files/Articles/1741133/fnhum-20-1741133-HTML/image_m/fnhum-20-1741133-g004.jpg Figure 4. Response direction of theta frequency band across brain regions for Sham and Active TMS co https://www.frontiersin.org/files/Articles/1741133/fnhum-20-1741133-HTML/image_m/fnhum-20-1741133-t002.jpg Table 2. Paired t-test results for theta oscillation changes (Sham vs. Active TMS). https://www.frontiersin.org/files/Articles/1741133/fnhum-20-1741133-HTML/image_m/fnhum-20-1741133-g005.jpg Figure 5. Paired t-test for theta-band oscillation changes in Harvard-Oxford Atlas Regions (Sham vs. https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2025.1731374/full https://www.frontiersin.org/files/Articles/1731374/fspas-12-1731374-HTML-r2/image_m/fspas-12-1731374-g001.jpg Figure 1. The plot displays the real (red) and imaginary (blue) components of the wave function Ψ (a https://www.frontiersin.org/files/Articles/1731374/fspas-12-1731374-HTML-r2/image_m/fspas-12-1731374-g002.jpg Figure 2. Redshift as a function of distance (in megaparsecs, Mpc) is shown for three regimes: the n https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1618501/full https://www.frontiersin.org/files/Articles/1618501/fonc-16-1618501-HTML/image_m/fonc-16-1618501-g001.jpg Figure 1. Comparative pre- and post-operative photographs of the cervical mass are provided. (A) Phy https://www.frontiersin.org/files/Articles/1618501/fonc-16-1618501-HTML/image_m/fonc-16-1618501-g002.jpg Figure 2. Results of preoperative cervical ultrasonography. (A) Ultrasonography revealed the hypoech https://www.frontiersin.org/files/Articles/1618501/fonc-16-1618501-HTML/image_m/fonc-16-1618501-g003.jpg Figure 3. Computed tomography findings of the cervical mass. (A) Low-density cystic cavity is presen https://www.frontiersin.org/files/Articles/1618501/fonc-16-1618501-HTML/image_m/fonc-16-1618501-g004.jpg Figure 4. Postoperative histopathology images (H&E staining, ×10 magnification). (A) Left papillary https://www.frontiersin.org/files/Articles/1618501/fonc-16-1618501-HTML/image_m/fonc-16-1618501-g005.jpg Figure 5. Results of Postoperative follow-up ultrasonography. (A) Ultrasonography performed on Decem https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1728553/full https://www.frontiersin.org/files/Articles/1728553/fnut-13-1728553-HTML/image_m/fnut-13-1728553-t001.jpg Table 1. Descriptive characteristics of pregnant women by survey year (2021–2024). https://www.frontiersin.org/files/Articles/1728553/fnut-13-1728553-HTML/image_m/fnut-13-1728553-t002.jpg Table 2. ANOVA results of urinary iodine and household salt iodine by gestational stage and survey y https://www.frontiersin.org/files/Articles/1728553/fnut-13-1728553-HTML/image_m/fnut-13-1728553-t003.jpg Table 3. Analysis results of the MEM model at different gestational ages. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1765741/full https://www.frontiersin.org/files/Articles/1765741/fendo-17-1765741-HTML-r1/image_m/fendo-17-1765741-g001.jpg Figure 1. The study flow diagram. https://www.frontiersin.org/files/Articles/1765741/fendo-17-1765741-HTML-r1/image_m/fendo-17-1765741-t001.jpg Table 1. Pre-study demographic and clinical characteristics of patients. https://www.frontiersin.org/files/Articles/1765741/fendo-17-1765741-HTML-r1/image_m/fendo-17-1765741-t002.jpg Table 2. Anthropometric and body composition before and after 12 weeks. https://www.frontiersin.org/files/Articles/1765741/fendo-17-1765741-HTML-r1/image_m/fendo-17-1765741-t003.jpg Table 3. Dietary intake before and after 12 weeks. https://www.frontiersin.org/files/Articles/1765741/fendo-17-1765741-HTML-r1/image_m/fendo-17-1765741-t004.jpg Table 4. Biochemical indicators before and after 12 weeks. https://www.frontiersin.org/files/Articles/1765741/fendo-17-1765741-HTML-r1/image_m/fendo-17-1765741-t005.jpg Table 5. Gastrointestinal side effects before and after 12 weeks. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1713650/full https://www.frontiersin.org/files/Articles/1713650/fimmu-16-1713650-HTML/image_m/fimmu-16-1713650-t001.jpg Table 1. Demographic and clinical characteristics of two groups. https://www.frontiersin.org/files/Articles/1713650/fimmu-16-1713650-HTML/image_m/fimmu-16-1713650-g001.jpg Figure 1. Microbial differential analysis between ALI and NALI groups. (A) Upset plot showing the nu https://www.frontiersin.org/files/Articles/1713650/fimmu-16-1713650-HTML/image_m/fimmu-16-1713650-g002.jpg Figure 2. Microbial composition and abundance in NALI and ALI groups. Subgroup analysis of gut micro https://www.frontiersin.org/files/Articles/1713650/fimmu-16-1713650-HTML/image_m/fimmu-16-1713650-g003.jpg Figure 3. Microbial gene function and clinical correlations. (A, B) Functional annotations at levels https://www.frontiersin.org/files/Articles/1713650/fimmu-16-1713650-HTML/image_m/fimmu-16-1713650-t002.jpg Table 2. The ability of specific microbiome biomarker in predicting ALI. https://www.frontiersin.org/files/Articles/1713650/fimmu-16-1713650-HTML/image_m/fimmu-16-1713650-g004.jpg Figure 4. PCA and PLS-DA scoring plots and volcano plot of metabolites. (A) PCA in NEG and POS modes https://www.frontiersin.org/files/Articles/1713650/fimmu-16-1713650-HTML/image_m/fimmu-16-1713650-g005.jpg Figure 5. Differential metabolites and KEGG pathways. (A, B) Heatmaps in NEG and POS modes. The colo https://www.frontiersin.org/files/Articles/1713650/fimmu-16-1713650-HTML/image_m/fimmu-16-1713650-g006.jpg Figure 6. Microbiota-metabolite correlations. (A, B) Spearman heatmaps of top 30 OTUs and metabolite https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1782217/full https://www.frontiersin.org/files/Articles/1782217/fnut-13-1782217-HTML-r1/image_m/fnut-13-1782217-g001.jpg Figure 1. Study flowchart. BMI, body mass index; ALP, alkaline phosphatase; CRP, C-reactive protein; https://www.frontiersin.org/files/Articles/1782217/fnut-13-1782217-HTML-r1/image_m/fnut-13-1782217-t001.jpg Table 1. Baseline characteristics by sex in the healthy population. https://www.frontiersin.org/files/Articles/1782217/fnut-13-1782217-HTML-r1/image_m/fnut-13-1782217-t002.jpg Table 2. Sex-stratified comparisons of vitamin B6 biomarkers between participants aged <50 and ≥50 y https://www.frontiersin.org/files/Articles/1782217/fnut-13-1782217-HTML-r1/image_m/fnut-13-1782217-t003.jpg Table 3. Reference intervals of vitamin B6 metabolites stratified by age and sex. https://www.frontiersin.org/files/Articles/1782217/fnut-13-1782217-HTML-r1/image_m/fnut-13-1782217-g002.jpg Figure 2. Sex-stratified multivariable linear regression forest plot of vitamin B6 biomarkers. The r https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2026.1762048/full https://www.frontiersin.org/files/Articles/1762048/fgwh-07-1762048-HTML/image_m/fgwh-07-1762048-t001.jpg Table 1. SPIRIT schedule of enrolment, interventions and assessment. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1721127/full https://www.frontiersin.org/files/Articles/1721127/fmicb-16-1721127-HTML/image_m/fmicb-16-1721127-g001.jpg Figure 1. Physicochemical properties of brown rice from different glutinous rice varieties: (A) Four https://www.frontiersin.org/files/Articles/1721127/fmicb-16-1721127-HTML/image_m/fmicb-16-1721127-g002.jpg Figure 2. Metabolic pathways of grain-derived compounds in dominant microbial taxa. The heatmap bloc https://www.frontiersin.org/files/Articles/1721127/fmicb-16-1721127-HTML/image_m/fmicb-16-1721127-g003.jpg Figure 3. Physicochemical parameters of Zaopei during saccharification and fermentation. (A) Crude s https://www.frontiersin.org/files/Articles/1721127/fmicb-16-1721127-HTML/image_m/fmicb-16-1721127-g004.jpg Figure 4. Metagenomic analysis of microbial communities in Zaopei. (A) Stacked bar chart of species- https://www.frontiersin.org/files/Articles/1721127/fmicb-16-1721127-HTML/image_m/fmicb-16-1721127-g005.jpg Figure 5. Differential metaproteomic analysis of Zaopei samples. (A) Taxonomic abundance at the spec https://www.frontiersin.org/files/Articles/1721127/fmicb-16-1721127-HTML/image_m/fmicb-16-1721127-g006.jpg Figure 6. Untargeted LC–MS-based metabolomic analysis of Zaopei. (A,B) Classification of metabolic p https://www.frontiersin.org/files/Articles/1721127/fmicb-16-1721127-HTML/image_m/fmicb-16-1721127-g007.jpg Figure 7. Metabolomic analysis of Baijiu volatile compounds. (A) Circular plot showing the number an https://www.frontiersin.org/files/Articles/1721127/fmicb-16-1721127-HTML/image_m/fmicb-16-1721127-t001.jpg Table 1. Significant differential VOCs (rOAV > 1) between DJG and other groups. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1744551/full https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-g001.jpg Figure 1. (a) the location of the study area; (b) the distribution of the 12 sampling sites, includ https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-t001.jpg Table 1. Functional groups and representative species in Wailingding Island marine ranch. https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-t002.jpg Table 2. Basic input and output parameters of functional groups estimated by the Ecopath model for t https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-g002.jpg Figure 2. Food web structure of the ecosystem Wailingding Island marine ranch; the gray lines denote https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-g003.jpg Figure 3. Lindeman spine flow network of organic matter and transfer efficiency for the artificial r https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-t003.jpg Table 3. Distribution of energy flow among trophic levels in the ecosystem of Wailingding Island mar https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-t004.jpg Table 4. Transfer efficiency among discrete trophic levels in the ecosystem of Wailingding Island ma https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-g004.jpg Figure 4. Mixed trophic impacts among functional groups in the ecosystem of Wailingding Island marin https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-g005.jpg Figure 5. Keystoneness for the functional groups of the ecosystem in Wailingding Island marine ranch https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-g006.jpg Figure 6. Trophic niche overlap plot of functional groups in Ecopath model (shows the degree to whic https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-t005.jpg Table 5. Maximum ecological carrying capacities of target functional groups in the ecosystem of the https://www.frontiersin.org/files/Articles/1744551/fmars-13-1744551-HTML/image_m/fmars-13-1744551-t006.jpg Table 6. Total system properties of the artificial reef ecosystem in the National marine ranch demon https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1804276/full https://www.frontiersin.org/files/Articles/1804276/fimmu-17-1804276-HTML/image_m/fimmu-17-1804276-g001.jpg Figure 1. Flowchart of the study design and analysis. KD, Kawasaki disease; IVIG, intravenous immuno https://www.frontiersin.org/files/Articles/1804276/fimmu-17-1804276-HTML/image_m/fimmu-17-1804276-t001.jpg Table 1. Comparison of clinical characteristics between patients with and without MGCAA in the Suzho https://www.frontiersin.org/files/Articles/1804276/fimmu-17-1804276-HTML/image_m/fimmu-17-1804276-t002.jpg Table 2. Predictive performance of 11 ML models for MGCAA in KD. https://www.frontiersin.org/files/Articles/1804276/fimmu-17-1804276-HTML/image_m/fimmu-17-1804276-g002.jpg Figure 2. Model performance comparison and evaluation of feature reduction, calibration, and clinica https://www.frontiersin.org/files/Articles/1804276/fimmu-17-1804276-HTML/image_m/fimmu-17-1804276-g003.jpg Figure 3. SHAP-based global feature importance and individual variable effects in the SVM kernlab mo https://www.frontiersin.org/files/Articles/1804276/fimmu-17-1804276-HTML/image_m/fimmu-17-1804276-g004.jpg Figure 4. SHAP-based local explanations for four individual patients. SHAP, SHapley Additive exPlana https://www.frontiersin.org/files/Articles/1804276/fimmu-17-1804276-HTML/image_m/fimmu-17-1804276-g005.jpg Figure 5. Online MGCAA risk calculator interface and probability distribution visualization. (A) Web https://www.frontiersin.org/files/Articles/1804276/fimmu-17-1804276-HTML/image_m/fimmu-17-1804276-g006.jpg Figure 6. External validation of the model. (A) Model performance evaluation in the external validat https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1643991/full https://www.frontiersin.org/files/Articles/1643991/fcimb-15-1643991-HTML/image_m/fcimb-15-1643991-g001.jpg Figure 1. Flow chart of this study. https://www.frontiersin.org/files/Articles/1643991/fcimb-15-1643991-HTML/image_m/fcimb-15-1643991-t001.jpg Table 1. Overview of testing of the 728 BALF specimens. https://www.frontiersin.org/files/Articles/1643991/fcimb-15-1643991-HTML/image_m/fcimb-15-1643991-g002.jpg Figure 2. Number of bacteria in six centers. https://www.frontiersin.org/files/Articles/1643991/fcimb-15-1643991-HTML/image_m/fcimb-15-1643991-t002.jpg Table 2. Number of samples in which multiple pathogens were detected using the respiratory pathogens https://www.frontiersin.org/files/Articles/1643991/fcimb-15-1643991-HTML/image_m/fcimb-15-1643991-t003.jpg Table 3. Pathogens identified using the respiratory pathogens multiplex nucleic acid diagnostic kit https://www.frontiersin.org/files/Articles/1643991/fcimb-15-1643991-HTML/image_m/fcimb-15-1643991-t004.jpg Table 4. Comparison between respiratory pathogens multiplex nucleic acid diagnostic kit results and https://www.frontiersin.org/files/Articles/1643991/fcimb-15-1643991-HTML/image_m/fcimb-15-1643991-t005.jpg Table 5. Performance summary and characteristics of the respiratory pathogens multiplex nucleic acid https://www.frontiersin.org/journals/malaria/articles/10.3389/fmala.2026.1751312/full https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-t001.jpg Table 1. Baseline prevalence, severity, and duration of malaria-associated symptoms at presentation https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-t002.jpg Table 2. Logistic regression analysis of the association between parasite density and the presence o https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-g001.jpg Figure 1. Non-linear relationship between parasite density and probability of severe symptoms (grade https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-t003.jpg Table 3. Quasi-Poisson regression analysis of the association between parasite density and total sym https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-g002.jpg Figure 2. Relationship between parasite density and total symptom count. Quasi-Poisson regression wi https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-t004.jpg Table 4. Negative binomial regression analysis of the association between parasite density and sympt https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-g003.jpg Figure 3. (a) Parasite density and body temperature at presentation. A smooth spline curve illustrat https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-t005.jpg Table 5. Ordinal logistic regression analysis of the association between parasite density and anemia https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-g004.jpg Figure 4. Distribution of hemoglobin levels among children with malaria. This histogram shows the di https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-g005.jpg Figure 5. Relationship between parasite density and hemoglobin concentration. Scatterplot with natur https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-t006.jpg Table 6. Logistic regression of any hematologic grade ≥ 3 vs. parasite density. https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-t007.jpg Table 7. Linear spline models for continuous hematologic indices vs. parasite density. https://www.frontiersin.org/files/Articles/1751312/fmala-04-1751312-HTML-r1/image_m/fmala-04-1751312-t008.jpg Table 8. Logistic regression of thrombocytopenia vs. parasite density. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1786573/full https://www.frontiersin.org/files/Articles/1786573/fmed-13-1786573-HTML/image_m/fmed-13-1786573-g001.jpg Figure 1. (A–C) Represent the ultrasound images of different gastric contents. (A) Empty, (B) Clear https://www.frontiersin.org/files/Articles/1786573/fmed-13-1786573-HTML/image_m/fmed-13-1786573-t001.jpg Table 1. Characteristics of the participants according to age, BMI, and sex. https://www.frontiersin.org/files/Articles/1786573/fmed-13-1786573-HTML/image_m/fmed-13-1786573-t002.jpg Table 2. Fluid emptying time and 4 h gastric emptying rates. https://www.frontiersin.org/files/Articles/1786573/fmed-13-1786573-HTML/image_m/fmed-13-1786573-g002.jpg Figure 2. (A–C) Represent Changes in blood glucose level, hunger NRS score, and gastric CSA. The sol https://www.frontiersin.org/files/Articles/1786573/fmed-13-1786573-HTML/image_m/fmed-13-1786573-g003.jpg Figure 3. Represents the relationship between the gastric CSA and hunger NRS score. The gastric CSA https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1783569/full https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g001.jpg Figure 1. The PRISMA statement flow chart. Risk of bias assessment in studies. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-t001.jpg Table 1. Basic characteristics of the included study. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g002.jpg Figure 2. Risk of bias assessment in studies. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g003.jpg Figure 3. Forest plots of meta-analysis of BASFI. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g004.jpg Figure 4. Forest plots of meta-analysis of BASDAI. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g005.jpg Figure 5. Forest plots of meta-analysis of BASMI. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g006.jpg Figure 6. Forest plots of meta-analysis of ASDAS. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g007.jpg Figure 7. Forest plots of meta-analysis of thoracic expansion capacity. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g008.jpg Figure 8. Forest plots of meta-analysis of fatigue. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g009.jpg Figure 9. Forest plots of meta-analysis of BASDAI subgroup analysis. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g010.jpg Figure 10. Forest plots of meta-analysis of ASDAS subgroup analysis. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g011.jpg Figure 11. Funnel plots of meta-analysis of BASFI. https://www.frontiersin.org/files/Articles/1783569/fmed-13-1783569-HTML/image_m/fmed-13-1783569-g012.jpg Figure 12. Funnel plots of meta-analysis of thoracic expansion capacity. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1764762/full https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-t001.jpg Table 1. Variable overview. https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-t002.jpg Table 2. Summary statistics. https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-t003.jpg Table 3. Correlation matrix. https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-g001.jpg Figure 1. CFP trends in the YREB from 2000 to 2021. Figure 1a shows the changing trends of the CFP f https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-t004.jpg Table 4. Panel-corrected standard error (PCSE) findings. https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-t005.jpg Table 5. General dominance statistics. https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-t006.jpg Table 6. Parameters of the GTWR model. https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-g002.jpg Figure 2. Time series variation in average regression coefficients for influencing factors of CFP in https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-g003.jpg Figure 3. Spatial distribution of average regression coefficients for influencing factors of CFP in https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-t007.jpg Table 7. Regional comparison of GTWR average coefficients and significance of differences. https://www.frontiersin.org/files/Articles/1764762/fenvs-14-1764762-HTML/image_m/fenvs-14-1764762-g004.jpg Figure 4. Significance coverage heatmap of local GTWR coefficients by province (2000–2021). https://www.frontiersin.org/journals/soft-matter/articles/10.3389/frsfm.2025.1708264/full https://www.frontiersin.org/files/Articles/1708264/frsfm-05-1708264-HTML/image_m/frsfm-05-1708264-t001.jpg Table 1. Results showing phases formed in the GMO-50/DGMO (+P80)-based system, hydrated to 60 wt% wa https://www.frontiersin.org/files/Articles/1708264/frsfm-05-1708264-HTML/image_m/frsfm-05-1708264-g001.jpg Figure 1. SAXS diffractograms showing 0–50 wt% DOPC added to (A) 30/70 GMO-50/DGMO and (B) 60/40 GMO https://www.frontiersin.org/files/Articles/1708264/frsfm-05-1708264-HTML/image_m/frsfm-05-1708264-g002.jpg Figure 2. Change in the lattice parameter, a, with 0–50 wt% DOPC added to (A) 30/70 GMO-50/DGMO, (B) https://www.frontiersin.org/files/Articles/1708264/frsfm-05-1708264-HTML/image_m/frsfm-05-1708264-g003.jpg Figure 3. SAXS diffractograms showing the replacement of DGMO with DOPC in (A) 30/70-X/X GMO-50/DGMO https://www.frontiersin.org/files/Articles/1708264/frsfm-05-1708264-HTML/image_m/frsfm-05-1708264-g004.jpg Figure 4. Change in the average lattice parameter, a, with replacement of DGMO with DOPC in (A) 30/7 https://www.frontiersin.org/files/Articles/1708264/frsfm-05-1708264-HTML/image_m/frsfm-05-1708264-g005.jpg Figure 5. SAXS diffractograms showing the effect of temperature on bulk phases (A) 25/75 GMO-50/DGMO https://www.frontiersin.org/files/Articles/1708264/frsfm-05-1708264-HTML/image_m/frsfm-05-1708264-g006.jpg Figure 6. SAXS data for (A) 60/40 GMO-50/DOPC + P80 bulk phases hydrated with 50–70 wt% MQ water and https://www.frontiersin.org/files/Articles/1708264/frsfm-05-1708264-HTML/image_m/frsfm-05-1708264-g007.jpg Figure 7. Synchrotron SAXS data for dispersions of bulk samples with 60/40 GMO-50/DOPC + 30 wt% P80 https://www.frontiersin.org/files/Articles/1708264/frsfm-05-1708264-HTML/image_m/frsfm-05-1708264-g008.jpg Figure 8. (A) Ternary phase map for the GMO-50/DGMO/DOPC system and (B) GMO-50/DGMO/DOPC/P80, where https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1766999/full https://www.frontiersin.org/files/Articles/1766999/fvets-13-1766999-HTML/image_m/fvets-13-1766999-g001.jpg Figure 1. Bichon Frisé case – (A) axial view and (B) sagittal view: CT image at T13–L1 showing a lef https://www.frontiersin.org/files/Articles/1766999/fvets-13-1766999-HTML/image_m/fvets-13-1766999-g002.jpg Figure 2. Visual summary of the image processing and segmentation pipeline used to quantify vertebra https://www.frontiersin.org/files/Articles/1766999/fvets-13-1766999-HTML/image_m/fvets-13-1766999-g003.jpg Figure 3. Illustrate 1–3: 3D Slicer axial, dorsal, and sagittal multiplanar reconstruction of the sc https://www.frontiersin.org/files/Articles/1766999/fvets-13-1766999-HTML/image_m/fvets-13-1766999-t001.jpg Table 1. Spinal canal volume by breed. https://www.frontiersin.org/files/Articles/1766999/fvets-13-1766999-HTML/image_m/fvets-13-1766999-g004.jpg Figure 4. Illustrate the distribution of canal volumes by breed. The box’s central line indicates th https://www.frontiersin.org/files/Articles/1766999/fvets-13-1766999-HTML/image_m/fvets-13-1766999-g005.jpg Figure 5. ROC curve for using spinal canal volume (per vertebra) to predict a severe neurologic outc https://www.frontiersin.org/files/Articles/1766999/fvets-13-1766999-HTML/image_m/fvets-13-1766999-g006.jpg Figure 6. Scattered plot showing the cases distribution according to spinal canal volume and severit https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1710669/full https://www.frontiersin.org/files/Articles/1710669/fimmu-16-1710669-HTML-r1/image_m/fimmu-16-1710669-g001.jpg Figure 1. Erythematous lesion in the left breast region, diagnosed as Tinea Corporis and tested posi https://www.frontiersin.org/files/Articles/1710669/fimmu-16-1710669-HTML-r1/image_m/fimmu-16-1710669-g002.jpg Figure 2. Clinical timeline illustrating treatments, relapses, and EDSS progression. EDSS, Expanded https://www.frontiersin.org/files/Articles/1710669/fimmu-16-1710669-HTML-r1/image_m/fimmu-16-1710669-g003.jpg Figure 3. On the left (A), axial FLAIR brain MRI reveals a large demyelinating lesion extending from https://www.frontiersin.org/files/Articles/1710669/fimmu-16-1710669-HTML-r1/image_m/fimmu-16-1710669-t001.jpg Table 1. Lymphocyte subset distribution before and after Ocrelizumab treatment. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1769162/full https://www.frontiersin.org/files/Articles/1769162/fpsyt-17-1769162-HTML/image_m/fpsyt-17-1769162-t001.jpg Table 1. Descriptive statistics for demographic and clinical variables (N = 33). https://www.frontiersin.org/files/Articles/1769162/fpsyt-17-1769162-HTML/image_m/fpsyt-17-1769162-t002.jpg Table 2. Descriptive statistics for inflammatory markers at baseline (V0) and follow-up (V1). https://www.frontiersin.org/files/Articles/1769162/fpsyt-17-1769162-HTML/image_m/fpsyt-17-1769162-g001.jpg Figure 1. Distribution of antipsychotic use in patient cohort (N = 33). OLA, olanzapine; RISP, rispe https://www.frontiersin.org/files/Articles/1769162/fpsyt-17-1769162-HTML/image_m/fpsyt-17-1769162-t003.jpg Table 3. Adjusted regression models predicting change in inflammatory markers. https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2026.1763096/full https://www.frontiersin.org/files/Articles/1763096/fmats-13-1763096-HTML/image_m/fmats-13-1763096-g001.jpg Figure 1. Specimens fabricated using three different techniques. https://www.frontiersin.org/files/Articles/1763096/fmats-13-1763096-HTML/image_m/fmats-13-1763096-g002.jpg Figure 2. (a) Specimen secured in a customized holder. (b) Specimens torqued using a digital gauge. https://www.frontiersin.org/files/Articles/1763096/fmats-13-1763096-HTML/image_m/fmats-13-1763096-g003.jpg Figure 3. Cyclic loading was employed using a four-station multimodal dual-axis chewing simulator. https://www.frontiersin.org/files/Articles/1763096/fmats-13-1763096-HTML/image_m/fmats-13-1763096-t001.jpg Table 1. Mean surface roughness (Ra, in μm) among study groups. https://www.frontiersin.org/files/Articles/1763096/fmats-13-1763096-HTML/image_m/fmats-13-1763096-g004.jpg Figure 4. Surface roughness micrographs among the study groups. (A) molar-SLM, (B) premolar-SLM, (C) https://www.frontiersin.org/files/Articles/1763096/fmats-13-1763096-HTML/image_m/fmats-13-1763096-t002.jpg Table 2. Mean and SD of RTV and RTD among the study groups. https://www.frontiersin.org/journals/fungal-biology/articles/10.3389/ffunb.2025.1641004/full https://www.frontiersin.org/files/Articles/1641004/ffunb-06-1641004-HTML/image_m/ffunb-06-1641004-t001.jpg Table 1. Comparative ecological effects of Trichoderma spp. on soil microbial communities. https://www.frontiersin.org/files/Articles/1641004/ffunb-06-1641004-HTML/image_m/ffunb-06-1641004-g001.jpg Figure 1. Effect of Trichoderma spp. inoculation on agricultural production and soil ecosystem. https://www.frontiersin.org/files/Articles/1641004/ffunb-06-1641004-HTML/image_m/ffunb-06-1641004-t002.jpg Table 2. Secondary metabolites produced by Trichoderma species and the biological effects of respect https://www.frontiersin.org/files/Articles/1641004/ffunb-06-1641004-HTML/image_m/ffunb-06-1641004-g002.jpg Figure 2. A conceptual model illustrating the interactions of Trichoderma secondary metabolites with https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1656739/full https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-t001.jpg Table 1. Distribution of the sample by district. https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-t002.jpg Table 2. Pesticide risk perception statements. https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-t003.jpg Table 3. Gender and age disaggregated demographic characteristics of farmers. https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-t004.jpg Table 4. Adoption level of pest management practices by gender and age. https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-g001.jpg Figure 1. Farmers’ risk perception of chemical pesticides and biopesticides. https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-g002.jpg Figure 2. Farmers’ risk perception of chemical pesticides by gender and age. https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-g003.jpg Figure 3. Farmers’ risk perceptions of biopesticides by gender and age. https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-g004.jpg Figure 4. Correlation between perceived risks of pesticides and adoption of pest management practice https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-g005.jpg Figure 5. Partial correlation between risk perceptions, controlling for the confounding effects of g https://www.frontiersin.org/files/Articles/1656739/fsufs-09-1656739-HTML-r1/image_m/fsufs-09-1656739-t005.jpg Table 5. Partial correlation of risk perceptions and use of pest management practices and confounder https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1799960/full https://www.frontiersin.org/files/Articles/1799960/fneur-17-1799960-HTML/image_m/fneur-17-1799960-g001.jpg Figure 1. Flowchart of the study. Patients with gaps of the switch between mAbs longer than 1 month https://www.frontiersin.org/files/Articles/1799960/fneur-17-1799960-HTML/image_m/fneur-17-1799960-t001.jpg Table 1. Baseline characteristics of the study sample. https://www.frontiersin.org/files/Articles/1799960/fneur-17-1799960-HTML/image_m/fneur-17-1799960-t002.jpg Table 2. Comparative analysis of the switch response in the total sample. https://www.frontiersin.org/files/Articles/1799960/fneur-17-1799960-HTML/image_m/fneur-17-1799960-g002.jpg Figure 2. Average MMDs (right) and MHDs (left) before and 3 and 6 months after switching, according https://www.frontiersin.org/files/Articles/1799960/fneur-17-1799960-HTML/image_m/fneur-17-1799960-t003.jpg Table 3. Comparative analysis of the switch response based on the underlying mechanism of action. https://www.frontiersin.org/files/Articles/1799960/fneur-17-1799960-HTML/image_m/fneur-17-1799960-t004.jpg Table 4. Comparative analysis of the response to the switch based on the kind of response to the fir https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1704090/full https://www.frontiersin.org/files/Articles/1704090/fonc-16-1704090-HTML/image_m/fonc-16-1704090-g001.jpg Figure 1. Timeline of this patient’s case. https://www.frontiersin.org/files/Articles/1704090/fonc-16-1704090-HTML/image_m/fonc-16-1704090-g002.jpg Figure 2. Alternate alleles reported by VarScan in blood and tumour samples with ClinVar classificat https://www.frontiersin.org/files/Articles/1704090/fonc-16-1704090-HTML/image_m/fonc-16-1704090-t001.jpg Table 1. Pathogenic alternate alleles detected in PIK3CA in the tumour using VarScan. https://www.frontiersin.org/files/Articles/1704090/fonc-16-1704090-HTML/image_m/fonc-16-1704090-g003.jpg Figure 3. (A) Small non-synonymous somatic variants and structural variants detected genome-wide. SN https://www.frontiersin.org/files/Articles/1704090/fonc-16-1704090-HTML/image_m/fonc-16-1704090-t002.jpg Table 2. Structural variants (SVs) affecting protein-coding genes detected in the tumour. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1784400/full https://www.frontiersin.org/files/Articles/1784400/fimmu-17-1784400-HTML/image_m/fimmu-17-1784400-t001.jpg Table 1. Baseline characteristics of four patients with active AS. https://www.frontiersin.org/files/Articles/1784400/fimmu-17-1784400-HTML/image_m/fimmu-17-1784400-t002.jpg Table 2. Changes in clinical and laboratory indicators before and after tofacitinib treatment (n=4). https://www.frontiersin.org/files/Articles/1784400/fimmu-17-1784400-HTML/image_m/fimmu-17-1784400-g001.jpg Figure 1. Comparison of sequencing depth and alpha diversity of fecal microbiota before and after to https://www.frontiersin.org/files/Articles/1784400/fimmu-17-1784400-HTML/image_m/fimmu-17-1784400-g002.jpg Figure 2. Gut microbiota composition and paired changes of key genera before and after AS treatment. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1731158/full https://www.frontiersin.org/files/Articles/1731158/fvets-13-1731158-HTML-r1/image_m/fvets-13-1731158-g001.jpg Figure 1. Cryptosporidiosis and One Health: interactions between human, animal, and environmental he https://www.frontiersin.org/files/Articles/1731158/fvets-13-1731158-HTML-r1/image_m/fvets-13-1731158-t001.jpg Table 1. Integrated summary of preclinical and clinical data on therapeutic efficacy against Cryptos https://www.frontiersin.org/files/Articles/1731158/fvets-13-1731158-HTML-r1/image_m/fvets-13-1731158-t002.jpg Table 2. Implications from previous studies informing Cryptosporidium vaccine development. https://www.frontiersin.org/journals/clinical-diabetes-and-healthcare/articles/10.3389/fcdhc.2026.1708124/full https://www.frontiersin.org/files/Articles/1708124/fcdhc-07-1708124-HTML/image_m/fcdhc-07-1708124-t001.jpg Table 1. Baseline characteristics of participants by glucose monitoring modality. https://www.frontiersin.org/files/Articles/1708124/fcdhc-07-1708124-HTML/image_m/fcdhc-07-1708124-t002.jpg Table 2. Comparison of diabetes distress, hypoglycemic confidence and WHO-5 well-being in the groups https://www.frontiersin.org/files/Articles/1708124/fcdhc-07-1708124-HTML/image_m/fcdhc-07-1708124-t003.jpg Table 3. Multivariable logistic regression models. https://www.frontiersin.org/files/Articles/1708124/fcdhc-07-1708124-HTML/image_m/fcdhc-07-1708124-t004.jpg Table 4. Good well-being (WHO-5 ≥50) in selected higher-risk subgroups. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1703264/full https://www.frontiersin.org/files/Articles/1703264/fped-13-1703264-HTML/image_m/fped-13-1703264-t001.jpg Table 1. Clinical and demographic features of the sample. https://www.frontiersin.org/files/Articles/1703264/fped-13-1703264-HTML/image_m/fped-13-1703264-t002.jpg Table 2. Differences between groups on D-N CAS. https://www.frontiersin.org/files/Articles/1703264/fped-13-1703264-HTML/image_m/fped-13-1703264-g001.jpg Figure 1. The ROC curve of planning, attention, and their combination in diagnosing ASD + ADHD in ch https://www.frontiersin.org/files/Articles/1703264/fped-13-1703264-HTML/image_m/fped-13-1703264-g002.jpg Figure 2. The ROC curve of successive processing in diagnosing ASD + ADHD in children with ADHD. https://www.frontiersin.org/files/Articles/1703264/fped-13-1703264-HTML/image_m/fped-13-1703264-g003.jpg Figure 3. Relations between ADHD/ASD features and the D-N CAS. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1561081/full https://www.frontiersin.org/files/Articles/1561081/fimmu-16-1561081-HTML/image_m/fimmu-16-1561081-g001.jpg Figure 1. The miRNA activity was significantly changed in TANs of LUAD based on scRNA-seq analysis. https://www.frontiersin.org/files/Articles/1561081/fimmu-16-1561081-HTML/image_m/fimmu-16-1561081-g002.jpg Figure 2. The miRNA activity was significantly changed between TANs subtypes in LUAD. (A) Heatmap di https://www.frontiersin.org/files/Articles/1561081/fimmu-16-1561081-HTML/image_m/fimmu-16-1561081-g003.jpg Figure 3. The miR-941 mimic facilitates LUAD tumor growth in vitro and in vivo. (A–C) CCK8 assays (A https://www.frontiersin.org/files/Articles/1561081/fimmu-16-1561081-HTML/image_m/fimmu-16-1561081-g004.jpg Figure 4. Prediction targets of miR-941 and prognostic significance of FOXN4 in LUAD. (A) miR-941 ta https://www.frontiersin.org/files/Articles/1561081/fimmu-16-1561081-HTML/image_m/fimmu-16-1561081-g005.jpg Figure 5. The miR-941-FOXN4 axis contributes to the malignant phenotype of LUAD cells. (A) Relative https://www.frontiersin.org/files/Articles/1561081/fimmu-16-1561081-HTML/image_m/fimmu-16-1561081-g006.jpg Figure 6. TANs promote LUAD progression via a miR-941/FOXN4/TGF-β feedback signaling loop (A) GSEA h https://www.frontiersin.org/files/Articles/1561081/fimmu-16-1561081-HTML/image_m/fimmu-16-1561081-g007.jpg Figure 7. The schematic diagram of this study. In this study, miR-941 served as a TAN-secreted oncog https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1539590/full https://www.frontiersin.org/files/Articles/1539590/fnut-12-1539590-HTML/image_m/fnut-12-1539590-t001.jpg Table 1. Descriptive characteristics of participants by Vitamin D status categories. https://www.frontiersin.org/files/Articles/1539590/fnut-12-1539590-HTML/image_m/fnut-12-1539590-t002.jpg Table 2. Descriptive characteristics of participants by Vitamin A status categories. https://www.frontiersin.org/files/Articles/1539590/fnut-12-1539590-HTML/image_m/fnut-12-1539590-t003.jpg Table 3. Association between Vitamin D and vitamin A. https://www.frontiersin.org/files/Articles/1539590/fnut-12-1539590-HTML/image_m/fnut-12-1539590-g001.jpg Figure 1. Relationship between Vitamin D and Vitamin A. Solid and dashed lines represent the predict https://www.frontiersin.org/files/Articles/1539590/fnut-12-1539590-HTML/image_m/fnut-12-1539590-t004.jpg Table 4. Threshold effect analysis of the relationship of Vitamin D and Vitamin A. https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2026.1735284/full https://www.frontiersin.org/files/Articles/1735284/fncel-20-1735284-HTML/image_m/fncel-20-1735284-t001.jpg Table 1. Summary of included studies. https://www.frontiersin.org/files/Articles/1735284/fncel-20-1735284-HTML/image_m/fncel-20-1735284-g001.jpg Figure 1. PRISMA chart depicting the search strategy for the systematic review. https://www.frontiersin.org/files/Articles/1735284/fncel-20-1735284-HTML/image_m/fncel-20-1735284-t002.jpg Table 2. Animals. https://www.frontiersin.org/files/Articles/1735284/fncel-20-1735284-HTML/image_m/fncel-20-1735284-t003.jpg Table 3. Human. https://www.frontiersin.org/files/Articles/1735284/fncel-20-1735284-HTML/image_m/fncel-20-1735284-t004.jpg Table 4. Summary of targets and the methods used to analyze them in culture-derived cells. https://www.frontiersin.org/files/Articles/1735284/fncel-20-1735284-HTML/image_m/fncel-20-1735284-t005.jpg Table 5. Results of the analysis of the culture derived cells. https://www.frontiersin.org/files/Articles/1735284/fncel-20-1735284-HTML/image_m/fncel-20-1735284-t006.jpg Table 6. Methods summary. https://www.frontiersin.org/files/Articles/1735284/fncel-20-1735284-HTML/image_m/fncel-20-1735284-g002.jpg Figure 2. Flow chart summarizing the correlation between sampling method (biopsy vs. brushing), cult https://www.frontiersin.org/files/Articles/1735284/fncel-20-1735284-HTML/image_m/fncel-20-1735284-t007.jpg Table 7. Quantitative trends and simplified quality appraisal across included studies. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1721116/full https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g001.jpg Figure 1. Chemical constitutes in YTA. (A) Total ion chromatograms of YTA in positive mode. (B) Tota https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g002.jpg Figure 2. YTA treatment alleviated DSS-induced colitis in mice. (A) Changes of body weight. (B) Chan https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g003.jpg Figure 3. YTA treatment alleviated inflammation, oxidative stress and intestinal barrier injured in https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g004.jpg Figure 4. Colon metabolomics analysis of YTA’s therapeutic effect on DSS-induced colitis in mice. (A https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g005.jpg Figure 5. Feces metabolomics analysis of YTA’s therapeutic effect on DSS-induced colitis in mice. (A https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g006.jpg Figure 6. Gut microbiota diversity analysis in feces through YTA treatment on DSS-induced colitis in https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g007.jpg Figure 7. Correlation analysis. (A) Correlation heatmap between gut microbiota and metabolites. (B) https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g008.jpg Figure 8. The effect of intestinal flora in DSS-induced colitis mice. (A) Changes of body weight, an https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g009.jpg Figure 9. LJ regulated inflammation in vitro. (A) Changes of five Lactobacillus spp. in mice feces. https://www.frontiersin.org/files/Articles/1721116/fphar-16-1721116-HTML-r1/image_m/fphar-16-1721116-g010.jpg Figure 10. LJ regulated DSS-induced colitis in mice. (A) Changes of body weight. (B) Changes of DAI https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1785618/full https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-g001.jpg Figure 1. The experiments: schematic representation. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-t001.jpg Table 1. Pollutant concentrations in actual water bodies. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-t002.jpg Table 2. Experimental design parameters for EFI devices. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-t003.jpg Table 3. Microbiological samples and sampling conditions. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-g002.jpg Figure 2. (a) COD, (b) TN, (c) NH4+−N and (d) NO3−−N removal by EFI under different C/N conditions. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-g003.jpg Figure 3. (a) COD, (b) TN, (c) NH4+−N, and (d) NO3−−N removal by EFI under different DO conditions. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-g004.jpg Figure 4. (a) COD, (b) TN, (c) NH4+−N, and (d) NO3−−N removal by EFI under different TN conditions. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-g005.jpg Figure 5. Chlorophyll content across different plant groups. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-g006.jpg Figure 6. Changes in CAT activity in different plant groups. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-t004.jpg Table 4. Microbial richness and diversity indices. https://www.frontiersin.org/files/Articles/1785618/fmicb-17-1785618-HTML/image_m/fmicb-17-1785618-g007.jpg Figure 7. Community abundance percentages at the microbial (a) phylum and (b) order levels. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1723195/full https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-t001.jpg Table 1. Sociodemographic variables of motorcycle riders. https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-t002.jpg Table 2. Knowledge of commercial motorcycle riders on personal protective equipment. https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-g001.jpg Figure 1. Motorcycle riders’ knowledge of the different types of personal protective equipment. https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-g002.jpg Figure 2. Overall knowledge of bike riders on personal protective equipment. https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-t003.jpg Table 3. Attitudes of commercial motorcycle riders toward personal protective equipment. https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-g003.jpg Figure 3. Overall attitudes of motorcycle riders toward personal protective equipment. https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-t004.jpg Table 4. Factors associated with knowledge via simple logistic regression. https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-t005.jpg Table 5. Factors associated with knowledge via multiple logistic regression. https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-t006.jpg Table 6. Factors associated with attitudes via simple logistic regression. https://www.frontiersin.org/files/Articles/1723195/fpubh-14-1723195-HTML-r1/image_m/fpubh-14-1723195-t007.jpg Table 7. Factors associated with knowledge via multiple logistic regression. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1734865/full https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-t001.jpg Table 1. Comprehensive summary of related literature in plant pathology and agricultural vision. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g001.jpg Figure 1. High-level schematic of the classification process. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g002.jpg Figure 2. Overall architecture of HASPNet. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g003.jpg Figure 3. Squeeze-and-excitation block architecture. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g004.jpg Figure 4. Architecture of CBAM attention. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-t002.jpg Table 2. HASPNet architectural components and their associated function. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-t003.jpg Table 3. Consolidated class distribution: original vs. augmented datasets. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g005.jpg Figure 5. Class distribution of the BDPapayaLeaf dataset. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g006.jpg Figure 6. Sample class images: (a) Anthracnose, (b) Bacterial spot, (c) Curl, (d) Healthy and (e) Ri https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-t004.jpg Table 4. Core augmentation and motivation. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g007.jpg Figure 7. (a) Training and validation accuracy, (b) confusion matrix, and (c) ROC curve for the comp https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-t005.jpg Table 5. Ablation study results. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g008.jpg Figure 8. (a) Training and validation accuracy, (b) confusion matrix, and (c) ROC curve for HASPNet https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g009.jpg Figure 9. (a) Accuracy and loss plots, (b) confusion matrix, and (c) ROC curve for HASPNet without S https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g010.jpg Figure 10. (a) Accuracy and loss plots, (b) confusion matrix, and (c) ROC curve for HASPNet without https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-t006.jpg Table 6. Performance across activation functions. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g011.jpg Figure 11. Class activation maps (Grad-CAM) for representative samples from each disease category: ( https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-t007.jpg Table 7. Grad-CAM explainability summary across disease classes. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-t008.jpg Table 8. Performance analysis across various SOTA models. https://www.frontiersin.org/files/Articles/1734865/frai-09-1734865-HTML/image_m/frai-09-1734865-g012.jpg Figure 12. Pareto comparison of classification accuracy versus inference time per image for all eval https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1759332/full https://www.frontiersin.org/files/Articles/1759332/fped-14-1759332-HTML/image_m/fped-14-1759332-g001.jpg Figure 1. Chest xray PA view demonstrating bilateral pleural effusions and trace pericardial effusio https://www.frontiersin.org/files/Articles/1759332/fped-14-1759332-HTML/image_m/fped-14-1759332-g002.jpg Figure 2. Chest xray lateral view demonstrating bilateral pleural effusions and trace pericardial ef https://www.frontiersin.org/files/Articles/1759332/fped-14-1759332-HTML/image_m/fped-14-1759332-g003.jpg Figure 3. Non- contrast CT chest demonstrating bilateral pleural effusions and trace pericardial eff https://www.frontiersin.org/files/Articles/1759332/fped-14-1759332-HTML/image_m/fped-14-1759332-g004.jpg Figure 4. Timeline for the resolution of clinical features of post-infectious glomerulonephritis (PI https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1628588/full https://www.frontiersin.org/files/Articles/1628588/fsufs-09-1628588-HTML/image_m/fsufs-09-1628588-g001.jpg Figure 1. Research framework of digital adoption on farmer livelihood resilience. https://www.frontiersin.org/files/Articles/1628588/fsufs-09-1628588-HTML/image_m/fsufs-09-1628588-t001.jpg Table 1. Farmer livelihood resilience indicator system. https://www.frontiersin.org/files/Articles/1628588/fsufs-09-1628588-HTML/image_m/fsufs-09-1628588-t002.jpg Table 2. Descriptive statistics of key variables. https://www.frontiersin.org/files/Articles/1628588/fsufs-09-1628588-HTML/image_m/fsufs-09-1628588-t003.jpg Table 3. Impact of digital adoption on farmer livelihood resilience. https://www.frontiersin.org/files/Articles/1628588/fsufs-09-1628588-HTML/image_m/fsufs-09-1628588-t004.jpg Table 4. Endogeneity test. https://www.frontiersin.org/files/Articles/1628588/fsufs-09-1628588-HTML/image_m/fsufs-09-1628588-t005.jpg Table 5. Robustness test. https://www.frontiersin.org/files/Articles/1628588/fsufs-09-1628588-HTML/image_m/fsufs-09-1628588-t006.jpg Table 6. Signal mechanism test of digital adoption. https://www.frontiersin.org/files/Articles/1628588/fsufs-09-1628588-HTML/image_m/fsufs-09-1628588-t007.jpg Table 7. Heterogeneous effects of digital adoption on farmer livelihood resilience. https://www.frontiersin.org/files/Articles/1628588/fsufs-09-1628588-HTML/image_m/fsufs-09-1628588-t008.jpg Table 8. Digital adoption and heterogeneous effects on livelihood capital. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1710545/full https://www.frontiersin.org/files/Articles/1710545/fmicb-16-1710545-HTML/image_m/fmicb-16-1710545-g001.jpg Figure 1. Antibacterial activity of β-acids against MRSA. (A) MBC of β-acids. (B) Inhibition zone of https://www.frontiersin.org/files/Articles/1710545/fmicb-16-1710545-HTML/image_m/fmicb-16-1710545-g002.jpg Figure 2. SEM and laser confocal microscopy images of MRSA. (A) SEM of MRSA. (B) Laser confocal micr https://www.frontiersin.org/files/Articles/1710545/fmicb-16-1710545-HTML/image_m/fmicb-16-1710545-g003.jpg Figure 3. (A) Inhibition of β-acids on MRSA biofilm formation. (B) The intracellular ROS level. (C) https://www.frontiersin.org/files/Articles/1710545/fmicb-16-1710545-HTML/image_m/fmicb-16-1710545-g004.jpg Figure 4. Effect of β-acids on cell membrane permeability. (A) The extracellular nucleic acid level https://www.frontiersin.org/files/Articles/1710545/fmicb-16-1710545-HTML/image_m/fmicb-16-1710545-g005.jpg Figure 5. Antibacterial mode of β-acids against MRSA. https://www.frontiersin.org/files/Articles/1710545/fmicb-16-1710545-HTML/image_m/fmicb-16-1710545-t001.jpg Table 1. Anti-MRSA mechanism of β-acids and other natural agents. https://www.frontiersin.org/files/Articles/1710545/fmicb-16-1710545-HTML/image_m/fmicb-16-1710545-g006.jpg Figure 6. The interaction of ManP with colupulone (A,B,C), lupulone (D,E,F), and adlupulone (G,H,I). https://www.frontiersin.org/files/Articles/1710545/fmicb-16-1710545-HTML/image_m/fmicb-16-1710545-g007.jpg Figure 7. Impact of β-acids on wound healing in MRSA-infected mice. (A) Images of wounds on the mice https://www.frontiersin.org/files/Articles/1710545/fmicb-16-1710545-HTML/image_m/fmicb-16-1710545-g008.jpg Figure 8. Evaluation of wound healing performance in Control and β-acids-treated groups via tissue s https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1675480/full https://www.frontiersin.org/files/Articles/1675480/fimmu-16-1675480-HTML/image_m/fimmu-16-1675480-g001.jpg Figure 1. The lactate-driven lactylation modification network in tumors. EGFR activation triggers th https://www.frontiersin.org/files/Articles/1675480/fimmu-16-1675480-HTML/image_m/fimmu-16-1675480-g002.jpg Figure 2. Functional mechanisms and clinical significance of lactylation in tumors. Lactylation serv https://www.frontiersin.org/files/Articles/1675480/fimmu-16-1675480-HTML/image_m/fimmu-16-1675480-g003.jpg Figure 3. Cross-regulation between lactylation and the tumor microenvironment. Tumor metabolic repro https://www.frontiersin.org/files/Articles/1675480/fimmu-16-1675480-HTML/image_m/fimmu-16-1675480-g004.jpg Figure 4. Lactylation drives tumor resistance to chemotherapy, targeted therapy, and immunotherapy. https://www.frontiersin.org/files/Articles/1675480/fimmu-16-1675480-HTML/image_m/fimmu-16-1675480-g005.jpg Figure 5. Therapeutic strategies targeting lactylation focus on metabolic intervention and epigeneti https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1635515/full https://www.frontiersin.org/files/Articles/1635515/fonc-15-1635515-HTML-r4/image_m/fonc-15-1635515-g001.jpg Figure 1. (A, B) demonstrate ultrasonographic findings of a solid hypoechoic nodule in the right axi https://www.frontiersin.org/files/Articles/1635515/fonc-15-1635515-HTML-r4/image_m/fonc-15-1635515-g002.jpg Figure 2. Histopathological and Immunohistochemical of Male Breast Carcinoma (A) The tumor is situat https://www.frontiersin.org/files/Articles/1635515/fonc-15-1635515-HTML-r4/image_m/fonc-15-1635515-t001.jpg Table 1. Common disease types and differential diagnosis points of axillary masses. https://www.frontiersin.org/files/Articles/1635515/fonc-15-1635515-HTML-r4/image_m/fonc-15-1635515-t002.jpg Table 2. Histopathological features of ductal carcinoma in situ (DCIS) by nuclear grade. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1721879/full https://www.frontiersin.org/files/Articles/1721879/fphar-16-1721879-HTML/image_m/fphar-16-1721879-g001.jpg Figure 1. Schematic representation of the biogenesis and main components of PELNs. Route ① shows the https://www.frontiersin.org/files/Articles/1721879/fphar-16-1721879-HTML/image_m/fphar-16-1721879-t001.jpg Table 1. Literature examples of PELNs in anti-UV-induced photoaging. https://www.frontiersin.org/files/Articles/1721879/fphar-16-1721879-HTML/image_m/fphar-16-1721879-g002.jpg Figure 2. Schematic representation of the mechanism of ultraviolet-induced skin photoaging and the p https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1664329/full https://www.frontiersin.org/files/Articles/1664329/fmed-13-1664329-HTML/image_m/fmed-13-1664329-t001.jpg Table 1. Common method bias test. https://www.frontiersin.org/files/Articles/1664329/fmed-13-1664329-HTML/image_m/fmed-13-1664329-t002.jpg Table 2. Demographic characteristics of early-stage nursing interns based on different feedback-seek https://www.frontiersin.org/files/Articles/1664329/fmed-13-1664329-HTML/image_m/fmed-13-1664329-t003.jpg Table 3. Feedback-seeking behavior scale score (n = 1,308). https://www.frontiersin.org/files/Articles/1664329/fmed-13-1664329-HTML/image_m/fmed-13-1664329-t004.jpg Table 4. Model fit indices for LPA concerning feedback-seeking behaviors (n = 1,308). https://www.frontiersin.org/files/Articles/1664329/fmed-13-1664329-HTML/image_m/fmed-13-1664329-t005.jpg Table 5. Average membership probabilities of each latent category. https://www.frontiersin.org/files/Articles/1664329/fmed-13-1664329-HTML/image_m/fmed-13-1664329-g001.jpg Figure 1. Mean value characteristics for the three-profile solution (n = 1,308). https://www.frontiersin.org/files/Articles/1664329/fmed-13-1664329-HTML/image_m/fmed-13-1664329-t006.jpg Table 6. Logistic regression results of different feedback-seeking behavior profiles (n = 1,308). https://www.frontiersin.org/journals/environmental-chemistry/articles/10.3389/fenvc.2026.1694851/full https://www.frontiersin.org/files/Articles/1694851/fenvc-07-1694851-HTML/image_m/fenvc-07-1694851-g001.jpg Figure 1. Sample location map. Callouts show samples collected (1) in the Las Vegas Valley, (2) from https://www.frontiersin.org/files/Articles/1694851/fenvc-07-1694851-HTML/image_m/fenvc-07-1694851-g002.jpg Figure 2. (A) Percent of targeted PFAS mass loading at Lake Mead Boulder Basin (LM-BB) attributed to https://www.frontiersin.org/files/Articles/1694851/fenvc-07-1694851-HTML/image_m/fenvc-07-1694851-g003.jpg Figure 3. Percent of targeted PFAS mass loading in the Las Vegas Wash at Rainbow Garden during dry w https://www.frontiersin.org/files/Articles/1694851/fenvc-07-1694851-HTML/image_m/fenvc-07-1694851-g004.jpg Figure 4. Fractional contribution by mass of individual PFAS detected via targeted LC-MS/MS to the t https://www.frontiersin.org/files/Articles/1694851/fenvc-07-1694851-HTML/image_m/fenvc-07-1694851-t001.jpg Table 1. Relative abundance of PFAS compounds tentatively identified via non-targeted analysis which https://www.frontiersin.org/files/Articles/1694851/fenvc-07-1694851-HTML/image_m/fenvc-07-1694851-g005.jpg Figure 5. Percent of total PFAS (targeted + TOP) mass flow in WWTP 3 influent that (1) can be attrib https://www.frontiersin.org/files/Articles/1694851/fenvc-07-1694851-HTML/image_m/fenvc-07-1694851-g006.jpg Figure 6. Fractional contribution by moles of individual targeted PFAS and other PFAS precursors mea https://www.frontiersin.org/files/Articles/1694851/fenvc-07-1694851-HTML/image_m/fenvc-07-1694851-t002.jpg Table 2. PFAS contributions to domestic wastewater from selected personal care products and bodily e https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1764394/full https://www.frontiersin.org/files/Articles/1764394/fenvs-14-1764394-HTML/image_m/fenvs-14-1764394-g001.jpg Figure 1. Boolean search strategy used to identify relevant studies in the Scopus database. https://www.frontiersin.org/files/Articles/1764394/fenvs-14-1764394-HTML/image_m/fenvs-14-1764394-t001.jpg Table 1. Inclusion and exclusion criteria. https://www.frontiersin.org/files/Articles/1764394/fenvs-14-1764394-HTML/image_m/fenvs-14-1764394-g002.jpg Figure 2. PRISMA 2020 flow diagram of the study selection. https://www.frontiersin.org/files/Articles/1764394/fenvs-14-1764394-HTML/image_m/fenvs-14-1764394-t002.jpg Table 2. PRISMA Summary of Included Studies. https://www.frontiersin.org/files/Articles/1764394/fenvs-14-1764394-HTML/image_m/fenvs-14-1764394-g003.jpg Figure 3. Conceptual framework of heavy-metal transport and leaching in agricultural soil–groundwate https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2026.1739828/full https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-g001.jpg Figure 1. Study site. https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-t001.jpg Table 1. Tree-crop composition of agroforestry systems. https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-t002.jpg Table 2. Bulk density variations across different land use types and soil depths. https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-t003.jpg Table 3. Soil organic carbon and carbon stock variations across different land use types and soil de https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-t004.jpg Table 4. Total nitrogen and nitrogen stock variations across different land use types and soil depth https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-t005.jpg Table 5. Soil microbial biomass carbon and nitrogen and C: N ratio variations across different land https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-g002.jpg Figure 2. Principle component analysis bi-plot. https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-t006.jpg Table 6. Statistics of PCA. https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-g003.jpg Figure 3. (a) Relation between soil bulk density (Mg m-3) and organic carbon (%) at two soil depths https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-g004.jpg Figure 4. (a) Relation between soil OC (%) and Total N (%) at two soil depths (0.0-0.15m & 0.15-0.30 https://www.frontiersin.org/files/Articles/1739828/fagro-08-1739828-HTML/image_m/fagro-08-1739828-g005.jpg Figure 5. (a) Relation between SMBC (μg g-1) and organic carbon (%) at two soil depths (0.0-0.15m & https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1773679/full https://www.frontiersin.org/files/Articles/1773679/fneur-17-1773679-HTML-r1/image_m/fneur-17-1773679-t001.jpg Table 1. Demographic and injury characteristics of participants with chronic spinal cord injury (SCI https://www.frontiersin.org/files/Articles/1773679/fneur-17-1773679-HTML-r1/image_m/fneur-17-1773679-g001.jpg Figure 1. Recruitment of motoneurons in AIS grade A-B injury before and after tsDCS. Transspinal evo https://www.frontiersin.org/files/Articles/1773679/fneur-17-1773679-HTML-r1/image_m/fneur-17-1773679-g002.jpg Figure 2. Recruitment of motoneurons in AIS grade D injuries before and after tsDCS. Transspinal evo https://www.frontiersin.org/files/Articles/1773679/fneur-17-1773679-HTML-r1/image_m/fneur-17-1773679-g003.jpg Figure 3. Recruitment of motoneurons in healthy subjects before and after tsDCS. Transspinal evoked https://www.frontiersin.org/files/Articles/1773679/fneur-17-1773679-HTML-r1/image_m/fneur-17-1773679-g004.jpg Figure 4. Postactivation depression before and after tsDCS in people with SCI. Transspinal evoked po https://www.frontiersin.org/files/Articles/1773679/fneur-17-1773679-HTML-r1/image_m/fneur-17-1773679-g005.jpg Figure 5. Postactivation depression before and after tsDCS in healthy subjects. Transspinal evoked p https://www.frontiersin.org/files/Articles/1773679/fneur-17-1773679-HTML-r1/image_m/fneur-17-1773679-g006.jpg Figure 6. Homosynaptic depression before and after tsDCS in people with SCI. Transspinal evoked pote https://www.frontiersin.org/files/Articles/1773679/fneur-17-1773679-HTML-r1/image_m/fneur-17-1773679-g007.jpg Figure 7. Homosynaptic depression before and after tsDCS in healthy subjects. Transspinal evoked pot https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1788232/full https://www.frontiersin.org/files/Articles/1788232/fpubh-14-1788232-HTML/image_m/fpubh-14-1788232-g001.jpg Figure 1. Annual and cumulative evolution of scientific publications on psychosocial risks in Latin https://www.frontiersin.org/files/Articles/1788232/fpubh-14-1788232-HTML/image_m/fpubh-14-1788232-g002.jpg Figure 2. Annual evolution of citations received by scientific production on psychosocial risks in L https://www.frontiersin.org/files/Articles/1788232/fpubh-14-1788232-HTML/image_m/fpubh-14-1788232-t001.jpg Table 1. Most productive and influential journals in the scientific production on psychosocial risks https://www.frontiersin.org/files/Articles/1788232/fpubh-14-1788232-HTML/image_m/fpubh-14-1788232-g003.jpg Figure 3. International co-authorship network in scientific production on psychosocial risks in Lati https://www.frontiersin.org/files/Articles/1788232/fpubh-14-1788232-HTML/image_m/fpubh-14-1788232-g004.jpg Figure 4. Thematic keyword co-occurrence network of psychosocial risks research in Latin America. https://www.frontiersin.org/files/Articles/1788232/fpubh-14-1788232-HTML/image_m/fpubh-14-1788232-t002.jpg Table 2. Influential studies on psychosocial risks in Latin America. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1766793/full https://www.frontiersin.org/files/Articles/1766793/fimmu-17-1766793-HTML/image_m/fimmu-17-1766793-t001.jpg Table 1. Baseline clinicopathologic characteristics by preoperative treatment regimen. https://www.frontiersin.org/files/Articles/1766793/fimmu-17-1766793-HTML/image_m/fimmu-17-1766793-t002.jpg Table 2. Surgical procedures, hospital stay, and postoperative complications by regimen. https://www.frontiersin.org/files/Articles/1766793/fimmu-17-1766793-HTML/image_m/fimmu-17-1766793-t003.jpg Table 3. Multivariable logistic regression for factors associated with pathologic response. https://www.frontiersin.org/files/Articles/1766793/fimmu-17-1766793-HTML/image_m/fimmu-17-1766793-t004.jpg Table 4. Pathologic outcomes and downstaging by preoperative treatment regimen. https://www.frontiersin.org/files/Articles/1766793/fimmu-17-1766793-HTML/image_m/fimmu-17-1766793-t005.jpg Table 5. Extended multivariable logistic regression model including biomarker variables for patholog https://www.frontiersin.org/files/Articles/1766793/fimmu-17-1766793-HTML/image_m/fimmu-17-1766793-t006.jpg Table 6. Pathologic response by regimen stratified by PD-L1 CPS. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1704672/full https://www.frontiersin.org/files/Articles/1704672/fpsyt-16-1704672-HTML/image_m/fpsyt-16-1704672-t001.jpg Table 1. Baseline characteristics and differences in MCI(n=329). https://www.frontiersin.org/files/Articles/1704672/fpsyt-16-1704672-HTML/image_m/fpsyt-16-1704672-t002.jpg Table 2. Current status of sleep quality in patients with CHF(n=329). https://www.frontiersin.org/files/Articles/1704672/fpsyt-16-1704672-HTML/image_m/fpsyt-16-1704672-t003.jpg Table 3. Correlation between MCI and sleep quality in patients with CHF(n=329). https://www.frontiersin.org/files/Articles/1704672/fpsyt-16-1704672-HTML/image_m/fpsyt-16-1704672-t004.jpg Table 4. Hierarchical regression analysis regarding predictors of MCI in patients with CHF(n=329). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1609435/full https://www.frontiersin.org/files/Articles/1609435/fpsyg-17-1609435-HTML/image_m/fpsyg-17-1609435-g001.jpg Figure 1. The theoretical model. https://www.frontiersin.org/files/Articles/1609435/fpsyg-17-1609435-HTML/image_m/fpsyg-17-1609435-t001.jpg Table 1. Comparison between the groups before and after the matching procedure. https://www.frontiersin.org/files/Articles/1609435/fpsyg-17-1609435-HTML/image_m/fpsyg-17-1609435-t002.jpg Table 2. Internal reliabilities, means, standard deviations, and zero-order intercorrelations of var https://www.frontiersin.org/files/Articles/1609435/fpsyg-17-1609435-HTML/image_m/fpsyg-17-1609435-t003.jpg Table 3. Results of independent sample tests between the secular and the ultra-Orthodox samples. https://www.frontiersin.org/files/Articles/1609435/fpsyg-17-1609435-HTML/image_m/fpsyg-17-1609435-t004.jpg Table 4. Path analysis for the full research model. https://www.frontiersin.org/files/Articles/1609435/fpsyg-17-1609435-HTML/image_m/fpsyg-17-1609435-t005.jpg Table 5. Path analysis for the final research model. https://www.frontiersin.org/files/Articles/1609435/fpsyg-17-1609435-HTML/image_m/fpsyg-17-1609435-g002.jpg Figure 2. The final model. Numbers above lines indicate standardized path coefficients and significa https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1774104/full https://www.frontiersin.org/files/Articles/1774104/fcvm-13-1774104-HTML-r1/image_m/fcvm-13-1774104-g001.jpg Figure 1. Patient flow chart. https://www.frontiersin.org/files/Articles/1774104/fcvm-13-1774104-HTML-r1/image_m/fcvm-13-1774104-g002.jpg Figure 2. Frequencies of GNRI classes. https://www.frontiersin.org/files/Articles/1774104/fcvm-13-1774104-HTML-r1/image_m/fcvm-13-1774104-t001.jpg Table 1. Demographics. https://www.frontiersin.org/files/Articles/1774104/fcvm-13-1774104-HTML-r1/image_m/fcvm-13-1774104-t002.jpg Table 2. Cox regression analysis on mortality for preoperative demographic parameters. https://www.frontiersin.org/files/Articles/1774104/fcvm-13-1774104-HTML-r1/image_m/fcvm-13-1774104-g003.jpg Figure 3. Survival. https://www.frontiersin.org/files/Articles/1774104/fcvm-13-1774104-HTML-r1/image_m/fcvm-13-1774104-g004.jpg Figure 4. Prediction of all-cause 30-day mortality: GNRI vs EuroSCORE II and GNRI vs STS score. https://www.frontiersin.org/files/Articles/1774104/fcvm-13-1774104-HTML-r1/image_m/fcvm-13-1774104-g005.jpg Figure 5. Calibration plots for GNRI, EuroSCORE II, and STS score. https://www.frontiersin.org/files/Articles/1774104/fcvm-13-1774104-HTML-r1/image_m/fcvm-13-1774104-g006.jpg Figure 6. Data requirements for individual score calculation. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1646128/full https://www.frontiersin.org/files/Articles/1646128/fpls-16-1646128-HTML/image_m/fpls-16-1646128-g001.jpg Figure 1. Distribution of diploid performance, haploid performance, and haploid frailty %. PH, Plant https://www.frontiersin.org/files/Articles/1646128/fpls-16-1646128-HTML/image_m/fpls-16-1646128-t001.jpg Table 1. Statistical summary of agronomic traits by ploidy level, haploid frailty %, and their herit https://www.frontiersin.org/files/Articles/1646128/fpls-16-1646128-HTML/image_m/fpls-16-1646128-g002.jpg Figure 2. Examples of six perfect isogenic haploid and diploid line pairs. Haploids are, in most cas https://www.frontiersin.org/files/Articles/1646128/fpls-16-1646128-HTML/image_m/fpls-16-1646128-t002.jpg Table 2. Top 5 isogenic diploid-haploid line pairs with highest HF and top 5 isogenic diploid-haploi https://www.frontiersin.org/files/Articles/1646128/fpls-16-1646128-HTML/image_m/fpls-16-1646128-t003.jpg Table 3. Correlation between the performance of haploids, diploids, and haploid frailty %. https://www.frontiersin.org/files/Articles/1646128/fpls-16-1646128-HTML/image_m/fpls-16-1646128-g003.jpg Figure 3. Correlation matrix of HFF, HMF, and the eight agronomic traits across haploids, diploids, https://www.frontiersin.org/files/Articles/1646128/fpls-16-1646128-HTML/image_m/fpls-16-1646128-g004.jpg Figure 4. Genomic predictive abilities of diploid performance for haploid performance, and haploid f https://www.frontiersin.org/files/Articles/1646128/fpls-16-1646128-HTML/image_m/fpls-16-1646128-t004.jpg Table 4. Significant SNPs detected for diploid performance, haploid performance, haploid frailty per https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1718372/full https://www.frontiersin.org/files/Articles/1718372/fspor-08-1718372-HTML-r2/image_m/fspor-08-1718372-t001.jpg Table 1. Participant pseudonyms and demographics. https://www.frontiersin.org/files/Articles/1718372/fspor-08-1718372-HTML-r2/image_m/fspor-08-1718372-t002.jpg Table 2. Perceived protective and risk factors influencing women athletes' resilience across the soc https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1780966/full https://www.frontiersin.org/files/Articles/1780966/fcvm-13-1780966-HTML/image_m/fcvm-13-1780966-t001.jpg Table 1. Search strategy in pubMed. https://www.frontiersin.org/files/Articles/1780966/fcvm-13-1780966-HTML/image_m/fcvm-13-1780966-g001.jpg Figure 1. PRISMA flowchart showing the study selection process. https://www.frontiersin.org/files/Articles/1780966/fcvm-13-1780966-HTML/image_m/fcvm-13-1780966-t002.jpg Table 2. Basic characteristics of the included literature (n = 16). https://www.frontiersin.org/files/Articles/1780966/fcvm-13-1780966-HTML/image_m/fcvm-13-1780966-t003.jpg Table 3. Methodological quality evaluation of the included literature (n = 16). https://www.frontiersin.org/files/Articles/1780966/fcvm-13-1780966-HTML/image_m/fcvm-13-1780966-t004.jpg Table 4. Meta-synthesis results. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1775690/full https://www.frontiersin.org/files/Articles/1775690/fmars-13-1775690-HTML-r2/image_m/fmars-13-1775690-t001.jpg Table 1. Comparative analysis of PSMA implementation mechanisms in selected countries. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1679193/full https://www.frontiersin.org/files/Articles/1679193/fmars-12-1679193-HTML/image_m/fmars-12-1679193-t001.jpg Table 1. Relevant legislation of China’s fishing moratorium system. https://www.frontiersin.org/files/Articles/1679193/fmars-12-1679193-HTML/image_m/fmars-12-1679193-t002.jpg Table 2. Marine summer fishing moratorium zones and periods in China. https://www.frontiersin.org/files/Articles/1679193/fmars-12-1679193-HTML/image_m/fmars-12-1679193-t003.jpg Table 3. China’s high seas fishing moratorium zones and periods. https://www.frontiersin.org/files/Articles/1679193/fmars-12-1679193-HTML/image_m/fmars-12-1679193-t004.jpg Table 4. Key recommendations in this section. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1655535/full https://www.frontiersin.org/files/Articles/1655535/fmars-12-1655535-HTML/image_m/fmars-12-1655535-g001.jpg Figure 1. Framework diagram of the article’s logical structure. https://www.frontiersin.org/journals/clinical-diabetes-and-healthcare/articles/10.3389/fcdhc.2026.1791782/full https://www.frontiersin.org/files/Articles/1791782/fcdhc-07-1791782-HTML/image_m/fcdhc-07-1791782-t001.jpg Table 1. PRISMA-like overview of literature identification and selection (Structured narrative revie https://www.frontiersin.org/files/Articles/1791782/fcdhc-07-1791782-HTML/image_m/fcdhc-07-1791782-g001.jpg Figure 1. Schematic representation of Toll-like receptor 2 (TLR2) as central immunometabolic hub lin https://www.frontiersin.org/files/Articles/1791782/fcdhc-07-1791782-HTML/image_m/fcdhc-07-1791782-g002.jpg Figure 2. TLR2 mediates neuroinflammatory progression in diabetes-associated neurodegeneration by li https://www.frontiersin.org/files/Articles/1791782/fcdhc-07-1791782-HTML/image_m/fcdhc-07-1791782-g003.jpg Figure 3. TLR2-driven neuroinflammatory research gap: peripheral DAMPs/PAMPs activate monocyte TLR2 https://www.frontiersin.org/files/Articles/1791782/fcdhc-07-1791782-HTML/image_m/fcdhc-07-1791782-t002.jpg Table 2. Anti-diabetic medications with neuroprotective potential. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1716930/full https://www.frontiersin.org/files/Articles/1716930/fmicb-17-1716930-HTML/image_m/fmicb-17-1716930-g006.jpg Graphical Abstract. DeepSolNet is a multi-module deep learning framework for predicting protein solu https://www.frontiersin.org/files/Articles/1716930/fmicb-17-1716930-HTML/image_m/fmicb-17-1716930-g001.jpg Figure 1. DeepSolNet architectures. Illustration of the DeepSolNet model used for predicting protein https://www.frontiersin.org/files/Articles/1716930/fmicb-17-1716930-HTML/image_m/fmicb-17-1716930-g002.jpg Figure 2. Cross-validation of DeepSolNet variants. (A–C) Show the best-performing fold from k-fold c https://www.frontiersin.org/files/Articles/1716930/fmicb-17-1716930-HTML/image_m/fmicb-17-1716930-g003.jpg Figure 3. Validation of DeepSolNet variants using an independent dataset. Performance evaluation of https://www.frontiersin.org/files/Articles/1716930/fmicb-17-1716930-HTML/image_m/fmicb-17-1716930-g004.jpg Figure 4. Model comparison on independent dataset. Performance comparison of various machine learnin https://www.frontiersin.org/files/Articles/1716930/fmicb-17-1716930-HTML/image_m/fmicb-17-1716930-g005.jpg Figure 5. Validating performance of DeepSolNet using curated dataset. (A) Comparison of evaluation m https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1768302/full https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-g000.jpg Graphical Abstract. Diagnostic contribution of NHR, SIRI, and ALT to MASLD identification. This sche https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-g001.jpg Figure 1. Flowchart of participant selection. The diagram illustrates the selection process for pati https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-t001.jpg Table 1. Comparison of general characteristics between the two groups before and after propensity sc https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-g002.jpg Figure 2. Love plot of standardized mean differences (SMDs) for covariates after propensity score ma https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-t002.jpg Table 2. Comparison of inflammation, lipid metabolism, and liver function parameters between the two https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-t003.jpg Table 3. Results of variance inflation factor testing. https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-t004.jpg Table 4. Univariate logistic regression analysis of factors associated with MASLD. https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-g003.jpg Figure 3. Multivariable associations between immune-related indices and MASLD. This forest plot disp https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-t005.jpg Table 5. Diagnostic performance of NHR, SIRI, ALT, and their combination for MASLD. https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-g004.jpg Figure 4. Diagnostic Performance and Calibration of the Combined Model for MASLD. (A) Receiver Opera https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-t006.jpg Table 6. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) of t https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-g005.jpg Figure 5. Internal validation of the combined ALT–NHR–SIRI diagnostic model using 2,000 bootstrap re https://www.frontiersin.org/files/Articles/1768302/fimmu-17-1768302-HTML-r1/image_m/fimmu-17-1768302-g006.jpg Figure 6. The immunometabolic mechanisms of NHR and SIRI in the development of MASLD. In Metabolic D https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1591130/full https://www.frontiersin.org/files/Articles/1591130/fpls-16-1591130-HTML-r2/image_m/fpls-16-1591130-g001.jpg Figure 1. Chromosomal distribution, gene structure and subcellular localization of PPR genes in toba https://www.frontiersin.org/files/Articles/1591130/fpls-16-1591130-HTML-r2/image_m/fpls-16-1591130-g002.jpg Figure 2. Phylogenetic relationships and conserved motif analysis among the NtPPR family genes. (a) https://www.frontiersin.org/files/Articles/1591130/fpls-16-1591130-HTML-r2/image_m/fpls-16-1591130-t001.jpg Table 1. Hysicochemical property analysis of NtRFLs. https://www.frontiersin.org/files/Articles/1591130/fpls-16-1591130-HTML-r2/image_m/fpls-16-1591130-g003.jpg Figure 3. Evolutionary relationship of RFL genes in tobacco. (a) Distribution of collinear RFL gene https://www.frontiersin.org/files/Articles/1591130/fpls-16-1591130-HTML-r2/image_m/fpls-16-1591130-t002.jpg Table 2. Collinear gene pairs in the tobacco, tomato and potato. https://www.frontiersin.org/files/Articles/1591130/fpls-16-1591130-HTML-r2/image_m/fpls-16-1591130-g004.jpg Figure 4. Prediction results of cis-acting elements of NtRFL genes. The number represents the elemen https://www.frontiersin.org/files/Articles/1591130/fpls-16-1591130-HTML-r2/image_m/fpls-16-1591130-g005.jpg Figure 5. Morphological and cytological observation of anther development in three different tobacco https://www.frontiersin.org/files/Articles/1591130/fpls-16-1591130-HTML-r2/image_m/fpls-16-1591130-g006.jpg Figure 6. RNA-seq analysis of NtRFL genes in tobacco lines with different fertility. The up- and dow https://www.frontiersin.org/files/Articles/1591130/fpls-16-1591130-HTML-r2/image_m/fpls-16-1591130-g007.jpg Figure 7. qPCR analysis and subcellular localization prediction of NtRFLs. (a-l) The expression of N https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1702601/full https://www.frontiersin.org/files/Articles/1702601/fonc-16-1702601-HTML/image_m/fonc-16-1702601-g001.jpg Figure 1. The oncolytic viruses expressing decorin (OAV-Decorin) inhibit the proliferation and migra https://www.frontiersin.org/files/Articles/1702601/fonc-16-1702601-HTML/image_m/fonc-16-1702601-g002.jpg Figure 2. Gemcitabine inhibits pancreatic cancer cells proliferation in a density-dependent manner. https://www.frontiersin.org/files/Articles/1702601/fonc-16-1702601-HTML/image_m/fonc-16-1702601-g003.jpg Figure 3. OAV-Decorin enhances antitumor activity in combination chemotherapy in vitro, and down-reg https://www.frontiersin.org/files/Articles/1702601/fonc-16-1702601-HTML/image_m/fonc-16-1702601-g004.jpg Figure 4. Combination of OAV-Decorin and chemotherapy improved the antitumor effect in vivo. NTG mic https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1655272/full https://www.frontiersin.org/files/Articles/1655272/fnut-12-1655272-HTML/image_m/fnut-12-1655272-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. The diagram outlines the sequential https://www.frontiersin.org/files/Articles/1655272/fnut-12-1655272-HTML/image_m/fnut-12-1655272-g002.jpg Figure 2. Propensity score density distributions before and after matching. The left panel shows dis https://www.frontiersin.org/files/Articles/1655272/fnut-12-1655272-HTML/image_m/fnut-12-1655272-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1655272/fnut-12-1655272-HTML/image_m/fnut-12-1655272-t002.jpg Table 2. Association between zinc deficiency and 6-month outcomes. https://www.frontiersin.org/files/Articles/1655272/fnut-12-1655272-HTML/image_m/fnut-12-1655272-t003.jpg Table 3. Association between zinc deficiency and 12-month outcomes. https://www.frontiersin.org/files/Articles/1655272/fnut-12-1655272-HTML/image_m/fnut-12-1655272-t004.jpg Table 4. Association between severe zinc deficiency and outcomes. https://www.frontiersin.org/files/Articles/1655272/fnut-12-1655272-HTML/image_m/fnut-12-1655272-t005.jpg Table 5. Association between high zinc levels and outcomes. https://www.frontiersin.org/files/Articles/1655272/fnut-12-1655272-HTML/image_m/fnut-12-1655272-t006.jpg Table 6. Subgroup analyses of association between zinc deficiency and risk of mortality at 6-m follo https://www.frontiersin.org/files/Articles/1655272/fnut-12-1655272-HTML/image_m/fnut-12-1655272-t007.jpg Table 7. Risk factors for mortality at 6-month follow up. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1718261/full https://www.frontiersin.org/files/Articles/1718261/fnut-12-1718261-HTML/image_m/fnut-12-1718261-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. The flowchart illustrates the exclu https://www.frontiersin.org/files/Articles/1718261/fnut-12-1718261-HTML/image_m/fnut-12-1718261-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1718261/fnut-12-1718261-HTML/image_m/fnut-12-1718261-t002.jpg Table 2. Association between iron deficiency anemia and hearing loss at 5-year follow up. https://www.frontiersin.org/files/Articles/1718261/fnut-12-1718261-HTML/image_m/fnut-12-1718261-t003.jpg Table 3. Association between iron deficiency anemia and hearing loss at 1- and 3-year follow-up. https://www.frontiersin.org/files/Articles/1718261/fnut-12-1718261-HTML/image_m/fnut-12-1718261-g002.jpg Figure 2. Kaplan–Meier curves for cumulative incidence of hearing loss among matched female patients https://www.frontiersin.org/files/Articles/1718261/fnut-12-1718261-HTML/image_m/fnut-12-1718261-t004.jpg Table 4. Dose–response of IDA and hearing loss in female with moderate to severe anemia (Hb < 10) (n https://www.frontiersin.org/files/Articles/1718261/fnut-12-1718261-HTML/image_m/fnut-12-1718261-t005.jpg Table 5. Subgroup analysis based on age. https://www.frontiersin.org/files/Articles/1718261/fnut-12-1718261-HTML/image_m/fnut-12-1718261-t006.jpg Table 6. Association between iron deficiency anemia and hearing loss in male population (n = 11,993 https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1727992/full https://www.frontiersin.org/files/Articles/1727992/fnut-13-1727992-HTML/image_m/fnut-13-1727992-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. IDA, Iron deficiency anemia HCOs, H https://www.frontiersin.org/files/Articles/1727992/fnut-13-1727992-HTML/image_m/fnut-13-1727992-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1727992/fnut-13-1727992-HTML/image_m/fnut-13-1727992-g002.jpg Figure 2. Propensity score density distributions before and after matching. The left panel displays https://www.frontiersin.org/files/Articles/1727992/fnut-13-1727992-HTML/image_m/fnut-13-1727992-t002.jpg Table 2. Five-year and extended follow-up outcomes in matched cohorts with and without iron deficien https://www.frontiersin.org/files/Articles/1727992/fnut-13-1727992-HTML/image_m/fnut-13-1727992-t003.jpg Table 3. Sex-stratified analysis of tuberculosis and related outcomes in patients with iron deficien https://www.frontiersin.org/files/Articles/1727992/fnut-13-1727992-HTML/image_m/fnut-13-1727992-t004.jpg Table 4. Age-stratified analysis of tuberculosis and related outcomes in patients with iron deficien https://www.frontiersin.org/files/Articles/1727992/fnut-13-1727992-HTML/image_m/fnut-13-1727992-t005.jpg Table 5. Multivariable analysis of risk factors for pulmonary and extrapulmonary tuberculosis. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1755607/full https://www.frontiersin.org/files/Articles/1755607/fnut-13-1755607-HTML/image_m/fnut-13-1755607-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. HCOs, healthcare organizations; OSA https://www.frontiersin.org/files/Articles/1755607/fnut-13-1755607-HTML/image_m/fnut-13-1755607-t001.jpg Table 1. Baseline characteristics of patients with obstructive sleep apnea before and after propensi https://www.frontiersin.org/files/Articles/1755607/fnut-13-1755607-HTML/image_m/fnut-13-1755607-t002.jpg Table 2. Association between vitamin D deficiency and cardiopulmonary complications at 5-year follow https://www.frontiersin.org/files/Articles/1755607/fnut-13-1755607-HTML/image_m/fnut-13-1755607-t003.jpg Table 3. Association between vitamin D deficiency and cardiopulmonary complications at 3-year follow https://www.frontiersin.org/files/Articles/1755607/fnut-13-1755607-HTML/image_m/fnut-13-1755607-t004.jpg Table 4. Association between vitamin D insufficiency and cardiopulmonary complications at 5-year fol https://www.frontiersin.org/files/Articles/1755607/fnut-13-1755607-HTML/image_m/fnut-13-1755607-g002.jpg Figure 2. Forest plot of subgroup analyses examining the association between vitamin D deficiency an https://www.frontiersin.org/journals/gastroenterology/articles/10.3389/fgstr.2025.1699508/full https://www.frontiersin.org/files/Articles/1699508/fgstr-04-1699508-HTML/image_m/fgstr-04-1699508-t001.jpg Table 1. Adult cardiometabolic criteria for diagnosing MASLD (6). https://www.frontiersin.org/files/Articles/1699508/fgstr-04-1699508-HTML/image_m/fgstr-04-1699508-g001.jpg Figure 1. Comparison of MASLD characteristics in Asia and West populations. https://www.frontiersin.org/files/Articles/1699508/fgstr-04-1699508-HTML/image_m/fgstr-04-1699508-t002.jpg Table 2. Key differences in between lean and obese MASLD characteristics. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1725720/full https://www.frontiersin.org/files/Articles/1725720/fmed-13-1725720-HTML/image_m/fmed-13-1725720-g001.jpg Figure 1. WHO five-step capacity building model (3). Sequential steps include development of the Glo https://www.frontiersin.org/files/Articles/1725720/fmed-13-1725720-HTML/image_m/fmed-13-1725720-g002.jpg Figure 2. WHO member states benchmarked using the WHO global benchmarking tool (6). Map and tabulati https://www.frontiersin.org/files/Articles/1725720/fmed-13-1725720-HTML/image_m/fmed-13-1725720-g003.jpg Figure 3. Progression of WHO member states along the regulatory maturity continuum (2018–2024) (6). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1698010/full https://www.frontiersin.org/files/Articles/1698010/fpsyg-16-1698010-HTML/image_m/fpsyg-16-1698010-t001.jpg Table 1. Descriptive statistics of psychological traits by competitive level and gender. https://www.frontiersin.org/files/Articles/1698010/fpsyg-16-1698010-HTML/image_m/fpsyg-16-1698010-t002.jpg Table 2. Model fit indices and proportional odds assumption tests for gender-stratified ordered logi https://www.frontiersin.org/files/Articles/1698010/fpsyg-16-1698010-HTML/image_m/fpsyg-16-1698010-t003.jpg Table 3. Ordered logistic regression results for a female badminton athlete. https://www.frontiersin.org/files/Articles/1698010/fpsyg-16-1698010-HTML/image_m/fpsyg-16-1698010-t004.jpg Table 4. Ordered logistic regression results for male badminton athletes. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1747027/full https://www.frontiersin.org/files/Articles/1747027/fpsyt-16-1747027-HTML/image_m/fpsyt-16-1747027-g001.jpg Figure 1. (a) Conceptual schematic framework for Centre for Advanced Research on Addictive Behaviour https://www.frontiersin.org/files/Articles/1747027/fpsyt-16-1747027-HTML/image_m/fpsyt-16-1747027-g002.jpg Figure 2. Conceptual schematic of various phases of Centre for Advanced Research on Addictive Behavi https://www.frontiersin.org/files/Articles/1747027/fpsyt-16-1747027-HTML/image_m/fpsyt-16-1747027-g003.jpg Figure 3. Conceptual; schematic of various phases and timeline for in the initial set of studies pro https://www.frontiersin.org/files/Articles/1747027/fpsyt-16-1747027-HTML/image_m/fpsyt-16-1747027-t001.jpg Table 1. Overview of CAR-AB studies. https://www.frontiersin.org/files/Articles/1747027/fpsyt-16-1747027-HTML/image_m/fpsyt-16-1747027-g004.jpg Figure 4. Conceptual schematic of various resources targeted at different stakeholders that shall be https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1637509/full https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g001.jpg Figure 1. Study area. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t001.jpg Table 1. Specific obstacle factors. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g002.jpg Figure 2. Research framework. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t002.jpg Table 2. Obstacle factor direct influence matrix. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t003.jpg Table 3. Index system for ecological governance. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t004.jpg Table 4. Indicator system of explanatory variables. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t005.jpg Table 5. Main parameters and meanings. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g003.jpg Figure 3. Scatter plot of centrality and causality degree values. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t006.jpg Table 6. Stratification of obstacle factors. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g004.jpg Figure 4. The annual average EGI of YRB. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g005.jpg Figure 5. The spatiotemporal evolution of EGI ((a), (b), (c), (d) correspond to 2014, 2017, 2020, 20 https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t007.jpg Table 7. Temporal stability test. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t008.jpg Table 8. Global Moran’s I. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g006.jpg Figure 6. Moran’s I Spatial Autocorrelation ((a), (b), (c), (d) correspond to 2014, 2017, 2020, 2023 https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g007.jpg Figure 7. Spatial linkage strength ((a), (b) correspond to 2014, 2023). https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t009.jpg Table 9. Results of spatial Lagrange multiplier test. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t010.jpg Table 10. Fitting results of the SLM. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g008.jpg Figure 8. Coefficients of Obstacle Factors and Effect Diagram (a,b). https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t011.jpg Table 11. The strategy and payoff matrix of the game players. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g009.jpg Figure 9. Simplified schematic of evolutionary game. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t012.jpg Table 12. Eigenvalues of strategy points. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t013.jpg Table 13. ESS conditions. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g010.jpg Figure 10. (1, 1, 1) Numerical simulation process ((a), (b) correspond to 2D, 3Dgraph). https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g011.jpg Figure 11. Dynamic rewards and punishments. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g012.jpg Figure 12. Multi - parameter sensitivity analysis. (a–e) show the sensitivity trends of different ke https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g013.jpg Figure 13. Stochastic evolutionary model. https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-g014.jpg Figure 14. Convergence time and terminal standard deviation. (a) Convergence time under different no https://www.frontiersin.org/files/Articles/1637509/fenvs-13-1637509-HTML/image_m/fenvs-13-1637509-t014.jpg Table 14. Comparison of Key Dimensions of Ecological governance. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1638650/full https://www.frontiersin.org/files/Articles/1638650/fcvm-12-1638650-HTML/image_m/fcvm-12-1638650-t001.jpg Table 1. Sequences of primers used in RT-PCR. https://www.frontiersin.org/files/Articles/1638650/fcvm-12-1638650-HTML/image_m/fcvm-12-1638650-g001.jpg Figure 1. Hypoxia stimulated collagen synthesis in CFs. (A) Western blots showing the protein levels https://www.frontiersin.org/files/Articles/1638650/fcvm-12-1638650-HTML/image_m/fcvm-12-1638650-g002.jpg Figure 2. Non-targeted metabolomics analysis of CF secretions under different oxygen conditions. CFs https://www.frontiersin.org/files/Articles/1638650/fcvm-12-1638650-HTML/image_m/fcvm-12-1638650-g003.jpg Figure 3. The top 10 most significantly altered metabolites in the secretions of hypoxia-stimulated https://www.frontiersin.org/files/Articles/1638650/fcvm-12-1638650-HTML/image_m/fcvm-12-1638650-g004.jpg Figure 4. L-glutamate levels were elevated in both the secretory products of CFs and the myocardial https://www.frontiersin.org/files/Articles/1638650/fcvm-12-1638650-HTML/image_m/fcvm-12-1638650-g005.jpg Figure 5. Activation of the glutamatergic transmitter system in hypoxia-induced CFs and myocardial t https://www.frontiersin.org/files/Articles/1638650/fcvm-12-1638650-HTML/image_m/fcvm-12-1638650-g006.jpg Figure 6. Exogenous supplementation of L-glutamate promoted collagen synthesis in CFs under normoxia https://www.frontiersin.org/files/Articles/1638650/fcvm-12-1638650-HTML/image_m/fcvm-12-1638650-g007.jpg Figure 7. L-glutamate activated the TGF-β/Smad signaling pathway in CFs under hypoxia. (A) Western b https://www.frontiersin.org/files/Articles/1638650/fcvm-12-1638650-HTML/image_m/fcvm-12-1638650-g008.jpg Figure 8. Schema illustrating the potential effects and underlying mechanisms of the glutamatergic t https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1687082/full https://www.frontiersin.org/files/Articles/1687082/fnut-12-1687082-HTML/image_m/fnut-12-1687082-t001.jpg Table 1. Demographic and clinical characteristics of the study participants. https://www.frontiersin.org/files/Articles/1687082/fnut-12-1687082-HTML/image_m/fnut-12-1687082-g001.jpg Figure 1. Comparisons of nutrient intakes and dietary n-6/n-3 PUFA ratio across the four groups. Box https://www.frontiersin.org/files/Articles/1687082/fnut-12-1687082-HTML/image_m/fnut-12-1687082-g002.jpg Figure 2. Comparison of diet quantity and quality between study groups. Panel (a) shows energy, macr https://www.frontiersin.org/files/Articles/1687082/fnut-12-1687082-HTML/image_m/fnut-12-1687082-g003.jpg Figure 3. Ridge regression analysis of nutrient intake among the study groups. Ridge regression was https://www.frontiersin.org/files/Articles/1687082/fnut-12-1687082-HTML/image_m/fnut-12-1687082-g004.jpg Figure 4. Correlations between nutrient intake and pulmonary function parameters. Pearson’s partial https://www.frontiersin.org/files/Articles/1687082/fnut-12-1687082-HTML/image_m/fnut-12-1687082-g005.jpg Figure 5. Correlations between nutrient intake and clinical outcomes. Pearson’s partial correlation https://www.frontiersin.org/files/Articles/1687082/fnut-12-1687082-HTML/image_m/fnut-12-1687082-g006.jpg Figure 6. Correlations between nutrient intake and plasma cytokine levels. Pearson’s partial correla https://www.frontiersin.org/files/Articles/1687082/fnut-12-1687082-HTML/image_m/fnut-12-1687082-g007.jpg Figure 7. Venn diagrams showing the overlap of significant nutrients identified in the correlation a https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1658228/full https://www.frontiersin.org/files/Articles/1658228/fnut-12-1658228-HTML-r1/image_m/fnut-12-1658228-g001.jpg Figure 1. (A) Global age-standardized Attention-Deficit/Hyperactivity Disorder (ADHD) prevalence (bl https://www.frontiersin.org/files/Articles/1658228/fnut-12-1658228-HTML-r1/image_m/fnut-12-1658228-g002.jpg Figure 2. Global association of macronutrient supplies and Attention-Deficit/Hyperactivity Disorder https://www.frontiersin.org/files/Articles/1658228/fnut-12-1658228-HTML-r1/image_m/fnut-12-1658228-g003.jpg Figure 3. Predicted effects of carbohydrate and protein, animal- and plant-based fat supplies on Att https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1659105/full https://www.frontiersin.org/files/Articles/1659105/fped-13-1659105-HTML/image_m/fped-13-1659105-g001.jpg Figure 1. Flow diagram of the studies selected for inclusion in the systematic review and meta-analy https://www.frontiersin.org/files/Articles/1659105/fped-13-1659105-HTML/image_m/fped-13-1659105-t001.jpg Table 1. Characteristics of the selected studies included in the systematic review and meta-analysis https://www.frontiersin.org/files/Articles/1659105/fped-13-1659105-HTML/image_m/fped-13-1659105-g002.jpg Figure 2. Forest plot of the association between hypertensive disorders in pregnancy and asthma in o https://www.frontiersin.org/files/Articles/1659105/fped-13-1659105-HTML/image_m/fped-13-1659105-g003.jpg Figure 3. Forest plot of the association between hypertensive disorders in pregnancy and asthma in o https://www.frontiersin.org/files/Articles/1659105/fped-13-1659105-HTML/image_m/fped-13-1659105-g004.jpg Figure 4. Forest plot of stratified analysis based on exposure type. (adjusted estimates*). HDP, hyp https://www.frontiersin.org/files/Articles/1659105/fped-13-1659105-HTML/image_m/fped-13-1659105-g005.jpg Figure 5. Forest plot of stratified analysis based on exposure type. (crude estimates). HDP, hyperte https://www.frontiersin.org/files/Articles/1659105/fped-13-1659105-HTML/image_m/fped-13-1659105-t002.jpg Table 2. Subgroup analysis: HDP and offspring asthma. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1706588/full https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g001.jpg Figure 1. Cell subpopulation identification and annotation. (A) UMAP plot showing the identification https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g002.jpg Figure 2. Pathway activity and cell communication analysis. (A) Heatmap showing the pathway activity https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g003.jpg Figure 3. Mendelian randomization analysis for GDM-associated genes. (A–O) Forest plots showing Mend https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g004.jpg Figure 4. Sensitivity analysis of causal relationships. (A–O) Sensitivity plots showing the robustne https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g005.jpg Figure 5. Gene trend validation and immune infiltration analysis. (A) Differential expression analys https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g006.jpg Figure 6. Immune infiltration and gene correlations with immune cells. (A) Distribution of immune in https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g007.jpg Figure 7. GSEA of key gene enrichment in signaling pathways. (A) Pathway enrichment analysis for CTS https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g008.jpg Figure 8. Transcription factor regulatory network and relationship between key genes and disease-ass https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g009.jpg Figure 9. Immune metabolism-related pathway activity in single cells, pseudotime analysis of cell di https://www.frontiersin.org/files/Articles/1706588/fmolb-13-1706588-HTML/image_m/fmolb-13-1706588-g010.jpg Figure 10. mRNA expression levels of key genes in human and mouse placentas. (A–C) represent the mRN https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1771616/full https://www.frontiersin.org/files/Articles/1771616/fimmu-17-1771616-HTML/image_m/fimmu-17-1771616-g001.jpg Figure 1. The flowchart of the overall study procedures. https://www.frontiersin.org/files/Articles/1771616/fimmu-17-1771616-HTML/image_m/fimmu-17-1771616-g002.jpg Figure 2. Identification and functional enrichment analysis of differentially expressed mitochondria https://www.frontiersin.org/files/Articles/1771616/fimmu-17-1771616-HTML/image_m/fimmu-17-1771616-g003.jpg Figure 3. Identification of hub mitochondrial-related genes (Mito-RGs) and development of the Mito-R https://www.frontiersin.org/files/Articles/1771616/fimmu-17-1771616-HTML/image_m/fimmu-17-1771616-g004.jpg Figure 4. Development and evaluation of a gestational diabetes mellitus (GDM) predictive model based https://www.frontiersin.org/files/Articles/1771616/fimmu-17-1771616-HTML/image_m/fimmu-17-1771616-g005.jpg Figure 5. Single-cell resolution reveals the expression of hub mitochondrial-related genes (Mito-RGs https://www.frontiersin.org/files/Articles/1771616/fimmu-17-1771616-HTML/image_m/fimmu-17-1771616-g006.jpg Figure 6. Cell-cell communication analysis and experimental validation of hub mitochondrial-related https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1751273/full https://www.frontiersin.org/files/Articles/1751273/fpls-16-1751273-HTML/image_m/fpls-16-1751273-g001.jpg Figure 1. Example plants representing different scores of the visual salt injury evaluation performe https://www.frontiersin.org/files/Articles/1751273/fpls-16-1751273-HTML/image_m/fpls-16-1751273-g002.jpg Figure 2. Distribution of seedling salt tolerance scores for 225 rice cultivars across the spring (A https://www.frontiersin.org/files/Articles/1751273/fpls-16-1751273-HTML/image_m/fpls-16-1751273-g003.jpg Figure 3. SNP density distributions across the 12 rice chromosomes, based on a 1-MB sliding window. https://www.frontiersin.org/files/Articles/1751273/fpls-16-1751273-HTML/image_m/fpls-16-1751273-t001.jpg Table 1. Information on the QTLs and associated significant SNPs identified in this study. https://www.frontiersin.org/files/Articles/1751273/fpls-16-1751273-HTML/image_m/fpls-16-1751273-g004.jpg Figure 4. Manhattan and QQ Plots. The left panel shows the Manhattan plot, with a red line at −log10 https://www.frontiersin.org/files/Articles/1751273/fpls-16-1751273-HTML/image_m/fpls-16-1751273-g005.jpg Figure 5. Functional enrichment of DEGs showing significantly enriched GO terms, organized by aspect https://www.frontiersin.org/files/Articles/1751273/fpls-16-1751273-HTML/image_m/fpls-16-1751273-g006.jpg Figure 6. Haplotype structure and phenotypic variation in Os07g0635500. The gene model (left) illust https://www.frontiersin.org/files/Articles/1751273/fpls-16-1751273-HTML/image_m/fpls-16-1751273-g007.jpg Figure 7. Combined effects of the identified QTLs on salt tolerance. The boxplots show (A) the BLUP https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1677128/full https://www.frontiersin.org/files/Articles/1677128/fpls-16-1677128-HTML/image_m/fpls-16-1677128-g001.jpg Figure 1. Conceptual framework illustrating the interconnected pathway between major climate-related https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1765179/full https://www.frontiersin.org/files/Articles/1765179/fendo-17-1765179-HTML/image_m/fendo-17-1765179-t001.jpg Table 1. Comparison of sociodemographic and clinical variables. https://www.frontiersin.org/files/Articles/1765179/fendo-17-1765179-HTML/image_m/fendo-17-1765179-t002.jpg Table 2. Comparison of hair cortisol levels by primary/secondary adrenal insufficiency and underlyin https://www.frontiersin.org/files/Articles/1765179/fendo-17-1765179-HTML/image_m/fendo-17-1765179-g001.jpg Figure 1. Correlation analyses between hair cortisol levels and clinical variables in patients with https://www.frontiersin.org/files/Articles/1765179/fendo-17-1765179-HTML/image_m/fendo-17-1765179-g002.jpg Figure 2. Multivariable linear regression analysis of factors associated with hair cortisol concentr https://www.frontiersin.org/files/Articles/1765179/fendo-17-1765179-HTML/image_m/fendo-17-1765179-t003.jpg Table 3. Percentile-based reclassification of AI patients. https://www.frontiersin.org/files/Articles/1765179/fendo-17-1765179-HTML/image_m/fendo-17-1765179-t004.jpg Table 4. Comparison of glucocorticoid dosing parameters and hair cortisol concentrations between und https://www.frontiersin.org/journals/clinical-diabetes-and-healthcare/articles/10.3389/fcdhc.2026.1719223/full https://www.frontiersin.org/files/Articles/1719223/fcdhc-07-1719223-HTML/image_m/fcdhc-07-1719223-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1719223/fcdhc-07-1719223-HTML/image_m/fcdhc-07-1719223-t001.jpg Table 1. Baseline characteristics of patients with de novo heart failure. https://www.frontiersin.org/files/Articles/1719223/fcdhc-07-1719223-HTML/image_m/fcdhc-07-1719223-t002.jpg Table 2. Physical examination, vital signs and laboratory tests upon hospital admission. https://www.frontiersin.org/files/Articles/1719223/fcdhc-07-1719223-HTML/image_m/fcdhc-07-1719223-t003.jpg Table 3. Investigations and management during index hospitalization. https://www.frontiersin.org/files/Articles/1719223/fcdhc-07-1719223-HTML/image_m/fcdhc-07-1719223-t004.jpg Table 4. Hospital discharge key laboratory tests, medications, and clinical outcomes. https://www.frontiersin.org/journals/insect-science/articles/10.3389/finsc.2025.1666457/full https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-g001.jpg Figure 1. Cage-based field trials to assess yield and quality losses by faba bean stem borer L. algi https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-t001.jpg Table 1. Average infestation rates of faba bean stem borer on 20 faba bean plants in artificial infe https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-g002.jpg Figure 2. Life cycle and type of damage caused by of both L. algirus adult and larva on faba bean pl https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-t002.jpg Table 2. Effect of L. algirus infestation on total weight yield, avoidable yield loss, and yield inc https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-t003.jpg Table 3. Impact of L. algirus infestation on the geometry of faba bean seeds and cooking time. https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-t004.jpg Table 4. Proximate composition of faba bean seeds from infested and uninfested faba bean plants, und https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-t005.jpg Table 5. Impact of L. algirus infestation on faba bean seed mineral composition. https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-t006.jpg Table 6. Average amino acid concentrations (mg/kg) in seeds from faba bean plants infested and non-i https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-t007.jpg Table 7. Impact of L. algirus infestation on faba bean antinutrients. https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-g003.jpg Figure 3. PCA biplot of faba bean samples showing separation by infestation status and year based on https://www.frontiersin.org/files/Articles/1666457/finsc-05-1666457-HTML/image_m/finsc-05-1666457-g004.jpg Figure 4. Pearson correlation heatmaps showing the relationships among mineral, biochemical, and mor https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1717105/full https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g001.jpg Figure 1. Frequency distribution of seed traits in the GWAS panel. The X-axis represents the trait v https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-t001.jpg Table 1. Analysis of variance (ANOVA) of 201 pearl millet genotypes for 27 seed phenotypic traits. https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g002.jpg Figure 2. Correlation heatmap representing the relationships between traits, with correlation coeffi https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g003.jpg Figure 3. (A) Principal components of 201 inbred lines from genotypic data of 2,015 SNPs illustratin https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g004.jpg Figure 4. Circos plots showing the distribution of significant MTAs identified under control conditi https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g005.jpg Figure 5. Circos plots showing the distribution of significant MTAs identified under accelerated agi https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g006.jpg Figure 6. Circos plots showing the distribution of significant MTAs identified for relative measures https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g007.jpg Figure 7. Box plots showing the phenotypic values of the different allele for significant SNPs ident https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g008.jpg Figure 8. Manhattan plot illustrating the pleiotropic SNP PMSnpB394 on chromosome 2, associated with https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g009.jpg Figure 9. Circos plot visualizing the association of genes on different chromosomes with different s https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-t002.jpg Table 2. Superior pearl millet genotypes identified for key traits contributing to seed vigor and lo https://www.frontiersin.org/files/Articles/1717105/fpls-17-1717105-HTML/image_m/fpls-17-1717105-g010.jpg Figure 10. Molecular pathway involved in the regulation of seed longevity, vigor and viability durin https://www.frontiersin.org/journals/thermal-engineering/articles/10.3389/fther.2025.1683632/full https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-g001.jpg Figure 1. Liquid desiccant dehumidification mechanism. https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-g002.jpg Figure 2. Experimental setup. https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-g003.jpg Figure 3. Photo of experimental facility. https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-t001.jpg Table 1. Domain of operating parameters. https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-t003.jpg https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-g004.jpg Figure 4. Effect of air flow rate on the moisture removal rate as a function of the solution inlet t https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-g005.jpg Figure 5. Effect of air flow rate on the outlet humidity ratio as a function of the solution inlet t https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-g006.jpg Figure 6. Effect of air flow rate on the outlet air temperature as a function of the solution inlet https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-g007.jpg Figure 7. Effect of inlet air temperature on the moisture removal rate as a function of solution inl https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-t002.jpg Table 2. Values of the inlet air’s specific humidity for different inlet air and desiccant inlet tem https://www.frontiersin.org/files/Articles/1683632/fther-05-1683632-HTML/image_m/fther-05-1683632-g008.jpg Figure 8. Effect of inlet air temperature on the humidity ratio difference as a function of solution https://www.frontiersin.org/journals/thermal-engineering/articles/10.3389/fther.2025.1682295/full https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g001.jpg Figure 1. Synthetic process of Al2O3 nanoparticles https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g002.jpg Figure 2. Powder XRD pattern of Al2O3 nanoparticles showing sharp, well-defined diffraction peaks in https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g003.jpg Figure 3. FESEM image of Al2O3 nanoparticles. https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g004.jpg Figure 4. Energy-Dispersive X-ray spectroscopy of Al2O3. https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g005.jpg Figure 5. HRTEM images of Al2O3 nanoparticles (A–C), and SAED pattern (D) of Al2O3-NPs. https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g006.jpg Figure 6. Isotherm shows the amount of gas adsorbed versus the relative pressure (P/P0) at a constan https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g007.jpg Figure 7. Linearised BET plot of 1WP0P−1 versus relative pressure for the adsorbent. https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g008.jpg Figure 8. Schematic diagram of experimental setup. https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-t001.jpg Table 1. Experimental test conditions. https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g009.jpg Figure 9. Variation of the coefficient of performance (COP) with Al2O3 nanoparticle concentration (W https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g010.jpg Figure 10. Suction temperature (Tsuc) as a function of Al2O3 nanoparticle concentration (Wt%) for te https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g011.jpg Figure 11. Discharge temperature (Tdis) versus Al2O3 nanoparticle concentration (Wt%) for test condi https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g012.jpg Figure 12. Suction pressure (Psuc) as a function of Al2O3 nanoparticle concentration (Wt%) for test https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g013.jpg Figure 13. Discharge pressure (Pdis) as a function of Al2O3 nanoparticle concentration (Wt%) for tes https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g014.jpg Figure 14. Evaporator superheat held constant at 5 K versus Al2O3 nanoparticle concentration (Wt%) f https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g015.jpg Figure 15. Subcooling (K) versus Al2O3 nanoparticle concentration (Wt%) showing a consistent rise fr https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g016.jpg Figure 16. Compression ratio as a function of Al2O3 nanoparticle concentration (Wt%) for TC1–TC5. Al https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g017.jpg Figure 17. Variation of dynamic viscosity with Al2O3 nanoparticle concentration (Wt%) for TC1–TC5. V https://www.frontiersin.org/files/Articles/1682295/fther-05-1682295-HTML/image_m/fther-05-1682295-g018.jpg Figure 18. Effective specific heat capacity versus Al2O3 nanoparticle concentration (Wt%) for TC1–TC https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1760166/full https://www.frontiersin.org/files/Articles/1760166/fcell-14-1760166-HTML/image_m/fcell-14-1760166-g001.jpg Figure 1. Structural organization and comparative domain architecture of cytosolic Hsp90, human Gp96 https://www.frontiersin.org/files/Articles/1760166/fcell-14-1760166-HTML/image_m/fcell-14-1760166-t001.jpg Table 1. Functional landscape of Gp96/Grp94: major client proteins, co-chaperones, and stress-relate https://www.frontiersin.org/files/Articles/1760166/fcell-14-1760166-HTML/image_m/fcell-14-1760166-g002.jpg Figure 2. Gp96-Driven ER Proteostasis and Immune Regulation in Malaria: Host Mechanisms and Knowledg https://www.frontiersin.org/files/Articles/1760166/fcell-14-1760166-HTML/image_m/fcell-14-1760166-t002.jpg Table 2. Clinical and translational evidence linking extracellular or circulating Gp96 to disease se https://www.frontiersin.org/files/Articles/1760166/fcell-14-1760166-HTML/image_m/fcell-14-1760166-t003.jpg Table 3. Overview of selective Gp96 and PfGp96 inhibitors highlighting chemical scaffolds, mechanism https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1789371/full https://www.frontiersin.org/files/Articles/1789371/fmed-13-1789371-HTML-r1/image_m/fmed-13-1789371-g001.jpg Figure 1. PRISMA flow diagram of the study selection. https://www.frontiersin.org/files/Articles/1789371/fmed-13-1789371-HTML-r1/image_m/fmed-13-1789371-t001.jpg Table 1. The basic characteristics of included studies. https://www.frontiersin.org/files/Articles/1789371/fmed-13-1789371-HTML-r1/image_m/fmed-13-1789371-g002.jpg Figure 2. Risk of bias graph. https://www.frontiersin.org/files/Articles/1789371/fmed-13-1789371-HTML-r1/image_m/fmed-13-1789371-g003.jpg Figure 3. Network evidence plot. (A) AHI, (B) ESS, (C) PSQI, (D) BMI. https://www.frontiersin.org/files/Articles/1789371/fmed-13-1789371-HTML-r1/image_m/fmed-13-1789371-g004.jpg Figure 4. Forest plots. (A) AHI, (B) ESS, (C) PSQI, (D) BMI. https://www.frontiersin.org/files/Articles/1789371/fmed-13-1789371-HTML-r1/image_m/fmed-13-1789371-t002.jpg Table 2. Ranking table of SUCRA values. https://www.frontiersin.org/files/Articles/1789371/fmed-13-1789371-HTML-r1/image_m/fmed-13-1789371-g005.jpg Figure 5. Funnel plots. (A) AHI, (B) ESS, (C) PSQI, (D) BMI. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1648661/full https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-g001.jpg Figure 1. The distribution of publications on AIHE across different academic disciplines. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-g002.jpg Figure 2. Workflow adopted for selecting AIHE-related articles in this research. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-t001.jpg Table 1. Inclusion and exclusion criteria for AIHE studies. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-g003.jpg Figure 3. Word cloud of the AIHE-related publications. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-g004.jpg Figure 4. Keyword co-occurrence network in AIHE-related studies. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-t002.jpg Table 2. Coding scheme for AIHE studies. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-g005.jpg Figure 5. Pedagogical mediation of AI for students engagement model. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-t003.jpg Table 3. The characteristics of AIHE studies. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-g006.jpg Figure 6. The distribution of publications from 2016 to 2025 related to AIHE. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-g007.jpg Figure 7. The geographical distribution of publications on AIHE studies. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-g008.jpg Figure 8. Research methods of publications in AIHE studies. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-t004.jpg Table 4. Data forms employed in the studies included in this review. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-g009.jpg Figure 9. Data form used in AIHE studies. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-t005.jpg Table 5. Application type of AIHE reviews. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-t006.jpg Table 6. Pedagogical strategies in AIHE. https://www.frontiersin.org/files/Articles/1648661/feduc-10-1648661-HTML-r1/image_m/feduc-10-1648661-t007.jpg Table 7. Teaching methods in AIHE. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1732718/full https://www.frontiersin.org/files/Articles/1732718/fimmu-16-1732718-HTML/image_m/fimmu-16-1732718-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1732718/fimmu-16-1732718-HTML/image_m/fimmu-16-1732718-g001.jpg Figure 1. Core signaling pathways governing macrophage polarization. M1 polarization is primarily dr https://www.frontiersin.org/files/Articles/1732718/fimmu-16-1732718-HTML/image_m/fimmu-16-1732718-g002.jpg Figure 2. Macrophage polarization is precisely regulated by multi-layered epigenetic mechanisms. (1) https://www.frontiersin.org/files/Articles/1732718/fimmu-16-1732718-HTML/image_m/fimmu-16-1732718-g003.jpg Figure 3. Core genes and pathway networks in metabolic reprogramming during macrophage polarization. https://www.frontiersin.org/files/Articles/1732718/fimmu-16-1732718-HTML/image_m/fimmu-16-1732718-g004.jpg Figure 4. Dysregulated macrophage polarization serves as a critical mechanism driving the progressio https://www.frontiersin.org/files/Articles/1732718/fimmu-16-1732718-HTML/image_m/fimmu-16-1732718-g005.jpg Figure 5. Precision modulation of macrophage polarization: emerging therapeutic strategies and trans https://www.frontiersin.org/files/Articles/1732718/fimmu-16-1732718-HTML/image_m/fimmu-16-1732718-g006.jpg Figure 6. Technological integration and future directions in macrophage research. This framework com https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1806471/full https://www.frontiersin.org/files/Articles/1806471/fnagi-18-1806471-HTML/image_m/fnagi-18-1806471-t001.jpg Table 1. Epidemiology of epilepsy across neurodegenerative dementias. https://www.frontiersin.org/files/Articles/1806471/fnagi-18-1806471-HTML/image_m/fnagi-18-1806471-t002.jpg Table 2. Characteristic EEG features across neurodegenerative dementias. https://www.frontiersin.org/files/Articles/1806471/fnagi-18-1806471-HTML/image_m/fnagi-18-1806471-t003.jpg Table 3. TMS cortical excitability profiles across neurodegenerative dementias. https://www.frontiersin.org/files/Articles/1806471/fnagi-18-1806471-HTML/image_m/fnagi-18-1806471-g001.jpg Figure 1. Disease-specific network vulnerability and clinical manifestations of epilepsy in neurodeg https://www.frontiersin.org/files/Articles/1806471/fnagi-18-1806471-HTML/image_m/fnagi-18-1806471-g002.jpg Figure 2. Conceptual comparison of three epilepsy paradigms. INS, insula; M1, primary motor cortex; https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1694567/full https://www.frontiersin.org/files/Articles/1694567/fimmu-16-1694567-HTML-r1/image_m/fimmu-16-1694567-g001.jpg Figure 1. Mechanism of pain sensing at the tumor-nociceptor interface. The unmyelinated C and thinly https://www.frontiersin.org/files/Articles/1694567/fimmu-16-1694567-HTML-r1/image_m/fimmu-16-1694567-t001.jpg Table 1. SASP factors for which a cascade to enhanced nociception is known. https://www.frontiersin.org/files/Articles/1694567/fimmu-16-1694567-HTML-r1/image_m/fimmu-16-1694567-g002.jpg Figure 2. The complex dialog among the actors of the innervated niche: focus on how cellular senesce https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1725493/full https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-g001.jpg Figure 1. Flowchart of patient selection. AE, autoimmune encephalitis; anti-NMDAR, anti-N-methyl-D-a https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-t001.jpg Table 1. Comparison of clinical characteristics in patients with anti-NMDAR encephalitis stratified https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-t002.jpg Table 2. Laboratory test and imaging results in patients with anti-NMDAR encephalitis stratified by https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-t003.jpg Table 3. Univariate binary logistic regression analysis of poor outcome in patients with anti-NMDAR https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-t004.jpg Table 4. Multivariate binary logistic regression analysis of poor outcome in patients with anti-NMDA https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-g002.jpg Figure 2. ROC curve analysis of the diagnostic value of NAR for disease prognosis in anti-NMDAR ence https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-t005.jpg Table 5. The mediating role of initial mRS between NAR and poor outcome of anti-NMDAR encephalitis. https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-g003.jpg Figure 3. The mediating role of initial mRS between NAR and disease prognosis in anti-NMDAR encephal https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-g004.jpg Figure 4. Kaplan–Meier curves categorized according to NAR. NAR, neutrophil-to-apolipoprotein A1 rat https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-t006.jpg Table 6. Univariate Cox analysis of relapse in patients with anti-NMDAR encephalitis. https://www.frontiersin.org/files/Articles/1725493/fneur-17-1725493-HTML/image_m/fneur-17-1725493-t007.jpg Table 7. Multivariate Cox analysis of relapse in patients with anti-NMDAR encephalitis. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1688885/full https://www.frontiersin.org/files/Articles/1688885/fgene-16-1688885-HTML/image_m/fgene-16-1688885-g001.jpg Figure 1. Magnetic resonance imaging showing extensive and symmetrical changes in bilateral cerebral https://www.frontiersin.org/files/Articles/1688885/fgene-16-1688885-HTML/image_m/fgene-16-1688885-g002.jpg Figure 2. Identification of compound heterozygous missense EIF2B5 gene variants causing VWM. (A) Vis https://www.frontiersin.org/files/Articles/1688885/fgene-16-1688885-HTML/image_m/fgene-16-1688885-t001.jpg Table 1. EIF2B5 gene variants identified using whole-exome sequencing. https://www.frontiersin.org/files/Articles/1688885/fgene-16-1688885-HTML/image_m/fgene-16-1688885-g003.jpg Figure 3. Structural analysis of pathogenic variants in the eIF2Bε protein. (A) Overall structure of https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1742670/full https://www.frontiersin.org/files/Articles/1742670/fphar-16-1742670-HTML/image_m/fphar-16-1742670-g001.jpg Figure 1. The chest CT of patient. (A) Chest CT showed the lung quality before furmonertinib (no evi https://www.frontiersin.org/files/Articles/1742670/fphar-16-1742670-HTML/image_m/fphar-16-1742670-g002.jpg Figure 2. The mediastinal window of the chest CT. (A) Chest CT showed the lesions in the left lung a https://www.frontiersin.org/files/Articles/1742670/fphar-16-1742670-HTML/image_m/fphar-16-1742670-g003.jpg Figure 3. Schematic of the patient’s treatment history (highlighting the complete hospitalization pr https://www.frontiersin.org/files/Articles/1742670/fphar-16-1742670-HTML/image_m/fphar-16-1742670-t001.jpg Table 1. EGFR-TKI-associated interstitial lung disease (ILD) incidence across pivotal trials and rea https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1769932/full https://www.frontiersin.org/files/Articles/1769932/fonc-16-1769932-HTML/image_m/fonc-16-1769932-g001.jpg Figure 1. Serial chest CT scans showing the evolution of bilateral lung lesions. (A) At initial diag https://www.frontiersin.org/files/Articles/1769932/fonc-16-1769932-HTML/image_m/fonc-16-1769932-g002.jpg Figure 2. Summary of Treatment Sequence. (A) Mediastinal window CT scan at initial diagnosis showing https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1651528/full https://www.frontiersin.org/files/Articles/1651528/fnut-12-1651528-HTML-r1/image_m/fnut-12-1651528-t001.jpg Table 1. Mean scores of parental food literacy (based on SFLQ score), perceived barriers and enabler https://www.frontiersin.org/files/Articles/1651528/fnut-12-1651528-HTML-r1/image_m/fnut-12-1651528-t002.jpg Table 2. Mean scores of parental food literacy (based on SFLQ score), perceived barriers and enabler https://www.frontiersin.org/files/Articles/1651528/fnut-12-1651528-HTML-r1/image_m/fnut-12-1651528-t003.jpg Table 3. Association between parental food literacy (based on SFLQ score), perceived barriers and en https://www.frontiersin.org/files/Articles/1651528/fnut-12-1651528-HTML-r1/image_m/fnut-12-1651528-g001.jpg Figure 1. Comparison parental food literacy (based on SFLQ score), perceived barriers and enablers ( https://www.frontiersin.org/files/Articles/1651528/fnut-12-1651528-HTML-r1/image_m/fnut-12-1651528-t004.jpg Table 4. (PBE score), healthy-eating attitudes (based on HEA score), and healthy-eating behavior (ba https://www.frontiersin.org/files/Articles/1651528/fnut-12-1651528-HTML-r1/image_m/fnut-12-1651528-t005.jpg Table 5. Associations between parental food literacy (based on SFLQ score), perceived barriers and e https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1695383/full https://www.frontiersin.org/files/Articles/1695383/fmed-12-1695383-HTML/image_m/fmed-12-1695383-t001.jpg Table 1. Clinical characteristics of patients with SLE with normal and low serum complement levels. https://www.frontiersin.org/files/Articles/1695383/fmed-12-1695383-HTML/image_m/fmed-12-1695383-g001.jpg Figure 1. The performance of different SLE classification criteria. The sensitivity (A), specificity https://www.frontiersin.org/files/Articles/1695383/fmed-12-1695383-HTML/image_m/fmed-12-1695383-t002.jpg Table 2. Sensitivity of different SLE classification criteria in identifying SLE with H-com and N-co https://www.frontiersin.org/files/Articles/1695383/fmed-12-1695383-HTML/image_m/fmed-12-1695383-t003.jpg Table 3. Classification time of different SLE classification criteria after excluding low complement https://www.frontiersin.org/files/Articles/1695383/fmed-12-1695383-HTML/image_m/fmed-12-1695383-t004.jpg Table 4. Clinical characteristics of patients with SLE with low C3/C4 and low C3 and C4 levels. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1746535/full https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g001.jpg Figure 1. Schematic of the DBD plasma setup used for the generation of plasma-activated water. https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g002.jpg Figure 2. RMS power of the plasma as a function of applied voltage frequency in the range of 20–90 k https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g003.jpg Figure 3. pH and ORP values (a), as well as conductivity and TDS (b), for PAW generated at different https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g004.jpg Figure 4. UV-Vis absorption spectra of plasma-activated water (PAW) obtained after different activat https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g005.jpg Figure 5. Deconvolution of UV-Vis absorption spectra of PAW and reference reactive species for quali https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-t001.jpg Table 1. H2O2, O3, HNO2, NO2–, and NO3– concentrations after PAW for 7 and 14 min. https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g006.jpg Figure 6. Mean and standard deviation of bacterial growth inhibition for Staphylococcus aureus after https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g007.jpg Figure 7. Mean and standard deviation of bacterial growth inhibition for Escherichia coli after (a) https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g008.jpg Figure 8. Mean and standard deviation of fungal growth inhibition for Candida albicans after (a) 7 m https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-t002.jpg Table 2. Energy efficiency of plasma-activated water antimicrobial activity at 24 h. https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g009.jpg Figure 9. Viability of L929 fibroblasts after exposure to PAW activated for 7 and 14 min, compared t https://www.frontiersin.org/files/Articles/1746535/fmicb-17-1746535-HTML/image_m/fmicb-17-1746535-g010.jpg Figure 10. Fluorescence microscopy images of L929 fibroblasts (a) and B16F10 melanoma cells (b) afte https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2026.1758411/full https://www.frontiersin.org/files/Articles/1758411/fncel-20-1758411-HTML/image_m/fncel-20-1758411-gr0001.jpg Graphical Abstract. Autopsy brains of newborn patients with hypoxic–ischemic encephalopathy (HIE) sh https://www.frontiersin.org/files/Articles/1758411/fncel-20-1758411-HTML/image_m/fncel-20-1758411-t001.jpg Table 1. Human infant autopsy cases used. https://www.frontiersin.org/files/Articles/1758411/fncel-20-1758411-HTML/image_m/fncel-20-1758411-g001.jpg Figure 1. Layer specific abnormal astrocytic GLAST localization in HIE patients. (A–C) Localization https://www.frontiersin.org/files/Articles/1758411/fncel-20-1758411-HTML/image_m/fncel-20-1758411-g002.jpg Figure 2. Layer-specific aggregation of GLAST immunoreactivity in the somatosensory cortex of HI-NT https://www.frontiersin.org/files/Articles/1758411/fncel-20-1758411-HTML/image_m/fncel-20-1758411-g003.jpg Figure 3. HI piglets have cEEG-identified seizures and layer-specific cortical neuropathology suppre https://www.frontiersin.org/files/Articles/1758411/fncel-20-1758411-HTML/image_m/fncel-20-1758411-g004.jpg Figure 4. Layer-specific neuropathology correlates with spontaneous recurrent seizures metrics after https://www.frontiersin.org/files/Articles/1758411/fncel-20-1758411-HTML/image_m/fncel-20-1758411-g005.jpg Figure 5. Complex cEEG seizure patterns are seen in HI-HT piglets at days 4 and 5. Representative cE https://www.frontiersin.org/files/Articles/1758411/fncel-20-1758411-HTML/image_m/fncel-20-1758411-g006.jpg Figure 6. Spike–wave form analysis reveals a distinct PSD pattern in focal vs. generalized seizure. https://www.frontiersin.org/files/Articles/1758411/fncel-20-1758411-HTML/image_m/fncel-20-1758411-g007.jpg Figure 7. Neocortical synaptophysin immunoreactivity is aberrant in HI-NT piglets and is insensitive https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1625852/full https://www.frontiersin.org/files/Articles/1625852/fendo-16-1625852-HTML/image_m/fendo-16-1625852-g001.jpg Figure 1. Study flow chart. https://www.frontiersin.org/files/Articles/1625852/fendo-16-1625852-HTML/image_m/fendo-16-1625852-t001.jpg Table 1. Study participant’s characteristics based on neutrophil tertiles in different sexes. https://www.frontiersin.org/files/Articles/1625852/fendo-16-1625852-HTML/image_m/fendo-16-1625852-t002.jpg Table 2. Associations of neutrophil and 5-year refracture rate in different sexes. https://www.frontiersin.org/files/Articles/1625852/fendo-16-1625852-HTML/image_m/fendo-16-1625852-g002.jpg Figure 2. The relationship between neutrophil counts and fracture risk stratified by gender. Adjuste https://www.frontiersin.org/files/Articles/1625852/fendo-16-1625852-HTML/image_m/fendo-16-1625852-t003.jpg Table 3. Threshold effect analysis of the association between neutrophil and 5-year refracture rate https://www.frontiersin.org/journals/animal-science/articles/10.3389/fanim.2025.1698465/full https://www.frontiersin.org/files/Articles/1698465/fanim-06-1698465-HTML-r1/image_m/fanim-06-1698465-t001.jpg Table 1. Effects of different treatments on broiler growth performance. https://www.frontiersin.org/files/Articles/1698465/fanim-06-1698465-HTML-r1/image_m/fanim-06-1698465-t002.jpg Table 2. Effects of different treatments on antioxidant enzyme activity and MDA. https://www.frontiersin.org/files/Articles/1698465/fanim-06-1698465-HTML-r1/image_m/fanim-06-1698465-g001.jpg Figure 1. Photomicrograph of the jejunum: hematoxylin and eosin stained. Pictures were observed at 1 https://www.frontiersin.org/files/Articles/1698465/fanim-06-1698465-HTML-r1/image_m/fanim-06-1698465-t003.jpg Table 3. Effects of different treatments on jejunum villus height and crypt depth. https://www.frontiersin.org/files/Articles/1698465/fanim-06-1698465-HTML-r1/image_m/fanim-06-1698465-g002.jpg Figure 2. Caecal microbiota diversity analysis. CON (Group A), AMO (Group B), SNL(Group D). (A) The https://www.frontiersin.org/files/Articles/1698465/fanim-06-1698465-HTML-r1/image_m/fanim-06-1698465-g003.jpg Figure 3. Microbiota analysis in cecum chyme. CON (Group A), AMO (Group B), SNL (Group D). (A) Commu https://www.frontiersin.org/files/Articles/1698465/fanim-06-1698465-HTML-r1/image_m/fanim-06-1698465-g004.jpg Figure 4. Transcriptome sequencing analysis of chicken ileum tissues in three treatments, CON (Group https://www.frontiersin.org/files/Articles/1698465/fanim-06-1698465-HTML-r1/image_m/fanim-06-1698465-g005.jpg Figure 5. Integrated analysis of the microbiome and transcriptome in the CON vs AMO (A), CON vs SNL https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2026.1769831/full https://www.frontiersin.org/files/Articles/1769831/fcomm-11-1769831-HTML-r2/image_m/fcomm-11-1769831-g001.jpg Figure 1. Conceptual framework structural model including direct and indirect paths for mediation an https://www.frontiersin.org/files/Articles/1769831/fcomm-11-1769831-HTML-r2/image_m/fcomm-11-1769831-t001.jpg Table 1. Study measures. https://www.frontiersin.org/files/Articles/1769831/fcomm-11-1769831-HTML-r2/image_m/fcomm-11-1769831-t002.jpg Table 2. Respondent’s demographic profile. https://www.frontiersin.org/files/Articles/1769831/fcomm-11-1769831-HTML-r2/image_m/fcomm-11-1769831-t003.jpg Table 3. Convergent validity. https://www.frontiersin.org/files/Articles/1769831/fcomm-11-1769831-HTML-r2/image_m/fcomm-11-1769831-g002.jpg Figure 2. PLS-SEM model structural model including direct and indirect paths for mediation analysis. https://www.frontiersin.org/files/Articles/1769831/fcomm-11-1769831-HTML-r2/image_m/fcomm-11-1769831-t004.jpg Table 4. Discriminant validity—Fornell-Larcker criterion. https://www.frontiersin.org/files/Articles/1769831/fcomm-11-1769831-HTML-r2/image_m/fcomm-11-1769831-t005.jpg Table 5. Path coefficients. https://www.frontiersin.org/files/Articles/1769831/fcomm-11-1769831-HTML-r2/image_m/fcomm-11-1769831-t006.jpg Table 6. Mediation analysis. https://www.frontiersin.org/files/Articles/1769831/fcomm-11-1769831-HTML-r2/image_m/fcomm-11-1769831-t007.jpg Table 7. (A) R-squared test and (B) Goodness fit. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1755141/full https://www.frontiersin.org/files/Articles/1755141/fimmu-17-1755141-HTML-r1/image_m/fimmu-17-1755141-g001.jpg Figure 1. Sublethal infection with SARS-CoV-2 MA30 or influenza A PR8 induces acute weight loss and https://www.frontiersin.org/files/Articles/1755141/fimmu-17-1755141-HTML-r1/image_m/fimmu-17-1755141-g002.jpg Figure 2. MA30 and PR8 infection leads to persistent pulmonary inflammation and unresolved collagen https://www.frontiersin.org/files/Articles/1755141/fimmu-17-1755141-HTML-r1/image_m/fimmu-17-1755141-g003.jpg Figure 3. KRT5+ progenitor cell migration and pod formation are impaired following MA30 infection bu https://www.frontiersin.org/files/Articles/1755141/fimmu-17-1755141-HTML-r1/image_m/fimmu-17-1755141-g004.jpg Figure 4. Long-term MA30 and PR8 infection induce distinct lung transcriptional profiles at 21 DPI. https://www.frontiersin.org/files/Articles/1755141/fimmu-17-1755141-HTML-r1/image_m/fimmu-17-1755141-g005.jpg Figure 5. Microhemorrhage and inflammatory glial morphology with MA30 infection. H&E exposed more fr https://www.frontiersin.org/files/Articles/1755141/fimmu-17-1755141-HTML-r1/image_m/fimmu-17-1755141-g006.jpg Figure 6. Long-term MA30 infection induces brain-specific transcriptional changes distinct from PR8. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1802173/full https://www.frontiersin.org/files/Articles/1802173/fmicb-17-1802173-HTML/image_m/fmicb-17-1802173-g001.jpg Figure 1. Summary of the overall experimental strategy. Schematic representation of the experimental https://www.frontiersin.org/files/Articles/1802173/fmicb-17-1802173-HTML/image_m/fmicb-17-1802173-g002.jpg Figure 2. Barplots representing the microbial community distribution in the Icelandic soil samples. https://www.frontiersin.org/files/Articles/1802173/fmicb-17-1802173-HTML/image_m/fmicb-17-1802173-g003.jpg Figure 3. Proteins from soil samples resolved by 2-DE. Numbered spots marked with circles correspond https://www.frontiersin.org/files/Articles/1802173/fmicb-17-1802173-HTML/image_m/fmicb-17-1802173-g004.jpg Figure 4. Comparison of OD600 values for Lysinibacillus and Ideonella in mineral medium with PET ove https://www.frontiersin.org/files/Articles/1802173/fmicb-17-1802173-HTML/image_m/fmicb-17-1802173-g005.jpg Figure 5. FE-SEM images of PET plastic strips. (A) Negative control (unexposed to bacterial culture) https://www.frontiersin.org/files/Articles/1802173/fmicb-17-1802173-HTML/image_m/fmicb-17-1802173-g006.jpg Figure 6. FE-SEM imaging of PET samples incubated with Lysinibacillus and Ideonella. (A) Control PET https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1732854/full https://www.frontiersin.org/files/Articles/1732854/fphar-16-1732854-HTML-r1/image_m/fphar-16-1732854-g001.jpg Figure 1. De novo synthesis, enterohepatic circulation, and microbial transformation of bile acids ( https://www.frontiersin.org/files/Articles/1732854/fphar-16-1732854-HTML-r1/image_m/fphar-16-1732854-g002.jpg Figure 2. Bile acid (BA) structure and hydrophilicity. The number, degree, position and stereochemis https://www.frontiersin.org/files/Articles/1732854/fphar-16-1732854-HTML-r1/image_m/fphar-16-1732854-g003.jpg Figure 3. The opposing cytotoxic and cytoprotective effects of bile acids (BAs) based on their physi https://www.frontiersin.org/files/Articles/1732854/fphar-16-1732854-HTML-r1/image_m/fphar-16-1732854-t001.jpg Table 1. Therapeutic applications, safety profile, dosage ranges, and regulatory status of selected https://www.frontiersin.org/files/Articles/1732854/fphar-16-1732854-HTML-r1/image_m/fphar-16-1732854-g004.jpg Figure 4. Chemical structures of the FDA-approved therapeutic bile acids (BAs). Chemical structures https://www.frontiersin.org/files/Articles/1732854/fphar-16-1732854-HTML-r1/image_m/fphar-16-1732854-t002.jpg Table 2. Therapeutic applications, safety profile, dosage ranges, and regulatory status of current b https://www.frontiersin.org/files/Articles/1732854/fphar-16-1732854-HTML-r1/image_m/fphar-16-1732854-t003.jpg Table 3. Cholestatic and non-cholestatic disease prevalence profile, recommended therapeutic bile ac https://www.frontiersin.org/files/Articles/1732854/fphar-16-1732854-HTML-r1/image_m/fphar-16-1732854-g005.jpg Figure 5. Structures of lipid nanocarriers and various bilosome surface-modifications. Lipid nanocar https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2026.1751006/full https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g001.jpg Figure 1. Spatial context and computational mesh. (a) The location of Langebaan Lagoon on the wester https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g002.jpg Figure 2. Examples of ICESat-2 ATL24 bathymetry elevations (referenced to EGM08). Each panel is labe https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g003.jpg Figure 3. All ICESat-2 ATL24 bathymetry tracks (referenced to EGM08) used in this study to drive bat https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-t001.jpg Table 1. Sentinel-2 predictors used to derive bathymetry. Band designations and central wavelengths https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-t002.jpg Table 2. Bathymetry validation for each Sentinel-2 image using the XGBoost model optimized with Baye https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g004.jpg Figure 4. Comparison of model performance for bathymetry prediction using different machine learning https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g005.jpg Figure 5. Final satellite-derived bathymetry for Langebaan Lagoon and Saldanha Bay. Depths represent https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-t003.jpg Table 3. Tidal harmonic constituents used to force the offshore boundary. Amplitudes (m) and phases https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g006.jpg Figure 6. SWOT L2 LR processing workflow used to generate geoid-referenced 250-m water surface eleva https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-t004.jpg Table 4. Sensitivity of model performance to the Chézy friction coefficient. Performance metrics are https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g007.jpg Figure 7. Comparison of modeled water surface elevation and SWOT observations for three acquisitions https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-t005.jpg Table 5. Per-pass validation statistics comparing modeled and SWOT LR Unsmoothed WSE. https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g008.jpg Figure 8. Scatter plot of collocated model versus SWOT water surface elevations for all 16 passes. https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g009.jpg Figure 9. Comparison between TPXO-reconstructed sea surface height time series and SWOT sea surface https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g010.jpg Figure 10. Scatter comparison between TPXO-reconstructed and SWOT observed sea surface heights at th https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-g011.jpg Figure 11. Cross-correlation analysis between SWOT and TPXO WSE time series at the offshore boundary https://www.frontiersin.org/files/Articles/1751006/frsen-07-1751006-HTML/image_m/frsen-07-1751006-t006.jpg Table 6. Summary of selected studies using SWOT to calibrate or validate hydrodynamic models or tida https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1761715/full https://www.frontiersin.org/files/Articles/1761715/fimmu-17-1761715-HTML/image_m/fimmu-17-1761715-g001.jpg Figure 1. IFNγ up-regulate PD-L1 expression in MC38 cells through IFN-STAT1 signaling. PD-L1 express https://www.frontiersin.org/files/Articles/1761715/fimmu-17-1761715-HTML/image_m/fimmu-17-1761715-g002.jpg Figure 2. IFN-γ modulate cancer cell stemness in MC38 cells indirectly through IFN-STAT3 pathway. (A https://www.frontiersin.org/files/Articles/1761715/fimmu-17-1761715-HTML/image_m/fimmu-17-1761715-g003.jpg Figure 3. Niclosamide has inhibition effect on both STAT1 and STAT3, which blocks IFN-γ-induced PD-L https://www.frontiersin.org/files/Articles/1761715/fimmu-17-1761715-HTML/image_m/fimmu-17-1761715-g004.jpg Figure 4. RNA-sequencing analysis from total RNA sample of MC38 cells treated with IFN-γ. (A) The vo https://www.frontiersin.org/files/Articles/1761715/fimmu-17-1761715-HTML/image_m/fimmu-17-1761715-g005.jpg Figure 5. Hypoxia enhances PD-L1 upregulation by IFN-γ, while Niclosamide down-regulates Hif1α under https://www.frontiersin.org/files/Articles/1761715/fimmu-17-1761715-HTML/image_m/fimmu-17-1761715-g006.jpg Figure 6. IFN-γ and Hypoxic conditions has synergistic effect on inducing T cell exhaustion, and IFN https://www.frontiersin.org/files/Articles/1761715/fimmu-17-1761715-HTML/image_m/fimmu-17-1761715-g007.jpg Figure 7. Niclosamide reduces tumor cell immune evasion while preserving IFN-γ’s effect in facilitat https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2025.1676316/full https://www.frontiersin.org/files/Articles/1676316/fncel-19-1676316-HTML/image_m/fncel-19-1676316-g001.jpg Figure 1. Differential expression of hippocampal miRNAs and transcripts in BTBR mice compared to B6 https://www.frontiersin.org/files/Articles/1676316/fncel-19-1676316-HTML/image_m/fncel-19-1676316-g002.jpg Figure 2. Interaction networks between BTBR hippocampal DEmiRNAs and DETs. (A) Venn diagram shows th https://www.frontiersin.org/files/Articles/1676316/fncel-19-1676316-HTML/image_m/fncel-19-1676316-g003.jpg Figure 3. Zeb2 is a validated target of the BTBR DEmiRNAs. (A) The 3’UTR of the BTBR differentially https://www.frontiersin.org/files/Articles/1676316/fncel-19-1676316-HTML/image_m/fncel-19-1676316-g004.jpg Figure 4. Non-canonical interactions between BTBR DEmiRNAs and DETs. (A) Identification of high affi https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1793162/full https://www.frontiersin.org/files/Articles/1793162/fmicb-17-1793162-HTML/image_m/fmicb-17-1793162-g001.jpg Figure 1. NaB acts to inhibit C. albicans growth, biofilm formation and acts as an HDAC inhibitor. C https://www.frontiersin.org/files/Articles/1793162/fmicb-17-1793162-HTML/image_m/fmicb-17-1793162-g002.jpg Figure 2. NaB modulates C. albicans mitochondrial respiration in a dose dependent manner. (A) Respir https://www.frontiersin.org/files/Articles/1793162/fmicb-17-1793162-HTML/image_m/fmicb-17-1793162-g003.jpg Figure 3. NaB has Set3 dependent and independent modes of action. C. albicans cells were grown to lo https://www.frontiersin.org/files/Articles/1793162/fmicb-17-1793162-HTML/image_m/fmicb-17-1793162-g004.jpg Figure 4. Acidic pH enhances the antifungal effects of NaB against C. albicans. C. albicans were gro https://www.frontiersin.org/files/Articles/1793162/fmicb-17-1793162-HTML/image_m/fmicb-17-1793162-g005.jpg Figure 5. ROS elevation and loss of calcium homeostasis underlies NaB induced C. albicans cell death https://www.frontiersin.org/files/Articles/1793162/fmicb-17-1793162-HTML/image_m/fmicb-17-1793162-g006.jpg Figure 6. The pH dependent antifungal effects of NaB on C. albicans. The addition of high levels of https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1778040/full https://www.frontiersin.org/files/Articles/1778040/fpubh-14-1778040-HTML-r2/image_m/fpubh-14-1778040-t001.jpg Table 1. Socio-demographic characteristics of subsamples. https://www.frontiersin.org/files/Articles/1778040/fpubh-14-1778040-HTML-r2/image_m/fpubh-14-1778040-t002.jpg Table 2. Reliability of the wrHCI-Q. https://www.frontiersin.org/files/Articles/1778040/fpubh-14-1778040-HTML-r2/image_m/fpubh-14-1778040-t003.jpg Table 3. EFA with oblique rotation (oblimin) (N = 625). https://www.frontiersin.org/files/Articles/1778040/fpubh-14-1778040-HTML-r2/image_m/fpubh-14-1778040-t004.jpg Table 4. Correlation matrix of the factors in the EFA model (N = 625). https://www.frontiersin.org/files/Articles/1778040/fpubh-14-1778040-HTML-r2/image_m/fpubh-14-1778040-g001.jpg Figure 1. The path diagram of the four-factor model. https://www.frontiersin.org/files/Articles/1778040/fpubh-14-1778040-HTML-r2/image_m/fpubh-14-1778040-t005.jpg Table 5. Correlation matrix of the factors in the CFA model (N = 573). https://www.frontiersin.org/files/Articles/1778040/fpubh-14-1778040-HTML-r2/image_m/fpubh-14-1778040-t006.jpg Table 6. Socio-demographic characteristics of wrHCI-Q. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1676394/full https://www.frontiersin.org/files/Articles/1676394/fonc-15-1676394-HTML/image_m/fonc-15-1676394-t001.jpg Table 1. Baseline characteristics of the cohort. https://www.frontiersin.org/files/Articles/1676394/fonc-15-1676394-HTML/image_m/fonc-15-1676394-g001.jpg Figure 1. Duodenal polyp count at 120 months by treatment group (mean ± SD). Groups: 0 = no therapy; https://www.frontiersin.org/files/Articles/1676394/fonc-15-1676394-HTML/image_m/fonc-15-1676394-g002.jpg Figure 2. Maximum polyp diameter at 120 months by treatment group (mean ± SD). Groups: 0 = no therap https://www.frontiersin.org/files/Articles/1676394/fonc-15-1676394-HTML/image_m/fonc-15-1676394-t002.jpg Table 2. Duodenal polyp outcomes at 10 years. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1705839/full https://www.frontiersin.org/files/Articles/1705839/fimmu-17-1705839-HTML/image_m/fimmu-17-1705839-g001.jpg Figure 1. An outline for the immunization strategy for P. falciparum circumsporozoite protein (PfCSP https://www.frontiersin.org/files/Articles/1705839/fimmu-17-1705839-HTML/image_m/fimmu-17-1705839-g002.jpg Figure 2. Repeated PfCSP immunization maintains durable IgG endpoint titers with different adjuvant https://www.frontiersin.org/files/Articles/1705839/fimmu-17-1705839-HTML/image_m/fimmu-17-1705839-g003.jpg Figure 3. Poly(I:C)+R848 is associated with altered IgG1 and IgG2a endpoint titers in PfCSP-immunize https://www.frontiersin.org/files/Articles/1705839/fimmu-17-1705839-HTML/image_m/fimmu-17-1705839-g004.jpg Figure 4. PbSLTRiP-specific IgG, IgG subclass, and IgM endpoint titers with and without Poly(I:C)+R8 https://www.frontiersin.org/files/Articles/1705839/fimmu-17-1705839-HTML/image_m/fimmu-17-1705839-g005.jpg Figure 5. PbSLTRiP antigen expression dynamics are determined by the chemoprophylaxis and sporozoite https://www.frontiersin.org/files/Articles/1705839/fimmu-17-1705839-HTML/image_m/fimmu-17-1705839-g006.jpg Figure 6. (A) In silico identification of B-cell, CD8+, and CD4+ T-cell epitope using IEDB for CSP a https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1694892/full https://www.frontiersin.org/files/Articles/1694892/fimmu-16-1694892-HTML/image_m/fimmu-16-1694892-g001.jpg Figure 1. Macrophage polarization in response to saturated and unsaturated fatty acids. Created in h https://www.frontiersin.org/files/Articles/1694892/fimmu-16-1694892-HTML/image_m/fimmu-16-1694892-t001.jpg Table 1. Impact of SFAs on macrophages’ metabolic pathways and their effects. https://www.frontiersin.org/files/Articles/1694892/fimmu-16-1694892-HTML/image_m/fimmu-16-1694892-t002.jpg Table 2. Metabolic pathways of PUFAs in macrophages and their effects. https://www.frontiersin.org/files/Articles/1694892/fimmu-16-1694892-HTML/image_m/fimmu-16-1694892-t003.jpg Table 3. Summary of MUFAs effects on metabolic and immunological pathways. https://www.frontiersin.org/files/Articles/1694892/fimmu-16-1694892-HTML/image_m/fimmu-16-1694892-g002.jpg Figure 2. Overview of the main macrophage activation pathways induced by saturated (SFAs) and unsatu https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1675277/full https://www.frontiersin.org/files/Articles/1675277/fcimb-15-1675277-HTML/image_m/fcimb-15-1675277-t001.jpg Table 1. The clinical characteristics between the survival and non-survival group* of IPA. https://www.frontiersin.org/files/Articles/1675277/fcimb-15-1675277-HTML/image_m/fcimb-15-1675277-g001.jpg Figure 1. (A) Restricted cubic spline for the association between serum GM levels and the odds of in https://www.frontiersin.org/files/Articles/1675277/fcimb-15-1675277-HTML/image_m/fcimb-15-1675277-g002.jpg Figure 2. (A) In-hospital mortality between the dual-negative, single-positive, and dual-positive gr https://www.frontiersin.org/files/Articles/1675277/fcimb-15-1675277-HTML/image_m/fcimb-15-1675277-t002.jpg Table 2. Prognostic performance of serum GM, NLR, and combined indicators. https://www.frontiersin.org/files/Articles/1675277/fcimb-15-1675277-HTML/image_m/fcimb-15-1675277-g003.jpg Figure 3. (A) Receiver operating characteristic curve of the model on the original data and bootstra https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1650764/full https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-g001.jpg Figure 1. Morphological characterization of three varieties of blue honeysuckle. https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-t001.jpg Table 1. The constituents and bitterness intensity of three varieties of blue honeysuckle samples. https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-t002.jpg Table 2. Content of free amino acids in different varieties of blue honeysuckle. https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-t003.jpg Table 3. Determination of electronic tongue taste characteristics of different varieties of blue hon https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-g002.jpg Figure 2. (A) Principal component analysis (PCA) of three blue honeysuckle varieties based on electr https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-g003.jpg Figure 3. Bitter taste levels of three blue honeysuckle cultivars. Data represent mean ± standard de https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-g004.jpg Figure 4. (A) PCA analysis of all metabolite profiles across different blue honeysuckle groups. (B) https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-t004.jpg Table 4. Statistical analysis of differential cumulative metabolite counts for three different varie https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-t005.jpg Table 5. Potential metabolites associated with bitterness detected by UPLC–MS/MS. https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-g005.jpg Figure 5. KEGG pathway analysis of differentially bitter metabolites in blue honeysuckle between (A) https://www.frontiersin.org/files/Articles/1650764/fnut-12-1650764-HTML/image_m/fnut-12-1650764-g006.jpg Figure 6. Differences in the biosynthesis of amino acids and flavonoid biosynthesis pathway bitter m https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1666919/full https://www.frontiersin.org/files/Articles/1666919/fnut-12-1666919-HTML/image_m/fnut-12-1666919-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1666919/fnut-12-1666919-HTML/image_m/fnut-12-1666919-t001.jpg Table 1. Primer sequences used for gene expression analysis by qPCR. https://www.frontiersin.org/files/Articles/1666919/fnut-12-1666919-HTML/image_m/fnut-12-1666919-g001.jpg Figure 1. Time (a) and temperature (b) dependent UV–Vis spectrometry demonstrates the formation of P https://www.frontiersin.org/files/Articles/1666919/fnut-12-1666919-HTML/image_m/fnut-12-1666919-g002.jpg Figure 2. XRD analysis (a) confirms the crystallinity of the Pk-AuNps, SAED patterns (b), and partic https://www.frontiersin.org/files/Articles/1666919/fnut-12-1666919-HTML/image_m/fnut-12-1666919-g003.jpg Figure 3. FTIR spectra of P. koreana root extract and Pk-AuNps (a). The antioxidant activity compari https://www.frontiersin.org/files/Articles/1666919/fnut-12-1666919-HTML/image_m/fnut-12-1666919-g004.jpg Figure 4. Evaluation of the cell viability in RAW 264.7 (a) and A549 (b) cells, 24 h and 48 h after https://www.frontiersin.org/files/Articles/1666919/fnut-12-1666919-HTML/image_m/fnut-12-1666919-g005.jpg Figure 5. Immunofluorescence staining of NF-κB p65 expression in RAW 264.7 macrophages. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1741782/full https://www.frontiersin.org/files/Articles/1741782/fonc-16-1741782-HTML-r1/image_m/fonc-16-1741782-g001.jpg Figure 1. The lactate–lactylation axis in CRC progression Oncogenic signaling and hypoxia in CRC—inc https://www.frontiersin.org/files/Articles/1741782/fonc-16-1741782-HTML-r1/image_m/fonc-16-1741782-g002.jpg Figure 2. Lactate metabolism and transport symbiosis in CRC. This schematic highlights the conceptua https://www.frontiersin.org/files/Articles/1741782/fonc-16-1741782-HTML-r1/image_m/fonc-16-1741782-t001.jpg Table 1. Evidence levels for key components of the lactate–lactylation axis in CRC. https://www.frontiersin.org/files/Articles/1741782/fonc-16-1741782-HTML-r1/image_m/fonc-16-1741782-g003.jpg Figure 3. The lactate–lactylation axis orchestrates immune suppression and angiogenesis in CRC. Exce https://www.frontiersin.org/files/Articles/1741782/fonc-16-1741782-HTML-r1/image_m/fonc-16-1741782-t002.jpg Table 2. Therapeutically actionable nodes along the lactate–lactylation axis in CRC. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1730155/full https://www.frontiersin.org/files/Articles/1730155/fonc-16-1730155-HTML/image_m/fonc-16-1730155-t001.jpg Table 1. Comparison of the baseline characteristics in patients. https://www.frontiersin.org/files/Articles/1730155/fonc-16-1730155-HTML/image_m/fonc-16-1730155-g001.jpg Figure 1. Overall workflow of patients enrollment, sample processing, and model construction. The di https://www.frontiersin.org/files/Articles/1730155/fonc-16-1730155-HTML/image_m/fonc-16-1730155-g002.jpg Figure 2. Identification and validation of differential serum metabolites associated with immunother https://www.frontiersin.org/files/Articles/1730155/fonc-16-1730155-HTML/image_m/fonc-16-1730155-g003.jpg Figure 3. Performance of the 5-metabolite predictive model across the training and validation cohort https://www.frontiersin.org/files/Articles/1730155/fonc-16-1730155-HTML/image_m/fonc-16-1730155-g004.jpg Figure 4. Performance of the integrated model combining 5-MPM with clinical M stage. (A). ROC curves https://www.frontiersin.org/files/Articles/1730155/fonc-16-1730155-HTML/image_m/fonc-16-1730155-g005.jpg Figure 5. SHAP-based interpretation of metabolic contributions in the 5-MPM model. (A). Bar plot of https://www.frontiersin.org/files/Articles/1730155/fonc-16-1730155-HTML/image_m/fonc-16-1730155-g006.jpg Figure 6. Decision curve analysis demonstrating the clinical application of the 5-MPM model. Decisio https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1631074/full https://www.frontiersin.org/files/Articles/1631074/fimmu-16-1631074-HTML/image_m/fimmu-16-1631074-g001.jpg Figure 1. Mechanisms of vitiligo pathogenesis. In normal skin, melanocytes are evenly distributed in https://www.frontiersin.org/files/Articles/1631074/fimmu-16-1631074-HTML/image_m/fimmu-16-1631074-g002.jpg Figure 2. Overview of factors triggering innate immune activation in vitiligo. Environmental stresso https://www.frontiersin.org/files/Articles/1631074/fimmu-16-1631074-HTML/image_m/fimmu-16-1631074-g003.jpg Figure 3. The mechanisms of innate and adaptive immune activation in vitiligo. Innate immune system, https://www.frontiersin.org/files/Articles/1631074/fimmu-16-1631074-HTML/image_m/fimmu-16-1631074-g004.jpg Figure 4. The interplay of innate immune activation and adaptive immune responses in vitiligo. The i https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1721287/full https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-g001.jpg Figure 1. Illustrates various multimodal fusion methods utilizing different attention mechanisms. (A https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-g002.jpg Figure 2. Chlorophyll fluorescence data acquisition process. (A) Soil-cultured soybean sprout stage https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-t001.jpg Table 1. Analysis of soybean chlorophyll fluorescence salt tolerance text data. https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-g003.jpg Figure 3. Chlorophyll fluorescence data acquisition process. (A) NPQ_Lss normal distribution Plot. ( https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-t002.jpg Table 2. Sample size of the salt stress dataset. https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-g004.jpg Figure 4. Chlorophyll fluorescence 5 phenotypic data categories. Level 1: Healthy green. Level 2: Sl https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-g005.jpg Figure 5. The Mm-VitnNet model architecture processes two types of multimodal data: images and text. https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-g006.jpg Figure 6. ITSAI module. First, both image and text modalities independently undergo multi-head self- https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-g007.jpg Figure 7. ITLeSAMM modules. This module employs two learnable tokens to capture information from eac https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-t003.jpg Table 3. Comparison of performance across different network models. https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-g008.jpg Figure 8. Training and evaluation results of the model. (A) Loss visualization. (B) Accuracy visuali https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-t004.jpg Table 4. Performance comparison with attention mechanism. https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-t005.jpg Table 5. Performance comparison based on stage computation proportion. https://www.frontiersin.org/files/Articles/1721287/fpls-17-1721287-HTML/image_m/fpls-17-1721287-t006.jpg Table 6. The number of principal components of the model text is selected. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1693472/full https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-t001.jpg Table 1. Primers used in this study for RT-qPCR. https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-g001.jpg Figure 1. WLWDS-11 ameliorates cardiac injury in MIRI mice: evaluation based on key detection indice https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-g002.jpg Figure 2. WLWDS-11 attenuates cardiac remodeling myocardial and apoptosis in MIRI mice. (A) Shows th https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-g003.jpg Figure 3. Metabolomic profiling reveals WLWDS-11-regulated metabolic perturbations and differential https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-t002.jpg Table 2. Statistical table of differential metabolites. https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-g004.jpg Figure 4. KEGG pathway enrichment analysis reveals WLWDS-11-mediated regulation of metabolic and sig https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-g005.jpg Figure 5. WLWDS-11 modulates gut microbiota composition, diversity, and key species abundance in MIR https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-t003.jpg Table 3. Table of species grace differences. https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-g006.jpg Figure 6. LEfSe-based identification of differential gut microbial taxa and functional enrichment an https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-g007.jpg Figure 7. Integrated IPCA/PCA visualization and correlation analysis of gut microbiota-metabolite in https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-g008.jpg Figure 8. Key regulators, microbiota-metabolite crosstalk, and functional pathway enrichment in the https://www.frontiersin.org/files/Articles/1693472/fmicb-16-1693472-HTML-r1/image_m/fmicb-16-1693472-g009.jpg Figure 9. WLWDS-11 regulates mitochondrial function-related genes, necroptosis pathway components, a https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1707232/full https://www.frontiersin.org/files/Articles/1707232/fbioe-14-1707232-HTML/image_m/fbioe-14-1707232-g001.jpg Figure 1. Stages of staphylococcal biofilm formation. https://www.frontiersin.org/files/Articles/1707232/fbioe-14-1707232-HTML/image_m/fbioe-14-1707232-g002.jpg Figure 2. The QS system is closely related to the formation and maturation of biofilms: turn upregul https://www.frontiersin.org/files/Articles/1707232/fbioe-14-1707232-HTML/image_m/fbioe-14-1707232-t001.jpg Table 1. Comparative overview of anti-biofilm coating strategies for orthopedic implants. https://www.frontiersin.org/files/Articles/1707232/fbioe-14-1707232-HTML/image_m/fbioe-14-1707232-g003.jpg Figure 3. Anti-adhesion/antifouling coatings. (a) Superhydrophilic surface; (b) superhydrophobic sur https://www.frontiersin.org/files/Articles/1707232/fbioe-14-1707232-HTML/image_m/fbioe-14-1707232-t002.jpg Table 2. Clinical scenario-oriented matching of anti-biofilm coating strategies in orthopedic implan https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1704211/full https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-g001.jpg Figure 1. The flowchart of the study. https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-t001.jpg Table 1. The baseline characteristics of participants. https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-g002.jpg Figure 2. Distribution of AST/ALT across different populations. The histograms show the distribution https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-g003.jpg Figure 3. AST/ALT density distribution by diabetes status across populations. Density plots showing https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-g004.jpg Figure 4. Age-specific incidence rates of diabetes stratified by sex and ethnicity The figure depict https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-t002.jpg Table 2. Incidence rate of incident diabetes. https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-g005.jpg Figure 5. Diabetes incidence by AST/ALT quartiles. Bar charts showing diabetes incidence (%) by AST/ https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-t003.jpg Table 3. The results of univariate analysis. https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-g006.jpg Figure 6. Kaplan-Meier curves for diabetes-free survival stratified by quartiles. The figure analysi https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-t004.jpg Table 4. Relationship between AST/ALT and the incident diabetes in different models. https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-t005.jpg Table 5. Relationship between AST/ALT and diabetes in different sensitivity analyses. https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-g007.jpg Figure 7. Dose-response relationship between AST/ALT and diabetes risk with ethnic-specific threshol https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-t006.jpg Table 6. The result of the two-piecewise Cox regression model. https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-t007.jpg Table 7. Effect size of AST/ALT on incident diabetes in prespecified and exploratory subgroups. https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-g008.jpg Figure 8. Discriminative performance of the AST/ALT for diabetes risk in overall, Chinese, and Japan https://www.frontiersin.org/files/Articles/1704211/fendo-16-1704211-HTML-r1/image_m/fendo-16-1704211-t008.jpg Table 8. Mediation analysis of the effect of BMI on the AST/ALT-diabetes relationship. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1718524/full https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-t001.jpg Table 1. Characteristics of the 688 bacterial isolates obtained from ginseng sprouts. https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-t002.jpg Table 2. Accession numbers of the five LAB strains using BLAST (16S rRNA gene). https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-g001.jpg Figure 1. Phylogenetic trees of various distantly related species of bacteria, based on the 16S rRNA https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-g002.jpg Figure 2. The presence of various stressors (pH, ethanol, and H2O2) did not affect the growth rate o https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-g003.jpg Figure 3. The five lactic acid bacterial (LAB) strains exhibit tolerance to acid and bile salts. (A) https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-t003.jpg Table 3. Representative responses of the five lactic acid bacterial isolates against six different a https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-t004.jpg Table 4. Antibacterial activity of selected lactic acid bacteria (LAB) strains against Escherichia c https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-g004.jpg Figure 4. Certain candidate probiotic strains showed higher adhesion ability compared to LGG. Shown https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-g005.jpg Figure 5. The five bacterial isolates from ginseng sprouts showed antioxidant properties as evidence https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-g006.jpg Figure 6. The relative mRNA expression of the pro-inflammatory cytokine TNF-α was inhibited by treat https://www.frontiersin.org/files/Articles/1718524/fnut-12-1718524-HTML/image_m/fnut-12-1718524-g007.jpg Figure 7. Treatment with protein extracts (PE) of the five candidate probiotic strains affects the v https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1774496/full https://www.frontiersin.org/files/Articles/1774496/feduc-11-1774496-HTML/image_m/feduc-11-1774496-g001.jpg Figure 1. Still images from the oyster reef exploration 360° video experience. (A) Natural oyster re https://www.frontiersin.org/files/Articles/1774496/feduc-11-1774496-HTML/image_m/feduc-11-1774496-g002.jpg Figure 2. Activities from the hands-on oyster reef lesson. (A) Students sorting through an oyster re https://www.frontiersin.org/files/Articles/1774496/feduc-11-1774496-HTML/image_m/feduc-11-1774496-g003.jpg Figure 3. Crossover study design used to compare the 360° video and hands-on lessons to each other ( https://www.frontiersin.org/files/Articles/1774496/feduc-11-1774496-HTML/image_m/feduc-11-1774496-g004.jpg Figure 4. Students’ knowledge assessment scores compared between the four assessment versions. N = 7 https://www.frontiersin.org/files/Articles/1774496/feduc-11-1774496-HTML/image_m/feduc-11-1774496-g005.jpg Figure 5. Connectedness to nature scale (CNS) mean scores compared across the different lesson versi https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1704105/full https://www.frontiersin.org/files/Articles/1704105/fpsyg-16-1704105-HTML/image_m/fpsyg-16-1704105-t001.jpg Table 1. Demographic characteristics. https://www.frontiersin.org/files/Articles/1704105/fpsyg-16-1704105-HTML/image_m/fpsyg-16-1704105-t002.jpg Table 2. Scale correlation analysis. https://www.frontiersin.org/files/Articles/1704105/fpsyg-16-1704105-HTML/image_m/fpsyg-16-1704105-t003.jpg Table 3. The influence of SRP on lottery addiction. https://www.frontiersin.org/files/Articles/1704105/fpsyg-16-1704105-HTML/image_m/fpsyg-16-1704105-t004.jpg Table 4. The moderating effect of SS on the relationship between SRP and addiction behavior. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1753885/full https://www.frontiersin.org/files/Articles/1753885/fpubh-14-1753885-HTML-r1/image_m/fpubh-14-1753885-g001.jpg Figure 1. Bio-psycho-social impact factors of coping. https://www.frontiersin.org/files/Articles/1753885/fpubh-14-1753885-HTML-r1/image_m/fpubh-14-1753885-g002.jpg Figure 2. CoviDrug consortium. https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2025.1577568/full https://www.frontiersin.org/files/Articles/1577568/fgwh-06-1577568-HTML/image_m/fgwh-06-1577568-t001.jpg Table 1. Self-reported demographic characteristics of the community stakeholders by project. https://www.frontiersin.org/files/Articles/1577568/fgwh-06-1577568-HTML/image_m/fgwh-06-1577568-t002.jpg Table 2. Alignment of project approach with CBPR principles. https://www.frontiersin.org/files/Articles/1577568/fgwh-06-1577568-HTML/image_m/fgwh-06-1577568-t003.jpg Table 3. Summary of the key lessons learned supporting evaluation data. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1710189/full https://www.frontiersin.org/files/Articles/1710189/fpubh-14-1710189-HTML-r1/image_m/fpubh-14-1710189-t001.jpg Table 1. Frequency (N) and relative frequency (%) of characteristics among student participants in t https://www.frontiersin.org/files/Articles/1710189/fpubh-14-1710189-HTML-r1/image_m/fpubh-14-1710189-t002.jpg Table 2. Descriptive measures of responses to the items of the Greek version of the SPRS-S scale. https://www.frontiersin.org/files/Articles/1710189/fpubh-14-1710189-HTML-r1/image_m/fpubh-14-1710189-g001.jpg Figure 1. Selection of number of factors based on Horn’s parallel analysis criterion. https://www.frontiersin.org/files/Articles/1710189/fpubh-14-1710189-HTML-r1/image_m/fpubh-14-1710189-t003.jpg Table 3. Factor matrix. https://www.frontiersin.org/files/Articles/1710189/fpubh-14-1710189-HTML-r1/image_m/fpubh-14-1710189-t004.jpg Table 4. Factor loadings from confirmatory factor analysis. https://www.frontiersin.org/files/Articles/1710189/fpubh-14-1710189-HTML-r1/image_m/fpubh-14-1710189-t005.jpg Table 5. Measurement invariance results from confirmatory factor analysis. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1787708/full https://www.frontiersin.org/files/Articles/1787708/fpsyg-17-1787708-HTML-r1/image_m/fpsyg-17-1787708-t001.jpg Table 1. Participants’ characteristics. https://www.frontiersin.org/files/Articles/1787708/fpsyg-17-1787708-HTML-r1/image_m/fpsyg-17-1787708-t002.jpg Table 2. The associations between background factors and IHSI/FHSI. https://www.frontiersin.org/files/Articles/1787708/fpsyg-17-1787708-HTML-r1/image_m/fpsyg-17-1787708-t003.jpg Table 3. Pearson correlations. https://www.frontiersin.org/files/Articles/1787708/fpsyg-17-1787708-HTML-r1/image_m/fpsyg-17-1787708-g001.jpg Figure 1. Results of structural equation modeling. The model was adjusted for age, sex, perceived fa https://www.frontiersin.org/files/Articles/1787708/fpsyg-17-1787708-HTML-r1/image_m/fpsyg-17-1787708-t004.jpg Table 4. Mediation analysis. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1699362/full https://www.frontiersin.org/files/Articles/1699362/fimmu-16-1699362-HTML/image_m/fimmu-16-1699362-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1699362/fimmu-16-1699362-HTML/image_m/fimmu-16-1699362-g001.jpg Figure 1. Kaplan-Meier curve for OS comparing male and female patients. https://www.frontiersin.org/files/Articles/1699362/fimmu-16-1699362-HTML/image_m/fimmu-16-1699362-t002.jpg Table 2. Cox regression for n=29 female patients with 20 events. https://www.frontiersin.org/files/Articles/1699362/fimmu-16-1699362-HTML/image_m/fimmu-16-1699362-t003.jpg Table 3. Cox regression for n=44 male patients with 27 events. https://www.frontiersin.org/files/Articles/1699362/fimmu-16-1699362-HTML/image_m/fimmu-16-1699362-g002.jpg Figure 2. (A) Kaplan-Meier curve for OS comparing male and female patients with abscopal benefit. (B https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1629038/full https://www.frontiersin.org/files/Articles/1629038/fcimb-15-1629038-HTML/image_m/fcimb-15-1629038-g001.jpg Figure 1. Annual Top Five Specimen Types (2021–2023) This figure shows the proportional distribution https://www.frontiersin.org/files/Articles/1629038/fcimb-15-1629038-HTML/image_m/fcimb-15-1629038-g002.jpg Figure 2. Trends in relative proportions of top five bacterial isolates (2021–2023). This line chart https://www.frontiersin.org/files/Articles/1629038/fcimb-15-1629038-HTML/image_m/fcimb-15-1629038-g003.jpg Figure 3. Taxonomic distribution of clinical pathogens (2021–2023). This composite figure presents t https://www.frontiersin.org/files/Articles/1629038/fcimb-15-1629038-HTML/image_m/fcimb-15-1629038-g004.jpg Figure 4. AMR in Enterobacteriaceae. This figure illustrates resistance trends among Enterobacteriac https://www.frontiersin.org/files/Articles/1629038/fcimb-15-1629038-HTML/image_m/fcimb-15-1629038-g005.jpg Figure 5. Resistance trends in MRSA and Enterococcus species. (A) MRSA. (B) E. faecalis. (C) E. faec https://www.frontiersin.org/files/Articles/1629038/fcimb-15-1629038-HTML/image_m/fcimb-15-1629038-g006.jpg Figure 6. AMR Trends in Pseudomonas aeruginosa and Acinetobacter spp. (A) Carbapenem-susceptible P. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1762886/full https://www.frontiersin.org/files/Articles/1762886/fpubh-14-1762886-HTML/image_m/fpubh-14-1762886-t001.jpg Table 1. Incidence of poverty. https://www.frontiersin.org/files/Articles/1762886/fpubh-14-1762886-HTML/image_m/fpubh-14-1762886-g001.jpg Figure 1. Share of medical expenditure in total consumption expenditure across income quintiles. https://www.frontiersin.org/files/Articles/1762886/fpubh-14-1762886-HTML/image_m/fpubh-14-1762886-t002.jpg Table 2. Share of medical expenditure in total consumption expenditure across income. https://www.frontiersin.org/files/Articles/1762886/fpubh-14-1762886-HTML/image_m/fpubh-14-1762886-t003.jpg Table 3. Descriptive statistics. https://www.frontiersin.org/files/Articles/1762886/fpubh-14-1762886-HTML/image_m/fpubh-14-1762886-t004.jpg Table 4. Logit regression results. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1737975/full https://www.frontiersin.org/files/Articles/1737975/fonc-16-1737975-HTML/image_m/fonc-16-1737975-g001.jpg Figure 1. The uPA/uPAR/PAI-1 axis in tumor biology. PAI-1 inhibits plasmin-mediated extracellular ma https://www.frontiersin.org/files/Articles/1737975/fonc-16-1737975-HTML/image_m/fonc-16-1737975-g002.jpg Figure 2. The PAI-1 paradox.PAI-1 inhibits plasmin generation, limiting extracellular matrix (ECM) d https://www.frontiersin.org/files/Articles/1737975/fonc-16-1737975-HTML/image_m/fonc-16-1737975-g003.jpg Figure 3. PAI-1 and the tumor microenvironment. Tumor cells secrete PAI-1, which binds to vitronecti https://www.frontiersin.org/files/Articles/1737975/fonc-16-1737975-HTML/image_m/fonc-16-1737975-t001.jpg Table 1. Tumor-specific PAI-1 (SERPINE1) expression, biological mechanisms, clinical associations, a https://www.frontiersin.org/files/Articles/1737975/fonc-16-1737975-HTML/image_m/fonc-16-1737975-g004.jpg Figure 4. Therapeutic strategies targeting PAI-1. PAI-1 can be inhibited by multiple approaches, inc https://www.frontiersin.org/files/Articles/1737975/fonc-16-1737975-HTML/image_m/fonc-16-1737975-t002.jpg Table 2. – PAI-1 inhibitors and pharmacologic profiles. https://www.frontiersin.org/files/Articles/1737975/fonc-16-1737975-HTML/image_m/fonc-16-1737975-t003.jpg Table 3. Overview of combination strategies involving PAI-1 inhibition across tumor models. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1792645/full https://www.frontiersin.org/files/Articles/1792645/fcell-14-1792645-HTML/image_m/fcell-14-1792645-g001.jpg Figure 1. Structural and functional basis of mitochondrial quality control (MQC). https://www.frontiersin.org/files/Articles/1792645/fcell-14-1792645-HTML/image_m/fcell-14-1792645-g002.jpg Figure 2. Synergistic mechanisms of exercise in regulating the dysregulation of mitochondrial qualit https://www.frontiersin.org/files/Articles/1792645/fcell-14-1792645-HTML/image_m/fcell-14-1792645-t001.jpg Table 1. Exercise-mediated modulation of mitochondrial quality control (MQC) in aging. https://www.frontiersin.org/files/Articles/1792645/fcell-14-1792645-HTML/image_m/fcell-14-1792645-g003.jpg Figure 3. Mechanisms by which Different Exercise Modes Regulate MQC to Delay Aging. https://www.frontiersin.org/files/Articles/1792645/fcell-14-1792645-HTML/image_m/fcell-14-1792645-t002.jpg Table 2. Comparison of mechanisms by which different exercise modalities regulate aging-related mito https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1796675/full https://www.frontiersin.org/files/Articles/1796675/fimmu-17-1796675-HTML/image_m/fimmu-17-1796675-g001.jpg Figure 1. Schematic representation of key barriers in solid tumor CAR-T cell therapy: (A) antigen he https://www.frontiersin.org/files/Articles/1796675/fimmu-17-1796675-HTML/image_m/fimmu-17-1796675-t001.jpg Table 1. Major translational barriers limiting CAR-T therapy in solid tumors. https://www.frontiersin.org/files/Articles/1796675/fimmu-17-1796675-HTML/image_m/fimmu-17-1796675-t002.jpg Table 2. Engineering strategies in development (next-gen CARs) and their status. https://www.frontiersin.org/files/Articles/1796675/fimmu-17-1796675-HTML/image_m/fimmu-17-1796675-t003.jpg Table 3. Representative clinical trials of CAR-T therapy in solid tumors. https://www.frontiersin.org/files/Articles/1796675/fimmu-17-1796675-HTML/image_m/fimmu-17-1796675-g002.jpg Figure 2. Roadmap timeline for translation of CAR-T in solid tumors (2025-2035). This figure illustr https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1699285/full https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-g001.jpg Figure 1. Conceptual framework for gastric adenocarcinoma patient education resources, linking infor https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-t001.jpg Table 1. Inclusion and exclusion criteria. https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-g002.jpg Figure 2. Website selection process for gastric adenocarcinoma patient information. https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-t002.jpg Table 2. Flesch-Kincaid scores with corresponding reading grade levels. https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-t003.jpg Table 3. Comprehensiveness criteria for gastric adenocarcinoma. https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-g003.jpg Figure 3. (A) Mean total DISCERN score and scores by affiliation (p = 0.55), (B) mean DISCERN scores https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-g004.jpg Figure 4. Mean PEMAT understandability scores with 70% recommendation threshold line indicated. (A) https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-g005.jpg Figure 5. Mean PEMAT actionability scores with 70% recommendation threshold line indicated. (A) Mean https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-g006.jpg Figure 6. (A) Mean total Flesch-Kincaid Reading ease score and scores by affiliation with NIH recomm https://www.frontiersin.org/files/Articles/1699285/fdgth-08-1699285-HTML/image_m/fdgth-08-1699285-g007.jpg Figure 7. (A) Mean total comprehensiveness score and scores by affiliation (p = 0.28), (B) mean comp https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1774495/full https://www.frontiersin.org/files/Articles/1774495/fmed-13-1774495-HTML/image_m/fmed-13-1774495-t001.jpg Table 1. Seven pairs of PCR primers used in this study. https://www.frontiersin.org/files/Articles/1774495/fmed-13-1774495-HTML/image_m/fmed-13-1774495-g001.jpg Figure 1. Clinical manifestations, dermoscopy, RCM, pathology, immunohistochemical staining, and San https://www.frontiersin.org/files/Articles/1774495/fmed-13-1774495-HTML/image_m/fmed-13-1774495-g002.jpg Figure 2. (A, B): Clinical manifestations and Sanger sequencing of the second to the fourth proband' https://www.frontiersin.org/files/Articles/1774495/fmed-13-1774495-HTML/image_m/fmed-13-1774495-g003.jpg Figure 3. Clinical manifestations, dermoscopy, RCM, and Sanger sequencing of the sixth proband's fam https://www.frontiersin.org/files/Articles/1774495/fmed-13-1774495-HTML/image_m/fmed-13-1774495-t002.jpg Table 2. Clinical and molecular findings in the cases with CM-AVM patients. https://www.frontiersin.org/files/Articles/1774495/fmed-13-1774495-HTML/image_m/fmed-13-1774495-g004.jpg Figure 4. The impacted amino acid residues of EphB4 and RASA1 protein are highly conserved across sp https://www.frontiersin.org/files/Articles/1774495/fmed-13-1774495-HTML/image_m/fmed-13-1774495-g005.jpg Figure 5. Three-dimensional structure prediction map of the EphB4 protein. (A). Overall (A1) and loc https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1644399/full https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-t001.jpg Table 1. FICI results of MIC (Palmatine) and FICI (Palmatine × cefquinome) for 20 E. coli strains. https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g001.jpg Figure 1. FIC index in tested bacterial strains. (a) Chemical structure of cefquinome. (b) Chemical https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g002.jpg Figure 2. Palmatine improves the activity of cefquinome against E.coli. (a) Growth curve of E. coli https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g003.jpg Figure 3. SEM and CLSI observation. (a) Palmatine enhances the damage caused by cefquinome to E.coli https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g004.jpg Figure 4. Palmatine enhances the destructive effect of cefquinome on E.coli. (a) Palmatine enhances https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g005.jpg Figure 5. Transcriptomic analysis of E. coli E93 treated with no drug, palmatine, or the combination https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g006.jpg Figure 6. Transcriptomic differential gene. Filtered differentially expressed genes related to ABC t https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g007.jpg Figure 7. Validation of differential gene expression in transcriptomics. According to the results of https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g008.jpg Figure 8. Phenotypic validation tests based on transcriptome sequencing. (a,b) The addition of palma https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-t002.jpg Table 2. Fractional inhibitory concentration index (FICI) of tested strains before and after the add https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g009.jpg Figure 9. Palmatine restores cefquinome activity in two mouse infection models. (a) In the mouse let https://www.frontiersin.org/files/Articles/1644399/fmicb-16-1644399-HTML-r1/image_m/fmicb-16-1644399-g010.jpg Figure 10. Palmatine restores cefquinome activity to lower bacterial load and tissue damage in the i https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1773990/full https://www.frontiersin.org/files/Articles/1773990/fimmu-17-1773990-HTML/image_m/fimmu-17-1773990-g001.jpg Figure 1. Genetically predicted circulating levels of 35 pyroptosis-related proteins and their assoc https://www.frontiersin.org/files/Articles/1773990/fimmu-17-1773990-HTML/image_m/fimmu-17-1773990-g002.jpg Figure 2. Genetically predicted gut microbial traits associated with ulcerative colitis (UC) risk. I https://www.frontiersin.org/files/Articles/1773990/fimmu-17-1773990-HTML/image_m/fimmu-17-1773990-t001.jpg Table 1. Mediation analysis of gut microbiota–pyroptosis–UC axis. https://www.frontiersin.org/files/Articles/1773990/fimmu-17-1773990-HTML/image_m/fimmu-17-1773990-g003.jpg Figure 3. KLF4 is downregulated in ulcerative colitis and regulates immune-related pathways. (A) Vol https://www.frontiersin.org/files/Articles/1773990/fimmu-17-1773990-HTML/image_m/fimmu-17-1773990-g004.jpg Figure 4. KLF4 expression is associated with anti-TNF-α treatment response in ulcerative colitis (UC https://www.frontiersin.org/files/Articles/1773990/fimmu-17-1773990-HTML/image_m/fimmu-17-1773990-g005.jpg Figure 5. Systemic KLF4 overexpression attenuates dextran sulfate sodium (DSS)-induced colitis. (A) https://www.frontiersin.org/files/Articles/1773990/fimmu-17-1773990-HTML/image_m/fimmu-17-1773990-g006.jpg Figure 6. KLF4 strengthens epithelial barrier markers, lowers permeability, and reduces gasdermin-D https://www.frontiersin.org/files/Articles/1773990/fimmu-17-1773990-HTML/image_m/fimmu-17-1773990-g007.jpg Figure 7. KLF4 reshapes splenic leukocyte composition and reduces inflammatory cytokines. (A) Flow c https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1606273/full https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-g001.jpg Figure 1. Prevalence of mental health issues among Malaysian professionals. https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-g002.jpg Figure 2. The proposed model of integrated concepts of UTAUT and TPB for mental health chatbot behav https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-t001.jpg Table 1. Summary of related studies. https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-t002.jpg Table 2. Summary of demographic profile of the respondents. https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-t003.jpg Table 3. Measurement model results. https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-g003.jpg Figure 3. Structural model. https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-t004.jpg Table 4. Fornell–Larcker results. https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-t005.jpg Table 5. Heterotrait–monotrait ratio (HTMT). https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-t006.jpg Table 6. Summary of hypothesis statements results. https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-t007.jpg Table 7. Summary of re-analysed results (removed factor loadings below 0.5). https://www.frontiersin.org/files/Articles/1606273/fdgth-07-1606273-HTML/image_m/fdgth-07-1606273-t008.jpg https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1768124/full https://www.frontiersin.org/files/Articles/1768124/fmolb-13-1768124-HTML/image_m/fmolb-13-1768124-g001.jpg Figure 1. Schematic diagram of the mechanism by which CST6 inhibits tumor cell growth by regulating https://www.frontiersin.org/files/Articles/1768124/fmolb-13-1768124-HTML/image_m/fmolb-13-1768124-g002.jpg Figure 2. Dual mechanisms of recombinant mouse CST6 (rmCST6) in suppressing osteoclastogenesis and m https://www.frontiersin.org/files/Articles/1768124/fmolb-13-1768124-HTML/image_m/fmolb-13-1768124-g003.jpg Figure 3. Schematic diagram of the mechanism of the CST6–CTSB–SPHK1 signaling axis in breast cancer https://www.frontiersin.org/files/Articles/1768124/fmolb-13-1768124-HTML/image_m/fmolb-13-1768124-g004.jpg Figure 4. Schematic diagram of the oncogenic mechanism of the TBX2–CST6–LGMN signaling axis in breas https://www.frontiersin.org/files/Articles/1768124/fmolb-13-1768124-HTML/image_m/fmolb-13-1768124-t001.jpg Table 1. The inhibitory role of CST6 in tumor-related diseases. https://www.frontiersin.org/files/Articles/1768124/fmolb-13-1768124-HTML/image_m/fmolb-13-1768124-t002.jpg Table 2. The role of CST6 in promoting tumor-related diseases. https://www.frontiersin.org/files/Articles/1768124/fmolb-13-1768124-HTML/image_m/fmolb-13-1768124-t003.jpg Table 3. The role of CST6 in non tumor diseases. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1756355/full https://www.frontiersin.org/files/Articles/1756355/fcell-14-1756355-HTML-r1/image_m/fcell-14-1756355-g001.jpg Figure 1. The picture above is a relevant potential marker for primary dysmenorrhea. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1770614/full https://www.frontiersin.org/files/Articles/1770614/fmicb-17-1770614-HTML/image_m/fmicb-17-1770614-g001.jpg Figure 1. TENS treatment improves the function and bone quality in rats with ATFL injuries. (A) The https://www.frontiersin.org/files/Articles/1770614/fmicb-17-1770614-HTML/image_m/fmicb-17-1770614-g002.jpg Figure 2. Osteogenesis and anti-inflammatory effects of TENS in rats with ATFL injury via regulation https://www.frontiersin.org/files/Articles/1770614/fmicb-17-1770614-HTML/image_m/fmicb-17-1770614-g003.jpg Figure 3. Changes in the intestinal OTUs of ATFL rats after TENS treatment. (A) The species abundanc https://www.frontiersin.org/files/Articles/1770614/fmicb-17-1770614-HTML/image_m/fmicb-17-1770614-g004.jpg Figure 4. FMT which induce by TENS improves ankle function and bone quality via the gut-knee joint a https://www.frontiersin.org/files/Articles/1770614/fmicb-17-1770614-HTML/image_m/fmicb-17-1770614-g005.jpg Figure 5. FMT induced by TENS improved ATFL injury in rats by regulating the NOD2/BMP2/TGF-β signali https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1655811/full https://www.frontiersin.org/files/Articles/1655811/fnut-12-1655811-HTML/image_m/fnut-12-1655811-g001.jpg Figure 1. Comprehensive overview of animal models used to study malnutrition, organized by taxonomic https://www.frontiersin.org/files/Articles/1655811/fnut-12-1655811-HTML/image_m/fnut-12-1655811-t001.jpg Table 1. Overview of experimentally induced malnutrition and metabolic–immune challenge models in ro https://www.frontiersin.org/files/Articles/1655811/fnut-12-1655811-HTML/image_m/fnut-12-1655811-t002.jpg Table 2. Comparative overview of animal species employed in nutritional and malnutrition research. https://www.frontiersin.org/files/Articles/1655811/fnut-12-1655811-HTML/image_m/fnut-12-1655811-t003.jpg Table 3. Comparative overview of animal models in malnutrition research. https://www.frontiersin.org/journals/microbiomes/articles/10.3389/frmbi.2025.1619859/full https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-t001.jpg Table 1. Sampling dates and incubation durations for standard and in situ cultivation devices. https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-t002.jpg Table 2. Number of colonies isolated, categorized by cultivation method and subculture media type. https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-g001.jpg Figure 1. Overlap of OTUs cultured by each approach. Numbers within the diagram indicate OTUs unique https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-g002.jpg Figure 2. Distribution of recovered isolates, grouped by the taxonomic order of their corresponding https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-g003.jpg Figure 3. Overlap of OTUs recovered using different in situ methods. N indicates the total number of https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-g004.jpg Figure 4. Unique in situ cultivation methods yield distinct taxonomic profiles among recovered isola https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-g005.jpg Figure 5. Overlap of OTUs initially recovered from diffusion chambers and subsequently subcultured u https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-t003.jpg Table 3. Cultivation preference of different taxonomic groups. https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-g006.jpg Figure 6. Different cultivation methodologies provide access to unique branches of the phylogenetic https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-t004.jpg Table 4. Number of OTUs captured using different cultivation methods, categorized by their sequence https://www.frontiersin.org/files/Articles/1619859/frmbi-04-1619859-HTML-r1/image_m/frmbi-04-1619859-t005.jpg Table 5. Comparison of cultivation methods based on isolation power, isolation efficiency, and most https://www.frontiersin.org/journals/anesthesiology/articles/10.3389/fanes.2025.1650491/full https://www.frontiersin.org/files/Articles/1650491/fanes-04-1650491-HTML/image_m/fanes-04-1650491-g001.jpg Figure 1. Multi-system variables that influence clinical and patient-reported outcomes. Visual repre https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1786023/full https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-t001.jpg Table 1. General characteristics of elderly participants in CNHS 2015–2017. https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-g001.jpg Figure 1. Association of atherogenic index of plasma and dietary inflammation index with hypertensio https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-g002.jpg Figure 2. Association of atherogenic index of plasma and dietary inflammatory index with hypertensio https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-t002.jpg Table 2. Interactive effects of atherogenic index of plasma and dietary inflammatory index on hypert https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-g003.jpg Figure 3. Joint associations of atherogenic index of plasma and dietary inflammatory index with hype https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-t003.jpg Table 3. Interactive effects of atherogenic index of plasma and dietary inflammatory index on hypert https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-t004.jpg Table 4. Joint associations of atherogenic index of plasma and dietary inflammatory index with hyper https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-t005.jpg Table 5. Sensitivity analysis for the association between dietary inflammatory index and hypertensio https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-t006.jpg Table 6. Sensitivity analysis for the interactive effects of atherogenic index of plasma and dietary https://www.frontiersin.org/files/Articles/1786023/fnut-13-1786023-HTML-r2/image_m/fnut-13-1786023-t007.jpg Table 7. Sensitivity analysis for the joint associations of atherogenic index of plasma and dietary https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1738650/full https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-gr0001.jpg Graphical Abstract. Illustration of a plant showing the effects of bio-priming with PGPR (plant grow https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-g001.jpg Figure 1. Schematic overview of the research process: the figure illustrates the sequential steps in https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-g002.jpg Figure 2. The phylogenetic tree shows the evolutionary relationships of Pseudomonas sp. (VITK-1), Bu https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-g003.jpg Figure 3. The effect of plant growth-promoting rhizobacteria (PGPR) on seed priming for germination https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-t001.jpg Table 1. Characterization of soil (VIT agriculture field). https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-g004.jpg Figure 4. Effect of bacterial seed priming on chlorophyll content and SPAD values of tomato plants u https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-t002.jpg Table 2. Effect of bacterial inoculation on physiological growth parameters of tomato plants under a https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-t003.jpg Table 3. Efficacy of bacterial treatment on nutrient accumulation in dried root samples of plants gr https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-g005.jpg Figure 5. The impact of Pseudomonas sp., Burkholderia sp., and their combined inoculation on the gro https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-g006.jpg Figure 6. Relative expression of nutrient transporter and stress-responsive genes in tomato seedling https://www.frontiersin.org/files/Articles/1738650/fmicb-17-1738650-HTML/image_m/fmicb-17-1738650-g007.jpg Figure 7. Schematic overview of the discussion process. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1667459/full https://www.frontiersin.org/files/Articles/1667459/fcell-14-1667459-HTML-r1/image_m/fcell-14-1667459-g001.jpg Figure 1. Neuronal regulation of cancer hallmarks. The complex cellular architecture of the tumor mi https://www.frontiersin.org/files/Articles/1667459/fcell-14-1667459-HTML-r1/image_m/fcell-14-1667459-g002.jpg Figure 2. Neuron-tumor crosstalk as a driver of tumor progression and therapeutic resistance. Bidire https://www.frontiersin.org/files/Articles/1667459/fcell-14-1667459-HTML-r1/image_m/fcell-14-1667459-t001.jpg Table 1. Neural ligands, receptors and downstream pathways in cancer progression. https://www.frontiersin.org/files/Articles/1667459/fcell-14-1667459-HTML-r1/image_m/fcell-14-1667459-g003.jpg Figure 3. Neuronal regulation of tumor microenvironment. Cancer cells and neurons release neurotroph https://www.frontiersin.org/files/Articles/1667459/fcell-14-1667459-HTML-r1/image_m/fcell-14-1667459-g004.jpg Figure 4. Tumor–nerve interactions leading to neural infiltration and perineural invasion. The tumor https://www.frontiersin.org/files/Articles/1667459/fcell-14-1667459-HTML-r1/image_m/fcell-14-1667459-t002.jpg Table 2. Neural-targeted therapeutic strategies in cancer. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1680896/full https://www.frontiersin.org/files/Articles/1680896/fimmu-16-1680896-HTML/image_m/fimmu-16-1680896-g001.jpg Figure 1. IDO1 catalytic inhibitors increase IDO1 protein expression in human tumor cell lines. (A) https://www.frontiersin.org/files/Articles/1680896/fimmu-16-1680896-HTML/image_m/fimmu-16-1680896-g002.jpg Figure 2. IDO1 catalytic inhibitors prolong the IDO1 protein half-life in FTC-133 cells. (A, B) Cycl https://www.frontiersin.org/files/Articles/1680896/fimmu-16-1680896-HTML/image_m/fimmu-16-1680896-g003.jpg Figure 3. IDO1 catalytic inhibitors enhance the non-enzymatic function of IDO1 in FTC-133 cells. (A) https://www.frontiersin.org/files/Articles/1680896/fimmu-16-1680896-HTML/image_m/fimmu-16-1680896-g004.jpg Figure 4. Pro-tumorigenic effects of IDO1 catalytic inhibitors in FTC-133 cells. (A) Analysis of the https://www.frontiersin.org/files/Articles/1680896/fimmu-16-1680896-HTML/image_m/fimmu-16-1680896-g005.jpg Figure 5. IDO1 protein knockdown abrogates the pro-tumorigenic phenotype of FTC-133 cells. (A) Immun https://www.frontiersin.org/files/Articles/1680896/fimmu-16-1680896-HTML/image_m/fimmu-16-1680896-g006.jpg Figure 6. IDO1 protein degradation abrogates the pro-tumorigenic phenotype of FTC-133 cells. (A) Imm https://www.frontiersin.org/files/Articles/1680896/fimmu-16-1680896-HTML/image_m/fimmu-16-1680896-g007.jpg Figure 7. Dual role of IDO1 in the TME and therapeutic strategies. IDO1 exerts a dual role in the TM https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1660480/full https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-t001.jpg Table 1. Comparison of key features and mAP@0.5 performance of YOLOv8-based models by different auth https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g001.jpg Figure 1. Polymorphic greenhouse tomato dataset, including (a) Horizontal shot, (b) Diagonal downwar https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g002.jpg Figure 2. Greenhouse tomato dataset enhancement. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g003.jpg Figure 3. Make Sense’s labeling of tomato targets. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g004.jpg Figure 4. Structure of SPD-Conv with scale = 2. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g005.jpg Figure 5. Structure diagram of the PPA attention mechanism. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g006.jpg Figure 6. Structure diagram of GSConv convolution. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g007.jpg Figure 7. Structure diagram of the VoV-GSCSP module. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g008.jpg Figure 8. Structure diagram of the Detect_CBAM detection head. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g009.jpg Figure 9. Geometric meanings of DIoU parameters. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g010.jpg Figure 10. Structure diagram of the improved YOLOv8n network. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-t002.jpg Table 2. Summary of experimental environment configuration. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-t003.jpg Table 3. Summary of key parameter settings for the model’s training process. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-t004.jpg Table 4. Ablation experiments on YOLOv8n with Individual innovative modules, using precision, recall https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-t005.jpg Table 5. Ablation experiments on YOLOv8n with multiple innovative modules, using precision, recall, https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-t006.jpg Table 6. A comparison of six loss functions with YOLOv8’s CIoU is made, assessing performance via pr https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g011.jpg Figure 11. Loss Curves of Various Loss Functions. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-t007.jpg Table 7. A comparison of lightweight YOLOv8n variant performance with different module combinations— https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g012.jpg Figure 12. Evaluation metrics of various lightweight models. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-t008.jpg Table 8. A comparison of key metrics for various object detection models in tomato detection tasks, https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g013.jpg Figure 13. Mean average precision curves of various models. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-t009.jpg Table 9. Comparison of key metrics for the improved YOLOv8n model across different extreme weather s https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g014.jpg Figure 14. Comparison of tomato detection effects of various models in polymorphic scenarios. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g015.jpg Figure 15. Comparison of tomato detection effects of various models in complex weather scenarios. https://www.frontiersin.org/files/Articles/1660480/fpls-16-1660480-HTML-r1/image_m/fpls-16-1660480-g016.jpg Figure 16. Visualization of heatmaps for various models. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1805720/full https://www.frontiersin.org/files/Articles/1805720/fmars-13-1805720-HTML/image_m/fmars-13-1805720-g001.jpg Figure 1. Study area map showing the northern South American shelf, the Amazon and Orinoco rivers, t https://www.frontiersin.org/files/Articles/1805720/fmars-13-1805720-HTML/image_m/fmars-13-1805720-g002.jpg Figure 2. Amazon River discharge at Óbidos (2003–2023): (a) seasonal–trend decomposition (STL) of mo https://www.frontiersin.org/files/Articles/1805720/fmars-13-1805720-HTML/image_m/fmars-13-1805720-g003.jpg Figure 3. Spatial distribution of particulate organic carbon (POC) on the North Brazil Shelf provinc https://www.frontiersin.org/files/Articles/1805720/fmars-13-1805720-HTML/image_m/fmars-13-1805720-g004.jpg Figure 4. Spatial polygons and clustering based on POC across the Amazonia–Guiana shelf: map showing https://www.frontiersin.org/files/Articles/1805720/fmars-13-1805720-HTML/image_m/fmars-13-1805720-g005.jpg Figure 5. Spatial distribution of benthic occurrence records across the Amazonian–Guiana shelf: dots https://www.frontiersin.org/files/Articles/1805720/fmars-13-1805720-HTML/image_m/fmars-13-1805720-g006.jpg Figure 6. Class-level composition of occurrence records by region for selected phyla: stacked bar ch https://www.frontiersin.org/files/Articles/1805720/fmars-13-1805720-HTML/image_m/fmars-13-1805720-g007.jpg Figure 7. Regional composition of OBIS benthic records across the Amazonia–Guiana shelf: heatmap sho https://www.frontiersin.org/files/Articles/1805720/fmars-13-1805720-HTML/image_m/fmars-13-1805720-g008.jpg Figure 8. Random forest drivers of phylum composition across regions: (a) heatmap of random forest ( https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1727864/full https://www.frontiersin.org/files/Articles/1727864/fpubh-14-1727864-HTML/image_m/fpubh-14-1727864-g001.jpg Figure 1. Geographical location of Jining City. https://www.frontiersin.org/files/Articles/1727864/fpubh-14-1727864-HTML/image_m/fpubh-14-1727864-g002.jpg Figure 2. Number and incidence rate of PTB in the whole population and by sex in Jining from 2010 to https://www.frontiersin.org/files/Articles/1727864/fpubh-14-1727864-HTML/image_m/fpubh-14-1727864-t001.jpg Table 1. Global autocorrelation results of PTB in Jining from 2010 to 2024. https://www.frontiersin.org/files/Articles/1727864/fpubh-14-1727864-HTML/image_m/fpubh-14-1727864-g003.jpg Figure 3. Local autocorrelation results of PTB in Jining from 2010 to 2024. https://www.frontiersin.org/files/Articles/1727864/fpubh-14-1727864-HTML/image_m/fpubh-14-1727864-t002.jpg Table 2. Spatiotemporal scanning results of PTB in Jining from 2010 to 2024. https://www.frontiersin.org/files/Articles/1727864/fpubh-14-1727864-HTML/image_m/fpubh-14-1727864-g004.jpg Figure 4. Joinpoint regression results of PTB in Jining from 2010 to 2024. https://www.frontiersin.org/files/Articles/1727864/fpubh-14-1727864-HTML/image_m/fpubh-14-1727864-g005.jpg Figure 5. APC model results of PTB in Jining from 2010 to 2024. (A) Local drifts; (B) age effect; (C https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2025.1715516/full https://www.frontiersin.org/files/Articles/1715516/fpos-07-1715516-HTML/image_m/fpos-07-1715516-t001.jpg Table 1. Citizen frames identified via BERTopic-based hybrid content analysis (N = 2024). https://www.frontiersin.org/files/Articles/1715516/fpos-07-1715516-HTML/image_m/fpos-07-1715516-t002.jpg Table 2. Frames identified via BERTopic-based hybrid content analysis in the G20 leaders’ declaratio https://www.frontiersin.org/files/Articles/1715516/fpos-07-1715516-HTML/image_m/fpos-07-1715516-g001.jpg Figure 1. Prioritization of frame categories in citizens’ responses and G20 policy document [as % of https://www.frontiersin.org/files/Articles/1715516/fpos-07-1715516-HTML/image_m/fpos-07-1715516-t003.jpg Table 3. Average marginal effects of individual-level characteristics on the selection of the policy https://www.frontiersin.org/journals/nephrology/articles/10.3389/fneph.2025.1677030/full https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-t002.jpg Table 2. Patient and graft survival outcomes. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g001.jpg Figure 1. Graft survival for first nations transplant recipients. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g002.jpg Figure 2. Death censored graft survival for first nations transplant recipients. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g003.jpg Figure 3. Patient survival for first nations transplant recipients. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g004.jpg Figure 4. Change in PIRCHE and eplet scores for first nations and non-indigenous recipients pre and https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g005.jpg Figure 5. Graft survival first nations vs. non-indigenous transplant recipients. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g006.jpg Figure 6. Patient survival first nations vs. non-indigenous transplant recipients. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g007.jpg Figure 7. Death censored graft survival first nations vs. non-indigenous transplant recipients. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g008.jpg Figure 8. Combined dataset of first nations and non-indigenous into either DSA positive or negative. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g009.jpg Figure 9. Combined cohort graft survival with dnDSA positive vs. negative. https://www.frontiersin.org/files/Articles/1677030/fneph-05-1677030-HTML/image_m/fneph-05-1677030-g010.jpg Figure 10. Combined cohort death censored graft survival dnDSA positive vs. negative. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1684062/full https://www.frontiersin.org/files/Articles/1684062/fpls-16-1684062-HTML/image_m/fpls-16-1684062-g001.jpg Figure 1. (A) Chloroplast genome of G.vaccaria. (B) Mitochondrial genome of of G.vaccaria. Genes are https://www.frontiersin.org/files/Articles/1684062/fpls-16-1684062-HTML/image_m/fpls-16-1684062-t001.jpg Table 1. Gene composition in the chloroplast genome of Gypsophila vaccaria plants. https://www.frontiersin.org/files/Articles/1684062/fpls-16-1684062-HTML/image_m/fpls-16-1684062-t002.jpg Table 2. Genetic composition of mitochondrial genome of the G. vaccaria. https://www.frontiersin.org/files/Articles/1684062/fpls-16-1684062-HTML/image_m/fpls-16-1684062-g002.jpg Figure 2. The repeat analysis of the G.vaccaria chloroplast genomes. (A) The repeat sequences identi https://www.frontiersin.org/files/Articles/1684062/fpls-16-1684062-HTML/image_m/fpls-16-1684062-g003.jpg Figure 3. G. vaccaria of the transfer of chloroplast genes to the mitochondrial genome. The orange a https://www.frontiersin.org/files/Articles/1684062/fpls-16-1684062-HTML/image_m/fpls-16-1684062-g004.jpg Figure 4. Analysis of relative synonymous codon usage (RSCU) in the G vaccaria mitochondrial (A) and https://www.frontiersin.org/files/Articles/1684062/fpls-16-1684062-HTML/image_m/fpls-16-1684062-g005.jpg Figure 5. RNA editing events in the (A) mitochondrial and (B) chloroplast genomes of G vaccaria. The https://www.frontiersin.org/files/Articles/1684062/fpls-16-1684062-HTML/image_m/fpls-16-1684062-g006.jpg Figure 6. Ka/Ks ratio of G. vaccaria and eight other Caryophyllales plant species. https://www.frontiersin.org/files/Articles/1684062/fpls-16-1684062-HTML/image_m/fpls-16-1684062-g007.jpg Figure 7. Phylogenetic relationships of G vaccaria and eight Caryophyllales species based on shared https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1773290/full https://www.frontiersin.org/files/Articles/1773290/fpubh-14-1773290-HTML/image_m/fpubh-14-1773290-t001.jpg Table 1. Main characteristics of the sample. https://www.frontiersin.org/files/Articles/1773290/fpubh-14-1773290-HTML/image_m/fpubh-14-1773290-t002.jpg Table 2. T-test independent sample. https://www.frontiersin.org/files/Articles/1773290/fpubh-14-1773290-HTML/image_m/fpubh-14-1773290-t003.jpg Table 3. Effect size for independent sample. https://www.frontiersin.org/files/Articles/1773290/fpubh-14-1773290-HTML/image_m/fpubh-14-1773290-t004.jpg Table 4. Multiple linear regression predicting perceived stress (PSSTOT). https://www.frontiersin.org/files/Articles/1773290/fpubh-14-1773290-HTML/image_m/fpubh-14-1773290-t005.jpg Table 5. Cluster analysis of lifestyle variables. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1709556/full https://www.frontiersin.org/files/Articles/1709556/fendo-17-1709556-HTML/image_m/fendo-17-1709556-g001.jpg Figure 1. Possible interrelationships among obesity, oxidative stress, and the endometrium. https://www.frontiersin.org/files/Articles/1709556/fendo-17-1709556-HTML/image_m/fendo-17-1709556-g002.jpg Figure 2. Mechanisms underlying obesity-induced oxidative stress. https://www.frontiersin.org/files/Articles/1709556/fendo-17-1709556-HTML/image_m/fendo-17-1709556-g003.jpg Figure 3. Mechanisms underlying the impact of oxidative stress on endometrial structure and function https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1715475/full https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g001.jpg Figure 1. Literature screening process. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t001.jpg Table 1. Baseline data. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g002.jpg Figure 2. ROB 2.0 assessment. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g003.jpg Figure 3. Network diagram. (A) Network Diagram of Summary Data; (B) Network Diagram of BDI Results; https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t002.jpg Table 2. League table for summary data. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g004.jpg Figure 4. SUCRA of summary data. SUCRA, surface under the cumulative ranking curve. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t003.jpg Table 3. League table for BDI. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g005.jpg Figure 5. SUCRA for BDI. SUCRA, surface under the cumulative ranking curve; BDI, Beck Depression Inv https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t004.jpg Table 4. League table for BDI-II. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g006.jpg Figure 6. SUCRA for BDI-II. SUCRA, surface under the cumulative ranking curve; BDI, Beck Depression https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t005.jpg Table 5. League table for PHQ-9. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g007.jpg Figure 7. SUCRA for PHQ-9. SUCRA, surface under the cumulative ranking curve; PHQ-9, Patient Health https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t006.jpg Table 6. League table for CES-D. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g008.jpg Figure 8. SUCRA for CES-D. SUCRA, surface under the cumulative ranking curve; CES-D, Center for Epid https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t007.jpg Table 7. League table for HADS. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g009.jpg Figure 9. SUCRA for HADS. SUCRA, surface under the cumulative ranking curve; HADS, Hospital Anxiety https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t008.jpg Table 8. SUCRA rankings for summary data of outcomes. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t009.jpg Table 9. SUCRA rankings for BDI. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t010.jpg Table 10. SUCRA rankings for BDI-II. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t011.jpg Table 11. SUCRA rankings for PHQ-9. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t012.jpg Table 12. SUCRA rankings for CES-D. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t013.jpg Table 13. SUCRA rankings for HADS. https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-g010.jpg Figure 10. Funnel plot. (A) Funnel Plot for Summary Data; (B) BDI Funnel Plot; (C) BDI-II Funnel Plo https://www.frontiersin.org/files/Articles/1715475/fpsyt-17-1715475-HTML/image_m/fpsyt-17-1715475-t014.jpg Table 14. Summary of key clinical and methodological characteristics across included trials. https://www.frontiersin.org/journals/ocean-sustainability/articles/10.3389/focsu.2026.1752532/full https://www.frontiersin.org/files/Articles/1752532/focsu-04-1752532-HTML/image_m/focsu-04-1752532-g001.jpg Figure 1. Summary of the principal ecological, economic, and social ecosystem services provided by m https://www.frontiersin.org/files/Articles/1752532/focsu-04-1752532-HTML/image_m/focsu-04-1752532-g002.jpg Figure 2. Red mangrove (Rhizophora spp.) exhibiting characteristic prop root structures that provide https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1776308/full https://www.frontiersin.org/files/Articles/1776308/feduc-11-1776308-HTML/image_m/feduc-11-1776308-t001.jpg Table 1. Correlations between AI usage dimensions, critical thinking dimensions, and academic integr https://www.frontiersin.org/files/Articles/1776308/feduc-11-1776308-HTML/image_m/feduc-11-1776308-t002.jpg Table 2. Conditional effects (Simple slopes) of AI usage on critical thinking at low (−1 SD) and Hig https://www.frontiersin.org/files/Articles/1776308/feduc-11-1776308-HTML/image_m/feduc-11-1776308-g001.jpg Figure 1. Conceptual illustration of the moderating role of academic integrity. Conditional level (+ https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1659901/full https://www.frontiersin.org/files/Articles/1659901/fcvm-12-1659901-HTML/image_m/fcvm-12-1659901-g001.jpg Figure 1. Flowchart of patient enrollment. https://www.frontiersin.org/files/Articles/1659901/fcvm-12-1659901-HTML/image_m/fcvm-12-1659901-g002.jpg Figure 2. Postoperative x-ray images of three types of pacemaker mplantations. (A–C) Are the x-ray i https://www.frontiersin.org/files/Articles/1659901/fcvm-12-1659901-HTML/image_m/fcvm-12-1659901-t001.jpg Table 1. Baseline characteristics between RAAVD LUVP and standard BVP group. https://www.frontiersin.org/files/Articles/1659901/fcvm-12-1659901-HTML/image_m/fcvm-12-1659901-t002.jpg Table 2. Comparison between standard BiV and RAAVD LUV pacing group. https://www.frontiersin.org/files/Articles/1659901/fcvm-12-1659901-HTML/image_m/fcvm-12-1659901-t003.jpg Table 3. Comparison between RAAVD LUVP and standard SVP group. https://www.frontiersin.org/files/Articles/1659901/fcvm-12-1659901-HTML/image_m/fcvm-12-1659901-g003.jpg Figure 3. Comparison of differences in various pacing types and different left ventricular pacing el https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1689818/full https://www.frontiersin.org/files/Articles/1689818/fpsyg-17-1689818-HTML/image_m/fpsyg-17-1689818-t001.jpg Table 1. Descriptive statistics of the whole sample and the analyzed subgroups. https://www.frontiersin.org/files/Articles/1689818/fpsyg-17-1689818-HTML/image_m/fpsyg-17-1689818-t002.jpg Table 2. Results of independent samples t-tests comparing AQ-short and SPQ-BRU-CogPer scores between https://www.frontiersin.org/files/Articles/1689818/fpsyg-17-1689818-HTML/image_m/fpsyg-17-1689818-g001.jpg Figure 1. Autistic trait and positive schizotypy subscale scores were compared between participants https://www.frontiersin.org/files/Articles/1689818/fpsyg-17-1689818-HTML/image_m/fpsyg-17-1689818-t003.jpg Table 3. Results of one-way between-subjects ANOVA comparing AQ-short and SPQ-BRU-CogPer scores betw https://www.frontiersin.org/files/Articles/1689818/fpsyg-17-1689818-HTML/image_m/fpsyg-17-1689818-t004.jpg Table 4. Results of pairwise comparisons between faith change groups on AQ-short and SPQ-BRU-CogPer https://www.frontiersin.org/files/Articles/1689818/fpsyg-17-1689818-HTML/image_m/fpsyg-17-1689818-g002.jpg Figure 2. Boxplots showing mean scores of autistic traits and positive schizotypy among faith change https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1730758/full https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-g001.jpg Figure 1. Flowchart for the screening of patients with brainstem glioma (BSG). https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-t001.jpg Table 1. Fundamental Information of Patients. https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-t002.jpg Table 2. Therapeutic approaches. https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-t003.jpg Table 3. Post-radiotherapy symptomatology evolution. https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-g002.jpg Figure 2. Kaplan-Meier survival estimates: (a) mOS = 20.9 months for the entire cohort; (b) mOS = 15 https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-t004.jpg Table 4. Survival datas. https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-t005.jpg Table 5. Univariate analysis. https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-g003.jpg Figure 3. Log-rank analyses: (a) Comparison of KPS status with mOS = 25.2 months for KPS ≥ 70 group https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-g004.jpg Figure 4. Forest plot of the multivariable Cox proportional-hazards model for overall survival: Adju https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-t006.jpg Table 6. Multivariate analysis. https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-t007.jpg Table 7. Radiation adverse reactions/radiation necrosis. https://www.frontiersin.org/files/Articles/1730758/fonc-16-1730758-HTML/image_m/fonc-16-1730758-t008.jpg Table 8. Comparisontables for various studies. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1771014/full https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-t001.jpg Table 1. Research stages. https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-t002.jpg Table 2. Schools and classes participating in the experiment. https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-t003.jpg Table 3. Pre- and post-test results of the experiment. https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-t004.jpg Table 4. Performance by mathematical competency domain. https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-t005.jpg Table 5. Classroom observation results (mean scores on 5-point scale). https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-t006.jpg Table 6. Summary of student-generated digital artifacts and STEM competency indicators. https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-t007.jpg Table 7. University survey results (Likert scale: 1 = strongly disagree, 5 = strongly agree). https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-t008.jpg Table 8. Major categories. https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-t009.jpg Table 9. Comparison of school and university data. https://www.frontiersin.org/files/Articles/1771014/feduc-11-1771014-HTML/image_m/feduc-11-1771014-g001.jpg Figure 1. Cyclical model of school–university continuity through digitalized STEM education. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1757302/full https://www.frontiersin.org/files/Articles/1757302/fmed-13-1757302-HTML/image_m/fmed-13-1757302-g001.jpg Figure 1. The biochemical mechanisms of CD147. CyPA, Cyclophilin A; ERK, extracellular regulated pro https://www.frontiersin.org/files/Articles/1757302/fmed-13-1757302-HTML/image_m/fmed-13-1757302-t001.jpg Table 1. Expression patterns of CD147 in human gestational tissues. https://www.frontiersin.org/files/Articles/1757302/fmed-13-1757302-HTML/image_m/fmed-13-1757302-g002.jpg Figure 2. The mechanism of CD147 in physiological and pathological pregnancy. https://www.frontiersin.org/files/Articles/1757302/fmed-13-1757302-HTML/image_m/fmed-13-1757302-t002.jpg Table 2. CD147 in pathological pregnancies and potential mechanisms. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1679602/full https://www.frontiersin.org/files/Articles/1679602/fpubh-13-1679602-HTML-r1/image_m/fpubh-13-1679602-t001.jpg Table 1. Aim 1 outcome measures and their descriptions. https://www.frontiersin.org/files/Articles/1679602/fpubh-13-1679602-HTML-r1/image_m/fpubh-13-1679602-g001.jpg Figure 1. Flow and organization of participants. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1669004/full https://www.frontiersin.org/files/Articles/1669004/fnut-12-1669004-HTML/image_m/fnut-12-1669004-g001.jpg Figure 1. Pairwise comparison and multiple comparison diagram. CA, conditioning activity; the left b https://www.frontiersin.org/files/Articles/1669004/fnut-12-1669004-HTML/image_m/fnut-12-1669004-g002.jpg Figure 2. PRISMA flow diagram of study selection. https://www.frontiersin.org/files/Articles/1669004/fnut-12-1669004-HTML/image_m/fnut-12-1669004-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/files/Articles/1669004/fnut-12-1669004-HTML/image_m/fnut-12-1669004-t002.jpg Table 2. The characteristics of the studies included. https://www.frontiersin.org/files/Articles/1669004/fnut-12-1669004-HTML/image_m/fnut-12-1669004-g003.jpg Figure 3. Risk of bias assessment. (a) Risk of bias summary plot. (b) Risk of bias traffic light plo https://www.frontiersin.org/files/Articles/1669004/fnut-12-1669004-HTML/image_m/fnut-12-1669004-g004.jpg Figure 4. The effects of PAPE + supplement compared to PAPE + placebo on sports performance (a) and https://www.frontiersin.org/files/Articles/1669004/fnut-12-1669004-HTML/image_m/fnut-12-1669004-t003.jpg Table 3. Subgroup analysis. https://www.frontiersin.org/files/Articles/1669004/fnut-12-1669004-HTML/image_m/fnut-12-1669004-g005.jpg Figure 5. (a) Network diagram of multiple comparisons. (b) Risk of publication bias in multiple comp https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2025.1726096/full https://www.frontiersin.org/files/Articles/1726096/falgy-06-1726096-HTML/image_m/falgy-06-1726096-t001.jpg Table 1. Summary of the study cohorts included in genotyping, spatial transcriptomics, and CyTOF ana https://www.frontiersin.org/files/Articles/1726096/falgy-06-1726096-HTML/image_m/falgy-06-1726096-g001.jpg Figure 1. Prevalence of HαT in biobanked IBD samples and distribution across UC and CD. (A) Study co https://www.frontiersin.org/files/Articles/1726096/falgy-06-1726096-HTML/image_m/falgy-06-1726096-g002.jpg Figure 2. Mast cells from IBD patients with HαT demonstrate increased MRGPRX2 expression. Spatial tr https://www.frontiersin.org/files/Articles/1726096/falgy-06-1726096-HTML/image_m/falgy-06-1726096-g003.jpg Figure 3. CyTOF analysis reveals increased SIGLEC8 in MRGPRX2+ mast cells within the small intestine https://www.frontiersin.org/files/Articles/1726096/falgy-06-1726096-HTML/image_m/falgy-06-1726096-g004.jpg Figure 4. Individuals with IBD and HαT exhibit increased SIGLEC8 expression in colon tissue. Spatial https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1700010/full https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-t001.jpg Table 1. Ingredients and proximate chemical analysis of the experimental diets (g/kg diet, on a dry https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-t002.jpg Table 2. Effects of titanium dioxide nanoparticle (TDNPs) exposure and/or dietary α-sitosterol (STL) https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-t003.jpg Table 3. Effects of titanium dioxide nanoparticle (TDNPs) exposure and/or dietary α-sitosterol (STL) https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-t004.jpg Table 4. Effects of titanium dioxide nanoparticle (TDNPs) exposure and/or dietary α-sitosterol (STL) https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-g001.jpg Figure 1. Effects of titanium dioxide nanoparticle (TDNPs) exposure and/or dietary α-sitosterol (STL https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-g002.jpg Figure 2. Effects of titanium dioxide nanoparticle (TDNPs) exposure and/or dietary α-sitosterol (STL https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-g003.jpg Figure 3. Effects of titanium dioxide nanoparticle (TDNPs) exposure and/or dietary α-sitosterol (STL https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-g004.jpg Figure 4. Effects of titanium dioxide nanoparticle (TDNPs) exposure and/or dietary α-sitosterol (STL https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-g005.jpg Figure 5. (A–D) Representative photomicrographs of H&E-stained sections from the intestine showing n https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-g006.jpg Figure 6. (A–D) Representative photomicrographs of H&E-stained sections from liver showing normal hi https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-g007.jpg Figure 7. (A–D) Representative photomicrographs of H&E-stained sections from the kidney showing norm https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-g008.jpg Figure 8. (A–D) Representative photomicrographs of H&E-stained sections from spleen showing normal h https://www.frontiersin.org/files/Articles/1700010/fmars-12-1700010-HTML/image_m/fmars-12-1700010-t005.jpg Table 5. Effects of titanium dioxide nanoparticle (TDNPs) exposure and/or dietary α-sitosterol (STL) https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1698653/full https://www.frontiersin.org/files/Articles/1698653/fped-13-1698653-HTML/image_m/fped-13-1698653-g001.jpg Figure 1. Schematic representation of the computational model used for PVR estimation using CFD. Inl https://www.frontiersin.org/files/Articles/1698653/fped-13-1698653-HTML/image_m/fped-13-1698653-t001.jpg Table 1. Clinically measured mean flow and pressure values across three centers. https://www.frontiersin.org/files/Articles/1698653/fped-13-1698653-HTML/image_m/fped-13-1698653-g002.jpg Figure 2. Comparison of total PVR between the three PVR estimation methods across all three centers https://www.frontiersin.org/files/Articles/1698653/fped-13-1698653-HTML/image_m/fped-13-1698653-g003.jpg Figure 3. The Fick-based total PVR estimates show poor correlation with those of the CFD-based metho https://www.frontiersin.org/files/Articles/1698653/fped-13-1698653-HTML/image_m/fped-13-1698653-t002.jpg Table 2. Summary of mean total PVR estimates of the three methods. https://www.frontiersin.org/files/Articles/1698653/fped-13-1698653-HTML/image_m/fped-13-1698653-g004.jpg Figure 4. The individual lung resistances using the Cath-CMR method correlate well with those of the https://www.frontiersin.org/files/Articles/1698653/fped-13-1698653-HTML/image_m/fped-13-1698653-g005.jpg Figure 5. The ratio of LPA to RPA PVR reveals no significant difference between the Cath-CMR and CFD https://www.frontiersin.org/files/Articles/1698653/fped-13-1698653-HTML/image_m/fped-13-1698653-g006.jpg Figure 6. Confusion matrix of Fontan failure risk stratification agreement for (a) Fick vs. CFD and https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1747487/full https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g001.jpg Figure 1. PRISMA 2020 flow diagram showing the process of study identification, screening, eligibili https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-t001.jpg Table 1. Characteristics of included literature. https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g002.jpg Figure 2. Risk of bias assessment of the included studies using the ROBINS-I tool. https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g003.jpg Figure 3. The funnel plot for the countermovement jump outcome showed evidence of publication bias. https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g004.jpg Figure 4. The funnel plot for the squat jump outcome showed evidence of publication bias. https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g005.jpg Figure 5. The funnel plot for the 20 m sprint outcome showed evidence of publication bias. https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g006.jpg Figure 6. Forest plot of the effect of plyometric training on countermovement jump compared with con https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g007.jpg Figure 7. Subgroup analysis of countermovement jump by training weeks (≤6 weeks vs. > 6 weeks). https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g008.jpg Figure 8. Forest plot of the effect of plyometric training on squat jump compared with controls (ran https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g009.jpg Figure 9. Forest plot of the effect of plyometric training on 20 m sprint compared with controls (ra https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g010.jpg Figure 10. Forest plot of the effect of plyometric training on squat jump performance in basketball https://www.frontiersin.org/files/Articles/1747487/fphys-17-1747487-HTML/image_m/fphys-17-1747487-g011.jpg Figure 11. Subgroup analysis of the effect of plyometric training on countermovement jump performanc https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1691658/full https://www.frontiersin.org/files/Articles/1691658/fmicb-16-1691658-HTML/image_m/fmicb-16-1691658-t001.jpg Table 1. Overview of microplastic exposure studies and associated sample metadata from NCBI SRA. https://www.frontiersin.org/files/Articles/1691658/fmicb-16-1691658-HTML/image_m/fmicb-16-1691658-g001.jpg Figure 1. A systematic workflow for processing FASTQ data from published NCBI datasets used in this https://www.frontiersin.org/files/Articles/1691658/fmicb-16-1691658-HTML/image_m/fmicb-16-1691658-g002.jpg Figure 2. Plastisphere alpha and beta diversity in environmental samples across different habitats ( https://www.frontiersin.org/files/Articles/1691658/fmicb-16-1691658-HTML/image_m/fmicb-16-1691658-g003.jpg Figure 3. Relative abundance of potential PDB genera, expressed as percentage values (e.g., 0.2 = 20 https://www.frontiersin.org/files/Articles/1691658/fmicb-16-1691658-HTML/image_m/fmicb-16-1691658-g004.jpg Figure 4. (A) NDB exhibiting Pearson’s correlation coefficient greater than 0.60 with potential PDB. https://www.frontiersin.org/files/Articles/1691658/fmicb-16-1691658-HTML/image_m/fmicb-16-1691658-g005.jpg Figure 5. (A) Mutual linear interactions between potential PDB and NDB based on Pearson’s correlatio https://www.frontiersin.org/files/Articles/1691658/fmicb-16-1691658-HTML/image_m/fmicb-16-1691658-g006.jpg Figure 6. PCA of potential PDB and NDB abundance across different habitats. (A) 2D scatter plot show https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1696120/full https://www.frontiersin.org/files/Articles/1696120/fimmu-17-1696120-HTML/image_m/fimmu-17-1696120-g001.jpg Figure 1. The mechanism of mitochondrial dysfunction in OA. https://www.frontiersin.org/files/Articles/1696120/fimmu-17-1696120-HTML/image_m/fimmu-17-1696120-g002.jpg Figure 2. Mitochondrial therapy as a target in OA treatment. https://www.frontiersin.org/files/Articles/1696120/fimmu-17-1696120-HTML/image_m/fimmu-17-1696120-g003.jpg Figure 3. Mitochondrial transfer/transplantation treatment in OA. https://www.frontiersin.org/files/Articles/1696120/fimmu-17-1696120-HTML/image_m/fimmu-17-1696120-t001.jpg Table 1. Summary of key studies on natural products improving mitochondrial function in OA. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1750568/full https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t001.jpg Table 1. Comparison of sociodemographic data between the ID caregiver group and the comparison group https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t002.jpg Table 2. Comparison of QOL between the ID caregiver group and the comparison group. https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t003.jpg Table 3. QOL of primary caregivers of children with ID with different sociodemographic data. https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t004.jpg Table 4. Correlation between sociodemographic data and QOL scores of primary caregivers of children https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t005.jpg Table 5. Comparison of anxiety scores between study and comparison groups. https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t006.jpg Table 6. Correlation between anxiety and QOL of primary caregivers of children with ID. https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t007.jpg Table 7. Comparison of depression score between study and comparison groups. https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t008.jpg Table 8. Correlation between depression and QOL of primary caregivers of children with ID. https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t009.jpg Table 9A. Hierarchical regression predicting PC. https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t010.jpg Table 9B. Hierarchical regression predicting PW. https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t011.jpg Table 9C. Hierarchical regression predicting SR. https://www.frontiersin.org/files/Articles/1750568/fpsyt-17-1750568-HTML/image_m/fpsyt-17-1750568-t012.jpg Table 9D. Hierarchical regression predicting EN. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1661125/full https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-t001.jpg Table 1. Exercise intensity progression for combined balance and strength program. https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-g001.jpg Figure 1. The combined balance and strength exercise intervention. https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-g002.jpg Figure 2. Marker placements on anatomical reference points used for motion capture. https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-g003.jpg Figure 3. Three-dimensional marker trajectories captured by the Qualisys motion analysis system duri https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-g004.jpg Figure 4. Calculation workflow for joint force power (JFP), segmental power due to torque (STP), and https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-t002.jpg Table 2. Demographic profile of study participants. https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-t003.jpg Table 3. Paired-samples t-test results comparing pre- and post-intervention outcomes (n = 23). https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-t004.jpg Table 4. Total variance explained by the four principal components derived from PCA (n = 24 variable https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-t005.jpg Table 5. Rotated component matrix from principal component analysis of energy flow and gait variable https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-t006.jpg Table 6. Component transformation matrix of energy flow variables. https://www.frontiersin.org/files/Articles/1661125/fspor-07-1661125-HTML/image_m/fspor-07-1661125-g005.jpg Figure 5. Dendrogram illustrating the results of hierarchical clustering using Ward’s method and squ https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1707847/full https://www.frontiersin.org/files/Articles/1707847/fneur-16-1707847-HTML/image_m/fneur-16-1707847-g001.jpg Figure 1. CONSORT flow diagram. https://www.frontiersin.org/files/Articles/1707847/fneur-16-1707847-HTML/image_m/fneur-16-1707847-g002.jpg Figure 2. Improvement in prognosis of stroke patients with dysphagia by discharge planning. (A) The https://www.frontiersin.org/files/Articles/1707847/fneur-16-1707847-HTML/image_m/fneur-16-1707847-g003.jpg Figure 3. Improvement in discharge readiness of stroke patients with dysphagia by the discharge plan https://www.frontiersin.org/files/Articles/1707847/fneur-16-1707847-HTML/image_m/fneur-16-1707847-g004.jpg Figure 4. Improvement in self-management ability of stroke patients with dysphagia by discharge plan https://www.frontiersin.org/files/Articles/1707847/fneur-16-1707847-HTML/image_m/fneur-16-1707847-g005.jpg Figure 5. Improvement of safe feeding in stroke patients with dysphagia by the discharge planning. D https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1744657/full https://www.frontiersin.org/files/Articles/1744657/fmed-12-1744657-HTML/image_m/fmed-12-1744657-g001.jpg Figure 1. Developmental workflow for generating the 10 tips. https://www.frontiersin.org/files/Articles/1744657/fmed-12-1744657-HTML/image_m/fmed-12-1744657-t001.jpg Table 1. Literature themes and implications. https://www.frontiersin.org/files/Articles/1744657/fmed-12-1744657-HTML/image_m/fmed-12-1744657-g002.jpg Figure 2. Conceptual framework for AI-supported OSCE station development. https://www.frontiersin.org/files/Articles/1744657/fmed-12-1744657-HTML/image_m/fmed-12-1744657-t002.jpg Table 2. Blueprint snapshot (AI-assisted). https://www.frontiersin.org/files/Articles/1744657/fmed-12-1744657-HTML/image_m/fmed-12-1744657-t003.jpg Table 3. Structured case template. https://www.frontiersin.org/files/Articles/1744657/fmed-12-1744657-HTML/image_m/fmed-12-1744657-t004.jpg Table 4. Mini-curriculum for AI training in assessment. https://www.frontiersin.org/files/Articles/1744657/fmed-12-1744657-HTML/image_m/fmed-12-1744657-t005.jpg Table 5. AI case validation workflow. https://www.frontiersin.org/files/Articles/1744657/fmed-12-1744657-HTML/image_m/fmed-12-1744657-t006.jpg Table 6. Case variations for acute chest pain. https://www.frontiersin.org/files/Articles/1744657/fmed-12-1744657-HTML/image_m/fmed-12-1744657-t007.jpg Table 7. Checklist (excerpt). https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1752283/full https://www.frontiersin.org/files/Articles/1752283/fmicb-17-1752283-HTML/image_m/fmicb-17-1752283-g001.jpg Figure 1. Sampling sites and plant characteristics of FTPs. Map of Jiangsu and Zhejiang Provinces sh https://www.frontiersin.org/files/Articles/1752283/fmicb-17-1752283-HTML/image_m/fmicb-17-1752283-g002.jpg Figure 2. Diversity patterns of the FTP microbiome. (a) Heat tree depicting the taxonomic structure https://www.frontiersin.org/files/Articles/1752283/fmicb-17-1752283-HTML/image_m/fmicb-17-1752283-g003.jpg Figure 3. Composition and taxonomy of the FTP microbiome. (a) Distribution of microbial classes acco https://www.frontiersin.org/files/Articles/1752283/fmicb-17-1752283-HTML/image_m/fmicb-17-1752283-g004.jpg Figure 4. Processes driving FTP biogeographic patterns. (a) DDPs for the FTP bacterial and fungal mi https://www.frontiersin.org/files/Articles/1752283/fmicb-17-1752283-HTML/image_m/fmicb-17-1752283-g005.jpg Figure 5. Co-occurrence network properties of bacterial and fungal communities vary across FTPs. Co- https://www.frontiersin.org/files/Articles/1752283/fmicb-17-1752283-HTML/image_m/fmicb-17-1752283-g006.jpg Figure 6. Phylogenetic structure of the FTP microbiome. (a) Phylogenetic tree annotated with phylum- https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1762596/full https://www.frontiersin.org/files/Articles/1762596/fendo-17-1762596-HTML/image_m/fendo-17-1762596-g001.jpg Figure 1. Proposed and experimentally supported mechanisms linking hyperprolactinemia and obesity. D https://www.frontiersin.org/files/Articles/1762596/fendo-17-1762596-HTML/image_m/fendo-17-1762596-g002.jpg Figure 2. Potential mechanism by which obesity may promote an increase in prolactin secretion. These https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1652622/full https://www.frontiersin.org/files/Articles/1652622/fcell-14-1652622-HTML/image_m/fcell-14-1652622-g001.jpg Figure 1. Schematic diagram of bmpr2a/b gene knockout in zebrafish. (A,B) Schematic diagrams of sgRN https://www.frontiersin.org/files/Articles/1652622/fcell-14-1652622-HTML/image_m/fcell-14-1652622-g002.jpg Figure 2. Ventricular morphology and heart parameters analysis via M-mode at 48 and 72 hpf. (A) M-mo https://www.frontiersin.org/files/Articles/1652622/fcell-14-1652622-HTML/image_m/fcell-14-1652622-g003.jpg Figure 3. bmpr2a−/−;bmpr2b−/− affects the myofibril substructures. (A) IF-detected subcellular local https://www.frontiersin.org/files/Articles/1652622/fcell-14-1652622-HTML/image_m/fcell-14-1652622-g004.jpg Figure 4. bmpr2a−/−;bmpr2b−/− affects the valve development. (A) Tg (flia:GFP) zebrafish line showin https://www.frontiersin.org/files/Articles/1652622/fcell-14-1652622-HTML/image_m/fcell-14-1652622-g005.jpg Figure 5. Transcriptomic analysis of differentially expressed genes in bmpr2a/b mutant zebrafish. (A https://www.frontiersin.org/files/Articles/1652622/fcell-14-1652622-HTML/image_m/fcell-14-1652622-g006.jpg Figure 6. Molecular mechanism verification: bmpr2a−/−;bmpr2b−/− affects heart contraction at 48 hpf. https://www.frontiersin.org/files/Articles/1652622/fcell-14-1652622-HTML/image_m/fcell-14-1652622-g007.jpg Figure 7. bmpr2a/b affects the valve development via the ECM–receptor interaction. (A) Heatmap of th https://www.frontiersin.org/files/Articles/1652622/fcell-14-1652622-HTML/image_m/fcell-14-1652622-g008.jpg Figure 8. PPI analyses of the regulatory network of valve development genes and cardiac contraction https://www.frontiersin.org/files/Articles/1652622/fcell-14-1652622-HTML/image_m/fcell-14-1652622-g009.jpg Figure 9. Diagram illustrating the molecular mechanism of bmpr2a/b affecting heart looping, cardiac https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1717992/full https://www.frontiersin.org/files/Articles/1717992/fenvs-13-1717992-HTML/image_m/fenvs-13-1717992-t001.jpg Table 1. The FAIR2 Compliance Certification presented here was generated through a Human-in-the-Loop https://www.frontiersin.org/files/Articles/1717992/fenvs-13-1717992-HTML/image_m/fenvs-13-1717992-g001.jpg Figure 1. Total municipal solid waste (MSW) generation by country and region: 2020 (historical) and https://www.frontiersin.org/files/Articles/1717992/fenvs-13-1717992-HTML/image_m/fenvs-13-1717992-g002.jpg Figure 2. Historical and projected trends in total municipal solid waste (MSW) generation for select https://www.frontiersin.org/files/Articles/1717992/fenvs-13-1717992-HTML/image_m/fenvs-13-1717992-g003.jpg Figure 3. Relationship between GDP per capita (PPP) and total municipal solid waste (MSW) generation https://www.frontiersin.org/files/Articles/1717992/fenvs-13-1717992-HTML/image_m/fenvs-13-1717992-g004.jpg Figure 4. Pairwise correlations among demographic, economic, waste, and emissions variables. Pearson https://www.frontiersin.org/files/Articles/1717992/fenvs-13-1717992-HTML/image_m/fenvs-13-1717992-t002.jpg Table a1. Country coverage of the dataset by continent. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1678811/full https://www.frontiersin.org/files/Articles/1678811/fpubh-13-1678811-HTML/image_m/fpubh-13-1678811-g001.jpg Figure 1. The theoretical model of this study. https://www.frontiersin.org/files/Articles/1678811/fpubh-13-1678811-HTML/image_m/fpubh-13-1678811-t001.jpg Table 1. Sociodemographic characteristics of the participants (n = 482). https://www.frontiersin.org/files/Articles/1678811/fpubh-13-1678811-HTML/image_m/fpubh-13-1678811-t002.jpg Table 2. Descriptive statistics and correlation analysis. https://www.frontiersin.org/files/Articles/1678811/fpubh-13-1678811-HTML/image_m/fpubh-13-1678811-t003.jpg Table 3. Model-fitting standard and fitting index of the final model. https://www.frontiersin.org/files/Articles/1678811/fpubh-13-1678811-HTML/image_m/fpubh-13-1678811-g002.jpg Figure 2. The mediating roles of MI, perceived social support, moral resilience, and HRPL. https://www.frontiersin.org/files/Articles/1678811/fpubh-13-1678811-HTML/image_m/fpubh-13-1678811-t004.jpg Table 4. Path analysis. https://www.frontiersin.org/files/Articles/1678811/fpubh-13-1678811-HTML/image_m/fpubh-13-1678811-t005.jpg Table 5. Bootstrap analysis of the mediating model. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1753229/full https://www.frontiersin.org/files/Articles/1753229/fpls-17-1753229-HTML-r1/image_m/fpls-17-1753229-g001.jpg Figure 1. Schematic diagram of the pot experiment setup and salinity treatments applied in this stud https://www.frontiersin.org/files/Articles/1753229/fpls-17-1753229-HTML-r1/image_m/fpls-17-1753229-g002.jpg Figure 2. Alpha diversity of root-zone soil microbial profiles across salinity treatments. Shannon a https://www.frontiersin.org/files/Articles/1753229/fpls-17-1753229-HTML-r1/image_m/fpls-17-1753229-g003.jpg Figure 3. Soil physicochemical properties and inorganic nitrogen forms in root-zone soil under diffe https://www.frontiersin.org/files/Articles/1753229/fpls-17-1753229-HTML-r1/image_m/fpls-17-1753229-g004.jpg Figure 4. P Alfalfa growth traits under different salinity treatments at harvest. Plant height, fres https://www.frontiersin.org/files/Articles/1753229/fpls-17-1753229-HTML-r1/image_m/fpls-17-1753229-g005.jpg Figure 5. Genus-level taxonomic composition of the root-zone soil microbiome across salinity treatme https://www.frontiersin.org/files/Articles/1753229/fpls-17-1753229-HTML-r1/image_m/fpls-17-1753229-g006.jpg Figure 6. Principal coordinates analysis (PCoA) of microbial community composition based on Bray–Cur https://www.frontiersin.org/files/Articles/1753229/fpls-17-1753229-HTML-r1/image_m/fpls-17-1753229-g007.jpg Figure 7. Heatmap of nitrogen cycling–related functional categories across salinity treatments. Valu https://www.frontiersin.org/files/Articles/1753229/fpls-17-1753229-HTML-r1/image_m/fpls-17-1753229-g008.jpg Figure 8. Heatmap of KEGG level 3 pathways across salinity treatments. Values are row-wise z-score–n https://www.frontiersin.org/files/Articles/1753229/fpls-17-1753229-HTML-r1/image_m/fpls-17-1753229-g009.jpg Figure 9. Associations among soil properties, plant growth traits, and microbial community compositi https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1675742/full https://www.frontiersin.org/files/Articles/1675742/fpubh-13-1675742-HTML/image_m/fpubh-13-1675742-g001.jpg Figure 1. PRISMA flow diagram of the scoping review, 2024. https://www.frontiersin.org/files/Articles/1675742/fpubh-13-1675742-HTML/image_m/fpubh-13-1675742-t001.jpg Table 1. Characteristics and context of wastewater and environmental surveillance (WES) conducted in https://www.frontiersin.org/files/Articles/1675742/fpubh-13-1675742-HTML/image_m/fpubh-13-1675742-t002.jpg Table 2. Public health actions in response to pathogen detection in wastewater. https://www.frontiersin.org/files/Articles/1675742/fpubh-13-1675742-HTML/image_m/fpubh-13-1675742-g002.jpg Figure 2. UpSet plot of the five domains of public health actions following pathogen detection in wa https://www.frontiersin.org/files/Articles/1675742/fpubh-13-1675742-HTML/image_m/fpubh-13-1675742-g003.jpg Figure 3. Heatmap of motives, commissioners, users, target pathogens and target populations for wast https://www.frontiersin.org/files/Articles/1675742/fpubh-13-1675742-HTML/image_m/fpubh-13-1675742-t003.jpg Table 3. Descriptives of wastewater sampling and analysis methods of wastewater and environmental su https://www.frontiersin.org/files/Articles/1675742/fpubh-13-1675742-HTML/image_m/fpubh-13-1675742-g004.jpg Figure 4. Heatmap of wastewater sampling and analysis methods for pathogen detection in wastewater a https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1706882/full https://www.frontiersin.org/files/Articles/1706882/fmed-13-1706882-HTML/image_m/fmed-13-1706882-g001.jpg Figure 1. Embryonic development of the tympanic ring (104). https://www.frontiersin.org/files/Articles/1706882/fmed-13-1706882-HTML/image_m/fmed-13-1706882-t001.jpg Table 1. Growth patterns of tympanic rings. https://www.frontiersin.org/files/Articles/1706882/fmed-13-1706882-HTML/image_m/fmed-13-1706882-g002.jpg Figure 2. Anatomical parameters of the tympanic ring (TR). (a) TRH: Vertical diameter at the longest https://www.frontiersin.org/files/Articles/1706882/fmed-13-1706882-HTML/image_m/fmed-13-1706882-g003.jpg Figure 3. Schematic diagram of ultrasound measurements of the tympanic ring (image acquisition was p https://www.frontiersin.org/files/Articles/1706882/fmed-13-1706882-HTML/image_m/fmed-13-1706882-g004.jpg Figure 4. MRI manifestations of the tympanic ring (image acquisition was performed following the met https://www.frontiersin.org/files/Articles/1706882/fmed-13-1706882-HTML/image_m/fmed-13-1706882-t002.jpg Table 2. Genes associated with tympanic ring development. https://www.frontiersin.org/files/Articles/1706882/fmed-13-1706882-HTML/image_m/fmed-13-1706882-t003.jpg Table 3. Malformations or syndromes associated with the middle ear. https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1634300/full https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g001.jpg Figure 1. The first step of the lossless Burrows-Wheeler compression transform of the words BAIGNADE https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g002.jpg Figure 2. Second step of lossless Burrows-Wheeler compression transform of the concatenated word BAI https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g003.jpg Figure 3. (A) Calculation of Heron area formula; (B) calculation of Isotropy index (distance to equi https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g004.jpg Figure 4. Phylogeny of the three domains of life—Archaea, Bacteria, and Eukarya—with indication of s https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g005.jpg Figure 5. de Vries in his garden with Oenethera Chilena Grandiflora. https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g006.jpg Figure 6. Hamiltonian path used on the amino-acids set to find the archetypal RNAs. https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g007.jpg Figure 7. (A) Five successive generations of simulated genomes (with random action at each generatio https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g008.jpg Figure 8. Examples showing the management of clusters with some not respecting the surface nor equil https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g009.jpg Figure 9. Matrices of distances between different species calculated by two different classifiers, N https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g010.jpg Figure 10. (A) Taxonomy of hominoides; (B) Three clusters obtained by Maxwell® confirming the antiqu https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g011.jpg Figure 11. (A) Cluster of giant viruses from Pandoravirus family with their target Acanthamoeba (rib https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g012.jpg Figure 12. Mammals mitochondrial genome Maxwell® classification and AL-codon-counter annotation; (A) https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g013.jpg Figure 13. Maxwell's phylogenetic tree of mammal species based on their mitochondrial genome. The bi https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g014.jpg Figure 14. Phylogenetic tree of crenoarchaeota (in green) and archaeoglobi (in red). (A) Classificat https://www.frontiersin.org/files/Articles/1634300/fams-11-1634300-HTML-r1/image_m/fams-11-1634300-g015.jpg Figure 15. Maxwell's classification of giant viruses and their putative targets chosen in amoebae, c https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1734075/full https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t001.jpg Table 1. Different combinations selected according to the mixture design and the corresponding respo https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t002.jpg Table 2. Analysis of variance for the proposed model applied to the mixture tested against S. aureus https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g001.jpg Figure 1. Plot of observed MIC values as a function of predicted MIC values for the S. aureus strain https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t003.jpg Table 3. Analysis of variance for the proposed model applied to the mixture tested against E. coli. https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g002.jpg Figure 2. Plot of observed MIC values as a function of predicted MIC values for the E. coli strain. https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t004.jpg Table 4. Different combinations selected according to the mixture design and the corresponding respo https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t005.jpg Table 5. Analysis of variance for the regression model applied to the mixture evaluated against Cand https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g003.jpg Figure 3. Plot of observed MIC values as a function of predicted MIC values for the C. albicans stra https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t006.jpg Table 6. Analysis of variance (ANOVA) for the proposed model applied to the mixture tested against G https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g004.jpg Figure 4. Plot of observed MIC values as a function of predicted MIC values for the G. candidum stra https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t007.jpg Table 7. Estimated regression coefficients and statistical significance for the effects of safranal, https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t008.jpg Table 8. Estimated regression coefficients and Statistical significance for the effects of safranal, https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g005.jpg Figure 5. 2D and 3D mixture diagrams showing variations in minimum inhibitory concentration (MIC) ag https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g006.jpg Figure 6. Desirability profile illustrating the optimal proportions of safranal, crocin, and croceti https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g007.jpg Figure 7. 2D and 3D mixture diagrams showing variations in minimum inhibitory concentration (MIC) ag https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g008.jpg Figure 8. Desirability profile indicating the optimal proportions of safranal, crocin, and crocetin https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g009.jpg Figure 9. 2D and 3D mixture diagrams illustrating variations in minimum inhibitory concentration (MI https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g010.jpg Figure 10. Desirability profile showing the optimal proportions of safranal, crocin, and crocetin fo https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g011.jpg Figure 11. 2D and 3D mixture diagrams illustrating variations in minimum inhibitory concentration (M https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g012.jpg Figure 12. Desirability profile showing the optimal proportions of safranal, crocin, and crocetin fo https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g013.jpg Figure 13. Mixture profile tested simultaneously against S. aureus and E. coli. https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g014.jpg Figure 14. Mixture profile tested simultaneously against C. albicans and G. candidum. https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t009.jpg Table 9. Expected and observed responses for the test point that the best-fit mixes were able to ach https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-t010.jpg Table 10. Identification of independent variables used in the mixture. https://www.frontiersin.org/files/Articles/1734075/fphar-17-1734075-HTML-r1/image_m/fphar-17-1734075-g015.jpg Figure 15. Equilateral triangle of the arrangement of mixtures using the simplex centroid design met https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1749036/full https://www.frontiersin.org/files/Articles/1749036/fpubh-14-1749036-HTML/image_m/fpubh-14-1749036-g001.jpg Figure 1. Distribution of four CHD patient subgroups based on the RPA framework. The scatter plot il https://www.frontiersin.org/files/Articles/1749036/fpubh-14-1749036-HTML/image_m/fpubh-14-1749036-t001.jpg Table 1. Sample characteristics and categorical variable univariate analysis of subgroups of coronar https://www.frontiersin.org/files/Articles/1749036/fpubh-14-1749036-HTML/image_m/fpubh-14-1749036-t002.jpg Table 2. Post-hoc analysis of univariate tests for categorical variables in each subgroup. https://www.frontiersin.org/files/Articles/1749036/fpubh-14-1749036-HTML/image_m/fpubh-14-1749036-g002.jpg Figure 2. Distribution of HISB levels across the four RPA subgroups. The area chart displays the pro https://www.frontiersin.org/files/Articles/1749036/fpubh-14-1749036-HTML/image_m/fpubh-14-1749036-t003.jpg Table 3. Continuous variable correlation analysis of subgroups of coronary heart disease patients. https://www.frontiersin.org/files/Articles/1749036/fpubh-14-1749036-HTML/image_m/fpubh-14-1749036-t004.jpg Table 4. Multifactorial analysis of characteristics of subgroups of patients with coronary heart dis https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1759157/full https://www.frontiersin.org/files/Articles/1759157/fpubh-14-1759157-HTML/image_m/fpubh-14-1759157-g001.jpg Figure 1. Research flowchart. https://www.frontiersin.org/files/Articles/1759157/fpubh-14-1759157-HTML/image_m/fpubh-14-1759157-t001.jpg Table 1. Characteristics of the participants (n = 468). https://www.frontiersin.org/files/Articles/1759157/fpubh-14-1759157-HTML/image_m/fpubh-14-1759157-t002.jpg Table 2. Model fit indices of latent profile analysis of presenteeism (n = 468). https://www.frontiersin.org/files/Articles/1759157/fpubh-14-1759157-HTML/image_m/fpubh-14-1759157-g002.jpg Figure 2. Distribution of three potential classes of fall risk perception. https://www.frontiersin.org/files/Articles/1759157/fpubh-14-1759157-HTML/image_m/fpubh-14-1759157-t003.jpg Table 3. Characteristics of individuals among risk perception categories [n = 468, n (%)]. https://www.frontiersin.org/files/Articles/1759157/fpubh-14-1759157-HTML/image_m/fpubh-14-1759157-t004.jpg Table 4. The multinational logistic regression results. https://www.frontiersin.org/files/Articles/1759157/fpubh-14-1759157-HTML/image_m/fpubh-14-1759157-t005.jpg Table 5. Meshing table between perceived fall risk and objective fall risk. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1627916/full https://www.frontiersin.org/files/Articles/1627916/fpubh-13-1627916-HTML/image_m/fpubh-13-1627916-t001.jpg Table 1. Participant demographics by types of health information sources (verified vs. unverified). https://www.frontiersin.org/files/Articles/1627916/fpubh-13-1627916-HTML/image_m/fpubh-13-1627916-t002.jpg Table 2. Chi-square associations between types of health information sources and child vaccination s https://www.frontiersin.org/files/Articles/1627916/fpubh-13-1627916-HTML/image_m/fpubh-13-1627916-t003.jpg Table 3. Adjusted odds ratios for the association between types of health information sources and ch https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1772361/full https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-t001.jpg Table 1. Physical and chemical characterization of poultry litter biochar (BC), poultry litter, swin https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-t002.jpg Table 2. Percentage distribution of biochar granulometry. https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-t003.jpg Table 3. Quantities of N, P, and K applied per treatment. https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-g001.jpg Figure 1. Root dry mass (RDM) (a), shoot dry mass (SDM) (b), and total dry mass (TDM) (c) of maize i https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-t004.jpg Table 4. C, N, S and C:N ratio accumulated in the root dry mass (RDM). https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-t005.jpg Table 5. C, N, S and C/N ratio accumulated in the shoot dry mass (SDM) of maize in the different tre https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-g002.jpg Figure 2. Stem diameter (cm) over time (47, 76, 83, 93, and 123 DAS) as affected by nutrient source https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-t006.jpg Table 6. Estimated marginal means (emmeans) of stem diameter (cm) at 123 DAS under inoculation condi https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-g003.jpg Figure 3. SPAD index in the different treatments, with and without inoculation. SPAD index in maize https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-g004.jpg Figure 4. Chlorophyll and carotenoids in the different treatments. (a) Chlorophyll A, (b) Chlorophyl https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-t007.jpg Table 7. Total C, N, S contents and C:N ratio in the soil after maize cultivation in the different t https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-g005.jpg Figure 5. Available P and K contents and exchangeable Ca, Mg, and Na contents in the soil after maiz https://www.frontiersin.org/files/Articles/1772361/fenvs-14-1772361-HTML-r1/image_m/fenvs-14-1772361-t008.jpg Table 8. pH in water and exchangeable Al contents and H + Al, CEC at pH 7.0, effective CEC, Base Sat https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1597079/full https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-t001.jpg Table 1. Properties of the different models of Gafchromic™ RCFs used at CLEAR. https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g001.jpg Figure 1. Left: The relative change in PV for the red channel of an EBT3 RCF as a function of time a https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g002.jpg Figure 2. Left: An example of an RCF with water residues. Right: The corresponding vertical dose pro https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g003.jpg Figure 3. Left: The scanner mask used for reproducible positioning of the RCFs. Right: The Epson Per https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-t002.jpg Table 2. The beam parameters used for RCF calibration at eRT6 at CHUV and the Varian TrueBeam medica https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g004.jpg Figure 4. RCF calibration setups in solid water using low-energy electrons and ionisation chambers. https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g005.jpg Figure 5. Calibration curves for different RCF models. The colours correspond to the respective colo https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g006.jpg Figure 6. Sensitvity of the different calibration functions in Figure 5. https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g007.jpg Figure 7. A drawing of the robot holding a sample holder inside the water phantom, with the path of https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g008.jpg Figure 8. A typical percentage-depth-dose (PDD) curve and beam size evolution for the CLEAR beam in https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g009.jpg Figure 9. Left: The robot holder with a stack of four alanine pellets (indicated in red) packed in p https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g010.jpg Figure 10. Left: Bar chart showing dose measurements from each sample holder of the ADs and EBT-XD R https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g011.jpg Figure 11. Left: The robot holder with an RPLD (indicated in red) positioned between two RCFs. Right https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g012.jpg Figure 12. Left: Bar chart of the individual dose measurements of the RPLDs and various RCF types an https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g013.jpg Figure 13. Left: The robot holder with the DP positioned between two RCFs. Right: The structure of t https://www.frontiersin.org/files/Articles/1597079/fphy-13-1597079-HTML-r1/image_m/fphy-13-1597079-g014.jpg Figure 14. Left: Bar chart of dose measurements from each sample holder, displaying the dose from th https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1726976/full https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g001.jpg Figure 1. Flow chat for selecting the articles in this systematic review. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-t001.jpg Table 1. Basic characteristics of the included studies. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g002.jpg Figure 2. Risk of summary. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g003.jpg Figure 3. Risk of graph. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g004.jpg Figure 4. Forest plot of the effect of all-cause mortality in different environments. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g005.jpg Figure 5. Forest plot of the effect of ICU LOS of all patients. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g006.jpg Figure 6. Forest plot of the effect of ICU LOS of patients who died. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g007.jpg Figure 7. Forest plot of the effect of ICU LOS of hospital length of study. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g008.jpg Figure 8. Forest plot of depression symptoms. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g009.jpg Figure 9. Forest plot of anxiety symptoms. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g010.jpg Figure 10. Forest plot of posttraumatic stress disorder. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g011.jpg Figure 11. Forest plot of quality of decision-making by surrogates. https://www.frontiersin.org/files/Articles/1726976/fmed-13-1726976-HTML-r1/image_m/fmed-13-1726976-g012.jpg Figure 12. Forest plot of overall quality of communication. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1736679/full https://www.frontiersin.org/files/Articles/1736679/fneur-16-1736679-HTML/image_m/fneur-16-1736679-t001.jpg Table 1. Comparison of baseline characteristics between SMT and EVT groups in ABAO patients with ext https://www.frontiersin.org/files/Articles/1736679/fneur-16-1736679-HTML/image_m/fneur-16-1736679-g001.jpg Figure 1. Distribution of modified Rankin scale score at 90 days in ABAO patients with extremely sev https://www.frontiersin.org/files/Articles/1736679/fneur-16-1736679-HTML/image_m/fneur-16-1736679-t002.jpg Table 2. Clinical outcomes at 90 days between SMT and EVT groups in ABAO patients with extremely sev https://www.frontiersin.org/files/Articles/1736679/fneur-16-1736679-HTML/image_m/fneur-16-1736679-g002.jpg Figure 2. Distribution of modified Rankin Scale score at 90 days and 1 year in ABAO patients stratif https://www.frontiersin.org/files/Articles/1736679/fneur-16-1736679-HTML/image_m/fneur-16-1736679-g003.jpg Figure 3. Association of onset to treatment time with the predicted probability of clinical outcomes https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1780241/full https://www.frontiersin.org/files/Articles/1780241/fped-14-1780241-HTML/image_m/fped-14-1780241-g001.jpg Figure 1. Patient recruitment flowchart. RDS, respiratory distress syndrome; QI, quality improvement https://www.frontiersin.org/files/Articles/1780241/fped-14-1780241-HTML/image_m/fped-14-1780241-t001.jpg Table 1. Infant demographic and clinical characteristics. https://www.frontiersin.org/files/Articles/1780241/fped-14-1780241-HTML/image_m/fped-14-1780241-t002.jpg Table 2. Comparison of primary and secondary outcomes between two groups. https://www.frontiersin.org/files/Articles/1780241/fped-14-1780241-HTML/image_m/fped-14-1780241-t003.jpg Table 3. Multivariable regression analysis of primary and secondary outcomes. https://www.frontiersin.org/files/Articles/1780241/fped-14-1780241-HTML/image_m/fped-14-1780241-t004.jpg Table 4. Interrupted time series analysis of Key outcomes. https://www.frontiersin.org/files/Articles/1780241/fped-14-1780241-HTML/image_m/fped-14-1780241-g002.jpg Figure 2. Interrupted time series analysis of key outcomes (A) IMV duration within 72 h. (B) Total I https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1760239/full https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-t001.jpg Table 1. Eligibility criteria (PICOS). https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g001.jpg Figure 1. Systematic review search and screening procedure. https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-t002.jpg Table 2. Data extraction from selected article. https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g002.jpg Figure 2. Risk of overall bias. https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g003.jpg Figure 3. RoB-2 assessments. https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-t003.jpg Table 3. GRADE analyses. https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-t004.jpg Table 4. Synthesis of results across included studies regarding the effects of plyometric training o https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g004.jpg Figure 4. Forest plot of the effect of plyometric training on CMJ in adolescent team-sport athletes. https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g005.jpg Figure 5. Forest plot of the effect of plyometric training on SJ in adolescent team-sport athletes. https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g006.jpg Figure 6. Forest plot of the effect of plyometric training on CMJA in adolescent team-sport athletes https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g007.jpg Figure 7. Forest plot of the effect of plyometric training on SLJ in adolescent team-sport athletes. https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g008.jpg Figure 8. Forest plot of the effect of plyometric training on ≤10-m linear sprint in adolescent team https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g009.jpg Figure 9. Forest plot of the effect of plyometric training on 20-m linear sprint in adolescent team- https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g010.jpg Figure 10. Forest plot of the effect of plyometric training on 30-m linear sprint in adolescent team https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-g011.jpg Figure 11. Forest plot of the effect of plyometric training on COD in adolescent team-sport athletes https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-t005.jpg Table 5. Meta-subgroup analysis results. https://www.frontiersin.org/files/Articles/1760239/fphys-17-1760239-HTML/image_m/fphys-17-1760239-t006.jpg Table 6. Multivariate meta-regression for training variables to predict plyometric training effects. https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1704719/full https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g001.jpg Figure 1. Part of the standard specimens after processing. https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g002.jpg Figure 2. Experiments equipment. https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-t001.jpg Table 1. Tensile strength of dry and water-saturated specimens. https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g003.jpg Figure 3. Schematic diagram of the stress path of cyclic loading and unloading. https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g004.jpg Figure 4. Total stress-strain of dried specimens under different cyclic paths:(a)D1-1,cyclic path 1; https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g005.jpg Figure 5. Total stress-strain of water-saturated samples under different circulation paths: (a)W1-1, https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g006.jpg Figure 6. Energy calculation analysis model. https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g007.jpg Figure 7. Energy evolution of dried specimens under different cyclic paths:(a) cyclic path 1; (b) cy https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-t002.jpg Table 2. Fitting equations for energy evolution under different cyclic paths. https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g008.jpg Figure 8. Energy evolution of saturated specimens under different cyclic paths:(a) cyclic path 1; (b https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g009.jpg Figure 9. Characteristic division of dissipated energy stages under cyclic path 1:(a)D1-1; (b)D1-2; https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g010.jpg Figure 10. Characteristic division of dissipated energy stages under cyclic path 2:(a)D2-1; (b)D2-2; https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g011.jpg Figure 11. Characteristic division of dissipated energy stages under cyclic path 3:(a)D3-1; (b)D3-2; https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g012.jpg Figure 12. Evolution of damage variables of dried specimens under each cyclic path. https://www.frontiersin.org/files/Articles/1704719/feart-13-1704719-HTML-r2/image_m/feart-13-1704719-g013.jpg Figure 13. Evolution of damage variables of saturated specimens under each cyclic path. https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2026.1738703/full https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-t001.jpg Table 1. Physical and mechanical parameters of coal and rock. https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g001.jpg Figure 1. Numerical model: (a) Whole model; (b) Working face. https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g002.jpg Figure 2. Initial stress distribution. https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g003.jpg Figure 3. Stress along the strike of 22,214 working face at different advancements: (a) 800 m, (b) 1 https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g004.jpg Figure 4. Stress along the inclination of 22,214 working face at different advancements: (a) 800 m, https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g005.jpg Figure 5. Plastic zone of 22,214 working face at different advancements: (a) 800 m, (b) 1,600 m, (c) https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g006.jpg Figure 6. Stress along the strike of 22,215 working face at different advancements: (a) 800 m, (b) 1 https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g007.jpg Figure 7. Stress along the inclination of 22,215 working face at different advancements: (a) 800 m, https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g008.jpg Figure 8. Plastic zone of 22,215 working face at different advancements: (a) 800 m, (b) 1,600 m, (c) https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-t002.jpg Table 2. Comparative analysis of stress concentration factors in working faces. https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g009.jpg Figure 9. Stress–distance curves of coal wall at different advancing distances. https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g010.jpg Figure 10. Iσ variation with advancing distance for the 22,214 and 22,215 working faces. https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g011.jpg Figure 11. Maximum principal stress vectors along the A-A′ section. https://www.frontiersin.org/files/Articles/1738703/feart-14-1738703-HTML/image_m/feart-14-1738703-g012.jpg Figure 12. Ip variation with advancing distance for the 22,214 and 22,215 working faces. https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2026.1756693/full https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g001.jpg Figure 1. Block diagram of distributed fiber optic sensing system. https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g002.jpg Figure 2. Schematic diagram of overlying strata deformation height monitoring. https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-t001.jpg Table 1. Characteristic parameters of overlying strata. https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g003.jpg Figure 3. 3D model framework (a) and similarity material model (b). https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-t002.jpg Table 2. Parameters of the similarity material model. https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g004.jpg Figure 4. Layout diagram of optical fibers and displacement monitoring points. https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g005.jpg Figure 5. Roof weighting phenomenon at working face: (a) advance of 560 mm; (b) advance of 720 mm; ( https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g006.jpg Figure 6. Roof weighting and face spalling height. https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g007.jpg Figure 7. Measurement curves of internal displacement and surface displacement; (A) 11 # monitoring https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g008.jpg Figure 8. Internal displacement variation of the model; (A) 11 # monitoring tube, (B) 12 # monitorin https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g009.jpg Figure 9. Illustrates the Brillouin frequency shift and its average variation rate as the working fa https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g010.jpg Figure 10. Brillouin shift and average variation rate, working face: 1,200 mm–1,360 mm; (A) V11Brill https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g011.jpg Figure 11. Brillouin shift and average variation rate, working face: 1,680 mm–1880 mm; (A) V11Brillo https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g012.jpg Figure 12. Brillouin shift and average variation rate, working face: 2,080 mm–2,240 mm; (A) V11 Bril https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g013.jpg Figure 13. Variation curve of internal deformation-failure height in the model. https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g014.jpg Figure 14. Accumulated displacement variation curve of overburden strata above the working face. https://www.frontiersin.org/files/Articles/1756693/feart-14-1756693-HTML/image_m/feart-14-1756693-g015.jpg Figure 15. Brillouin frequency shift curve of optical fiber during the complete advance process of t https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1672589/full https://www.frontiersin.org/files/Articles/1672589/fmicb-17-1672589-HTML-r1/image_m/fmicb-17-1672589-g001.jpg Figure 1. Study design and flow chart. https://www.frontiersin.org/files/Articles/1672589/fmicb-17-1672589-HTML-r1/image_m/fmicb-17-1672589-g002.jpg Figure 2. (A) Observed OTUs index among the CA, AA, and H groups. (B) Chao1 index among the CA, AA, https://www.frontiersin.org/files/Articles/1672589/fmicb-17-1672589-HTML-r1/image_m/fmicb-17-1672589-g003.jpg Figure 3. Panel A shows the LEfSe analysis of differential taxa among the AA, CA, and H groups, and https://www.frontiersin.org/files/Articles/1672589/fmicb-17-1672589-HTML-r1/image_m/fmicb-17-1672589-g004.jpg Figure 4. Associations between upper respiratory tract microbiota and lung function. (A) ROC curve o https://www.frontiersin.org/files/Articles/1672589/fmicb-17-1672589-HTML-r1/image_m/fmicb-17-1672589-g005.jpg Figure 5. Metabolite profiles and clinical relevance in asthma. (A) Metabolite classification propor https://www.frontiersin.org/files/Articles/1672589/fmicb-17-1672589-HTML-r1/image_m/fmicb-17-1672589-t001.jpg Table 1. The top 10 metabolites with differences were identified in the CA and AA groups. https://www.frontiersin.org/files/Articles/1672589/fmicb-17-1672589-HTML-r1/image_m/fmicb-17-1672589-g006.jpg Figure 6. KEGG enrichment pathway analysis bubble chart. (A) KEGG pathway enrichment analysis of dif https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1789986/full https://www.frontiersin.org/files/Articles/1789986/fmed-13-1789986-HTML/image_m/fmed-13-1789986-g001.jpg Figure 1. Chest CT scans of Case 1. Prior to treatment initiation: (a–c) multiple ill-defined focal https://www.frontiersin.org/files/Articles/1789986/fmed-13-1789986-HTML/image_m/fmed-13-1789986-g002.jpg Figure 2. Medication chart for Case 1. https://www.frontiersin.org/files/Articles/1789986/fmed-13-1789986-HTML/image_m/fmed-13-1789986-g003.jpg Figure 3. Chest CT of Case 2. Prior to treatment initiation: (a,b) multiple nodular opacities are se https://www.frontiersin.org/files/Articles/1789986/fmed-13-1789986-HTML/image_m/fmed-13-1789986-g004.jpg Figure 4. Medication chart for Case 2. https://www.frontiersin.org/files/Articles/1789986/fmed-13-1789986-HTML/image_m/fmed-13-1789986-g005.jpg Figure 5. Chest CT of Case 3. After 2 months of treatment: (a,b) multiple nodular and cystic lucenci https://www.frontiersin.org/files/Articles/1789986/fmed-13-1789986-HTML/image_m/fmed-13-1789986-g006.jpg Figure 6. Medication chart for Case 3. https://www.frontiersin.org/files/Articles/1789986/fmed-13-1789986-HTML/image_m/fmed-13-1789986-t001.jpg Table 1. Findings from the literature analysis. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1641928/full https://www.frontiersin.org/files/Articles/1641928/fimmu-16-1641928-HTML/image_m/fimmu-16-1641928-g001.jpg Figure 1. Preparation process of CPPs. https://www.frontiersin.org/files/Articles/1641928/fimmu-16-1641928-HTML/image_m/fimmu-16-1641928-g002.jpg Figure 2. Advantages and disadvantages of different extraction methods for CPPs. https://www.frontiersin.org/files/Articles/1641928/fimmu-16-1641928-HTML/image_m/fimmu-16-1641928-g003.jpg Figure 3. (A) Predicted structure of 50WCP (B) Predicted structure of 100WCP (C) Predicted structure https://www.frontiersin.org/files/Articles/1641928/fimmu-16-1641928-HTML/image_m/fimmu-16-1641928-t001.jpg Table 1. Summary of the immunomodulatory activity of CPPs. https://www.frontiersin.org/files/Articles/1641928/fimmu-16-1641928-HTML/image_m/fimmu-16-1641928-g004.jpg Figure 4. Macrophage-centric immunomodulatory mechanism of CPPs. https://www.frontiersin.org/files/Articles/1641928/fimmu-16-1641928-HTML/image_m/fimmu-16-1641928-t002.jpg Table 2. Structural motifs of CPPs and their immunological activities. https://www.frontiersin.org/files/Articles/1641928/fimmu-16-1641928-HTML/image_m/fimmu-16-1641928-g005.jpg Figure 5. Immune regulatory mechanisms of sCPPs. https://www.frontiersin.org/files/Articles/1641928/fimmu-16-1641928-HTML/image_m/fimmu-16-1641928-g006.jpg Figure 6. Immune regulatory mechanisms of Se-CPPs. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1666994/full https://www.frontiersin.org/files/Articles/1666994/fphys-16-1666994-HTML/image_m/fphys-16-1666994-g001.jpg Figure 1. Representative VDAC Structure (human VDAC1): hVDAC1 generally consists of ∼283 amino acid https://www.frontiersin.org/files/Articles/1666994/fphys-16-1666994-HTML/image_m/fphys-16-1666994-t001.jpg Table 1. Phosphorylation of VDAC. https://www.frontiersin.org/files/Articles/1666994/fphys-16-1666994-HTML/image_m/fphys-16-1666994-g002.jpg Figure 2. Representative roles of VDAC in apoptosis, cancer, redox regulation, and Ca2+ transfer: Th https://www.frontiersin.org/files/Articles/1666994/fphys-16-1666994-HTML/image_m/fphys-16-1666994-t002.jpg Table 2. VDAC-protein interaction. https://www.frontiersin.org/files/Articles/1666994/fphys-16-1666994-HTML/image_m/fphys-16-1666994-t003.jpg Table 3. Physiological modulator of VDAC. https://www.frontiersin.org/files/Articles/1666994/fphys-16-1666994-HTML/image_m/fphys-16-1666994-t004.jpg Table 4. Pharmacological modulator of VDAC. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1716306/full https://www.frontiersin.org/files/Articles/1716306/fmed-12-1716306-HTML/image_m/fmed-12-1716306-g001.jpg Figure 1. Bacteriophage-antibiotic therapy for extensively drug-resistant A. baumannii. (A) Timeline https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2026.1783025/full https://www.frontiersin.org/files/Articles/1783025/fdmed-07-1783025-HTML/image_m/fdmed-07-1783025-t001.jpg Table 1. Demographic and professional characteristics of study participants. https://www.frontiersin.org/files/Articles/1783025/fdmed-07-1783025-HTML/image_m/fdmed-07-1783025-g001.jpg Figure 1. Distribution of conventional behavioral management techniques used by dentists according t https://www.frontiersin.org/files/Articles/1783025/fdmed-07-1783025-HTML/image_m/fdmed-07-1783025-t002.jpg Table 2. Alternative therapy implementation by dental specialty. https://www.frontiersin.org/files/Articles/1783025/fdmed-07-1783025-HTML/image_m/fdmed-07-1783025-g002.jpg Figure 2. Predictors of the implementation of alternative behavioral management techniques. PTO, ped https://www.frontiersin.org/files/Articles/1783025/fdmed-07-1783025-HTML/image_m/fdmed-07-1783025-g003.jpg Figure 3. Dentists’ perceived effectiveness of alternative behavioral management techniques by speci https://www.frontiersin.org/files/Articles/1783025/fdmed-07-1783025-HTML/image_m/fdmed-07-1783025-g004.jpg Figure 4. Dentists’ ratings of the most effective alternative behavioral management technique. The f https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1667350/full https://www.frontiersin.org/files/Articles/1667350/fmed-12-1667350-HTML/image_m/fmed-12-1667350-g001.jpg Figure 1. Patient knee joint X-ray images at different stages. (A,B) Before the primary total knee a https://www.frontiersin.org/files/Articles/1667350/fmed-12-1667350-HTML/image_m/fmed-12-1667350-g002.jpg Figure 2. Patient skin presentations at various stages. (A) Before the primary total knee arthroplas https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1720212/full https://www.frontiersin.org/files/Articles/1720212/feduc-10-1720212-HTML-r1/image_m/feduc-10-1720212-t001.jpg Table 1. Trial design. https://www.frontiersin.org/files/Articles/1720212/feduc-10-1720212-HTML-r1/image_m/feduc-10-1720212-t002.jpg Table 2. Univariate hypothesis test results. https://www.frontiersin.org/files/Articles/1720212/feduc-10-1720212-HTML-r1/image_m/feduc-10-1720212-t003.jpg Table 3. Results of N-gain critical thinking ability score based on gender. https://www.frontiersin.org/files/Articles/1720212/feduc-10-1720212-HTML-r1/image_m/feduc-10-1720212-g001.jpg Figure 1. Students’ critical thinking skills based on gender. https://www.frontiersin.org/files/Articles/1720212/feduc-10-1720212-HTML-r1/image_m/feduc-10-1720212-t004.jpg Table 4. Univariate hypothesis test results. https://www.frontiersin.org/files/Articles/1720212/feduc-10-1720212-HTML-r1/image_m/feduc-10-1720212-t005.jpg Table 5. Results of N-gain cultural literacy scores based on gender. https://www.frontiersin.org/files/Articles/1720212/feduc-10-1720212-HTML-r1/image_m/feduc-10-1720212-g002.jpg Figure 2. Students’ cultural literacy based on gender. https://www.frontiersin.org/files/Articles/1720212/feduc-10-1720212-HTML-r1/image_m/feduc-10-1720212-t006.jpg Table 6. Multivariate hypothesis test results. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1626345/full https://www.frontiersin.org/files/Articles/1626345/fmed-12-1626345-HTML/image_m/fmed-12-1626345-t001.jpg Table 1. Demographic and clinical characteristics of patients participating in this study. https://www.frontiersin.org/files/Articles/1626345/fmed-12-1626345-HTML/image_m/fmed-12-1626345-g001.jpg Figure 1. HS patients feel poorly involved in the decision regarding the therapy of their skin disea https://www.frontiersin.org/files/Articles/1626345/fmed-12-1626345-HTML/image_m/fmed-12-1626345-g002.jpg Figure 2. Good involvement of HS patients in the decision making regarding their treatment leads to https://www.frontiersin.org/files/Articles/1626345/fmed-12-1626345-HTML/image_m/fmed-12-1626345-g003.jpg Figure 3. HS patients' perceived involvement in the therapy decision-making process stratified accor https://www.frontiersin.org/files/Articles/1626345/fmed-12-1626345-HTML/image_m/fmed-12-1626345-t002.jpg Table 2. Healthcare-related parameters that correlate with HS and psoriasis patients' perceived invo https://www.frontiersin.org/files/Articles/1626345/fmed-12-1626345-HTML/image_m/fmed-12-1626345-g004.jpg Figure 4. Specific healthcare-related parameters are associated with patients' perceived involvement https://www.frontiersin.org/files/Articles/1626345/fmed-12-1626345-HTML/image_m/fmed-12-1626345-g005.jpg Figure 5. HS patients' perceived involvement in the therapy decision-making process is strongly asso https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1800577/full https://www.frontiersin.org/files/Articles/1800577/fendo-17-1800577-HTML/image_m/fendo-17-1800577-g001.jpg Figure 1. The PRISMA flow diagram of the identification, screening, and inclusion methodology of the https://www.frontiersin.org/files/Articles/1800577/fendo-17-1800577-HTML/image_m/fendo-17-1800577-t001.jpg Table 1. Main characteristics of studies in this meta-analysis. https://www.frontiersin.org/files/Articles/1800577/fendo-17-1800577-HTML/image_m/fendo-17-1800577-g002.jpg Figure 2. Forest plot showing the difference in AIP levels between NAFLD/MAFLD patients and control https://www.frontiersin.org/files/Articles/1800577/fendo-17-1800577-HTML/image_m/fendo-17-1800577-g003.jpg Figure 3. Forest plot showing the association between AIP and risk of NAFLD/MAFLD (odds ratio, 95% C https://www.frontiersin.org/files/Articles/1800577/fendo-17-1800577-HTML/image_m/fendo-17-1800577-g004.jpg Figure 4. Summary receiver operating characteristic (SROC) curve of AIP for the diagnosis of NAFLD/M https://www.frontiersin.org/files/Articles/1800577/fendo-17-1800577-HTML/image_m/fendo-17-1800577-t002.jpg Table 2. Subgroup analyses of the relationship between AIP and NAFLD/MAFLD based on diagnostic crite https://www.frontiersin.org/files/Articles/1800577/fendo-17-1800577-HTML/image_m/fendo-17-1800577-t003.jpg Table 3. Meta-regression of the association between AIP and NAFLD/MAFLD. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1749396/full https://www.frontiersin.org/files/Articles/1749396/fmolb-12-1749396-HTML/image_m/fmolb-12-1749396-g001.jpg Figure 1. Spectrum of regulatory QTLs (regQTLs) influencing miRNA expression, processing, and oncoge https://www.frontiersin.org/files/Articles/1749396/fmolb-12-1749396-HTML/image_m/fmolb-12-1749396-t001.jpg Table 1. Summarizes major regQTL studies, resources, and databases that have systematically mapped m https://www.frontiersin.org/files/Articles/1749396/fmolb-12-1749396-HTML/image_m/fmolb-12-1749396-g002.jpg Figure 2. Workflow for Interaction Regression Models that Identify Allele-Specific miRNA–mRNA Regula https://www.frontiersin.org/files/Articles/1749396/fmolb-12-1749396-HTML/image_m/fmolb-12-1749396-g003.jpg Figure 3. Integrated Computational and Biological Frameworks for miRNA QTL Analyses (a) IsomiR-eQTL https://www.frontiersin.org/files/Articles/1749396/fmolb-12-1749396-HTML/image_m/fmolb-12-1749396-t002.jpg Table 2. Computational tools and resources for regQTL and related miRNA regulatory analyses. https://www.frontiersin.org/files/Articles/1749396/fmolb-12-1749396-HTML/image_m/fmolb-12-1749396-t003.jpg Table 3. Summary of functionally validated miRNA-targeting SNPs (regQTLs) across cancer types. https://www.frontiersin.org/files/Articles/1749396/fmolb-12-1749396-HTML/image_m/fmolb-12-1749396-t004.jpg Table 4. Experimentally validated regQTLs in cancer: functional assays and clinical associations. https://www.frontiersin.org/files/Articles/1749396/fmolb-12-1749396-HTML/image_m/fmolb-12-1749396-t005.jpg Table 5. Validated regQTLs and their clinical/therapeutic relevance in cancer. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1639257/full https://www.frontiersin.org/files/Articles/1639257/fnut-12-1639257-HTML/image_m/fnut-12-1639257-g001.jpg Figure 1. Patient selection from the TriNetX database. https://www.frontiersin.org/files/Articles/1639257/fnut-12-1639257-HTML/image_m/fnut-12-1639257-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1639257/fnut-12-1639257-HTML/image_m/fnut-12-1639257-t002.jpg Table 2. Association between vitamin D deficiency and 1-year outcomes. https://www.frontiersin.org/files/Articles/1639257/fnut-12-1639257-HTML/image_m/fnut-12-1639257-t003.jpg Table 3. Association between vitamin D deficiency and 2-year outcomes. https://www.frontiersin.org/files/Articles/1639257/fnut-12-1639257-HTML/image_m/fnut-12-1639257-t004.jpg Table 4. Association between vitamin D insufficiency and outcomes. https://www.frontiersin.org/files/Articles/1639257/fnut-12-1639257-HTML/image_m/fnut-12-1639257-t005.jpg Table 5. Subgroup analysis of association between vitamin D deficiency and risk of deep vein thrombo https://www.frontiersin.org/files/Articles/1639257/fnut-12-1639257-HTML/image_m/fnut-12-1639257-t006.jpg Table 6. Subgroup analysis of association between vitamin D deficiency and risk of pulmonary embolis https://www.frontiersin.org/files/Articles/1639257/fnut-12-1639257-HTML/image_m/fnut-12-1639257-t007.jpg Table 7. Multivariate predictors of deep vein thrombosis at 1-year follow-up. https://www.frontiersin.org/files/Articles/1639257/fnut-12-1639257-HTML/image_m/fnut-12-1639257-t008.jpg Table 8. Multivariate predictors of pulmonary embolism at 1-year follow-up. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1653151/full https://www.frontiersin.org/files/Articles/1653151/fnut-12-1653151-HTML/image_m/fnut-12-1653151-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. The flowchart illustrates the syste https://www.frontiersin.org/files/Articles/1653151/fnut-12-1653151-HTML/image_m/fnut-12-1653151-g002.jpg Figure 2. Propensity score density distributions before and after matching. The left panel illustrat https://www.frontiersin.org/files/Articles/1653151/fnut-12-1653151-HTML/image_m/fnut-12-1653151-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1653151/fnut-12-1653151-HTML/image_m/fnut-12-1653151-t002.jpg Table 2. Association between zinc deficiency and 2-year outcomes. https://www.frontiersin.org/files/Articles/1653151/fnut-12-1653151-HTML/image_m/fnut-12-1653151-g003.jpg Figure 3. Kaplan–Meier survival curves for new-onset diabetic kidney disease (DKD) in patients with https://www.frontiersin.org/files/Articles/1653151/fnut-12-1653151-HTML/image_m/fnut-12-1653151-t003.jpg Table 3. Association between zinc deficiency and 1-year outcomes. https://www.frontiersin.org/files/Articles/1653151/fnut-12-1653151-HTML/image_m/fnut-12-1653151-g004.jpg Figure 4. Forest plot showing subgroup analyses of association between zinc deficiency and risk of d https://www.frontiersin.org/files/Articles/1653151/fnut-12-1653151-HTML/image_m/fnut-12-1653151-t004.jpg Table 4. Risk factors for new-onset diabetic kidney disease (DKD). https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1658308/full https://www.frontiersin.org/files/Articles/1658308/fnut-12-1658308-HTML/image_m/fnut-12-1658308-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. The flowchart illustrates the syste https://www.frontiersin.org/files/Articles/1658308/fnut-12-1658308-HTML/image_m/fnut-12-1658308-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1658308/fnut-12-1658308-HTML/image_m/fnut-12-1658308-g002.jpg Figure 2. Propensity score density distributions before and after matching. The left panel shows imb https://www.frontiersin.org/files/Articles/1658308/fnut-12-1658308-HTML/image_m/fnut-12-1658308-t002.jpg Table 2. Association between zinc deficiency and 12-month outcomes. https://www.frontiersin.org/files/Articles/1658308/fnut-12-1658308-HTML/image_m/fnut-12-1658308-t003.jpg Table 3. Association between zinc deficiency and 6-month outcomes. https://www.frontiersin.org/files/Articles/1658308/fnut-12-1658308-HTML/image_m/fnut-12-1658308-t004.jpg Table 4. Sensitivity analysis of association between zinc deficiency and 1-year outcomes after exclu https://www.frontiersin.org/files/Articles/1658308/fnut-12-1658308-HTML/image_m/fnut-12-1658308-g003.jpg Figure 3. Subgroup analysis of the association between zinc deficiency and risk of acute kidney inju https://www.frontiersin.org/files/Articles/1658308/fnut-12-1658308-HTML/image_m/fnut-12-1658308-t005.jpg Table 5. Risk factors for new-onset acute kidney injury at 12-month follow-up. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1660475/full https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. The flowchart illustrates the syste https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-g002.jpg Figure 2. Propensity score density distributions before and after matching. The left panel shows the https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-t002.jpg Table 2. Association between zinc deficiency and 1-year outcomes. https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-g003.jpg Figure 3. Kaplan–Meier survival curves for intracerebral hemorrhage (ICH)-free survival at the 12-mo https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-t003.jpg Table 3. Association between zinc deficiency and 2-year outcomes. https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-t004.jpg Table 4. Sensitivity analysis of association between zinc deficiency and 1-year outcomes. https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-t005.jpg Table 5. Impact of severe zinc deficiency on 1-year outcomes. https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-t006.jpg Table 6. Subgroup analyses of association between zinc deficiency and risk of intracranial hemorrhag https://www.frontiersin.org/files/Articles/1660475/fnut-12-1660475-HTML/image_m/fnut-12-1660475-t007.jpg Table 7. Risk factors for new-onset intracranial hemorrhage. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1660622/full https://www.frontiersin.org/files/Articles/1660622/fnut-12-1660622-HTML/image_m/fnut-12-1660622-g001.jpg Figure 1. Patient selection from the TriNetx database. IDA, iron deficiency anemia. https://www.frontiersin.org/files/Articles/1660622/fnut-12-1660622-HTML/image_m/fnut-12-1660622-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1660622/fnut-12-1660622-HTML/image_m/fnut-12-1660622-t002.jpg Table 2. Association between iron deficiency anemia and 12-month outcomes. https://www.frontiersin.org/files/Articles/1660622/fnut-12-1660622-HTML/image_m/fnut-12-1660622-t003.jpg Table 3. Association between iron deficiency anemia and 3-month outcomes. https://www.frontiersin.org/files/Articles/1660622/fnut-12-1660622-HTML/image_m/fnut-12-1660622-t004.jpg Table 4. Association between iron deficiency anemia and 1-year outcomes after excluding patients wit https://www.frontiersin.org/files/Articles/1660622/fnut-12-1660622-HTML/image_m/fnut-12-1660622-t005.jpg Table 5. Subgroup analysis of association between iron deficiency anemia and risk of VTE at 1-year f https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1666887/full https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. The flowchart illustrates the syste https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-g002.jpg Figure 2. Propensity score density distributions before and after matching. The left panel shows the https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-t002.jpg Table 2. Association between zinc deficiency and 3-year risk of dementia. https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-t003.jpg Table 3. Association between zinc deficiency and 5-year risk of dementia. https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-t004.jpg Table 4. Sensitivity analysis: association between zinc deficiency and 3-year risk of dementia in pr https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-t005.jpg Table 5. Dose-response relationship across different levels of zinc deficiency at 3-y follow up. https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-t006.jpg Table 6. Subgroup analysis based on sex at 3-y follow up. https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-t007.jpg Table 7. Subgroup analysis based on age at 3-y follow up. https://www.frontiersin.org/files/Articles/1666887/fnut-12-1666887-HTML/image_m/fnut-12-1666887-t008.jpg Table 8. Risk factors for new-onset dementia at 3-year follow up. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1704946/full https://www.frontiersin.org/files/Articles/1704946/fnut-12-1704946-HTML/image_m/fnut-12-1704946-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. The flowchart illustrates the exclu https://www.frontiersin.org/files/Articles/1704946/fnut-12-1704946-HTML/image_m/fnut-12-1704946-g002.jpg Figure 2. Propensity score density distributions before and after matching. The density plots demons https://www.frontiersin.org/files/Articles/1704946/fnut-12-1704946-HTML/image_m/fnut-12-1704946-t001.jpg Table 1. Baseline characteristics of female patients with and without iron deficiency anemia before https://www.frontiersin.org/files/Articles/1704946/fnut-12-1704946-HTML/image_m/fnut-12-1704946-t002.jpg Table 2. Association between iron deficiency anemia and new-onset tinnitus at 1-year and 3-year foll https://www.frontiersin.org/files/Articles/1704946/fnut-12-1704946-HTML/image_m/fnut-12-1704946-t003.jpg Table 3. Dose-dependent association between iron deficiency anemia severity and new-onset tinnitus b https://www.frontiersin.org/files/Articles/1704946/fnut-12-1704946-HTML/image_m/fnut-12-1704946-t004.jpg Table 4. Age-stratified analysis of the association between iron deficiency anemia and new-onset tin https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1707271/full https://www.frontiersin.org/files/Articles/1707271/fnut-12-1707271-HTML/image_m/fnut-12-1707271-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. The flowchart illustrates the exclu https://www.frontiersin.org/files/Articles/1707271/fnut-12-1707271-HTML/image_m/fnut-12-1707271-t001.jpg Table 1. Baseline characteristics of newly diagnosed rheumatoid arthritis patients by hemoglobin lev https://www.frontiersin.org/files/Articles/1707271/fnut-12-1707271-HTML/image_m/fnut-12-1707271-t002.jpg Table 2. Three-year clinical outcomes in newly diagnosed rheumatoid arthritis patients by baseline h https://www.frontiersin.org/files/Articles/1707271/fnut-12-1707271-HTML/image_m/fnut-12-1707271-t003.jpg Table 3. One-year clinical outcomes in newly diagnosed rheumatoid arthritis patients by baseline hem https://www.frontiersin.org/files/Articles/1707271/fnut-12-1707271-HTML/image_m/fnut-12-1707271-t004.jpg Table 4. Three-year clinical outcomes in contemporary cohort (2018–2023): sensitivity analysis. https://www.frontiersin.org/files/Articles/1707271/fnut-12-1707271-HTML/image_m/fnut-12-1707271-t005.jpg Table 5. Dose-response relationship between baseline hemoglobin categories and 3-year clinical outco https://www.frontiersin.org/files/Articles/1707271/fnut-12-1707271-HTML/image_m/fnut-12-1707271-t006.jpg Table 6. Subgroup analysis of association between low hemoglobin and 3-year mortality risk. https://www.frontiersin.org/files/Articles/1707271/fnut-12-1707271-HTML/image_m/fnut-12-1707271-t007.jpg Table 7. Comparative mortality risk analysis: rheumatoid arthritis vs. osteoarthritis by hemoglobin https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1709491/full https://www.frontiersin.org/files/Articles/1709491/fnut-12-1709491-HTML-r1/image_m/fnut-12-1709491-t001.jpg Table 1. Baseline characteristics of patients with ischemic stroke before and after propensity score https://www.frontiersin.org/files/Articles/1709491/fnut-12-1709491-HTML-r1/image_m/fnut-12-1709491-g001.jpg Figure 1. Patient selection from the TriNetX databases. AIS – Acute Ischemic Stroke; 25(OH)D – 25-hy https://www.frontiersin.org/files/Articles/1709491/fnut-12-1709491-HTML-r1/image_m/fnut-12-1709491-g002.jpg Figure 2. Propensity score distribution before and after matching. (a) Propensity score distribution https://www.frontiersin.org/files/Articles/1709491/fnut-12-1709491-HTML-r1/image_m/fnut-12-1709491-t002.jpg Table 2. Association between vitamin D deficiency (VDD) and 30-day outcomes. https://www.frontiersin.org/files/Articles/1709491/fnut-12-1709491-HTML-r1/image_m/fnut-12-1709491-t003.jpg Table 3. Sensitivity analyses of the association between vitamin D deficiency (VDD) and 30-day outco https://www.frontiersin.org/files/Articles/1709491/fnut-12-1709491-HTML-r1/image_m/fnut-12-1709491-t004.jpg Table 4. Association between vitamin D insufficiency (VDI) and 30-day outcomes. https://www.frontiersin.org/files/Articles/1709491/fnut-12-1709491-HTML-r1/image_m/fnut-12-1709491-t005.jpg Table 5. Risk factors for acute kidney injury in patients with vitamin D deficiency. https://www.frontiersin.org/files/Articles/1709491/fnut-12-1709491-HTML-r1/image_m/fnut-12-1709491-t006.jpg Table 6. Association between vitamin D deficiency (VDD) and 1–12 m outcomes. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1743873/full https://www.frontiersin.org/files/Articles/1743873/fnut-13-1743873-HTML/image_m/fnut-13-1743873-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. HCOs, Healthcare Organizations; CKD https://www.frontiersin.org/files/Articles/1743873/fnut-13-1743873-HTML/image_m/fnut-13-1743873-t001.jpg Table 1. Baseline characteristics of patients with obstructive sleep apnea before and after propensi https://www.frontiersin.org/files/Articles/1743873/fnut-13-1743873-HTML/image_m/fnut-13-1743873-t002.jpg Table 2. Association between iron deficiency anemia and adverse outcomes at 5-year follow-up. https://www.frontiersin.org/files/Articles/1743873/fnut-13-1743873-HTML/image_m/fnut-13-1743873-g002.jpg Figure 2. Cumulative incidence of composite renal function decline at 5-year follow-up among patient https://www.frontiersin.org/files/Articles/1743873/fnut-13-1743873-HTML/image_m/fnut-13-1743873-t003.jpg Table 3. Association between iron deficiency anemia and adverse outcomes at 7-year follow-up. https://www.frontiersin.org/files/Articles/1743873/fnut-13-1743873-HTML/image_m/fnut-13-1743873-t004.jpg Table 4. Sensitivity analyses of the association between iron deficiency anemia and clinical outcome https://www.frontiersin.org/files/Articles/1743873/fnut-13-1743873-HTML/image_m/fnut-13-1743873-t005.jpg Table 5. Subgroup analyses of the association between iron deficiency anemia and composite renal fun https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1746078/full https://www.frontiersin.org/files/Articles/1746078/fnut-13-1746078-HTML/image_m/fnut-13-1746078-g001.jpg Figure 1. Patient selection flowchart from the TriNetX database. The flowchart illustrates the syste https://www.frontiersin.org/files/Articles/1746078/fnut-13-1746078-HTML/image_m/fnut-13-1746078-g002.jpg Figure 2. Propensity score density distributions before and after matching. The left panel shows the https://www.frontiersin.org/files/Articles/1746078/fnut-13-1746078-HTML/image_m/fnut-13-1746078-t001.jpg Table 1. Baseline characteristics of patients before and after propensity score matching. https://www.frontiersin.org/files/Articles/1746078/fnut-13-1746078-HTML/image_m/fnut-13-1746078-t002.jpg Table 2. Association Between zinc deficiency and risk of atrial fibrillation/flutter. https://www.frontiersin.org/files/Articles/1746078/fnut-13-1746078-HTML/image_m/fnut-13-1746078-t003.jpg Table 3. Sensitivity analysis: Association between zinc deficiency and atrial fibrillation/flutter a https://www.frontiersin.org/files/Articles/1746078/fnut-13-1746078-HTML/image_m/fnut-13-1746078-t004.jpg Table 4. Dose-response relationship across different levels of zinc deficiency at early (1–6 m) and https://www.frontiersin.org/files/Articles/1746078/fnut-13-1746078-HTML/image_m/fnut-13-1746078-t005.jpg Table 5. Subgroup analyses of association between zinc deficiency and risk of atrial fibrillation/fl https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1801558/full https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-g001.jpg Figure 1. Flow diagram illustrating cohort selection from the TriNetX research network. HCOs, health https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-t001.jpg Table 1. Baseline characteristics of patients with dementia according to zinc status before and afte https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-g002.jpg Figure 2. Propensity score distributions before and after matching. The figure displays the distribu https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-g003.jpg Figure 3. Outcome-free survival after landmark follow-up by zinc status. The figure shows outcome-fr https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-t002.jpg Table 2. Association between zinc deficiency and clinical outcomes during 1-year follow-up (n = 1,24 https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-t003.jpg Table 3. Association between zinc deficiency and clinical outcomes during extended follow-up (1–3 ye https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-t004.jpg Table 4. Sensitivity analyses for the association between zinc deficiency and 1-year outcomes. https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-t005.jpg Table 5. Subgroup analysis of the association between zinc deficiency and 1-year outcomes stratified https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-t006.jpg Table 6. Association between severe zinc deficiency and 1-year outcomes (n = 441 for each group). https://www.frontiersin.org/files/Articles/1801558/fnut-13-1801558-HTML/image_m/fnut-13-1801558-t007.jpg Table 7. Multivariable cox proportional hazards model for 1-year mortality. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1822195/full https://www.frontiersin.org/files/Articles/1822195/fnut-13-1822195-HTML/image_m/fnut-13-1822195-g001.jpg Figure 1. Patient selection flowchart. Adults aged ≥50 years with at least one serum 25-hydroxyvitam https://www.frontiersin.org/files/Articles/1822195/fnut-13-1822195-HTML/image_m/fnut-13-1822195-t001.jpg Table 1. Baseline characteristics of patients with vitamin D deficiency and vitamin D sufficiency. https://www.frontiersin.org/files/Articles/1822195/fnut-13-1822195-HTML/image_m/fnut-13-1822195-g002.jpg Figure 2. Propensity score density plots before and after matching. The left panel displays the dist https://www.frontiersin.org/files/Articles/1822195/fnut-13-1822195-HTML/image_m/fnut-13-1822195-t002.jpg Table 2. Association between vitamin D deficiency and dementia risk during the 10-year follow-up (n  https://www.frontiersin.org/files/Articles/1822195/fnut-13-1822195-HTML/image_m/fnut-13-1822195-g003.jpg Figure 3. Kaplan–Meier outcome-free probability curves for incident dementia in the vitamin D defici https://www.frontiersin.org/files/Articles/1822195/fnut-13-1822195-HTML/image_m/fnut-13-1822195-t003.jpg Table 3. Sensitivity analyses for the association between vitamin D deficiency and dementia risk dur https://www.frontiersin.org/files/Articles/1822195/fnut-13-1822195-HTML/image_m/fnut-13-1822195-g004.jpg Figure 4. Forest plot of subgroup analyses for the association between vitamin D deficiency and inci https://www.frontiersin.org/files/Articles/1822195/fnut-13-1822195-HTML/image_m/fnut-13-1822195-t004.jpg Table 4. Multivariate Cox regression analysis for predictors of incident dementia in the propensity https://www.frontiersin.org/files/Articles/1822195/fnut-13-1822195-HTML/image_m/fnut-13-1822195-t005.jpg Table 5. Association between vitamin D insufficiency and dementia risk during the 10-year follow-up https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1590484/full https://www.frontiersin.org/files/Articles/1590484/fpls-16-1590484-HTML/image_m/fpls-16-1590484-t001.jpg Table 1. The characterization details of the proline metabolism genes in the kiwifruit genome. https://www.frontiersin.org/files/Articles/1590484/fpls-16-1590484-HTML/image_m/fpls-16-1590484-g001.jpg Figure 1. Comprehensive analysis of evolutionary relationship and gene structure in the proline-meta https://www.frontiersin.org/files/Articles/1590484/fpls-16-1590484-HTML/image_m/fpls-16-1590484-g002.jpg Figure 2. Cis-elements in promoter sequences and chromosomal distribution of the proline-metabolism https://www.frontiersin.org/files/Articles/1590484/fpls-16-1590484-HTML/image_m/fpls-16-1590484-g003.jpg Figure 3. Phylogenetic trees of kiwifruit gene families—P5CS (A), P5CR (B), OAT (C), PDH (D), and P5 https://www.frontiersin.org/files/Articles/1590484/fpls-16-1590484-HTML/image_m/fpls-16-1590484-g004.jpg Figure 4. Electronic Fluorescent Pictograph (eFP) browser images of AcP5CS1 (A), AcP5CS2 (B), AcP5CR https://www.frontiersin.org/files/Articles/1590484/fpls-16-1590484-HTML/image_m/fpls-16-1590484-g005.jpg Figure 5. illustrates the expression profiles of the proline-metabolism gene family in response to a https://www.frontiersin.org/files/Articles/1590484/fpls-16-1590484-HTML/image_m/fpls-16-1590484-g006.jpg Figure 6. The expression levels of the proline-metabolism gene family genes at days 0, 2, 4, 6, 8 an https://www.frontiersin.org/files/Articles/1590484/fpls-16-1590484-HTML/image_m/fpls-16-1590484-g007.jpg Figure 7. AcNAC30 Regulates directly targets and transcriptionally modulates the AcP5CS1 promoter. ( https://www.frontiersin.org/files/Articles/1590484/fpls-16-1590484-HTML/image_m/fpls-16-1590484-g008.jpg Figure 8. Overexpression of AcP5CS1 enhances salt tolerance. (A) Phenotypic observations and root le https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1590396/full https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-g001.jpg Figure 1. Flowchart showing the process of patient selection. https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-t001.jpg Table 1. Clinicopathological characteristics in the validation and test groups. https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-t002.jpg Table 2. Univariate and multivariate analyses. https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-g002.jpg Figure 2. (A) Forest plots of influencing factors associated with prostate cancer, as identified in https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-g003.jpg Figure 3. Scatter plot features. icc1 represents inter-reader ICCs; icc2 represents intra-reader ICC https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-t003.jpg Table 3. Selected LASSO regression-derived radiomics features. https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-g004.jpg Figure 4. Selection of the optimal penalization coefficient (λ). (A) Ten-fold cross-validation for t https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-g005.jpg Figure 5. Spearman’s correlation coefficients for the indicated features. https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-t004.jpg Table 4. Model-specific testing and validation. https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-g006.jpg Figure 6. (A, B) Machine learning confusion matrices in the internal validation and test cohorts. (C https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-g007.jpg Figure 7. ROC curves for the six evaluated models. (A, B) ROC curves for the six models in the inter https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-g008.jpg Figure 8. SHAP summary plots for the XGBoost model. (A) Beeswarm plot and (B) bar plot illustrations https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-g009.jpg Figure 9. Calibration curves and decision curve. (A, B) Calibration curves of the six models for pre https://www.frontiersin.org/files/Articles/1590396/fonc-15-1590396-HTML/image_m/fonc-15-1590396-t005.jpg Table 5. Brier scores of six models. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1686443/full https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g001.jpg Figure 1. Expression of MPST in the pan-cancer (A) Expression of MPST mRNA in the 33 tumors in the T https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g002.jpg Figure 2. The relation between MPST expression and DSS in pan-cancer. (A) Effect of MPST expression https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g003.jpg Figure 3. Correlation between MPST expression and clinicopathological parameters. (A) In ACC, MPST e https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-t001.jpg Table 1. Univariate and multivariate Cox regression analysis between ACC. https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-t002.jpg Table 2. Univariate and multivariate Cox regression analysis between SARC. https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g004.jpg Figure 4. Correlation between MPST expression and tumor immune microenvironment. (A) heat map betwee https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g005.jpg Figure 5. Associations between MPST expression and immune infiltration across multiple cancer types https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g006.jpg Figure 6. Combined with the expression of MPST and immune cell infiltration, B cell infiltration aff https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g007.jpg Figure 7. Functional enrichment analysis of the genes associated with MPST. (A) A PPI network based https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g008.jpg Figure 8. Expression level of the MPST at the single-cell level. (A, B, D, F) The relationship betwe https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g009.jpg Figure 9. Differential methylation of the MPST promoter in 15 cancer types (A–O). The panel compares https://www.frontiersin.org/files/Articles/1686443/fonc-15-1686443-HTML/image_m/fonc-15-1686443-g010.jpg Figure 10. MPST gene mutations in various cancers. (A, B) cBioPortal shows the altered frequency of https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2026.1653126/full https://www.frontiersin.org/files/Articles/1653126/fgwh-07-1653126-HTML/image_m/fgwh-07-1653126-t001.jpg Table 1. Descriptive statistics of key study variables by categorical groups in the PdP sample (N =  https://www.frontiersin.org/files/Articles/1653126/fgwh-07-1653126-HTML/image_m/fgwh-07-1653126-g001.jpg Figure 1. Structural equation model examining associations among general psychological distress, COV https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1694688/full https://www.frontiersin.org/files/Articles/1694688/fmed-12-1694688-HTML/image_m/fmed-12-1694688-g001.jpg Figure 1. Flowchart using the FAERS database. https://www.frontiersin.org/files/Articles/1694688/fmed-12-1694688-HTML/image_m/fmed-12-1694688-t001.jpg Table 1. Two-by-two contingency table for disproportionality analyses. https://www.frontiersin.org/files/Articles/1694688/fmed-12-1694688-HTML/image_m/fmed-12-1694688-t002.jpg Table 2. Four major algorithms used for signal detection. https://www.frontiersin.org/files/Articles/1694688/fmed-12-1694688-HTML/image_m/fmed-12-1694688-t003.jpg Table 3. Clinical characteristics of infections and infestations related to JAK-1 inhibitors in atop https://www.frontiersin.org/files/Articles/1694688/fmed-12-1694688-HTML/image_m/fmed-12-1694688-t004.jpg Table 4. Signal strength of positive infection and infestation-related adverse events associated wit https://www.frontiersin.org/files/Articles/1694688/fmed-12-1694688-HTML/image_m/fmed-12-1694688-g002.jpg Figure 2. Venn diagram showing the types of AEs related to infection caused by two JAK1 inhibitors. https://www.frontiersin.org/files/Articles/1694688/fmed-12-1694688-HTML/image_m/fmed-12-1694688-g003.jpg Figure 3. The forest plot of two drugs under the same PT conditions. (A) Upadacitinib; (B) Abrocitin https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1734355/full https://www.frontiersin.org/files/Articles/1734355/fspor-07-1734355-HTML-r1/image_m/fspor-07-1734355-t001.jpg Table 1. Mean values, standard deviations, and effect sizes for the 1st order factor scales of the R https://www.frontiersin.org/files/Articles/1734355/fspor-07-1734355-HTML-r1/image_m/fspor-07-1734355-t002.jpg Table 2. Mean values, standard deviations, and effect sizes for the 2nd order factor scales of the R https://www.frontiersin.org/files/Articles/1734355/fspor-07-1734355-HTML-r1/image_m/fspor-07-1734355-t003.jpg Table 3. Intraclass correlation coefficient (ICC), 95% confidence intervals, and determination facto https://www.frontiersin.org/files/Articles/1734355/fspor-07-1734355-HTML-r1/image_m/fspor-07-1734355-g001.jpg Figure 1. Power parameters [(a) CMJ, (b) PPA, (c) PPR and (d) AH] at different times [Baseline (blac https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1721893/full https://www.frontiersin.org/files/Articles/1721893/fchem-13-1721893-HTML/image_m/fchem-13-1721893-g001.jpg Figure 1. Schematic diagram summarizing the main contents of this review. https://www.frontiersin.org/files/Articles/1721893/fchem-13-1721893-HTML/image_m/fchem-13-1721893-t001.jpg Table 1. Comparative analysis of online DGA techniques. https://www.frontiersin.org/files/Articles/1721893/fchem-13-1721893-HTML/image_m/fchem-13-1721893-g002.jpg Figure 2. (A) Gas flow schematics of the chromatographic setup (Paris et al., 2024). (B) Sample cycl https://www.frontiersin.org/files/Articles/1721893/fchem-13-1721893-HTML/image_m/fchem-13-1721893-g003.jpg Figure 3. (A) Dissolved C2H2 sensor system in transformer oil based on FF-RF-WMS-2f/1f (Li et al., 2 https://www.frontiersin.org/files/Articles/1721893/fchem-13-1721893-HTML/image_m/fchem-13-1721893-g004.jpg Figure 4. (A) Principle of Raman scattering (Dai et al., 2025). (B) Experimental setup (Wang P. et a https://www.frontiersin.org/files/Articles/1721893/fchem-13-1721893-HTML/image_m/fchem-13-1721893-g005.jpg Figure 5. (A) Schematic diagram of the MPAEPAS experimental setup (Li et al., 2022); (B) Schematic d https://www.frontiersin.org/files/Articles/1721893/fchem-13-1721893-HTML/image_m/fchem-13-1721893-g006.jpg Figure 6. (A) Schematic diagram of TDLAS for analyzing DGA (Dai et al., 2025); (B) Methodological di https://www.frontiersin.org/files/Articles/1721893/fchem-13-1721893-HTML/image_m/fchem-13-1721893-g007.jpg Figure 7. (A) Schematic of the sensing mechanism (Tang et al., 2025); (B) Schematic explanation of t https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1739543/full https://www.frontiersin.org/files/Articles/1739543/fped-13-1739543-HTML/image_m/fped-13-1739543-g001.jpg Figure 1. Podoscope-based plantar image acquisition process. (A) A participant stands barefoot on an https://www.frontiersin.org/files/Articles/1739543/fped-13-1739543-HTML/image_m/fped-13-1739543-g002.jpg Figure 2. Schematic illustration of the line-based footprint method for assessing flatfoot severity. https://www.frontiersin.org/files/Articles/1739543/fped-13-1739543-HTML/image_m/fped-13-1739543-t001.jpg Table 1. Baseline characteristics of schoolchildren in Kunming and Kandahar. https://www.frontiersin.org/files/Articles/1739543/fped-13-1739543-HTML/image_m/fped-13-1739543-t002.jpg Table 2. Distribution of flatfoot and normal foot by demographic, anthropometric, lifestyle, and cli https://www.frontiersin.org/files/Articles/1739543/fped-13-1739543-HTML/image_m/fped-13-1739543-t003.jpg Table 3. Factors associated with flatfoot by site: crude and adjusted odds ratios. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1754743/full https://www.frontiersin.org/files/Articles/1754743/fendo-17-1754743-HTML/image_m/fendo-17-1754743-t001.jpg Table 1. Primer sequences for real-time PCR. https://www.frontiersin.org/files/Articles/1754743/fendo-17-1754743-HTML/image_m/fendo-17-1754743-t002.jpg Table 2. Baseline demographic and clinical characteristics. https://www.frontiersin.org/files/Articles/1754743/fendo-17-1754743-HTML/image_m/fendo-17-1754743-t003.jpg Table 3. Comparison of laboratory parameters among the three groups. https://www.frontiersin.org/files/Articles/1754743/fendo-17-1754743-HTML/image_m/fendo-17-1754743-t004.jpg Table 4. Comparison of complications and comorbidities between the DM and DF groups. https://www.frontiersin.org/files/Articles/1754743/fendo-17-1754743-HTML/image_m/fendo-17-1754743-g001.jpg Figure 1. Untargeted metabolomic analysis of serum metabolic profiles among the three groups. (A–C) https://www.frontiersin.org/files/Articles/1754743/fendo-17-1754743-HTML/image_m/fendo-17-1754743-t005.jpg Table 5. Logistic regression analysis of differential metabolites for DF risk. https://www.frontiersin.org/files/Articles/1754743/fendo-17-1754743-HTML/image_m/fendo-17-1754743-g002.jpg Figure 2. Effects of different concentrations of CDCA treatment on HSF cells. (A) Morphological obse https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1678319/full https://www.frontiersin.org/files/Articles/1678319/fendo-16-1678319-HTML/image_m/fendo-16-1678319-g001.jpg Figure 1. Flow chart showing inclusion and exclusion in this study. BMI, body mass index; GWG, gesta https://www.frontiersin.org/files/Articles/1678319/fendo-16-1678319-HTML/image_m/fendo-16-1678319-t001.jpg Table 1. Comparison of potential risk factors between s-GDM and s-ND group. https://www.frontiersin.org/files/Articles/1678319/fendo-16-1678319-HTML/image_m/fendo-16-1678319-g002.jpg Figure 2. Associations between f-BW and s-GDM evaluated by RCS analyses s-GDM, gestational diabetes https://www.frontiersin.org/files/Articles/1678319/fendo-16-1678319-HTML/image_m/fendo-16-1678319-g003.jpg Figure 3. Associations between the category of f-BW percentiles and s-GDM evaluated by RCS analyses https://www.frontiersin.org/files/Articles/1678319/fendo-16-1678319-HTML/image_m/fendo-16-1678319-t002.jpg Table 2. Analyses of the association of f-LGA and f-MAC with s-GDM across unadjusted and adjusted mo https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1736779/full https://www.frontiersin.org/files/Articles/1736779/fendo-17-1736779-HTML-r1/image_m/fendo-17-1736779-g001.jpg Figure 1. Flowchart of study inclusion and exclusion. https://www.frontiersin.org/files/Articles/1736779/fendo-17-1736779-HTML-r1/image_m/fendo-17-1736779-t001.jpg Table 1. Comparison of risk factors between the s-HDP and s-NBP groups. https://www.frontiersin.org/files/Articles/1736779/fendo-17-1736779-HTML-r1/image_m/fendo-17-1736779-t002.jpg Table 2. Impact of GDM status across two pregnancies on s-HDP in unadjusted and adjusted models. https://www.frontiersin.org/files/Articles/1736779/fendo-17-1736779-HTML-r1/image_m/fendo-17-1736779-g002.jpg Figure 2. Stratified analysis of the association between GDM patterns and HDP in subsequent pregnanc https://www.frontiersin.org/files/Articles/1736779/fendo-17-1736779-HTML-r1/image_m/fendo-17-1736779-t003.jpg Table 3. Association between GDM patterns across two pregnancies and s-PE. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1672241/full https://www.frontiersin.org/files/Articles/1672241/fimmu-17-1672241-HTML/image_m/fimmu-17-1672241-g001.jpg Figure 1. General state, motor symptoms and behavioral changes of rotenone-induced PD mouse model. ( https://www.frontiersin.org/files/Articles/1672241/fimmu-17-1672241-HTML/image_m/fimmu-17-1672241-t001.jpg Table 1. Paired primers for RT-qPCR. https://www.frontiersin.org/files/Articles/1672241/fimmu-17-1672241-HTML/image_m/fimmu-17-1672241-g002.jpg Figure 2. Histological characteristics of α-syn in rotenone-induced PD mouse model. (A) Western blot https://www.frontiersin.org/files/Articles/1672241/fimmu-17-1672241-HTML/image_m/fimmu-17-1672241-g003.jpg Figure 3. The effect of rotenone induced-PD mouse model on the TLR4/NF-κB signaling pathway in the m https://www.frontiersin.org/files/Articles/1672241/fimmu-17-1672241-HTML/image_m/fimmu-17-1672241-g004.jpg Figure 4. The effects of rotenone-induced PD mouse models on neuroinflammation and intestinal inflam https://www.frontiersin.org/files/Articles/1672241/fimmu-17-1672241-HTML/image_m/fimmu-17-1672241-g005.jpg Figure 5. Species annotation and evaluation of gut microbiota in rotenone-induced PD mouse models. ( https://www.frontiersin.org/files/Articles/1672241/fimmu-17-1672241-HTML/image_m/fimmu-17-1672241-g006.jpg Figure 6. Gut microbiota dysbiosis in rotenone-induced PD mouse model. (A, B) Bar charts of species https://www.frontiersin.org/files/Articles/1672241/fimmu-17-1672241-HTML/image_m/fimmu-17-1672241-g007.jpg Figure 7. Correlation analysis between gut microbiota and inflammatory markers in rotenone-induced P https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1564307/full https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t001.jpg Table 1. Sociodemographic, general health, and COVID-19-related variables for TD children and childr https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t002.jpg Table 2. Sociodemographic characteristic of survey respondents, N=548. https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t003.jpg Table 3. Reported level of anxiety and worry in TD children and children with SEND around COVID-19, https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t004.jpg Table 4. Use of coping strategies by children with SEND compared to TD peers during the COVID-19 pan https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t005.jpg Table 5. Coping efficacy in children with SEND compared to TD peers during the COVID-19 pandemic, N https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t006.jpg Table 6. Comparison of mean adaptive coping, maladaptive coping, and coping efficacy in children wit https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t007.jpg Table 7. Multivariate logistic binary regression analysis of odds for “High” adaptive coping among T https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t008.jpg Table 8. Multivariate logistic binary regression analysis of odds for “High” maladaptive coping amon https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t009.jpg Table 9. Multivariate logistic binary regression analysis of odds for “High” coping efficacy among T https://www.frontiersin.org/files/Articles/1564307/fpubh-13-1564307-HTML/image_m/fpubh-13-1564307-t010.jpg Table A1. Principal components analysis (Promax rotated) factor solution for children's coping strat https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1753749/full https://www.frontiersin.org/files/Articles/1753749/fimmu-16-1753749-HTML/image_m/fimmu-16-1753749-g001.jpg Figure 1. Overview of patient enrollment and follow-up. (A) Patients’ treatment process. Patients wi https://www.frontiersin.org/files/Articles/1753749/fimmu-16-1753749-HTML/image_m/fimmu-16-1753749-t001.jpg Table 1. Baseline demographic and treatment characteristics. https://www.frontiersin.org/files/Articles/1753749/fimmu-16-1753749-HTML/image_m/fimmu-16-1753749-g002.jpg Figure 2. Clinical efficacy of overall patients. (A) Kaplan-Meier curves of overall survival (OS) of https://www.frontiersin.org/files/Articles/1753749/fimmu-16-1753749-HTML/image_m/fimmu-16-1753749-g003.jpg Figure 3. Clinical efficacy stratified by R0 and MPR status. (A) Kaplan-Meier curves of OS and EFS s https://www.frontiersin.org/files/Articles/1753749/fimmu-16-1753749-HTML/image_m/fimmu-16-1753749-g004.jpg Figure 4. Clinical efficacy analysis in patients with cIV. (A) Rates of pathological complete respon https://www.frontiersin.org/files/Articles/1753749/fimmu-16-1753749-HTML/image_m/fimmu-16-1753749-g005.jpg Figure 5. Clinical efficacy according to different clinicopathological characteristics. (A) Patholog https://www.frontiersin.org/files/Articles/1753749/fimmu-16-1753749-HTML/image_m/fimmu-16-1753749-g006.jpg Figure 6. Association of KMT2D mutation with major pathological response and immune landscape. (A) M https://www.frontiersin.org/files/Articles/1753749/fimmu-16-1753749-HTML/image_m/fimmu-16-1753749-g007.jpg Figure 7. Correlation of major pathological response (MPR) with tumor cytochrome P450 activity. (A) https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1629307/full https://www.frontiersin.org/files/Articles/1629307/fonc-15-1629307-HTML/image_m/fonc-15-1629307-g001.jpg Figure 1. A nodule in the left upper lobe, with the edges appearing lobulated. https://www.frontiersin.org/files/Articles/1629307/fonc-15-1629307-HTML/image_m/fonc-15-1629307-g002.jpg Figure 2. The conventional paraffin pathology of the left upper lobe nodule indicates the coexistenc https://www.frontiersin.org/files/Articles/1629307/fonc-15-1629307-HTML/image_m/fonc-15-1629307-t001.jpg Table 1. Clinical features, pathological types, treatment methods, and follow-up of two C-SCLC patie https://www.frontiersin.org/files/Articles/1629307/fonc-15-1629307-HTML/image_m/fonc-15-1629307-g003.jpg Figure 3. Lobulated nodule in the left upper lobe. https://www.frontiersin.org/files/Articles/1629307/fonc-15-1629307-HTML/image_m/fonc-15-1629307-g004.jpg Figure 4. The conventional paraffin pathology of the left upper lobe nodule indicates the coexistenc https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1780034/full https://www.frontiersin.org/files/Articles/1780034/fmed-13-1780034-HTML-r3/image_m/fmed-13-1780034-g001.jpg Figure 1. Central role of NETs in the pathogenesis and therapeutic targeting of treatment-refractory https://www.frontiersin.org/files/Articles/1780034/fmed-13-1780034-HTML-r3/image_m/fmed-13-1780034-t001.jpg Table 1. Therapeutic strategies targeting NETs in treatment-refractory asthma. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1726800/full https://www.frontiersin.org/files/Articles/1726800/fped-13-1726800-HTML/image_m/fped-13-1726800-g001.jpg Figure 1. Family pedigree. https://www.frontiersin.org/files/Articles/1726800/fped-13-1726800-HTML/image_m/fped-13-1726800-t001.jpg Table 1. Family genetic testing results. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1690103/full https://www.frontiersin.org/files/Articles/1690103/fneur-16-1690103-HTML/image_m/fneur-16-1690103-g001.jpg Figure 1. Flowchart of study subject inclusion. https://www.frontiersin.org/files/Articles/1690103/fneur-16-1690103-HTML/image_m/fneur-16-1690103-t001.jpg Table 1. Characteristics of 13 patients with supratentorial stroke. https://www.frontiersin.org/files/Articles/1690103/fneur-16-1690103-HTML/image_m/fneur-16-1690103-g002.jpg Figure 2. MRI lesions of 13 patients with hemispheric stroke. The infarct is shown at the arrow. The https://www.frontiersin.org/files/Articles/1690103/fneur-16-1690103-HTML/image_m/fneur-16-1690103-t002.jpg Table 2. Neuro-ophthalmological and neuro-otological examination results of 13 patients with hemisph https://www.frontiersin.org/files/Articles/1690103/fneur-16-1690103-HTML/image_m/fneur-16-1690103-g003.jpg Figure 3. Neuro-ophthalmological and neuro-otological findings in four patients. (A) Optokinetic tes https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1724311/full https://www.frontiersin.org/files/Articles/1724311/fneur-17-1724311-HTML-r1/image_m/fneur-17-1724311-t001.jpg Table 1. Comparison of phase III RCTs of TNK vs. rt-PA for acute ischemic stroke. https://www.frontiersin.org/files/Articles/1724311/fneur-17-1724311-HTML-r1/image_m/fneur-17-1724311-t002.jpg Table 2. Selected studies of ultra-time window IVT in AIS with non-small vessel occlusion. https://www.frontiersin.org/files/Articles/1724311/fneur-17-1724311-HTML-r1/image_m/fneur-17-1724311-t003.jpg Table 3. Ongoing ultra-time-window intravenous thrombolysis studies. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1674679/full https://www.frontiersin.org/files/Articles/1674679/fendo-17-1674679-HTML/image_m/fendo-17-1674679-t001.jpg Table 1. Incident cases and ASIR of EMBC in 1990 and 2021, and its temporal trends from 1990 to 2021 https://www.frontiersin.org/files/Articles/1674679/fendo-17-1674679-HTML/image_m/fendo-17-1674679-g001.jpg Figure 1. Joinpoint regression analysis of EMBC for ASIR (A), ASMR (B), and ASDR (C), 1990–2021. Age https://www.frontiersin.org/files/Articles/1674679/fendo-17-1674679-HTML/image_m/fendo-17-1674679-g002.jpg Figure 2. Global map of ASIR of EMBC in 1990 (A) and 2021 (B), and corresponding AAPC from 1990 to 2 https://www.frontiersin.org/files/Articles/1674679/fendo-17-1674679-HTML/image_m/fendo-17-1674679-g003.jpg Figure 3. Age–period–cohort effects on incidence (A), mortality (B), and DALYs (C) of EMBC globally https://www.frontiersin.org/files/Articles/1674679/fendo-17-1674679-HTML/image_m/fendo-17-1674679-g004.jpg Figure 4. Decomposition of changes in incidence, mortality, and DALYs of EMBC from 1990 to 2021 by S https://www.frontiersin.org/files/Articles/1674679/fendo-17-1674679-HTML/image_m/fendo-17-1674679-g005.jpg Figure 5. SDI-related inequality in ASIR, ASMR, and ASDR of EMBC in 1990 and 2021: regression-based https://www.frontiersin.org/files/Articles/1674679/fendo-17-1674679-HTML/image_m/fendo-17-1674679-g006.jpg Figure 6. Temporal trends in ASMR (A) and ASDR (B) of EMBC attributable to alcohol use, dietary risk https://www.frontiersin.org/files/Articles/1674679/fendo-17-1674679-HTML/image_m/fendo-17-1674679-g007.jpg Figure 7. Observed and projected global ASIR (A), ASMR (B), and ASDR (C) of EMBC, 1990–2040. Age def https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1756102/full https://www.frontiersin.org/files/Articles/1756102/fmed-13-1756102-HTML/image_m/fmed-13-1756102-g001.jpg Figure 1. Multiphasic CT and iodine map images of the pancreatic lesion. (A) Axial contrast-enhanced https://www.frontiersin.org/files/Articles/1756102/fmed-13-1756102-HTML/image_m/fmed-13-1756102-g002.jpg Figure 2. Curved planar and volume-rendered reconstructions from contrast-enhanced CT. (A) Curved pl https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2025.1720425/full https://www.frontiersin.org/files/Articles/1720425/frhs-05-1720425-HTML/image_m/frhs-05-1720425-t001.jpg Table 1. The responsibilities of a SN. https://www.frontiersin.org/files/Articles/1720425/frhs-05-1720425-HTML/image_m/frhs-05-1720425-t002.jpg Table 2. Demographic characteristics of participants. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1624106/full https://www.frontiersin.org/files/Articles/1624106/fbioe-13-1624106-HTML/image_m/fbioe-13-1624106-g001.jpg Figure 1. Design strategy of multifunctional PEEK implants. (A) Schematic illustration of the mussel https://www.frontiersin.org/files/Articles/1624106/fbioe-13-1624106-HTML/image_m/fbioe-13-1624106-g002.jpg Figure 2. Characterization of various PEEK surfaces. (A) SEM image (Scale bars, 30 μm and 10 µm). (B https://www.frontiersin.org/files/Articles/1624106/fbioe-13-1624106-HTML/image_m/fbioe-13-1624106-g003.jpg Figure 3. Characterization of different PEEK surfaces. (A,B) Water contact angles of the different P https://www.frontiersin.org/files/Articles/1624106/fbioe-13-1624106-HTML/image_m/fbioe-13-1624106-g004.jpg Figure 4. The biocompatibility of Sr2+ and AMP co-modified PEEK surfaces. (A,B) SEM images of BMSCs https://www.frontiersin.org/files/Articles/1624106/fbioe-13-1624106-HTML/image_m/fbioe-13-1624106-g005.jpg Figure 5. Sr2+ and AMP co-modified PEEK surfaces regulate macrophage polarization in vitro. (A,B) Re https://www.frontiersin.org/files/Articles/1624106/fbioe-13-1624106-HTML/image_m/fbioe-13-1624106-g006.jpg Figure 6. Sr2+ and AMP co-modified PEEK surfaces enhance osteogenic differentiation in vitro. (A) Th https://www.frontiersin.org/files/Articles/1624106/fbioe-13-1624106-HTML/image_m/fbioe-13-1624106-g007.jpg Figure 7. In vitro antibacterial ability of different PEEKs. (A) SEM Images of S. aureus and E. coli https://www.frontiersin.org/files/Articles/1624106/fbioe-13-1624106-HTML/image_m/fbioe-13-1624106-g008.jpg Figure 8. Multifunctional PEEK implants promoted osseointegration in the presence of S. aureus in vi https://www.frontiersin.org/files/Articles/1624106/fbioe-13-1624106-HTML/image_m/fbioe-13-1624106-g009.jpg Figure 9. Histological analysis and immunohistochemical staining in the bone tissue treated with dif https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1742763/full https://www.frontiersin.org/files/Articles/1742763/fmed-13-1742763-HTML/image_m/fmed-13-1742763-g001.jpg Figure 1. The heterogeneous morbidity profile of the patients. (A) The percentage of multimorbid pat https://www.frontiersin.org/files/Articles/1742763/fmed-13-1742763-HTML/image_m/fmed-13-1742763-g002.jpg Figure 2. Characteristics of the three identified subgroups in the study cohort. (A) Bubble plot sum https://www.frontiersin.org/files/Articles/1742763/fmed-13-1742763-HTML/image_m/fmed-13-1742763-g003.jpg Figure 3. Standardized path coefficients of the mediation model, both unadjusted and adjusted. (A,B) https://www.frontiersin.org/files/Articles/1742763/fmed-13-1742763-HTML/image_m/fmed-13-1742763-g004.jpg Figure 4. A series of performance metrics in the multinomial logistic regression model. (A) Area und https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1777863/full https://www.frontiersin.org/files/Articles/1777863/fmicb-17-1777863-HTML/image_m/fmicb-17-1777863-t001.jpg Table 1. Two-way ANOVA of inhibition zones (disc and well diffusion assays) as affected by plant spe https://www.frontiersin.org/files/Articles/1777863/fmicb-17-1777863-HTML/image_m/fmicb-17-1777863-g001.jpg Figure 1. Antimicrobial activity of Cleome extracts in diffusion assays. (A) Disc diffusion and (B) https://www.frontiersin.org/files/Articles/1777863/fmicb-17-1777863-HTML/image_m/fmicb-17-1777863-g002.jpg Figure 2. Minimum inhibitory concentration (MIC) values of Cleome extracts against bacterial isolate https://www.frontiersin.org/files/Articles/1777863/fmicb-17-1777863-HTML/image_m/fmicb-17-1777863-g003.jpg Figure 3. Minimum bactericidal concentrations (MBCs) of Cleome extracts. Heatmap showing MBC values https://www.frontiersin.org/files/Articles/1777863/fmicb-17-1777863-HTML/image_m/fmicb-17-1777863-g004.jpg Figure 4. Correlation analyses between diffusion assay zones, MIC, and MBC values. (A) MIC vs. MBC. https://www.frontiersin.org/files/Articles/1777863/fmicb-17-1777863-HTML/image_m/fmicb-17-1777863-g005.jpg Figure 5. Effects of Cleome noeana extract, phage, and their combination on bacterial growth. (A) Ba https://www.frontiersin.org/files/Articles/1777863/fmicb-17-1777863-HTML/image_m/fmicb-17-1777863-g006.jpg Figure 6. Clustered heatmap of metabolites in three Cleome species. Row-normalized concentrations (0 https://www.frontiersin.org/files/Articles/1777863/fmicb-17-1777863-HTML/image_m/fmicb-17-1777863-t002.jpg Table 2. Top metabolites identified by PLS-VIP analysis and their correlations with antimicrobial ac https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1712376/full https://www.frontiersin.org/files/Articles/1712376/fpubh-13-1712376-HTML/image_m/fpubh-13-1712376-t001.jpg Table 1. Characteristics of participants (mean ± SD). https://www.frontiersin.org/files/Articles/1712376/fpubh-13-1712376-HTML/image_m/fpubh-13-1712376-t002.jpg Table 2. Body composition and fitness differences according to MetS status by sex (mean ± SD). https://www.frontiersin.org/files/Articles/1712376/fpubh-13-1712376-HTML/image_m/fpubh-13-1712376-t003.jpg Table 3. Multivariable logistic regression for MetS (Men). https://www.frontiersin.org/files/Articles/1712376/fpubh-13-1712376-HTML/image_m/fpubh-13-1712376-t004.jpg Table 4. Multivariable logistic regression for MetS (Women). https://www.frontiersin.org/files/Articles/1712376/fpubh-13-1712376-HTML/image_m/fpubh-13-1712376-g001.jpg Figure 1. Receiver operating characteristic (ROC) curves for women comparing relative grip strength https://www.frontiersin.org/files/Articles/1712376/fpubh-13-1712376-HTML/image_m/fpubh-13-1712376-g002.jpg Figure 2. Receiver operating characteristic (ROC) curves for men comparing relative grip strength (o https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1658993/full https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-g006.jpg https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-t001.jpg Table 1. Subject anthropometrics. https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-t002.jpg Table 2. The results of the pre experiment, which tests the average number of repetitions in the SLR https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-g001.jpg Figure 1. Overview of research design. SLRT: stepwise load reduction training; MLRT: medium load res https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-g002.jpg Figure 2. The reliability of thigh circumference and CMJ tests. NOTE: TC = thigh circumference, CMJ https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-t003.jpg Table 3. Time main effect, group main effect and time*group interaction effect in the test of within https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-t004.jpg Table 4. Effects of different exercise interventions on changes in blood lactate parameters. https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-g003.jpg Figure 3. Effects of SLRT and MLRT on the blood lactate concentration (mmol/L) at various time point https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-t005.jpg Table 5. Effect of different exercise interventions on the change in strength performance parameters https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-g004.jpg Figure 4. (a–c) Changes in maximum strength, thigh circumference and muscle endurance after the 8-we https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-t006.jpg Table 6. Effects of different exercise interventions on changes in cmj performance parameters. https://www.frontiersin.org/files/Articles/1658993/fphys-16-1658993-HTML/image_m/fphys-16-1658993-g005.jpg Figure 5. (a–d) Changes in counter movement jump height and peak power after the 8-week intervention https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1644508/full https://www.frontiersin.org/files/Articles/1644508/fmed-12-1644508-HTML/image_m/fmed-12-1644508-g001.jpg Figure 1. Theoretical model based on social cognitive career theory and cognitive information proces https://www.frontiersin.org/files/Articles/1644508/fmed-12-1644508-HTML/image_m/fmed-12-1644508-t001.jpg Table 1. The model fit indices for LPA. https://www.frontiersin.org/files/Articles/1644508/fmed-12-1644508-HTML/image_m/fmed-12-1644508-t002.jpg Table 2. Fit indices of LPA for career decision-making difficulties profiles (N = 562). https://www.frontiersin.org/files/Articles/1644508/fmed-12-1644508-HTML/image_m/fmed-12-1644508-g002.jpg Figure 2. Three profiles of career decision-making difficulties among undergraduate nursing students https://www.frontiersin.org/files/Articles/1644508/fmed-12-1644508-HTML/image_m/fmed-12-1644508-t003.jpg Table 3. Profiles differences in career decision-making difficulties and the results of post hoc ana https://www.frontiersin.org/files/Articles/1644508/fmed-12-1644508-HTML/image_m/fmed-12-1644508-t004.jpg Table 4. Unordered logistic regression analysis results for the information-driven advantage group o https://www.frontiersin.org/files/Articles/1644508/fmed-12-1644508-HTML/image_m/fmed-12-1644508-t005.jpg Table 5. Unordered logistic regression analysis results for the knowledge-action disconnection group https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2025.1635540/full https://www.frontiersin.org/files/Articles/1635540/falgy-06-1635540-HTML-r3/image_m/falgy-06-1635540-t001.jpg Table 1. Patient background at the initiation of biologics in the extended dosing interval group. https://www.frontiersin.org/files/Articles/1635540/falgy-06-1635540-HTML-r3/image_m/falgy-06-1635540-t002.jpg Table 2. Types of biologics dosage, administration interval, and extended regimen. https://www.frontiersin.org/files/Articles/1635540/falgy-06-1635540-HTML-r3/image_m/falgy-06-1635540-t003.jpg Table 3. Summary of clinical parameters before biologics administration, at the time of dosing inter https://www.frontiersin.org/files/Articles/1635540/falgy-06-1635540-HTML-r3/image_m/falgy-06-1635540-g001.jpg Figure 1. Temporal changes in ACT score, % predicted FEV1, and daily prednisolone dose. This figure https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2025.1653140/full https://www.frontiersin.org/files/Articles/1653140/fresc-06-1653140-HTML/image_m/fresc-06-1653140-g001.jpg Figure 1. Distribution of types of disabilities among children students. https://www.frontiersin.org/files/Articles/1653140/fresc-06-1653140-HTML/image_m/fresc-06-1653140-g002.jpg Figure 2. Initial thematic Map of the study reflecting all the relevant codes. https://www.frontiersin.org/files/Articles/1653140/fresc-06-1653140-HTML/image_m/fresc-06-1653140-g003.jpg Figure 3. (a) developed thematic map. (b): Developed Thematic map. https://www.frontiersin.org/files/Articles/1653140/fresc-06-1653140-HTML/image_m/fresc-06-1653140-g004.jpg Figure 4. Final thematic map of themes extracted. https://www.frontiersin.org/files/Articles/1653140/fresc-06-1653140-HTML/image_m/fresc-06-1653140-t001.jpg Table 1. Categorization of codes and frequency of code occurrences within categories. https://www.frontiersin.org/files/Articles/1653140/fresc-06-1653140-HTML/image_m/fresc-06-1653140-t002.jpg Table 2. Prevalence of themes. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1624111/full https://www.frontiersin.org/files/Articles/1624111/fonc-15-1624111-HTML/image_m/fonc-15-1624111-g001.jpg Figure 1. Sample images. https://www.frontiersin.org/files/Articles/1624111/fonc-15-1624111-HTML/image_m/fonc-15-1624111-g002.jpg Figure 2. Example of drawing segmentation regions in the image. https://www.frontiersin.org/files/Articles/1624111/fonc-15-1624111-HTML/image_m/fonc-15-1624111-g003.jpg Figure 3. Model framework workflow. https://www.frontiersin.org/files/Articles/1624111/fonc-15-1624111-HTML/image_m/fonc-15-1624111-g004.jpg Figure 4. IOU comparison between two methods. https://www.frontiersin.org/files/Articles/1624111/fonc-15-1624111-HTML/image_m/fonc-15-1624111-t001.jpg Table 1. Corresponding weight information of images. https://www.frontiersin.org/files/Articles/1624111/fonc-15-1624111-HTML/image_m/fonc-15-1624111-t002.jpg Table 2. Comparative experiments. https://www.frontiersin.org/files/Articles/1624111/fonc-15-1624111-HTML/image_m/fonc-15-1624111-g005.jpg Figure 5. Comparative experiments of methods. https://www.frontiersin.org/files/Articles/1624111/fonc-15-1624111-HTML/image_m/fonc-15-1624111-g006.jpg Figure 6. Confusion matrix. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1712781/full https://www.frontiersin.org/files/Articles/1712781/fped-14-1712781-HTML/image_m/fped-14-1712781-t001.jpg Table 1. General characteristics of hospitalized children with chronic kidney disease (CKD) stages 3 https://www.frontiersin.org/files/Articles/1712781/fped-14-1712781-HTML/image_m/fped-14-1712781-t002.jpg Table 2. Distribution of CKD stages by initial presentation (asymptomatic vs. symptomatic). https://www.frontiersin.org/files/Articles/1712781/fped-14-1712781-HTML/image_m/fped-14-1712781-t003.jpg Table 3. Laboratory parameters stratified by CKD stage. https://www.frontiersin.org/files/Articles/1712781/fped-14-1712781-HTML/image_m/fped-14-1712781-t004.jpg Table 4. Prevalence and management of anemia by CKD stage. https://www.frontiersin.org/files/Articles/1712781/fped-14-1712781-HTML/image_m/fped-14-1712781-t005.jpg Table 5. Prevalence of complications and antihypertensive medication use by CKD stage. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1802653/full https://www.frontiersin.org/files/Articles/1802653/fneur-17-1802653-HTML/image_m/fneur-17-1802653-g001.jpg Figure 1. Stimulus-induced IIC pattern. (A) Prominent parietotemporal skull defect (arrowheads). (B) https://www.frontiersin.org/files/Articles/1802653/fneur-17-1802653-HTML/image_m/fneur-17-1802653-g002.jpg Figure 2. Seizure and BIRDs. (A) Ten-second EEG epoch demonstrating the initial part of an epileptic https://www.frontiersin.org/files/Articles/1802653/fneur-17-1802653-HTML/image_m/fneur-17-1802653-g003.jpg Figure 3. Patient with a focal to bilateral tonic–clonic seizure lasting 3 min, followed by decrease https://www.frontiersin.org/files/Articles/1802653/fneur-17-1802653-HTML/image_m/fneur-17-1802653-g004.jpg Figure 4. Overlap of PMA-like imaging features and encephalitis-related MRI findings in a patient wi https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1771844/full https://www.frontiersin.org/files/Articles/1771844/fpubh-14-1771844-HTML/image_m/fpubh-14-1771844-t001.jpg Table 1. Pediatric antimicrobial resistance: One Health drivers, exposure pathways, and policy needs https://www.frontiersin.org/files/Articles/1771844/fpubh-14-1771844-HTML/image_m/fpubh-14-1771844-t002.jpg Table 2. Antimicrobial resistance in neonates and children within a One Health perspective. https://www.frontiersin.org/files/Articles/1771844/fpubh-14-1771844-HTML/image_m/fpubh-14-1771844-t003.jpg Table 3. Physiological and environmental health impacts associated with rising ambient temperatures. https://www.frontiersin.org/files/Articles/1771844/fpubh-14-1771844-HTML/image_m/fpubh-14-1771844-g001.jpg Figure 1. Multifactorial impact of air pollution on allergic disease burden. https://www.frontiersin.org/files/Articles/1771844/fpubh-14-1771844-HTML/image_m/fpubh-14-1771844-t004.jpg Table 4. Primary health and environmental consequences associated with extreme weather events such a https://www.frontiersin.org/files/Articles/1771844/fpubh-14-1771844-HTML/image_m/fpubh-14-1771844-g002.jpg Figure 2. Groups most vulnerable to climate change. https://www.frontiersin.org/files/Articles/1771844/fpubh-14-1771844-HTML/image_m/fpubh-14-1771844-t005.jpg Table 5. Climate-sensitive vector-borne diseases relevant to Europe and pediatric populations. https://www.frontiersin.org/files/Articles/1771844/fpubh-14-1771844-HTML/image_m/fpubh-14-1771844-t006.jpg Table 6. Framework linking climate and anthropogenic change to vector-borne disease emergence. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1636533/full https://www.frontiersin.org/files/Articles/1636533/fonc-15-1636533-HTML/image_m/fonc-15-1636533-g001.jpg Figure 1. (A) The overall survival difference between ES and LS SCLC patients. (B) The first-line PF https://www.frontiersin.org/files/Articles/1636533/fonc-15-1636533-HTML/image_m/fonc-15-1636533-g002.jpg Figure 2. (A) The OS difference between platinum-resistant patients and platinum-sensitive patients. https://www.frontiersin.org/files/Articles/1636533/fonc-15-1636533-HTML/image_m/fonc-15-1636533-g003.jpg Figure 3. (A) The mOS of patients receive platinum as the second line treatment is 17.7 months verse https://www.frontiersin.org/files/Articles/1636533/fonc-15-1636533-HTML/image_m/fonc-15-1636533-g004.jpg Figure 4. (A) The mPFS for IL in second line treatment is 3.3 months verse 4.2 months in PS patients https://www.frontiersin.org/files/Articles/1636533/fonc-15-1636533-HTML/image_m/fonc-15-1636533-g005.jpg Figure 5. (A) The difference in third-line PFS among different regimens. (B) The difference of OS be https://www.frontiersin.org/files/Articles/1636533/fonc-15-1636533-HTML/image_m/fonc-15-1636533-g006.jpg Figure 6. (A) First-line PFS comparison (irradiated vs. non-irradiated); (B) Cox regression for PFS https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1782626/full https://www.frontiersin.org/files/Articles/1782626/feduc-11-1782626-HTML/image_m/feduc-11-1782626-g001.jpg Figure 1. Growth of artificial intelligence and personalized learning publications from 2013 to 2025 https://www.frontiersin.org/files/Articles/1782626/feduc-11-1782626-HTML/image_m/feduc-11-1782626-t001.jpg Table 1. The search string. https://www.frontiersin.org/files/Articles/1782626/feduc-11-1782626-HTML/image_m/feduc-11-1782626-t002.jpg Table 2. Inclusion and exclusion criteria. https://www.frontiersin.org/files/Articles/1782626/feduc-11-1782626-HTML/image_m/feduc-11-1782626-g002.jpg Figure 2. Data retrieval process. Source: Ikram et al. (2025). https://www.frontiersin.org/files/Articles/1782626/feduc-11-1782626-HTML/image_m/feduc-11-1782626-g003.jpg Figure 3. Articles frequency distribution by research methods. https://www.frontiersin.org/files/Articles/1782626/feduc-11-1782626-HTML/image_m/feduc-11-1782626-g004.jpg Figure 4. Articles frequency distribution by research setting. https://www.frontiersin.org/files/Articles/1782626/feduc-11-1782626-HTML/image_m/feduc-11-1782626-g005.jpg Figure 5. Top authors. https://www.frontiersin.org/files/Articles/1782626/feduc-11-1782626-HTML/image_m/feduc-11-1782626-g006.jpg Figure 6. Subject area. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1772657/full https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-t001.jpg Table 1. Phenotypic variation and genetic analysis of the evaluated traits in F6:7 recombinant inbre https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-g001.jpg Figure 1. Distribution of seed size and yield-related traits of RP270× BRSImponente cowpea RIL popul https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-g002.jpg Figure 2. Correlation coefficients among seed size related traits and grain yield. PedLt, Peduncle l https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-t002.jpg Table 2. List of putative QTLs for seed- and yield related traits. https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-t003.jpg Table 3. Summary of QTL comparison. https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-g003.jpg Figure 3. QTL hotspots/cluster mapped on two chromosomal regions. https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-g004.jpg Figure 4. Manhattan plots of QTL clusters for measured traits. Each plot displays the LOD scores of https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-t004.jpg Table 4. Putative genes identified that regulate cowpea seed size traits. https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-t005.jpg Table 5. Marker effects of flanking SNPs associated with seed size traits. https://www.frontiersin.org/files/Articles/1772657/fpls-17-1772657-HTML-r1/image_m/fpls-17-1772657-g005.jpg Figure 5. Prediction of seed size traits based on SNP markers. https://www.frontiersin.org/journals/anesthesiology/articles/10.3389/fanes.2025.1610320/full https://www.frontiersin.org/files/Articles/1610320/fanes-04-1610320-HTML/image_m/fanes-04-1610320-t001.jpg Table 1. Mixed-method cost minimization illustration, addressing PONV prophylaxis options achievable https://www.frontiersin.org/files/Articles/1610320/fanes-04-1610320-HTML/image_m/fanes-04-1610320-t002.jpg Table 2. Mixed-method cost-benefit analysis illustration per 1,000 patients per treatment, addressin https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1689706/full https://www.frontiersin.org/files/Articles/1689706/fpubh-14-1689706-HTML-r1/image_m/fpubh-14-1689706-t001.jpg Table 1. Global self-harm incidence, YLDs, and DALYs: numbers and age-specific rates among all ages https://www.frontiersin.org/files/Articles/1689706/fpubh-14-1689706-HTML-r1/image_m/fpubh-14-1689706-g001.jpg Figure 1. Global distribution of self-harm incidence, YLDs, and DALYs rates (per 100,000 population) https://www.frontiersin.org/files/Articles/1689706/fpubh-14-1689706-HTML-r1/image_m/fpubh-14-1689706-g002.jpg Figure 2. Temporal trends in the self-harm burden and attributable risk factors among adolescents ag https://www.frontiersin.org/files/Articles/1689706/fpubh-14-1689706-HTML-r1/image_m/fpubh-14-1689706-g003.jpg Figure 3. Global average annual percentage change (AAPC) in self-harm incidence, YLDs, and DALYs rat https://www.frontiersin.org/files/Articles/1689706/fpubh-14-1689706-HTML-r1/image_m/fpubh-14-1689706-g004.jpg Figure 4. Associations between self-harm burden and the socio-demographic index (SDI) among adolesce https://www.frontiersin.org/files/Articles/1689706/fpubh-14-1689706-HTML-r1/image_m/fpubh-14-1689706-g005.jpg Figure 5. Socioeconomic inequality and frontier analysis of self-harm DALYs rates among adolescents https://www.frontiersin.org/files/Articles/1689706/fpubh-14-1689706-HTML-r1/image_m/fpubh-14-1689706-g006.jpg Figure 6. Global projections of adolescent self-harm burden (aged 10–19 years) to 2035. Left column https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2026.1752000/full https://www.frontiersin.org/files/Articles/1752000/fcomm-11-1752000-HTML/image_m/fcomm-11-1752000-g001.jpg Figure 1. Conceptual model. https://www.frontiersin.org/files/Articles/1752000/fcomm-11-1752000-HTML/image_m/fcomm-11-1752000-t001.jpg Table 1. Means and standard deviations for Likert items. https://www.frontiersin.org/files/Articles/1752000/fcomm-11-1752000-HTML/image_m/fcomm-11-1752000-t002.jpg Table 2. Measurement model for variables. https://www.frontiersin.org/files/Articles/1752000/fcomm-11-1752000-HTML/image_m/fcomm-11-1752000-t003.jpg Table 3. Discriminant validity (HTMT). https://www.frontiersin.org/files/Articles/1752000/fcomm-11-1752000-HTML/image_m/fcomm-11-1752000-g002.jpg Figure 2. Structural model. https://www.frontiersin.org/files/Articles/1752000/fcomm-11-1752000-HTML/image_m/fcomm-11-1752000-t004.jpg Table 4. Hypothesis testing direct effect. https://www.frontiersin.org/files/Articles/1752000/fcomm-11-1752000-HTML/image_m/fcomm-11-1752000-t005.jpg Table 5. Indirect effect. https://www.frontiersin.org/files/Articles/1752000/fcomm-11-1752000-HTML/image_m/fcomm-11-1752000-t006.jpg Table 6. PLS predict. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1669838/full https://www.frontiersin.org/files/Articles/1669838/fonc-15-1669838-HTML/image_m/fonc-15-1669838-t001.jpg Table 1. Characteristics of the study population with gastric cancer stratified by LCR and food inta https://www.frontiersin.org/files/Articles/1669838/fonc-15-1669838-HTML/image_m/fonc-15-1669838-g001.jpg Figure 1. Time-dependent changes in the area under the curve (AUC) for overall survival of 16 system https://www.frontiersin.org/files/Articles/1669838/fonc-15-1669838-HTML/image_m/fonc-15-1669838-t002.jpg Table 2. The association between LCR levels or Food intake and all-cause mortality in patients with https://www.frontiersin.org/files/Articles/1669838/fonc-15-1669838-HTML/image_m/fonc-15-1669838-t003.jpg Table 3. The association of the model constructed by LCR and Food intake with all-cause mortality in https://www.frontiersin.org/files/Articles/1669838/fonc-15-1669838-HTML/image_m/fonc-15-1669838-g002.jpg Figure 2. Kaplan–Meier analysis of overall survival according to the (A) LCR levels, (B) Food intake https://www.frontiersin.org/files/Articles/1669838/fonc-15-1669838-HTML/image_m/fonc-15-1669838-g003.jpg Figure 3. Receiver operating characteristics (ROC) curve for LCR, Food intake status and LCR combine https://www.frontiersin.org/files/Articles/1669838/fonc-15-1669838-HTML/image_m/fonc-15-1669838-g004.jpg Figure 4. The relationship between LCR or Food intake and all-cause mortality of patients with gastr https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1788723/full https://www.frontiersin.org/files/Articles/1788723/feduc-11-1788723-HTML-r1/image_m/feduc-11-1788723-t001.jpg Table 1. Interview protocol traceability matrix: guiding questions, analytical categories, and inter https://www.frontiersin.org/files/Articles/1788723/feduc-11-1788723-HTML-r1/image_m/feduc-11-1788723-t002.jpg Table 2. Data sources and collection techniques. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1687922/full https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g001.jpg Figure 1. Flow diagram of the study. https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g002.jpg Figure 2. Identification of srlncRNAs with significant prognostic value in EC. (A) The forest showed https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g003.jpg Figure 3. Survival curve of EC patients in different groups. (A) Comparison of survival rates betwee https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g004.jpg Figure 4. Screening of prognostic srlncRNA in EC. (A) A co-expression network of srlncRNAs and mRNAs https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g005.jpg Figure 5. The prognostic value of our predictive model in the entire, train, and test sets. (A) Exhi https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g006.jpg Figure 6. Assessment of the prognostic survival model based on seven srlncRNAs. (A,B) Univariate and https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g007.jpg Figure 7. (A) Heat map of seven srlncRNAs’ expression. (B) Survival analysis of the high-risk and lo https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g008.jpg Figure 8. The results of functional analysis based on seven srlncRNAs model by GSEA. (A) GO enrichme https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g009.jpg Figure 9. The investigation of tumor immune factors and immunotherapy. (A–C) Estimate score, immune https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g010.jpg Figure 10. (A) The organoid morphology of P0, P1, and P2. (B) Comparison of HE and IHC between organ https://www.frontiersin.org/files/Articles/1687922/fgene-16-1687922-HTML-r1/image_m/fgene-16-1687922-g011.jpg Figure 11. (A) Relative mRNA levels of targeted genes from four organoids; (B) Drug sensitivity anal https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1746188/full https://www.frontiersin.org/files/Articles/1746188/fvets-13-1746188-HTML/image_m/fvets-13-1746188-g001.jpg Figure 1. PRRSV genome. The PRRSV genome generates three polyprotein precursors (pp1a, pp1ab, and pp https://www.frontiersin.org/files/Articles/1746188/fvets-13-1746188-HTML/image_m/fvets-13-1746188-g002.jpg Figure 2. Virus assembly and immunomodulation. The PRRSV infection cycle can be broadly categorized https://www.frontiersin.org/files/Articles/1746188/fvets-13-1746188-HTML/image_m/fvets-13-1746188-g003.jpg Figure 3. NSP2 protein interaction map. Summary of host proteins and viral proteins interacting with https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1700166/full https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-t001.jpg Table 1. Detailed search strategy. https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-t002.jpg Table 2. PCC framework for digital neurosurgery. https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-g001.jpg Figure 1. Distribution of included studies across digital-neurosurgery technology domains. This pie https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-g002.jpg Figure 2. PRISMA-ScR flow diagram of study selection. https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-t003.jpg Table 3. Summary of widely used technologies in digital neurosurgery. https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-g003.jpg Figure 3. Application framework of digital 3D reconstruction. (A) Workflow converting multimodal 2D https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-g004.jpg Figure 4. Application framework of 3DP. Mind-map showing: definition, roles (pre-/intra-operative to https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-g005.jpg Figure 5. Application framework of DT and DTT. Mind-map of DT/DTT’s core, perioperative value (pre/i https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-g006.jpg Figure 6. Application framework of intraoperative navigation. Mind-map contrasting traditional stere https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-g007.jpg Figure 7. Application framework of robot-assisted surgery. Mind-map highlighting aims (fewer errors https://www.frontiersin.org/files/Articles/1700166/fmed-12-1700166-HTML/image_m/fmed-12-1700166-g008.jpg Figure 8. Application framework of AI. Mind-map outlining definition/promise, why neurosurgery is da https://www.frontiersin.org/journals/clinical-diabetes-and-healthcare/articles/10.3389/fcdhc.2025.1714205/full https://www.frontiersin.org/files/Articles/1714205/fcdhc-06-1714205-HTML/image_m/fcdhc-06-1714205-t001.jpg Table 1. Sociodemographic and clinical characteristics of Peruvian adults aged ≥50 years with diabet https://www.frontiersin.org/files/Articles/1714205/fcdhc-06-1714205-HTML/image_m/fcdhc-06-1714205-g001.jpg Figure 1. Temporal trends in healthcare check-ups among Peruvian adults aged ≥50 years with diabetes https://www.frontiersin.org/files/Articles/1714205/fcdhc-06-1714205-HTML/image_m/fcdhc-06-1714205-t002.jpg Table 2. Disparities in healthcare check-ups among Peruvian adults aged ≥50 years with diabetes by s https://www.frontiersin.org/files/Articles/1714205/fcdhc-06-1714205-HTML/image_m/fcdhc-06-1714205-t003.jpg Table 3. Disparities in healthcare check-ups among Peruvian adults aged ≥50 years with hypertension https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1799940/full https://www.frontiersin.org/files/Articles/1799940/fcvm-13-1799940-HTML/image_m/fcvm-13-1799940-g001.jpg Figure 1. Study flow diagram. https://www.frontiersin.org/files/Articles/1799940/fcvm-13-1799940-HTML/image_m/fcvm-13-1799940-t001.jpg Table 1. Comparison of baseline characteristics, comorbidities, and medications in the PSM-cohort. https://www.frontiersin.org/files/Articles/1799940/fcvm-13-1799940-HTML/image_m/fcvm-13-1799940-t002.jpg Table 2. Comparison of pre-procedural echocardiographic parameters, laboratory values, and procedura https://www.frontiersin.org/files/Articles/1799940/fcvm-13-1799940-HTML/image_m/fcvm-13-1799940-g002.jpg Figure 2. Acute kidney injury and need for dialysis in overall and propensity-matched cohorts. (a) A https://www.frontiersin.org/files/Articles/1799940/fcvm-13-1799940-HTML/image_m/fcvm-13-1799940-t003.jpg Table 3. Procedural and study outcomes in PSM-cohort. https://www.frontiersin.org/files/Articles/1799940/fcvm-13-1799940-HTML/image_m/fcvm-13-1799940-t004.jpg Table 4. Univariate and multivariable logistic regression analyses for the prediction of acute kidne https://www.frontiersin.org/files/Articles/1799940/fcvm-13-1799940-HTML/image_m/fcvm-13-1799940-g003.jpg Figure 3. Independent predictors of postoperative acute kidney injury in the overall cohort (forest https://www.frontiersin.org/files/Articles/1799940/fcvm-13-1799940-HTML/image_m/fcvm-13-1799940-t005.jpg Table 5. Univariate and multivariable logistic regression analyses for the prediction of acute kidne https://www.frontiersin.org/files/Articles/1799940/fcvm-13-1799940-HTML/image_m/fcvm-13-1799940-g004.jpg Figure 4. Independent predictors of acute kidney injury in the propensity score–matched cohort (fore https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1736489/full https://www.frontiersin.org/files/Articles/1736489/feduc-11-1736489-HTML/image_m/feduc-11-1736489-t001.jpg Table 1. Genre-based revision goals and feedback messages corresponding to each goal. https://www.frontiersin.org/files/Articles/1736489/feduc-11-1736489-HTML/image_m/feduc-11-1736489-g001.jpg Figure 1. Taxonomy of revision patterns. https://www.frontiersin.org/files/Articles/1736489/feduc-11-1736489-HTML/image_m/feduc-11-1736489-t002.jpg Table 2. Manifestation of revision patterns by revision goals. https://www.frontiersin.org/files/Articles/1736489/feduc-11-1736489-HTML/image_m/feduc-11-1736489-t003.jpg Table 3. Distribution of revision patterns for each revision goal (N = 330 essay pairs). https://www.frontiersin.org/files/Articles/1736489/feduc-11-1736489-HTML/image_m/feduc-11-1736489-g002.jpg Figure 2. Illustration of prompt design. https://www.frontiersin.org/files/Articles/1736489/feduc-11-1736489-HTML/image_m/feduc-11-1736489-t004.jpg Table 4. Intra-rater reliability across two prompting strategies. https://www.frontiersin.org/files/Articles/1736489/feduc-11-1736489-HTML/image_m/feduc-11-1736489-t005.jpg Table 5. Prediction accuracy across two prompting strategies. https://www.frontiersin.org/files/Articles/1736489/feduc-11-1736489-HTML/image_m/feduc-11-1736489-t006.jpg Table 6. F1 scores across two prompting strategies. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1662170/full https://www.frontiersin.org/files/Articles/1662170/fimmu-16-1662170-HTML/image_m/fimmu-16-1662170-g001.jpg Figure 1. Simvastatin inhibits IL-33-mediated mast cell function. (A) Mevalonate pathway schematic. https://www.frontiersin.org/files/Articles/1662170/fimmu-16-1662170-HTML/image_m/fimmu-16-1662170-g002.jpg Figure 2. Simvastatin effects are due to inhibition of isoprenylation and not by targeting cholester https://www.frontiersin.org/files/Articles/1662170/fimmu-16-1662170-HTML/image_m/fimmu-16-1662170-g003.jpg Figure 3. Inhibiting both isoprenylation transferases consistently reduces IL-33-mediated cytokine p https://www.frontiersin.org/files/Articles/1662170/fimmu-16-1662170-HTML/image_m/fimmu-16-1662170-g004.jpg Figure 4. Genetic background affects the response to simvastatin but not FGTI-2734. (A) 129/Sv/J BMM https://www.frontiersin.org/files/Articles/1662170/fimmu-16-1662170-HTML/image_m/fimmu-16-1662170-g005.jpg Figure 5. Simvastatin but not FGTI-2734 inhibits IL-33-mediated Akt activation. (A) C57BL/6J BMMC we https://www.frontiersin.org/files/Articles/1662170/fimmu-16-1662170-HTML/image_m/fimmu-16-1662170-g006.jpg Figure 6. Simvastatin and FGTI-2734 inhibit eosinophil responses to IL-33 and chemokine-induced migr https://www.frontiersin.org/files/Articles/1662170/fimmu-16-1662170-HTML/image_m/fimmu-16-1662170-g007.jpg Figure 7. FGTI-2734 inhibits IL-33-mediated peritonitis in vivo. (A) Experimental schematic showing https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1730953/full https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-g001.jpg Figure 1. Research framework. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t001.jpg Table 1. Demographics analysis. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-g002.jpg Figure 2. Measurement model. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t002.jpg Table 2. Measurement model statistics. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t003.jpg Table 3. Higher-order construct weights, loadings, and collinearity. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t004.jpg Table 4. Discriminant validity (HTMT). https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t005.jpg Table 5. Method factor model (higher-order constructs with marker variable “perception of blue”). https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t006.jpg Table 6. Predictive statistics. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-g003.jpg Figure 3. Structural model. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t007.jpg Table 7. Model fit statistics. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t008.jpg Table 8. Structural model statistics. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t009.jpg Table 9. Necessity analysis for high ESC. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t010.jpg Table 10. Minimally sufficient configurations for high ESC. https://www.frontiersin.org/files/Articles/1730953/fpsyg-16-1730953-HTML/image_m/fpsyg-16-1730953-t011.jpg Table 11. Configurations leading to high ESC. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1707116/full https://www.frontiersin.org/files/Articles/1707116/fpsyg-17-1707116-HTML-r1/image_m/fpsyg-17-1707116-g001.jpg Figure 1. Research framework. https://www.frontiersin.org/files/Articles/1707116/fpsyg-17-1707116-HTML-r1/image_m/fpsyg-17-1707116-t001.jpg Table 1. Demographic of the respondents. https://www.frontiersin.org/files/Articles/1707116/fpsyg-17-1707116-HTML-r1/image_m/fpsyg-17-1707116-g002.jpg Figure 2. Measurement model. https://www.frontiersin.org/files/Articles/1707116/fpsyg-17-1707116-HTML-r1/image_m/fpsyg-17-1707116-t002.jpg Table 2. Measurement model statistics. https://www.frontiersin.org/files/Articles/1707116/fpsyg-17-1707116-HTML-r1/image_m/fpsyg-17-1707116-t003.jpg Table 3. Discriminant validity. https://www.frontiersin.org/files/Articles/1707116/fpsyg-17-1707116-HTML-r1/image_m/fpsyg-17-1707116-t004.jpg Table 4. Predictive statistics. https://www.frontiersin.org/files/Articles/1707116/fpsyg-17-1707116-HTML-r1/image_m/fpsyg-17-1707116-t005.jpg Table 5. Structural model statistics. https://www.frontiersin.org/files/Articles/1707116/fpsyg-17-1707116-HTML-r1/image_m/fpsyg-17-1707116-g003.jpg Figure 3. Structural model. https://www.frontiersin.org/files/Articles/1707116/fpsyg-17-1707116-HTML-r1/image_m/fpsyg-17-1707116-t006.jpg https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1735049/full https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g001.jpg Figure 1. Flowchart of the study. https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-t001.jpg Table 1. Baseline characteristics and pathologic outcomes. https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-t002.jpg Table 2. Baseline characteristics of pCR patients. https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-t003.jpg Table 3. Baseline characteristics and pathologic outcomes of non-pCR patients. https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g002.jpg Figure 2. OS curves before and after sIPTW for the entire population, stratified by adjuvant therapy https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g003.jpg Figure 3. DFS curves before and after sIPTW for the entire population, stratified by adjuvant therap https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g004.jpg Figure 4. CSS curves before and after sIPTW for the entire population, stratified by adjuvant therap https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g005.jpg Figure 5. OS curves before and after sIPTW for patients achieving pCR, stratified by adjuvant therap https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g006.jpg Figure 6. DFS curves before and after sIPTW for patients achieving pCR, stratified by adjuvant thera https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g007.jpg Figure 7. CSS curves before and after sIPTW for patients achieving pCR, stratified by adjuvant thera https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g008.jpg Figure 8. OS curves before and after sIPTW for non-pCR patients, stratified by adjuvant therapy stat https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g009.jpg Figure 9. DFS curves before and after sIPTW for non-pCR patients, stratified by adjuvant therapy sta https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-g010.jpg Figure 10. CSS curves before and after sIPTW for non-pCR patients, stratified by adjuvant therapy st https://www.frontiersin.org/files/Articles/1735049/fonc-16-1735049-HTML-r1/image_m/fonc-16-1735049-t004.jpg Table 4. Patterns of recurrence and metastasis. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1712643/full https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g001.jpg Figure 1. HE staining of myodural bridge fibers in rats at different developmental stages. RCDmi: Re https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g002.jpg Figure 2. HE staining of ligamentum flavum fibers in rats at different developmental stages. →: Spin https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g003.jpg Figure 3. Gomori aldehyde fuchsin staining of myodural bridge fibers in rats at different developmen https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g004.jpg Figure 4. Gomori aldehyde fuchsin staining of ligamentum flavum fibers in rats at different developm https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g005.jpg Figure 5. Masson staining of MDB and LF in different groups of rats at P14. ▲: Myodural bridge; △: L https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g006.jpg Figure 6. Comparison of Lox and Loxl1 expression in MDB and LF of different groups at P14. X-axis: G https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g007.jpg Figure 7. LOX immunohistochemical staining and quantitative analysis of myodural bridge fibers in ra https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g008.jpg Figure 8. LOX immunohistochemical staining and quantitative analysis of ligamentum flavum fibers in https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g009.jpg Figure 9. LOXL1 immunohistochemical staining and quantitative analysis of ligamentum flavum fibers i https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g010.jpg Figure 10. Comparison of LOX and LOXL1 expression in ligamentum flavum fibers at different developme https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g011.jpg Figure 11. Comparison of LOX expression in myodural bridge and ligamentum flavum at different develo https://www.frontiersin.org/files/Articles/1712643/fcell-14-1712643-HTML/image_m/fcell-14-1712643-g012.jpg Figure 12. Comparison of Lox expression in MDB and LF at different developmental stages. X-axis: Gro https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1526105/full https://www.frontiersin.org/files/Articles/1526105/fpubh-13-1526105-HTML/image_m/fpubh-13-1526105-g001.jpg Figure 1. The workflow of the study. https://www.frontiersin.org/files/Articles/1526105/fpubh-13-1526105-HTML/image_m/fpubh-13-1526105-t001.jpg Table 1. Baseline characteristics of the included patients. https://www.frontiersin.org/files/Articles/1526105/fpubh-13-1526105-HTML/image_m/fpubh-13-1526105-g002.jpg Figure 2. An intelligent wearable and visual knee joint weight-bearing exercise device. (A) The user https://www.frontiersin.org/files/Articles/1526105/fpubh-13-1526105-HTML/image_m/fpubh-13-1526105-t002.jpg Table 2. Comparison of ROM and HSS scores before and after intervention. https://www.frontiersin.org/files/Articles/1526105/fpubh-13-1526105-HTML/image_m/fpubh-13-1526105-g003.jpg Figure 3. Comparison of knee joint ROM and HSS before and after intervention between two groups. https://www.frontiersin.org/files/Articles/1526105/fpubh-13-1526105-HTML/image_m/fpubh-13-1526105-t003.jpg Table 3. Comparison of pain scores at two different time points. https://www.frontiersin.org/files/Articles/1526105/fpubh-13-1526105-HTML/image_m/fpubh-13-1526105-g004.jpg Figure 4. Comparison of VAS in different time periods between the two groups. https://www.frontiersin.org/files/Articles/1526105/fpubh-13-1526105-HTML/image_m/fpubh-13-1526105-t004.jpg Table 4. Effects of the robot group as compared with the control group on the primary efficacy outco https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1645614/full https://www.frontiersin.org/files/Articles/1645614/fnut-12-1645614-HTML/image_m/fnut-12-1645614-g001.jpg Figure 1. Theoretical framework of the study. https://www.frontiersin.org/files/Articles/1645614/fnut-12-1645614-HTML/image_m/fnut-12-1645614-t001.jpg Table 1. Research designs of the three experiments. https://www.frontiersin.org/files/Articles/1645614/fnut-12-1645614-HTML/image_m/fnut-12-1645614-t002.jpg Table 2. Demographic characteristics of participants across experiments. https://www.frontiersin.org/files/Articles/1645614/fnut-12-1645614-HTML/image_m/fnut-12-1645614-g002.jpg Figure 2. Experimental stimulus materials: (A) sports-implied packaging and (B) traditional packagin https://www.frontiersin.org/files/Articles/1645614/fnut-12-1645614-HTML/image_m/fnut-12-1645614-t003.jpg Table 3. Independent-samples t-test results for pretest stimulus materials across dimensions. https://www.frontiersin.org/files/Articles/1645614/fnut-12-1645614-HTML/image_m/fnut-12-1645614-g003.jpg Figure 3. Path diagram of the chain mediation model with psychological empowerment and intrinsic mot https://www.frontiersin.org/files/Articles/1645614/fnut-12-1645614-HTML/image_m/fnut-12-1645614-g004.jpg Figure 4. Path diagram of the chain mediation model with psychological empowerment and extrinsic mot https://www.frontiersin.org/files/Articles/1645614/fnut-12-1645614-HTML/image_m/fnut-12-1645614-t004.jpg Table 4. Summary of indirect effects in the mediation models. https://www.frontiersin.org/files/Articles/1645614/fnut-12-1645614-HTML/image_m/fnut-12-1645614-g005.jpg Figure 5. Interaction between product packaging type and disease threat on purchase intention. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1652984/full https://www.frontiersin.org/files/Articles/1652984/fpsyg-16-1652984-HTML-r1/image_m/fpsyg-16-1652984-g001.jpg Figure 1. Hypothesis model diagram. https://www.frontiersin.org/files/Articles/1652984/fpsyg-16-1652984-HTML-r1/image_m/fpsyg-16-1652984-t001.jpg Table 1. Demographic and sample characteristics. https://www.frontiersin.org/files/Articles/1652984/fpsyg-16-1652984-HTML-r1/image_m/fpsyg-16-1652984-t002.jpg Table 2. The short grit scale. https://www.frontiersin.org/files/Articles/1652984/fpsyg-16-1652984-HTML-r1/image_m/fpsyg-16-1652984-t003.jpg Table 3. Self-efficacy scale. https://www.frontiersin.org/files/Articles/1652984/fpsyg-16-1652984-HTML-r1/image_m/fpsyg-16-1652984-t004.jpg Table 4. Descriptive statistics and correlation matrix. https://www.frontiersin.org/files/Articles/1652984/fpsyg-16-1652984-HTML-r1/image_m/fpsyg-16-1652984-t005.jpg Table 5. Regression analysis results. https://www.frontiersin.org/files/Articles/1652984/fpsyg-16-1652984-HTML-r1/image_m/fpsyg-16-1652984-g002.jpg Figure 2. Structural equation model. *P < 0.05, **P < 0.01, ***P < 0.001. https://www.frontiersin.org/files/Articles/1652984/fpsyg-16-1652984-HTML-r1/image_m/fpsyg-16-1652984-t006.jpg Table 6. Mediation effects and proportions. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1660164/full https://www.frontiersin.org/files/Articles/1660164/fpsyt-16-1660164-HTML/image_m/fpsyt-16-1660164-g001.jpg Figure 1. The proposed structural model. https://www.frontiersin.org/files/Articles/1660164/fpsyt-16-1660164-HTML/image_m/fpsyt-16-1660164-t001.jpg Table 1. Descriptive statistics and pearson correlations among key study variables. https://www.frontiersin.org/files/Articles/1660164/fpsyt-16-1660164-HTML/image_m/fpsyt-16-1660164-t002.jpg Table 2. Moderation effect regression results. https://www.frontiersin.org/files/Articles/1660164/fpsyt-16-1660164-HTML/image_m/fpsyt-16-1660164-g002.jpg Figure 2. Simple slopes illustrating the moderating effect of T2 physical exercise on the associatio https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1669753/full https://www.frontiersin.org/files/Articles/1669753/fmed-13-1669753-HTML-r1/image_m/fmed-13-1669753-g001.jpg Figure 1. (A) Total body hair loss present at birth. (B,C) Images obtained at the initial visit show https://www.frontiersin.org/files/Articles/1669753/fmed-13-1669753-HTML-r1/image_m/fmed-13-1669753-g002.jpg Figure 2. (A) Primary dentition showing sparse, hypoplastic deciduous teeth with a trapezoidal shape https://www.frontiersin.org/files/Articles/1669753/fmed-13-1669753-HTML-r1/image_m/fmed-13-1669753-g003.jpg Figure 3. Trichoscopy of the vertex scalp showing broken hairs (green arrows), short vellus hairs (r https://www.frontiersin.org/files/Articles/1669753/fmed-13-1669753-HTML-r1/image_m/fmed-13-1669753-g004.jpg Figure 4. Pedigree and LSS Sanger sequencing. (A) Pedigree of the family. Squares indicate males and https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1797275/full https://www.frontiersin.org/files/Articles/1797275/fmed-13-1797275-HTML/image_m/fmed-13-1797275-g001.jpg Figure 1. Clinical photographs of Case 1. (A) Frontal hairline. (B) Vertex. (C) Temporal region. (D) https://www.frontiersin.org/files/Articles/1797275/fmed-13-1797275-HTML/image_m/fmed-13-1797275-g002.jpg Figure 2. Trichoscopic findings in Case 1. (A) Vertex. (B) Whorl area. (C) Temporal region. (D) Occi https://www.frontiersin.org/files/Articles/1797275/fmed-13-1797275-HTML/image_m/fmed-13-1797275-t001.jpg Table 1. Paired vertex and occipital trichoscopic and histopathologic findings in two patients. https://www.frontiersin.org/files/Articles/1797275/fmed-13-1797275-HTML/image_m/fmed-13-1797275-g003.jpg Figure 3. Histopathologic findings in Case 1. (A) Vertex scalp, longitudinal section, showing follic https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1527309/full https://www.frontiersin.org/files/Articles/1527309/fcell-13-1527309-HTML/image_m/fcell-13-1527309-g001.jpg Figure 1. (A) Schematic depiction of constitutive splicing and five modes of alternative splicing: e https://www.frontiersin.org/files/Articles/1527309/fcell-13-1527309-HTML/image_m/fcell-13-1527309-g002.jpg Figure 2. The role of SRSF in cancer. https://www.frontiersin.org/files/Articles/1527309/fcell-13-1527309-HTML/image_m/fcell-13-1527309-t001.jpg Table 1. Dysregulated SRSF expression in cancer. https://www.frontiersin.org/files/Articles/1527309/fcell-13-1527309-HTML/image_m/fcell-13-1527309-g003.jpg Figure 3. The role of SRSF in tumor immunity. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1727398/full https://www.frontiersin.org/files/Articles/1727398/fcell-14-1727398-HTML-r1/image_m/fcell-14-1727398-g001.jpg Figure 1. Mechanisms linking cellular senescence to skeletal aging. (Left) Oxidative stress and telo https://www.frontiersin.org/files/Articles/1727398/fcell-14-1727398-HTML-r1/image_m/fcell-14-1727398-g002.jpg Figure 2. Molecular landscapes of estrogen-mediated skeletal protection. (Left) Estrogen deficiency https://www.frontiersin.org/files/Articles/1727398/fcell-14-1727398-HTML-r1/image_m/fcell-14-1727398-g003.jpg Figure 3. Microbial metabolites and immune signaling in bone homeostasis. (Left) Secondary bile acid https://www.frontiersin.org/files/Articles/1727398/fcell-14-1727398-HTML-r1/image_m/fcell-14-1727398-g004.jpg Figure 4. The integrative Gut-Bone axis: Estrobolome, SCFAs, and Senescence. (Left) The “Estrobolome https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1564649/full https://www.frontiersin.org/files/Articles/1564649/fcell-13-1564649-HTML-r1/image_m/fcell-13-1564649-g001.jpg Figure 1. Human diseases correlated to variants and dysregulation of ERAP1 and ERAP2. Figure created https://www.frontiersin.org/files/Articles/1564649/fcell-13-1564649-HTML-r1/image_m/fcell-13-1564649-g002.jpg Figure 2. Schematic representation illustrating the multifaceted roles of ERAP1 and ERAP2 in cellula https://www.frontiersin.org/files/Articles/1564649/fcell-13-1564649-HTML-r1/image_m/fcell-13-1564649-g003.jpg Figure 3. Schematic representation summarizing the role of ERAPs in the unfolded protein response (U https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1760517/full https://www.frontiersin.org/files/Articles/1760517/fcell-14-1760517-HTML/image_m/fcell-14-1760517-g001.jpg Figure 1. Different origins of widely studied EVs. Presently, in-depth research on EV primarily focu https://www.frontiersin.org/files/Articles/1760517/fcell-14-1760517-HTML/image_m/fcell-14-1760517-g002.jpg Figure 2. Classification of EVs is based on their biogenesis, release, and uptake. EVs comprise apop https://www.frontiersin.org/files/Articles/1760517/fcell-14-1760517-HTML/image_m/fcell-14-1760517-t001.jpg Table 1. The impact of ingesta-derived EVs on the gut barrier. https://www.frontiersin.org/files/Articles/1760517/fcell-14-1760517-HTML/image_m/fcell-14-1760517-g003.jpg Figure 3. EVs derived from CRC alter the TME, promoting tumor growth and metastasis. CRC-derived EVs https://www.frontiersin.org/files/Articles/1760517/fcell-14-1760517-HTML/image_m/fcell-14-1760517-g004.jpg Figure 4. Illustrations of the role of new inflammatory mediators in the progression of IBD-CRC. LXA https://www.frontiersin.org/files/Articles/1760517/fcell-14-1760517-HTML/image_m/fcell-14-1760517-t002.jpg Table 2. The specific role of EV and its assembled ncRNA in CRC progression. https://www.frontiersin.org/files/Articles/1760517/fcell-14-1760517-HTML/image_m/fcell-14-1760517-t003.jpg Table 3. Engineered EVs as drug delivery vehicles to treat IBD and CRC. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1643255/full https://www.frontiersin.org/files/Articles/1643255/fcell-13-1643255-HTML/image_m/fcell-13-1643255-t001.jpg Table 1. Fluid shear stress regulates the effects of ncRNAs on bone tissue or cells. https://www.frontiersin.org/files/Articles/1643255/fcell-13-1643255-HTML/image_m/fcell-13-1643255-t002.jpg Table 2. Stretching stress regulates the effects of ncRNAs on bone tissue or cells. https://www.frontiersin.org/files/Articles/1643255/fcell-13-1643255-HTML/image_m/fcell-13-1643255-t003.jpg Table 3. Compressive stress/Hydrostatic pressure regulates the effects of ncRNAs on bone tissue or c https://www.frontiersin.org/files/Articles/1643255/fcell-13-1643255-HTML/image_m/fcell-13-1643255-g001.jpg Figure 1. The force of contact regulates target genes by controlling the ceRNA network through vario https://www.frontiersin.org/files/Articles/1643255/fcell-13-1643255-HTML/image_m/fcell-13-1643255-t004.jpg Table 4. Hindlimb unloading/Simulated microgravity regulates the effects of ncRNAs on bone tissue or https://www.frontiersin.org/files/Articles/1643255/fcell-13-1643255-HTML/image_m/fcell-13-1643255-g002.jpg Figure 2. The non-contact force component alleviates the bone’s sensitivity to force via ncRNAs, ult https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1504834/full https://www.frontiersin.org/files/Articles/1504834/fcell-13-1504834-HTML/image_m/fcell-13-1504834-t001.jpg Table 1. Primers tailored for GAPDH (5′–3′). https://www.frontiersin.org/files/Articles/1504834/fcell-13-1504834-HTML/image_m/fcell-13-1504834-g001.jpg Figure 1. Effects of MP, RTX, or PAN on MPC5 podocyte stimulation. (A) Cell activity in different nu https://www.frontiersin.org/files/Articles/1504834/fcell-13-1504834-HTML/image_m/fcell-13-1504834-g002.jpg Figure 2. Differences in the podocyte apoptosis rate in noraml and under different drug intervention https://www.frontiersin.org/files/Articles/1504834/fcell-13-1504834-HTML/image_m/fcell-13-1504834-g003.jpg Figure 3. Relative expression levels of TRPC6 mRNA and different proteins in podocytes. (A) Expressi https://www.frontiersin.org/files/Articles/1504834/fcell-13-1504834-HTML/image_m/fcell-13-1504834-g004.jpg Figure 4. Levels of IL-1β and IL-18 in podocytes culture supernatants in control, PAN, MP, RTX, and https://www.frontiersin.org/files/Articles/1504834/fcell-13-1504834-HTML/image_m/fcell-13-1504834-g005.jpg Figure 5. Effects of PAN, MP, or RTX on the distribution and protein expression of TRPC6 at differen https://www.frontiersin.org/files/Articles/1504834/fcell-13-1504834-HTML/image_m/fcell-13-1504834-g006.jpg Figure 6. Differences in Ca2+ influx in control and under different drug intervetions of the PAN, MP https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1669591/full https://www.frontiersin.org/files/Articles/1669591/fphar-16-1669591-HTML/image_m/fphar-16-1669591-g001.jpg Figure 1. Risk factors and pathogenesis of sarcopenia. https://www.frontiersin.org/files/Articles/1669591/fphar-16-1669591-HTML/image_m/fphar-16-1669591-g002.jpg Figure 2. Lipophilic and hydrophilic statins. https://www.frontiersin.org/files/Articles/1669591/fphar-16-1669591-HTML/image_m/fphar-16-1669591-g003.jpg Figure 3. Molecular Mechanisms of Statins in sarcopenia. https://www.frontiersin.org/files/Articles/1669591/fphar-16-1669591-HTML/image_m/fphar-16-1669591-g004.jpg Figure 4. Mechanism of action of statins in sarcopenia. https://www.frontiersin.org/files/Articles/1669591/fphar-16-1669591-HTML/image_m/fphar-16-1669591-t001.jpg Table 1. Summary of clinical studies evaluating statin use and its impact on sarcopenia risk and out https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1685275/full https://www.frontiersin.org/files/Articles/1685275/fimmu-16-1685275-HTML/image_m/fimmu-16-1685275-t001.jpg Table 1. Demographic and clinical characteristics. https://www.frontiersin.org/files/Articles/1685275/fimmu-16-1685275-HTML/image_m/fimmu-16-1685275-g001.jpg Figure 1. SLEDAI. (A) Circulating levels of sCD25, sTim-3 and sGal-9 are higher in the disease group https://www.frontiersin.org/files/Articles/1685275/fimmu-16-1685275-HTML/image_m/fimmu-16-1685275-g002.jpg Figure 2. Kidney involvement and anemia. (A) Circulating levels of sCD25, sTim-3, sGal-9 and sPD-1 a https://www.frontiersin.org/files/Articles/1685275/fimmu-16-1685275-HTML/image_m/fimmu-16-1685275-g003.jpg Figure 3. Correlation. (A) Scatter plots showing correlations between sCD25, sTim-3 and sGal-9. Pear https://www.frontiersin.org/files/Articles/1685275/fimmu-16-1685275-HTML/image_m/fimmu-16-1685275-g004.jpg Figure 4. DORIS remission and LLDAS remission. (A) Circulating levels of sCD25 and sTim-3 are lower https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1753313/full https://www.frontiersin.org/files/Articles/1753313/fmed-13-1753313-HTML/image_m/fmed-13-1753313-g001.jpg Figure 1. Flow chart of inclusion and exclusion criteria of participants. https://www.frontiersin.org/files/Articles/1753313/fmed-13-1753313-HTML/image_m/fmed-13-1753313-t001.jpg Table 1. Baseline clinical characteristics of patients with pneumonia. https://www.frontiersin.org/files/Articles/1753313/fmed-13-1753313-HTML/image_m/fmed-13-1753313-t002.jpg Table 2. Comparisons of lymphocyte subsets between the ARDS and non-ARDS patients. https://www.frontiersin.org/files/Articles/1753313/fmed-13-1753313-HTML/image_m/fmed-13-1753313-g002.jpg Figure 2. Variable selection via least absolute shrinkage and selection operator (LASSO) regression. https://www.frontiersin.org/files/Articles/1753313/fmed-13-1753313-HTML/image_m/fmed-13-1753313-t003.jpg Table 3. Association between T cells (CD3 + CD4+) count and ARDS in different models. https://www.frontiersin.org/files/Articles/1753313/fmed-13-1753313-HTML/image_m/fmed-13-1753313-g003.jpg Figure 3. Restricted cubic spline curve describing the dose–response relationship between T cells CD https://www.frontiersin.org/files/Articles/1753313/fmed-13-1753313-HTML/image_m/fmed-13-1753313-t004.jpg Table 4. Association between CD3 + CD4+ T-cell count and ARDS: dose–response relationship and thresh https://www.frontiersin.org/files/Articles/1753313/fmed-13-1753313-HTML/image_m/fmed-13-1753313-t005.jpg Table 5. Comparison of CD3 + CD4 + T cell counts across ARDS severity groups. https://www.frontiersin.org/files/Articles/1753313/fmed-13-1753313-HTML/image_m/fmed-13-1753313-g004.jpg Figure 4. Subgroup analyses for the association of Log2 (T cells CD3+CD4+) and ARDS. OR, odds ratio; https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1644840/full https://www.frontiersin.org/files/Articles/1644840/fnut-12-1644840-HTML/image_m/fnut-12-1644840-t001.jpg Table 1. Characteristics and nutrition status of stroke survivors. https://www.frontiersin.org/files/Articles/1644840/fnut-12-1644840-HTML/image_m/fnut-12-1644840-g001.jpg Figure 1. Flowchart of study participants. https://www.frontiersin.org/files/Articles/1644840/fnut-12-1644840-HTML/image_m/fnut-12-1644840-t002.jpg Table 2. Anthropometric characteristics of stroke survivors according to GLIM and SGA. https://www.frontiersin.org/files/Articles/1644840/fnut-12-1644840-HTML/image_m/fnut-12-1644840-t003.jpg Table 3. Biochemical characteristics of stroke survivors according to GLIM and SGA. https://www.frontiersin.org/files/Articles/1644840/fnut-12-1644840-HTML/image_m/fnut-12-1644840-t004.jpg Table 4. Comorbidities and complications of stroke survivors according to GLIM and SGA. https://www.frontiersin.org/files/Articles/1644840/fnut-12-1644840-HTML/image_m/fnut-12-1644840-t005.jpg Table 5. Concurrent validity of GLIM criteria using SGA as reference. https://www.frontiersin.org/files/Articles/1644840/fnut-12-1644840-HTML/image_m/fnut-12-1644840-t006.jpg Table 6. Concurrent validity of GLIM criteria using SGA as a reference for male cases only. https://www.frontiersin.org/files/Articles/1644840/fnut-12-1644840-HTML/image_m/fnut-12-1644840-g002.jpg Figure 2. Receiver-operating characteristic (ROC) curve plot of the true positive rate (sensitivity) https://www.frontiersin.org/files/Articles/1644840/fnut-12-1644840-HTML/image_m/fnut-12-1644840-t007.jpg Table 7. Prevalence of phenotypic and etiologic components of GLIM criteria among stroke survivors. https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2025.1686370/full https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g001.jpg Figure 1. Identification of DEGs in allergic rhinitis and atopic dermatitis. (A) Volcano plot of DEG https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g002.jpg Figure 2. Identification of core interacting proteins shared by allergic rhinitis and atopic dermati https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g003.jpg Figure 3. Identification of potential shared diagnostic genes based on machine learning. (A) Curve o https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g004.jpg Figure 4. Expression validation of core diagnostic genes. (A) Box plot of core diagnostic gene expre https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g005.jpg Figure 5. Diagnostic value and validation of core diagnostic genes. (A) Bar plot showing the diagnos https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g006.jpg Figure 6. Construction nomograms for allergic rhinitis and atopic dermatitis diagnosis. (A) Nomogram https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g007.jpg Figure 7. Pathway enrichment associated with diagnostic hub biomarkers. (A–E) GSEA ridge plots illus https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g008.jpg Figure 8. Immune infiltration analysis reveals the crosstalk between the core diagnostic genes and a https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g009.jpg Figure 9. Construct the mRNA–miRNA regulatory network of the core diagnostic genes mRNA–miRNA regula https://www.frontiersin.org/files/Articles/1686370/falgy-06-1686370-HTML/image_m/falgy-06-1686370-g010.jpg Figure 10. Drug–gene target network of the core diagnostic genes. The blue nodes represent different https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1666573/full https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-t001.jpg Table 1. List of screened hits against CathS and their docking parameters. https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-t002.jpg Table 2. PASS analysis of the selected molecules with their predicted activity. https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-g001.jpg Figure 1. Binding interactions of the docked compounds with Cathepsin S (CathS). (A) Docked view sho https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-g002.jpg Figure 2. Two-dimensional interaction diagrams of Alectinib and Q1N with Cathepsin S. (A) Alectinib https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-g003.jpg Figure 3. Stability assessment of CathS, CathS-Q1N, and CathS-Alectinib complex by analyzing (A) RMS https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-t003.jpg Table 3. Mean values of the analyzed parameters for CathS, CathS-Q1N, and CathS-Alectinib complex. https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-g004.jpg Figure 4. Compactness and solvent exposure of CathS and ligand-bound complexes. (A) Radius of gyrati https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-g005.jpg Figure 5. Intramolecular hydrogen bond dynamics of CathS and ligand-bound complexes. (A) Time evolut https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-g006.jpg Figure 6. Intermolecular hydrogen bond dynamics between CathS and ligands. (A) Time evolution of hyd https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-g007.jpg Figure 7. Secondary structure evolution of CathS and ligand-bound complexes. Secondary structure ass https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-t004.jpg Table 4. Alterations of the residues that participate in secondary structure formation calculated fo https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-g008.jpg Figure 8. Principal component analysis plots. (A) PCA plot of CathS, CathS-Q1N, and CathS-Alectinib https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-g009.jpg Figure 9. Free energy landscape (FEL) analysis of CathS and ligand-bound complexes. Three-dimensiona https://www.frontiersin.org/files/Articles/1666573/fbinf-05-1666573-HTML-r1/image_m/fbinf-05-1666573-t005.jpg Table 5. Binding free energy parameters for CathS–ligand complexes calculated through the MM-PBSA ap https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1737141/full https://www.frontiersin.org/files/Articles/1737141/fcvm-13-1737141-HTML/image_m/fcvm-13-1737141-g001.jpg Figure 1. The flowchart of patient selection. CPET, cardiopulmonary exercise testing; CRF, cardioresp https://www.frontiersin.org/files/Articles/1737141/fcvm-13-1737141-HTML/image_m/fcvm-13-1737141-t001.jpg Table 1. Baseline characteristics stratified by cardiorespiratory fitness status. https://www.frontiersin.org/files/Articles/1737141/fcvm-13-1737141-HTML/image_m/fcvm-13-1737141-t002.jpg Table 2. Characteristics of variables grouped by fibrinogen tertiles. https://www.frontiersin.org/files/Articles/1737141/fcvm-13-1737141-HTML/image_m/fcvm-13-1737141-t003.jpg Table 3. Univariable logistic regression for moderate-to-severe cardiorespiratory fitness decline. https://www.frontiersin.org/files/Articles/1737141/fcvm-13-1737141-HTML/image_m/fcvm-13-1737141-g002.jpg Figure 2. ROC curve for fibrinogen in predicting moderate-to-severe cardiorespiratory fitness declin https://www.frontiersin.org/files/Articles/1737141/fcvm-13-1737141-HTML/image_m/fcvm-13-1737141-t004.jpg Table 4. Multivariable logistic regression analysis. https://www.frontiersin.org/files/Articles/1737141/fcvm-13-1737141-HTML/image_m/fcvm-13-1737141-t005.jpg Table 5. Subgroup analysis of the association between fibrinogen and moderate-to-severe cardiorespir https://www.frontiersin.org/files/Articles/1737141/fcvm-13-1737141-HTML/image_m/fcvm-13-1737141-t006.jpg Table 6. Sensitivity analyses excluding participants who did not achieve maximal effort. https://www.frontiersin.org/files/Articles/1737141/fcvm-13-1737141-HTML/image_m/fcvm-13-1737141-t007.jpg Table 7. Association between fibrinogen and peak O2 pulse. https://www.frontiersin.org/journals/blockchain/articles/10.3389/fbloc.2025.1713637/full https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-t001.jpg Table 1. Review studies on cryptocurrency exchange vulnerabilities (2014–2024). Panel A reports year https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-t002.jpg Table 2. Summary of problems addressed and limitations in prior studies. https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-g001.jpg Figure 1. Workflow showing the major stages of the PRISMA-ScR methodology used in this study. https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-g002.jpg Figure 2. PRISMA-ScR flow diagram for study selection. https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-t003.jpg Table 3. Summarized high impact CEX exchange crimes from 2009–2024. Full list of incidence in Supple https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-t004.jpg Table 4. Common Attack vectors on CEX platforms and their frequency. https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-t005.jpg Table 5. Summarized high impact dex exchange crimes from 2009–2024. Full List of incidence in Supple https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-t006.jpg Table 6. Common Attack Vectors on DEX platforms and their frequency. https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-t007.jpg Table 7. Repeated and high impact attacks vectors on CEX and DEX (2009–2024). Data extracted from Su https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-g003.jpg Figure 3. (a) Total CEX attack 2009_2024 (b) Total DEX 2009_2024 (c) CEX vs. DEX incidents (d) Attac https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-g004.jpg Figure 4. Repeated high impact attacks: (a) repeated high impact attacks CEX (b) repeated high impac https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-g005.jpg Figure 5. (a) Incident trends (b) Cumulative loss (c) Year-wise CEX DEX (d) Incidents by year. https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-t008.jpg Table 8. Attack vectors, methodologies, evolution, primary targets, defensive mechanisms, and refere https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-g006.jpg Figure 6. Attack vectors/types: (a) dominant attack vectors cex VS dex (b) most frequent attack meth https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-g007.jpg Figure 7. Financial impact views: (a) Financial Impact Over Time (b) Financial Impact by Region. https://www.frontiersin.org/files/Articles/1713637/fbloc-08-1713637-HTML/image_m/fbloc-08-1713637-t009.jpg Table 9. Post-quantum cryptographic algorithms. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1722444/full https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-g001.jpg Figure 1. Framework. https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-t001.jpg Table 1. Descriptive statistics of variables. https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-t002.jpg Table 2. Robustness checks. https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-t003.jpg Table 3. Effects of farmers’ use of the Internet on their adoption of biopesticides. https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-g002.jpg Figure 2. Common range of values of propensity scores under the nearest neighbor matching (1:1) meth https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-g003.jpg Figure 3. Common range of values of propensity scores under the radius matching (caliper 0.01) metho https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-g004.jpg Figure 4. Common range of values of propensity scores under the kernel-based matching (bandwidth 0.0 https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-t004.jpg Table 4. Results of balance test. https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-t005.jpg Table 5. Effectiveness of farmers’ use of the Internet in promoting their adoption of biopesticides. https://www.frontiersin.org/files/Articles/1722444/fsufs-10-1722444-HTML/image_m/fsufs-10-1722444-t006.jpg Table 6. Heterogeneity analysis of impact effects. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1643376/full https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-g001.jpg Figure 1. Pesticide use per area of cropland for the world and China (1990–2022). Data source: FAOST https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-g002.jpg Figure 2. Scale of rural internet users and internet penetration rate in China (2005–2024). Source: https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-g003.jpg Figure 3. Framework. https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-g004.jpg Figure 4. The sample distribution. https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-t001.jpg Table 1. Variable descriptions and sample characteristics. https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-t002.jpg Table 2. Baseline regression and robustness test. https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-t003.jpg Table 3. Results of endogeneity treatment using CF approach. https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-t004.jpg Table 4. Results of endogeneity treatment using the PSM method. https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-t005.jpg Table 5. Mediation mechanisms. https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-g005.jpg Figure 5. Heterogeneity in household head age. https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-g006.jpg Figure 6. Heterogeneity in rice cultivation area. https://www.frontiersin.org/files/Articles/1643376/fsufs-09-1643376-HTML/image_m/fsufs-09-1643376-g007.jpg Figure 7. Heterogeneity in housing location. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1722292/full https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-g001.jpg Figure 1. Scheme followed to create maps for Africa. https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-g002.jpg Figure 2. Available weather stations in Africa. https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-t001.jpg Table 1. Temperature increase (°C) in 2090–2099 relative to 1980–1999. https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-g003.jpg Figure 3. Mean all-day temperature (left), relative humidity (center), and atmospheric radiation (ri https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-g004.jpg Figure 4. All-day RC power potential (left) and RC energy potential (right) maps for the current TMY https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-g005.jpg Figure 5. All-day RC power potential maps for the future scenarios B1 (low emissions) (left), A1B (m https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-t002.jpg Table 2. Annual average nighttime RC power and energy potentials for different years and scenarios. https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-t003.jpg Table 3. Annual average all-day RC power and energy potentials for different years and scenarios. https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-t004.jpg Table 4. Percentages of increase of the mean annual RC power potential of Africa with respect to Eur https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-g006.jpg Figure 6. Mean all-day atmospheric radiation and surface emission increase compared to the current T https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-t005.jpg Table 5. Metrics for the models’ performance assessment. https://www.frontiersin.org/files/Articles/1722292/fenvs-13-1722292-HTML/image_m/fenvs-13-1722292-t006.jpg Table 6. Comparison of the mean annual RC power across different regions of the world. Ranges of RC https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1797372/full https://www.frontiersin.org/files/Articles/1797372/fnut-13-1797372-HTML/image_m/fnut-13-1797372-t001.jpg Table 1. Baseline characteristics of participants by sleep quality status. https://www.frontiersin.org/files/Articles/1797372/fnut-13-1797372-HTML/image_m/fnut-13-1797372-g001.jpg Figure 1. Rotated factor loading plot for dietary patterns derived from principal component analysis https://www.frontiersin.org/files/Articles/1797372/fnut-13-1797372-HTML/image_m/fnut-13-1797372-g002.jpg Figure 2. Associations between dietary pattern adherence and poor sleep quality. BMI, body mass inde https://www.frontiersin.org/files/Articles/1797372/fnut-13-1797372-HTML/image_m/fnut-13-1797372-t002.jpg Table 2. Associations between PCA3 adherence and PSQI component scores. https://www.frontiersin.org/files/Articles/1797372/fnut-13-1797372-HTML/image_m/fnut-13-1797372-t003.jpg Table 3. Associations between PCA3 score (per 1-SD increase) and poor sleep quality across subgroups https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1750281/full https://www.frontiersin.org/files/Articles/1750281/fpsyg-17-1750281-HTML/image_m/fpsyg-17-1750281-g001.jpg Figure 1. Conceptual framework of the study. https://www.frontiersin.org/files/Articles/1750281/fpsyg-17-1750281-HTML/image_m/fpsyg-17-1750281-t001.jpg Table 1. Variable names and quantification methods. https://www.frontiersin.org/files/Articles/1750281/fpsyg-17-1750281-HTML/image_m/fpsyg-17-1750281-t002.jpg Table 2. Reliability and convergent validity results. https://www.frontiersin.org/files/Articles/1750281/fpsyg-17-1750281-HTML/image_m/fpsyg-17-1750281-t003.jpg Table 3. Discriminant validity. https://www.frontiersin.org/files/Articles/1750281/fpsyg-17-1750281-HTML/image_m/fpsyg-17-1750281-t004.jpg Table 4. Descriptive statistics. https://www.frontiersin.org/files/Articles/1750281/fpsyg-17-1750281-HTML/image_m/fpsyg-17-1750281-t005.jpg Table 5. Baseline regression analysis results. https://www.frontiersin.org/files/Articles/1750281/fpsyg-17-1750281-HTML/image_m/fpsyg-17-1750281-t006.jpg Table 6. Mediation effect test results. https://www.frontiersin.org/files/Articles/1750281/fpsyg-17-1750281-HTML/image_m/fpsyg-17-1750281-t007.jpg Table 7. Robustness test results. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1749660/full https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g001.jpg Figure 1. Flow diagram of the literature search and study selection process. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-t001.jpg Table 1. Basic characteristics of included literature. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g002.jpg Figure 2. Risk of bias summary for included studies. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g003.jpg Figure 3. Forest plot of overall efficacy rate. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g004.jpg Figure 4. Forest plot of adverse reaction incidence. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g005.jpg Figure 5. Forest plot of PSQI score. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g006.jpg Figure 6. Forest plot of LH level. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g007.jpg Figure 7. Forest plot of FSH level. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g008.jpg Figure 8. Forest plot of E2 level. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g009.jpg Figure 9. Forest plot of KMI score. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g010.jpg Figure 10. Forest plot of TCMS score. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g011.jpg Figure 11. Forest plot of SAS score. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g012.jpg Figure 12. Forest plot of SDS score. https://www.frontiersin.org/files/Articles/1749660/fneur-17-1749660-HTML/image_m/fneur-17-1749660-g013.jpg Figure 13. Funnel plot of the outcomes. (A) OE; (B) AE; (C) PSQI; (D) LH; (E) FSH; (F) E2; (G) KMI; https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1731390/full https://www.frontiersin.org/files/Articles/1731390/fcimb-16-1731390-HTML/image_m/fcimb-16-1731390-g001.jpg Figure 1. Experimental design for the AECOPD mouse model induced by long-term cigarette smoke exposu https://www.frontiersin.org/files/Articles/1731390/fcimb-16-1731390-HTML/image_m/fcimb-16-1731390-g002.jpg Figure 2. Increased inflammatory response in the airways following IAV infection in cigarette smoke- https://www.frontiersin.org/files/Articles/1731390/fcimb-16-1731390-HTML/image_m/fcimb-16-1731390-g003.jpg Figure 3. Increased inflammatory response in pulmonary tissues following IAV infection in cigarette https://www.frontiersin.org/files/Articles/1731390/fcimb-16-1731390-HTML/image_m/fcimb-16-1731390-t001.jpg Table 1. CS exposure and IAV infection enhance cytokine expression in the lungs. https://www.frontiersin.org/files/Articles/1731390/fcimb-16-1731390-HTML/image_m/fcimb-16-1731390-g004.jpg Figure 4. CS exposure and IAV infection induce changes in lung structure and function. (A) Represent https://www.frontiersin.org/files/Articles/1731390/fcimb-16-1731390-HTML/image_m/fcimb-16-1731390-g005.jpg Figure 5. Altered gut microbiota composition following IAV infection in CS-exposed mice. (A) Shannon https://www.frontiersin.org/files/Articles/1731390/fcimb-16-1731390-HTML/image_m/fcimb-16-1731390-g006.jpg Figure 6. Linear discriminant analysis effect size (LEfSe) of gut microbiota. (A) LEfSe score plot o https://www.frontiersin.org/files/Articles/1731390/fcimb-16-1731390-HTML/image_m/fcimb-16-1731390-g007.jpg Figure 7. Altered serum and fecal metabolomic signatures following IAV infection in CS-exposed mice. https://www.frontiersin.org/files/Articles/1731390/fcimb-16-1731390-HTML/image_m/fcimb-16-1731390-t002.jpg Table 2. Results of random forest analysis of differential metabolites. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1788516/full https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-g001.jpg Figure 1. Overall framework of the proposed TFFBN-HDLF. https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-g002.jpg Figure 2. Overall framework of SeizureTransNet. https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-g003.jpg Figure 3. Performance on the CHB-MIT dataset. https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-t001.jpg Table 1. Experimental results based on the CHB-MIT dataset. https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-g004.jpg Figure 4. Performance on the Siena Dataset. https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-t002.jpg Table 2. Experimental results based on the Siena dataset. https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-t003.jpg Table 3. Ablation results for different functional brain networks. https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-g005.jpg Figure 5. The visualization results on the CHB-MIT. https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-g006.jpg Figure 6. The visualization results on the Siena. https://www.frontiersin.org/files/Articles/1788516/fmed-13-1788516-HTML/image_m/fmed-13-1788516-t004.jpg Table 4. Performance comparison with baseline models on the two datasets. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1767220/full https://www.frontiersin.org/files/Articles/1767220/fmicb-17-1767220-HTML/image_m/fmicb-17-1767220-g001.jpg Figure 1. Alpha diversity of bacterial and fungal communities across the restoration chronosequence. https://www.frontiersin.org/files/Articles/1767220/fmicb-17-1767220-HTML/image_m/fmicb-17-1767220-g002.jpg Figure 2. Beta diversity, successional convergence, and taxonomic composition of microbial communiti https://www.frontiersin.org/files/Articles/1767220/fmicb-17-1767220-HTML/image_m/fmicb-17-1767220-g003.jpg Figure 3. Unsupervised clustering of bacterial and fungal communities reveals two core ecological st https://www.frontiersin.org/files/Articles/1767220/fmicb-17-1767220-HTML/image_m/fmicb-17-1767220-g004.jpg Figure 4. Validation of ecological stages and identification of biomarker OTUs using a Random Forest https://www.frontiersin.org/files/Articles/1767220/fmicb-17-1767220-HTML/image_m/fmicb-17-1767220-g005.jpg Figure 5. Microbial co-occurrence networks in Chaos and Recovery stages. (A–D) Network visualization https://www.frontiersin.org/files/Articles/1767220/fmicb-17-1767220-HTML/image_m/fmicb-17-1767220-t001.jpg Table 1. Positive and negative edges in co-occurrence networks between ecological stages. https://www.frontiersin.org/files/Articles/1767220/fmicb-17-1767220-HTML/image_m/fmicb-17-1767220-t002.jpg Table 2. Topological properties and null model analysis between Chaotic and Recovery stages. https://www.frontiersin.org/files/Articles/1767220/fmicb-17-1767220-HTML/image_m/fmicb-17-1767220-g006.jpg Figure 6. Identification of keystone taxa, their taxonomic composition, and their impact on network https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1735150/full https://www.frontiersin.org/files/Articles/1735150/fmars-13-1735150-HTML/image_m/fmars-13-1735150-g001.jpg Figure 1. The location and spatial zoning of the Rongcheng Whooper Swan National Nature Reserve. https://www.frontiersin.org/files/Articles/1735150/fmars-13-1735150-HTML/image_m/fmars-13-1735150-t001.jpg Table 1. Satellite remote sensing data used in this study. https://www.frontiersin.org/files/Articles/1735150/fmars-13-1735150-HTML/image_m/fmars-13-1735150-g002.jpg Figure 2. Workflow summarizing the main steps of the analysis, including data acquisition, image pre https://www.frontiersin.org/files/Articles/1735150/fmars-13-1735150-HTML/image_m/fmars-13-1735150-g003.jpg Figure 3. Whooper swans’ distribution in the reserve during 2009 to 2016 and within the winter perio https://www.frontiersin.org/files/Articles/1735150/fmars-13-1735150-HTML/image_m/fmars-13-1735150-t002.jpg Table 2. The number of whooper swans in different locations and backgrounds of the reserve during 20 https://www.frontiersin.org/files/Articles/1735150/fmars-13-1735150-HTML/image_m/fmars-13-1735150-t003.jpg Table 3. The estimation error of whooper swan population number in different images. https://www.frontiersin.org/files/Articles/1735150/fmars-13-1735150-HTML/image_m/fmars-13-1735150-t004.jpg Table 4. Whooper swans in different zones of the reserve during 2009~2016 and the overwinter period https://www.frontiersin.org/files/Articles/1735150/fmars-13-1735150-HTML/image_m/fmars-13-1735150-g004.jpg Figure 4. The distribution of whooper swans interpreted from 5 very high-resolution satellite images https://www.frontiersin.org/files/Articles/1735150/fmars-13-1735150-HTML/image_m/fmars-13-1735150-t005.jpg Table 5. The matrix of suggested zone changes for the Rongcheng Whooper Swan National Natural Reserv https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1709939/full https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g001.jpg Figure 1. Sample display of seven apple leaf disease types from Plant-Pathology-2021-FGVC8 and Apple https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t001.jpg Table 1. Summary of related works in lightweight apple leaf disease detection. https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g002.jpg Figure 2. Experimental, technical design, and workflows. The figure illustrates the six main stages: https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g003.jpg Figure 3. An example of the enhancement methods. The augmentation techniques simulate various real-w https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t002.jpg Table 2. Distribution of apple leaf disease images in the Apple-Disease-Detection Dataset. https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g004.jpg Figure 4. SRC-YOLOv8n network structure. The framework integrates four key innovations: SDAC2f for s https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g005.jpg Figure 5. The overall architecture of the SDA-C2f module. (a) Left: Space-to-Depth transformation pr https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g006.jpg Figure 6. Architecture comparison between YOLOv8 neck and RepGFPN neck networks. The left side shows https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g007.jpg Figure 7. RepGFPN-fusion module for multi-scale feature integration. The network employs a dual-path https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g008.jpg Figure 8. CLLAHead architecture with cross-level local attention mechanism. The network processes th https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t003.jpg Table 3. Experimental environment. https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t004.jpg Table 4. Experimental parameters. https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t005.jpg Table 5. Results of ablation experiments with metrics of mAP50, F1 score, FPS, parameter size, GFLOP https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t006.jpg Table 6. Comparisons of different backbone C2f network modules with metrics of mAP50, F1 score, FPS, https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t007.jpg Table 7. Comparison of different feature fusion modules with metrics of mAP50, F1 score, FPS, parame https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t008.jpg Table 8. Comparison of different detection head modules with metrics of mAP50, F1 score, FPS, parame https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t009.jpg Table 9. Comparison of different loss functions with metrics of mAP50, precision, recall, and F1 sco https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g009.jpg Figure 9. Visual comparison of detection results across ablation stages. Each row shows the same tes https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t010.jpg Table 10. Comparison with state-of-the-art models on the Apple-Disease-Detection Dataset (combined P https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-t011.jpg Table 11. Generalization performance evaluation across different environmental conditions. https://www.frontiersin.org/files/Articles/1709939/fpls-16-1709939-HTML/image_m/fpls-16-1709939-g010.jpg Figure 10. Performance evaluation across various environmental conditions. (a) Performance metrics c https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1774071/full https://www.frontiersin.org/files/Articles/1774071/fmed-13-1774071-HTML/image_m/fmed-13-1774071-g001.jpg Figure 1. Protocol flow. PONV, postoperative nausea and vomiting; QoR-15 score, Quality of Recovery https://www.frontiersin.org/files/Articles/1774071/fmed-13-1774071-HTML/image_m/fmed-13-1774071-t001.jpg Table 1. Study schedule. https://www.frontiersin.org/files/Articles/1774071/fmed-13-1774071-HTML/image_m/fmed-13-1774071-g002.jpg Figure 2. Emergency unblinding protocol. HR, heart rate; SBP, systolic blood pressure; IV, intraveno https://www.frontiersin.org/files/Articles/1774071/fmed-13-1774071-HTML/image_m/fmed-13-1774071-g003.jpg Figure 3. Patients in both groups receive two thumb-tack devices: A real needle (0.25 mm × 2.0 mm) a https://www.frontiersin.org/files/Articles/1774071/fmed-13-1774071-HTML/image_m/fmed-13-1774071-g004.jpg Figure 4. Opaque sterile patches will be applied to both intervention sites to conceal the allocatio https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1619704/full https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-g001.jpg Figure 1. Study inclusion and exclusion criteria. FS-T2WI, fat-suppressed T2-weighted imaging; CE-T1 https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-g002.jpg Figure 2. Flowchart of radiomics analysis. https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-t001.jpg Table 1. Patient baseline characteristics. https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-t002.jpg Table 2. Predictive performance of baseline habitat signatures. https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-t003.jpg Table 3. Predictive performance of conventional radiomics signatures and habitat radiomics signature https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-g003.jpg Figure 3. The input features and corresponding regression coefficients of radiomics progression risk https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-t004.jpg Table 4. Predictive performance of radiomics signature, clinical model, and nomogram. https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-g004.jpg Figure 4. Time-dependent receiver operating characteristic curves and prediction error curves for th https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-g005.jpg Figure 5. (A) Calibration curves of the radiomics signature, nomogram, and clinical models in the tr https://www.frontiersin.org/files/Articles/1619704/fonc-15-1619704-HTML/image_m/fonc-15-1619704-g006.jpg Figure 6. Kaplan–Meier curves of progression-free survival in the patients with low and high risk of https://www.frontiersin.org/journals/anesthesiology/articles/10.3389/fanes.2025.1703717/full https://www.frontiersin.org/files/Articles/1703717/fanes-04-1703717-HTML/image_m/fanes-04-1703717-g001.jpg Figure 1. X-RAY: pneumomediastinum and subcutaneous emphysema. https://www.frontiersin.org/files/Articles/1703717/fanes-04-1703717-HTML/image_m/fanes-04-1703717-g002.jpg Figure 2. CT SCAN CHEST: significant subcutaneous emphysema extending to the cervical level. Trachea https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1781852/full https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g001.jpg Figure 1. Identification of differentially expressed genes. (A) Enrichment scores of butyrate metabo https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g002.jpg Figure 2. Candidate gene screening. (A) Volcano plot of differentially expressed genes. The X-axis r https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g003.jpg Figure 3. MR Randomization analysis results. MR Randomization analysis results of exposed factors (f https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g004.jpg Figure 4. Construction of risk model. (A) Univariate COX forest plot. From left to right are the pro https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g005.jpg Figure 5. Analysis of gene and protein expression of key genes. (A) Gene expression analysis. Blue r https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g006.jpg Figure 6. Independent prognostic analysis and nomogram construction. (A) Univariate COX forest plot. https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g007.jpg Figure 7. GSEA enrichment analysis. (A) GSEA analysis of prognostic genes. The first section shows g https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g008.jpg Figure 8. Immune infiltration analysis. (A) Relative abundance of immune cells. Different colors rep https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g009.jpg Figure 9. Construction of molecular regulatory networks. (A) lncRNA-miRNA-mRNA network. Red circles https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-t001.jpg Table 1. Molecular docking result. https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g010.jpg Figure 10. Single - cell data analysis. (A) Selection of highly variable genes. The x-axis represent https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g011.jpg Figure 11. Cell communication and trajectory analysis. (A) Cell - communication heatmap between all https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g012.jpg Figure 12. Differential expression of prognostic genes in LUAD and normal in TCGA-LUAD and GSE116959 https://www.frontiersin.org/files/Articles/1781852/fgene-17-1781852-HTML/image_m/fgene-17-1781852-g013.jpg Figure 13. RT-qPCR validation of the seven prognostic genes in lung cancer cell lines. The mRNA expr https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1764724/full https://www.frontiersin.org/files/Articles/1764724/fbioe-14-1764724-HTML/image_m/fbioe-14-1764724-g001.jpg Figure 1. Taking exosomes as an example, elucidating the basic morphology, constituents, and release https://www.frontiersin.org/files/Articles/1764724/fbioe-14-1764724-HTML/image_m/fbioe-14-1764724-g002.jpg Figure 2. Schematic illustration of common preprocessing methods for the isolation of PDEVs (Pulido- https://www.frontiersin.org/files/Articles/1764724/fbioe-14-1764724-HTML/image_m/fbioe-14-1764724-g003.jpg Figure 3. Methods for the Separation of PDEVs.Illustrated are commonly employed techniques for PDEV https://www.frontiersin.org/files/Articles/1764724/fbioe-14-1764724-HTML/image_m/fbioe-14-1764724-t001.jpg Table 1. Techniques for the separation and purification of PDEV and their applications. https://www.frontiersin.org/files/Articles/1764724/fbioe-14-1764724-HTML/image_m/fbioe-14-1764724-g004.jpg Figure 4. PDEVs and Tissue Engineering. PDEVs serve as natural bioactive carriers. Engineered PDEVs https://www.frontiersin.org/files/Articles/1764724/fbioe-14-1764724-HTML/image_m/fbioe-14-1764724-g005.jpg Figure 5. PDEVs exert multifaceted effects in wound healing PDEVs simultaneously influence multiple https://www.frontiersin.org/files/Articles/1764724/fbioe-14-1764724-HTML/image_m/fbioe-14-1764724-g006.jpg Figure 6. Repair Mechanism of PDEV on the Cartilage System. PDEV promotes cartilage repair by delive https://www.frontiersin.org/files/Articles/1764724/fbioe-14-1764724-HTML/image_m/fbioe-14-1764724-g007.jpg Figure 7. Mechanism of PDEV in Fracture Healing. PDEV intervenes in fracture healing by enhancing os https://www.frontiersin.org/files/Articles/1764724/fbioe-14-1764724-HTML/image_m/fbioe-14-1764724-t002.jpg Table 2. Summary of PDEV isolation methods and therapeutic applications. https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2025.1642315/full https://www.frontiersin.org/files/Articles/1642315/falgy-06-1642315-HTML/image_m/falgy-06-1642315-t001.jpg Table 1. Baseline patient characteristics. https://www.frontiersin.org/files/Articles/1642315/falgy-06-1642315-HTML/image_m/falgy-06-1642315-t002.jpg Table 2. Health-related quality of life (ESPRINT-15 questionnaire). https://www.frontiersin.org/files/Articles/1642315/falgy-06-1642315-HTML/image_m/falgy-06-1642315-g001.jpg Figure 1. Percentage of patients with different severity of rhinitis (left panel) and asthma (right https://www.frontiersin.org/files/Articles/1642315/falgy-06-1642315-HTML/image_m/falgy-06-1642315-g002.jpg Figure 2. Evolution of the percentage of patients without nasal, ocular and bronchial symptoms at ba https://www.frontiersin.org/files/Articles/1642315/falgy-06-1642315-HTML/image_m/falgy-06-1642315-t003.jpg Table 3. Daily medication score, asthma control and lung function during follow-up. https://www.frontiersin.org/files/Articles/1642315/falgy-06-1642315-HTML/image_m/falgy-06-1642315-t004.jpg Table 4. Adverse events. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1794713/full https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t001.jpg Table 1. The vineyards sampled for detecting of different biotic and abiotic stresses using hyperspe https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t002.jpg Table 2. The primer pairs used in the RT-PCR to detect virus infections in Germany. https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-g001.jpg Figure 1. Mobile platform for lab and field image acquisition. (A) image acquisition system with HSI https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-g002.jpg Figure 2. Mean spectral reflectance of the white (A) and black (B) cultivar leaves colored by their https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t003.jpg Table 3. Number of images used in model training and testing. https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t004.jpg Table 4. PCR results of phytoplasma detection in six grapevine cultivars in the German test plots in https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t005.jpg Table 5. The qPCR results of phytoplasma detection in two grapevine cultivars in the Italian test pl https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t006.jpg Table 6. RT-PCR results of virus detection in three grapevine cultivars in the German test plots in https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-g003.jpg Figure 3. Magnesium content ± SD in symptomatic and non-symptomatic leaf samples of three grapevine https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-g004.jpg Figure 4. Fe content ± SD in symptomatic and non-symptomatic leaf samples of two grapevine cultivars https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t007.jpg Table 7. Harmonic mean of precision and recall (F1-Score in %) for the disease classification models https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t008.jpg Table 8. Confusion matrix for the model classification among phytoplasmas on a) Kerner, b) Scheurebe https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t009.jpg Table 9. Confusion matrix for the classification models for phytoplasmas in white grapevine cultivar https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t010.jpg Table 10. Confusion matrix for the model classification of various diseases in white grapevine culti https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t011.jpg Table 11. Confusion matrix for model classification of various diseases in black grapevine cultivars https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-t012.jpg Table 12. Confusion matrix for the model classification of various stresses in white and black grape https://www.frontiersin.org/files/Articles/1794713/fpls-17-1794713-HTML/image_m/fpls-17-1794713-g005.jpg Figure 5. Contribution map of the individual wavelengths for the disease classification performance https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1780830/full https://www.frontiersin.org/files/Articles/1780830/fvets-13-1780830-HTML-r1/image_m/fvets-13-1780830-g001.jpg Figure 1. Contrast-enhanced computed tomography (CT) images showing an enlarged right adrenal gland https://www.frontiersin.org/files/Articles/1780830/fvets-13-1780830-HTML-r1/image_m/fvets-13-1780830-g002.jpg Figure 2. Right adrenal gland dog. On cross-section: the right adrenal gland showed a heterogeneous https://www.frontiersin.org/files/Articles/1780830/fvets-13-1780830-HTML-r1/image_m/fvets-13-1780830-g003.jpg Figure 3. Histologic section of the adrenal gland adenoma. The normal architecture of the gland was https://www.frontiersin.org/files/Articles/1780830/fvets-13-1780830-HTML-r1/image_m/fvets-13-1780830-g004.jpg Figure 4. Histologic section of the adrenal gland myelolipoma. The central region of the gland displ https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1703530/full https://www.frontiersin.org/files/Articles/1703530/fcimb-16-1703530-HTML/image_m/fcimb-16-1703530-g001.jpg Figure 1. Phylogeny constructed from the multi-locus sequence typing (MLST) profiles of 7 housekeepi https://www.frontiersin.org/files/Articles/1703530/fcimb-16-1703530-HTML/image_m/fcimb-16-1703530-t001.jpg Table 1. Characteristics of Streptococcus agalactiae isolates by sequence type (ST). https://www.frontiersin.org/files/Articles/1703530/fcimb-16-1703530-HTML/image_m/fcimb-16-1703530-t002.jpg Table 2. Comparative analysis of virulence gene carriage rates among strain groups. https://www.frontiersin.org/files/Articles/1703530/fcimb-16-1703530-HTML/image_m/fcimb-16-1703530-t003.jpg Table 3. Distribution of the CRISPR1 system among Streptococcus agalactiae STs. https://www.frontiersin.org/files/Articles/1703530/fcimb-16-1703530-HTML/image_m/fcimb-16-1703530-g002.jpg Figure 2. Association between CRISPR1 system and virulence genes in Streptococcus agalactiae isolate https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1604554/full https://www.frontiersin.org/files/Articles/1604554/fcimb-15-1604554-HTML/image_m/fcimb-15-1604554-g001.jpg Figure 1. Purification of lEVs from P. gingivalis-infected HCT116 cells. (a) Schematic showing the g https://www.frontiersin.org/files/Articles/1604554/fcimb-15-1604554-HTML/image_m/fcimb-15-1604554-g002.jpg Figure 2. miRNA profile of lEVs from P. gingivalis-infected HCT116 cells. (a) Bar plot showing the d https://www.frontiersin.org/files/Articles/1604554/fcimb-15-1604554-HTML/image_m/fcimb-15-1604554-g003.jpg Figure 3. Proteomic analysis of lEVs from P. gingivalis-infected HCT116 cells. (a) Venn diagram show https://www.frontiersin.org/files/Articles/1604554/fcimb-15-1604554-HTML/image_m/fcimb-15-1604554-g004.jpg Figure 4. P. gingivalis infection induced autophagy and degraded STING in HCT116 cells. (a) Immunobl https://www.frontiersin.org/files/Articles/1604554/fcimb-15-1604554-HTML/image_m/fcimb-15-1604554-g005.jpg Figure 5. P. gingivalis infection remodels the STING interactome in CRC. (a) Diagram showing the wor https://www.frontiersin.org/files/Articles/1604554/fcimb-15-1604554-HTML/image_m/fcimb-15-1604554-g006.jpg Figure 6. P. gingivalis infection confers resistance to STING-induced antitumor immunity. Radiation https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1706250/full https://www.frontiersin.org/files/Articles/1706250/fimmu-17-1706250-HTML/image_m/fimmu-17-1706250-t001.jpg Table 1. List of Antibodies used. https://www.frontiersin.org/files/Articles/1706250/fimmu-17-1706250-HTML/image_m/fimmu-17-1706250-g001.jpg Figure 1. Acidity inhibits T-cell function. Assessment of T-cell proliferation. (A) Representative h https://www.frontiersin.org/files/Articles/1706250/fimmu-17-1706250-HTML/image_m/fimmu-17-1706250-g002.jpg Figure 2. Differentially expressed genes in acidity. (A) Summary graph showing the concentration of https://www.frontiersin.org/files/Articles/1706250/fimmu-17-1706250-HTML/image_m/fimmu-17-1706250-g003.jpg Figure 3. DYV800 elevates urine and tumor pH in subcutaneous MB49OVA bladder tumors. Mice were injec https://www.frontiersin.org/files/Articles/1706250/fimmu-17-1706250-HTML/image_m/fimmu-17-1706250-g004.jpg Figure 4. Transdermal DYV800 reduces tumor burden in subcutaneous MB49OVA tumors. C57BL/6 mice were https://www.frontiersin.org/files/Articles/1706250/fimmu-17-1706250-HTML/image_m/fimmu-17-1706250-g005.jpg Figure 5. Transdermal DYV800 increases the activation and effector function of T cells. Tumor digest https://www.frontiersin.org/files/Articles/1706250/fimmu-17-1706250-HTML/image_m/fimmu-17-1706250-g006.jpg Figure 6. Transdermal DYV800 increases the percentage of OVA-specific CD8+T cells in tumor. (A) Repr https://www.frontiersin.org/files/Articles/1706250/fimmu-17-1706250-HTML/image_m/fimmu-17-1706250-g007.jpg Figure 7. DYV800 slows tumor progression in an orthotopic model of bladder cancer: Tumor growth was https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1635559/full https://www.frontiersin.org/files/Articles/1635559/fcvm-12-1635559-HTML-r1/image_m/fcvm-12-1635559-t001.jpg Table 1. Changes in blood gas analysis during hospitalization. https://www.frontiersin.org/files/Articles/1635559/fcvm-12-1635559-HTML-r1/image_m/fcvm-12-1635559-g001.jpg Figure 1. (A) Obtained via left lateral decubitus transthoracic echocardiography, comprises two imag https://www.frontiersin.org/files/Articles/1635559/fcvm-12-1635559-HTML-r1/image_m/fcvm-12-1635559-g002.jpg Figure 2. Panels (A–C) are all axial images from contrast-enhanced CT scans. Panels (A and B) demons https://www.frontiersin.org/files/Articles/1635559/fcvm-12-1635559-HTML-r1/image_m/fcvm-12-1635559-g003.jpg Figure 3. Panel (A), coronal MIP reconstruction, shows that the branches of the right inferior pulmo https://www.frontiersin.org/files/Articles/1635559/fcvm-12-1635559-HTML-r1/image_m/fcvm-12-1635559-g004.jpg Figure 4. 3D reconstruction shows inferior vena cava anomalous drainage into the left atrium (A and https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1704902/full https://www.frontiersin.org/files/Articles/1704902/fmed-12-1704902-HTML/image_m/fmed-12-1704902-g001.jpg Figure 1. The examination results of the patient before and after treatment are as follows. (a) FCM https://www.frontiersin.org/files/Articles/1704902/fmed-12-1704902-HTML/image_m/fmed-12-1704902-t001.jpg Table 1. Comparison of key clinical and laboratory parameters before and after treatment. https://www.frontiersin.org/journals/environmental-chemistry/articles/10.3389/fenvc.2026.1773001/full https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-g001.jpg Figure 1. Overview of experimental setup (A) lysimeters, (B) arrangement of lysimeters in a complete https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-t001.jpg Table 1. Basic physical and chemical properties of soil, poultry litter (PL), and biochar types (BC- https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-g002.jpg Figure 2. Average concentrations of (A) NO3–N (mg L−1) and (B) PO4–P (mg L−1) in leachate over five https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-g003.jpg Figure 3. Box plots showing the effects of (A) rainfall events, (B) amendments, and (C) their intera https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-g004.jpg Figure 4. Box plots illustrating the effects of (A) rainfall events, (B) amendments, and (C) their i https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-t002.jpg Table 2. Effect of different amendments on corn aboveground biomassa and grain nutrient uptake. https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-g005.jpg Figure 5. Average cumulative load (g ha−1) of nitrate-N (NO3-N) and phosphate-P (PO4-P) across treat https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-g006.jpg Figure 6. Relationships between (A) NO3–N and (B) PO4–P and dissolved organic carbon (DOC) in leacha https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-g007.jpg Figure 7. Effect of treatments on corn above-ground biomass (AGB) and grain yield (GY). Bars sharing https://www.frontiersin.org/files/Articles/1773001/fenvc-07-1773001-HTML/image_m/fenvc-07-1773001-t003.jpg Table 3. Nitrogen (NH4-N, NO3-N) and phosphorus (P) concentration across various treatments at pre-s https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1804917/full https://www.frontiersin.org/files/Articles/1804917/fnut-13-1804917-HTML/image_m/fnut-13-1804917-g001.jpg Figure 1. Proposed integrative mechanisms by which L-carnitine ameliorates metabolic dysfunction–ass https://www.frontiersin.org/files/Articles/1804917/fnut-13-1804917-HTML/image_m/fnut-13-1804917-t001.jpg Table 1. Animal and in vitro studies on L-carnitine supplementation in MASLD. https://www.frontiersin.org/files/Articles/1804917/fnut-13-1804917-HTML/image_m/fnut-13-1804917-t002.jpg Table 2. Human studies on L-carnitine supplementation in MASLD. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1699999/full https://www.frontiersin.org/files/Articles/1699999/fphar-16-1699999-HTML-r1/image_m/fphar-16-1699999-t001.jpg Table 1. Sociodemographic chracteristics associated with medication adherence in older adults. https://www.frontiersin.org/files/Articles/1699999/fphar-16-1699999-HTML-r1/image_m/fphar-16-1699999-t002.jpg Table 2. Clinical and health system characteristics associated with medication adherence in older ad https://www.frontiersin.org/files/Articles/1699999/fphar-16-1699999-HTML-r1/image_m/fphar-16-1699999-t003.jpg Table 3. Pharmacological characteristics associated with medication adherence in older adults. https://www.frontiersin.org/files/Articles/1699999/fphar-16-1699999-HTML-r1/image_m/fphar-16-1699999-t004.jpg Table 4. Crude and adjusted prevalence ratios of sociodemographic, clinical, and pharmacological cha https://www.frontiersin.org/files/Articles/1699999/fphar-16-1699999-HTML-r1/image_m/fphar-16-1699999-t005.jpg Table 5. Principal components for non-adherence to medications in older adults. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1680840/full https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-g001.jpg Figure 1. PRISMA 2020 flow diagram outlining the selection process for eligible studies. From 1, 946 https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-t001.jpg Table 1. Eligibility criteria (PICOS). https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-t002.jpg Table 2. Characteristics of included trials (n = 17). https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-g002.jpg Figure 2. Forest plot showing pooled mean difference in total testosterone levels (ng/mL) between pr https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-t003.jpg Table 3. Pooled hormonal outcomes. https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-g003.jpg Figure 3. Standardized mean difference (SMD) in LH/FSH ratio across 10 trials. The pooled effect (SM https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-g004.jpg Figure 4. Random-effects Forest plot for HOMA-IR scores across 15 comparisons. The pooled mean diffe https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-t004.jpg Table 4. Pooled metabolic outcomes. https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-g005.jpg Figure 5. Forest plot showing changes in HDL-cholesterol (mg/dL) in eight trials. Interventions led https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-t005.jpg Table 5. Sensitivity analyses. https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-g006.jpg Figure 6. Heat-map showing risk-of-bias (RoB 2) assessments across five domains for each included tr https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-t006.jpg Table 6. Sub-group analysis. https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-g007.jpg Figure 7. Meta-regression bubble plot illustrating the relationship between circulating butyrate lev https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-t007.jpg Table 7. Grade evidence profile. https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-t008.jpg Table 8. Metabolite concentration changes. https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-g008.jpg Figure 8. Contour-enhanced funnel plot for total testosterone, assessing publication bias. The symme https://www.frontiersin.org/files/Articles/1680840/fcimb-15-1680840-HTML/image_m/fcimb-15-1680840-t009.jpg Table 9. Adverse events. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1714681/full https://www.frontiersin.org/files/Articles/1714681/fneur-16-1714681-HTML/image_m/fneur-16-1714681-g001.jpg Figure 1. An example of bilateral cortical venous filling (CVF) time points. (A) Showed the occlusio https://www.frontiersin.org/files/Articles/1714681/fneur-16-1714681-HTML/image_m/fneur-16-1714681-t001.jpg Table 1. Demographics and clinical data of FOG and FRG. https://www.frontiersin.org/files/Articles/1714681/fneur-16-1714681-HTML/image_m/fneur-16-1714681-t002.jpg Table 2. Wilcoxon rank-sum test of imaging metrics between FOG and FRG. https://www.frontiersin.org/files/Articles/1714681/fneur-16-1714681-HTML/image_m/fneur-16-1714681-t003.jpg Table 3. Independent clinical and imaging predictors for FR. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1672578/full https://www.frontiersin.org/files/Articles/1672578/fnagi-18-1672578-HTML/image_m/fnagi-18-1672578-g001.jpg Figure 1. Schematic overview of the experimental steps. Slices of human hemi-midbrain were scanned b https://www.frontiersin.org/files/Articles/1672578/fnagi-18-1672578-HTML/image_m/fnagi-18-1672578-g002.jpg Figure 2. Anatomical topography of NM-MRI and neurochemical measurements for all specimens. Each row https://www.frontiersin.org/files/Articles/1672578/fnagi-18-1672578-HTML/image_m/fnagi-18-1672578-t001.jpg Table 1. Concentrations of NM and Fe and NM-MRI signal in midbrain subregions of PD and AD subjects. https://www.frontiersin.org/files/Articles/1672578/fnagi-18-1672578-HTML/image_m/fnagi-18-1672578-g003.jpg Figure 3. Correlation between NM or Fe concentrations and the NM-MRI signal. Scatterplots showing th https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1748932/full https://www.frontiersin.org/files/Articles/1748932/fphar-17-1748932-HTML/image_m/fphar-17-1748932-t001.jpg Table 1. Patient characteristics of men and women enrolled in the study after 2 years of LD/DDCI tre https://www.frontiersin.org/files/Articles/1748932/fphar-17-1748932-HTML/image_m/fphar-17-1748932-t002.jpg Table 2. LD plasma concentrations (ng/mL) at baseline and Follow-up from T0 (at the time of drug int https://www.frontiersin.org/files/Articles/1748932/fphar-17-1748932-HTML/image_m/fphar-17-1748932-g001.jpg Figure 1. Differences in plasma levodopa (LD) concentrations between men and women at baseline and a https://www.frontiersin.org/files/Articles/1748932/fphar-17-1748932-HTML/image_m/fphar-17-1748932-g002.jpg Figure 2. Differences in AUC (A), Cmax (B), Tmax (C), and T1/2 (D) at Follow-up compared to baseline https://www.frontiersin.org/files/Articles/1748932/fphar-17-1748932-HTML/image_m/fphar-17-1748932-t003.jpg Table 3. PK parameters at baseline and Follow-up by sex and Type III tests of Fixed effects. https://www.frontiersin.org/files/Articles/1748932/fphar-17-1748932-HTML/image_m/fphar-17-1748932-g003.jpg Figure 3. Linear regression analysis with (A) AUC at Follow-up as dependent variable and AUC at base https://www.frontiersin.org/files/Articles/1748932/fphar-17-1748932-HTML/image_m/fphar-17-1748932-g004.jpg Figure 4. Linear regression analysis with (A) AUC at Follow-up as dependent variable and the AUC at https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1766378/full https://www.frontiersin.org/files/Articles/1766378/fbioe-14-1766378-HTML/image_m/fbioe-14-1766378-g001.jpg Figure 1. Decellularization efficiency of AVN tissue with Tergitol 15 S 9. (A) Decellularization pro https://www.frontiersin.org/files/Articles/1766378/fbioe-14-1766378-HTML/image_m/fbioe-14-1766378-g002.jpg Figure 2. Proteomic comparison between native and decellularized AVN. (A) Diagram of the proteomic w https://www.frontiersin.org/files/Articles/1766378/fbioe-14-1766378-HTML/image_m/fbioe-14-1766378-g003.jpg Figure 3. Ultrastructure analysis of collagen fibers after decellularization. (A) SHG of collagen fi https://www.frontiersin.org/files/Articles/1766378/fbioe-14-1766378-HTML/image_m/fbioe-14-1766378-g004.jpg Figure 4. 3D micro-CT analysis. (A) Representative coronal (Z-Y, in blue), sagittal (X-Y, in green), https://www.frontiersin.org/files/Articles/1766378/fbioe-14-1766378-HTML/image_m/fbioe-14-1766378-g005.jpg Figure 5. Mechanical characterization. Native (black) and decellularized (red) AVN representative hy https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1678149/full https://www.frontiersin.org/files/Articles/1678149/fimmu-16-1678149-HTML/image_m/fimmu-16-1678149-t001.jpg Table 1. Therapeutic strategies to overcome T-cell exhaustion in COVID-19. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1770291/full https://www.frontiersin.org/files/Articles/1770291/fimmu-17-1770291-HTML/image_m/fimmu-17-1770291-t001.jpg Table 1. Baseline characteristics of included subjects‡. https://www.frontiersin.org/files/Articles/1770291/fimmu-17-1770291-HTML/image_m/fimmu-17-1770291-g001.jpg Figure 1. Anti-synthetase syndrome patients show skewed B cell subset distribution from healthy cont https://www.frontiersin.org/files/Articles/1770291/fimmu-17-1770291-HTML/image_m/fimmu-17-1770291-g002.jpg Figure 2. An enriched interferon signature was observed in activated ASyS B cells compared to health https://www.frontiersin.org/files/Articles/1770291/fimmu-17-1770291-HTML/image_m/fimmu-17-1770291-g003.jpg Figure 3. Transcriptional differences were observed in memory B cells isolated from ASyS compared to https://www.frontiersin.org/files/Articles/1770291/fimmu-17-1770291-HTML/image_m/fimmu-17-1770291-g004.jpg Figure 4. Genes in the Negative Regulation of Viral Process Pathway (GO:0048525) are increased in th https://www.frontiersin.org/files/Articles/1770291/fimmu-17-1770291-HTML/image_m/fimmu-17-1770291-g005.jpg Figure 5. Intracellular reactive oxygen species are increased in ASyS compared to healthy B cells. C https://www.frontiersin.org/files/Articles/1770291/fimmu-17-1770291-HTML/image_m/fimmu-17-1770291-g006.jpg Figure 6. The frequency of FKBP5-expressing memory B cells is increased in ASyS patients compared to https://www.frontiersin.org/files/Articles/1770291/fimmu-17-1770291-HTML/image_m/fimmu-17-1770291-g007.jpg Figure 7. MYADM expression is increased in ASyS compared to healthy memory B cells and is associated https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1795403/full https://www.frontiersin.org/files/Articles/1795403/fphar-17-1795403-HTML-r1/image_m/fphar-17-1795403-g001.jpg Figure 1. Selection process for the patients in the rituximab and the control group. https://www.frontiersin.org/files/Articles/1795403/fphar-17-1795403-HTML-r1/image_m/fphar-17-1795403-t001.jpg Table 1. Baseline demographics. https://www.frontiersin.org/files/Articles/1795403/fphar-17-1795403-HTML-r1/image_m/fphar-17-1795403-t002.jpg Table 2. Clinical characteristics. https://www.frontiersin.org/files/Articles/1795403/fphar-17-1795403-HTML-r1/image_m/fphar-17-1795403-t003.jpg Table 3. Respiratory outcomes. https://www.frontiersin.org/files/Articles/1795403/fphar-17-1795403-HTML-r1/image_m/fphar-17-1795403-g002.jpg Figure 2. CT scan findings before (left) and after (right) rituximab therapy showing improvement in https://www.frontiersin.org/files/Articles/1795403/fphar-17-1795403-HTML-r1/image_m/fphar-17-1795403-g003.jpg Figure 3. Line diagram showing the trend of the percent predicted DLCO and FVC before and after ritu https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1701620/full https://www.frontiersin.org/files/Articles/1701620/fcvm-13-1701620-HTML/image_m/fcvm-13-1701620-g001.jpg Figure 1. Patient sample selection. https://www.frontiersin.org/files/Articles/1701620/fcvm-13-1701620-HTML/image_m/fcvm-13-1701620-t001.jpg Table 1. Baseline demographic characteristics. https://www.frontiersin.org/files/Articles/1701620/fcvm-13-1701620-HTML/image_m/fcvm-13-1701620-t002.jpg Table 2. Baseline clinical characteristics. https://www.frontiersin.org/files/Articles/1701620/fcvm-13-1701620-HTML/image_m/fcvm-13-1701620-t003.jpg Table 3. Propensity-matched in-hospital mortality. https://www.frontiersin.org/files/Articles/1701620/fcvm-13-1701620-HTML/image_m/fcvm-13-1701620-t004.jpg Table 4. Propensity-matched hospital length of stay. https://www.frontiersin.org/files/Articles/1701620/fcvm-13-1701620-HTML/image_m/fcvm-13-1701620-t005.jpg Table 5. Propensity-matched total hospital charges. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1722575/full https://www.frontiersin.org/files/Articles/1722575/fpsyt-16-1722575-HTML-r1/image_m/fpsyt-16-1722575-g001.jpg Figure 1. Summary of study procedures. Participants completed a baseline MRI scanning session, basel https://www.frontiersin.org/files/Articles/1722575/fpsyt-16-1722575-HTML-r1/image_m/fpsyt-16-1722575-g002.jpg Figure 2. CONSORT diagram. https://www.frontiersin.org/files/Articles/1722575/fpsyt-16-1722575-HTML-r1/image_m/fpsyt-16-1722575-g003.jpg Figure 3. Simulation of ultrasound delivery to the amPFC using k-Wave modeling. A matrix array trans https://www.frontiersin.org/files/Articles/1722575/fpsyt-16-1722575-HTML-r1/image_m/fpsyt-16-1722575-t001.jpg Table 1. Participant demographics. https://www.frontiersin.org/files/Articles/1722575/fpsyt-16-1722575-HTML-r1/image_m/fpsyt-16-1722575-t002.jpg Table 2. Per-participant mean (sd) targeting accuracy, skull-related pressure loss, and acoustic exp https://www.frontiersin.org/files/Articles/1722575/fpsyt-16-1722575-HTML-r1/image_m/fpsyt-16-1722575-g004.jpg Figure 4. Effect of transcranial-focused ultrasound treatment on depression symptoms and repetitive https://www.frontiersin.org/files/Articles/1722575/fpsyt-16-1722575-HTML-r1/image_m/fpsyt-16-1722575-g005.jpg Figure 5. ROI-to-ROI default mode network (DMN) functional connectivity changes from baseline to end https://www.frontiersin.org/files/Articles/1722575/fpsyt-16-1722575-HTML-r1/image_m/fpsyt-16-1722575-t003.jpg Table 3. Summary statistics for leftPFC-leftPCC connectivity, Beck Depression Inventory-II (BDI-II), https://www.frontiersin.org/files/Articles/1722575/fpsyt-16-1722575-HTML-r1/image_m/fpsyt-16-1722575-t004.jpg Table 4. Zero-order correlation matrix illustrating relationship between change in PFC – PCC connect https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1743010/full https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-t001.jpg Table 1. Descriptive clinical outcomes for radical hysterectomy by approach. https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-t002.jpg Table 2. Standardized effects and PPD-aligned scores. https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-t003.jpg Table 3. Demographic characteristics of the sample. https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-t004.jpg Table 4. Reliability and convergent validity tests summary. https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-t005.jpg Table 5. Heterotrait-Monotrait ratio (HTMT). https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-t006.jpg Table 6. Structural model evaluation. https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-g001.jpg Figure 1. Structural equation model results. Solid lines represent significant paths (p < 0.05); das https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-t007.jpg Table 7. Between-group path differences. https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-t008.jpg Table 8. Group differences in core construct scores. https://www.frontiersin.org/files/Articles/1743010/fpubh-14-1743010-HTML/image_m/fpubh-14-1743010-t009.jpg Table 9. Demographic characteristics across groups. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1795512/full https://www.frontiersin.org/files/Articles/1795512/fmed-13-1795512-HTML/image_m/fmed-13-1795512-t001.jpg Table 1. Risk of bias assessment using ROBINS-I tool. https://www.frontiersin.org/files/Articles/1795512/fmed-13-1795512-HTML/image_m/fmed-13-1795512-t002.jpg Table 2. Risk of bias assessment using Cochrane RoB 2 tool. https://www.frontiersin.org/files/Articles/1795512/fmed-13-1795512-HTML/image_m/fmed-13-1795512-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1795512/fmed-13-1795512-HTML/image_m/fmed-13-1795512-t003.jpg Table 3. Duration of efficacy by study. https://www.frontiersin.org/files/Articles/1795512/fmed-13-1795512-HTML/image_m/fmed-13-1795512-t004.jpg Table 4. Patient-reported satisfaction, improvement, and quality of life. https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2025.1680457/full https://www.frontiersin.org/files/Articles/1680457/fresc-06-1680457-HTML/image_m/fresc-06-1680457-g001.jpg Figure 1. Participant flowchart. https://www.frontiersin.org/files/Articles/1680457/fresc-06-1680457-HTML/image_m/fresc-06-1680457-g002.jpg Figure 2. Scenes from the sensor game. (a) Performing the Y-balance test while playing “SoapBubble”. https://www.frontiersin.org/files/Articles/1680457/fresc-06-1680457-HTML/image_m/fresc-06-1680457-t001.jpg Table 1. Characteristics of the participants. https://www.frontiersin.org/files/Articles/1680457/fresc-06-1680457-HTML/image_m/fresc-06-1680457-t002.jpg Table 2. Analysis of variance results of for each parameter https://www.frontiersin.org/files/Articles/1680457/fresc-06-1680457-HTML/image_m/fresc-06-1680457-t003.jpg Table 3. Comparison of parameters before and after efforts. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1572944/full https://www.frontiersin.org/files/Articles/1572944/fvets-12-1572944-HTML/image_m/fvets-12-1572944-g001.jpg Figure 1. Schematic of the experimental design to investigate the competence of Haemaphysalis longic https://www.frontiersin.org/files/Articles/1572944/fvets-12-1572944-HTML/image_m/fvets-12-1572944-g002.jpg Figure 2. Percentage of parasitized erythrocytes (PPE) during the larval and nymphal Haemaphysalis l https://www.frontiersin.org/files/Articles/1572944/fvets-12-1572944-HTML/image_m/fvets-12-1572944-g003.jpg Figure 3. Percentage of packed cell volume (PCV) (A) and total number of red blood cells (RBCs) (B) https://www.frontiersin.org/files/Articles/1572944/fvets-12-1572944-HTML/image_m/fvets-12-1572944-g004.jpg Figure 4. Total number of white blood cells (WBCs) (A), lymphocytes (B), neutrophils (C), and monocy https://www.frontiersin.org/files/Articles/1572944/fvets-12-1572944-HTML/image_m/fvets-12-1572944-g005.jpg Figure 5. The number of engorged Haemaphysalis longicornis larvae (A) and nymphs (B) collected every https://www.frontiersin.org/files/Articles/1572944/fvets-12-1572944-HTML/image_m/fvets-12-1572944-t001.jpg Table 1. Results of Theileria haneyi nPCR and Haemaphysalis longicornis GAPDH PCR in 10 acquisition https://www.frontiersin.org/files/Articles/1572944/fvets-12-1572944-HTML/image_m/fvets-12-1572944-t002.jpg Table 2. Results of Theileria haneyi nPCR and Haemaphysalis longicornis GAPDH PCR in 10 transmission https://www.frontiersin.org/files/Articles/1572944/fvets-12-1572944-HTML/image_m/fvets-12-1572944-t003.jpg Table 3. Results of Theileria haneyi nPCR, blood smear, and horse β-actin PCR in transmission horse https://www.frontiersin.org/files/Articles/1572944/fvets-12-1572944-HTML/image_m/fvets-12-1572944-g006.jpg Figure 6. Percentage of packed cell volume (PCV) (A; the left y-axis) and total number of red blood https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1671424/full https://www.frontiersin.org/files/Articles/1671424/feduc-10-1671424-HTML/image_m/feduc-10-1671424-g001.jpg Figure 1. EFCC program areas by percentages of abstracts presented. https://www.frontiersin.org/files/Articles/1671424/feduc-10-1671424-HTML/image_m/feduc-10-1671424-t001.jpg Table 1. Research productivity by thematic area. https://www.frontiersin.org/files/Articles/1671424/feduc-10-1671424-HTML/image_m/feduc-10-1671424-t002.jpg Table 2. Collaboration across thematic research areas. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1544170/full https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g001.jpg Figure 1. Network pharmacology-based analysis elucidates the impact of PEC on CC. (A) the overlappin https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-t001.jpg Table 1. The energy of different modes of PEC-AKT and their corresponding sites analysis. https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-t002.jpg Table 2. The energy of different modes of MK2206-AKT and their corresponding sites analysis. https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g002.jpg Figure 2. Molecular representations of PEC interacting with its anticipated protein targets. (A) Doc https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g003.jpg Figure 3. PEC demonstrates anti-tumor properties in human CC cell lines and anti-migration effects i https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g004.jpg Figure 4. PEC demonstrates anti-invasive properties in human CC cell lines and inhibits migration in https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g005.jpg Figure 5. PEC induces programmed cell death in human CC cells. (A) The apoptosis assay was performed https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g006.jpg Figure 6. PEC induces autophagy in CC cells. (A) Western blotting analysis was conducted to evaluate https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g007.jpg Figure 7. The PEC modulates the AKT/mTOR signaling pathway to induce apoptosis and autophagy in CC c https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g008.jpg Figure 8. Pectolinarigenin inhibits the growth of cervical cancer in the xenograft mouse model. (A) https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g009.jpg Figure 9. The potential toxic effects of pectolinarigenin in mice. (A) The weights of major organs w https://www.frontiersin.org/files/Articles/1544170/fphar-17-1544170-HTML/image_m/fphar-17-1544170-g010.jpg Figure 10. The process of exploring and validating the targets of PEC. Figure created by biorender. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1720006/full https://www.frontiersin.org/files/Articles/1720006/fmed-13-1720006-HTML/image_m/fmed-13-1720006-t001.jpg Table 1. Baseline demographics and characteristics. https://www.frontiersin.org/files/Articles/1720006/fmed-13-1720006-HTML/image_m/fmed-13-1720006-t002.jpg Table 2. Inpatient investigations, ventilatory support, and treatment patterns. https://www.frontiersin.org/files/Articles/1720006/fmed-13-1720006-HTML/image_m/fmed-13-1720006-t003.jpg Table 3. Clinical Outcomes. https://www.frontiersin.org/files/Articles/1720006/fmed-13-1720006-HTML/image_m/fmed-13-1720006-t004.jpg Table 4. Logistic regression analysis for clinical characteristics associated with RM admission. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1725327/full https://www.frontiersin.org/files/Articles/1725327/fmars-12-1725327-HTML/image_m/fmars-12-1725327-g001.jpg Figure 1. (A) Survival curve and clinical images of V. harveyi-infected diseased abalone (left) and https://www.frontiersin.org/files/Articles/1725327/fmars-12-1725327-HTML/image_m/fmars-12-1725327-g002.jpg Figure 2. The diversity changes of the intestinal microbiota in H. discus hannai. (A) Shannon index https://www.frontiersin.org/files/Articles/1725327/fmars-12-1725327-HTML/image_m/fmars-12-1725327-g003.jpg Figure 3. Dominant bacterial composition of intestinal microbiota under different infection times in https://www.frontiersin.org/files/Articles/1725327/fmars-12-1725327-HTML/image_m/fmars-12-1725327-g004.jpg Figure 4. Differential genera in the intestinal microbiota of H. discus hannai infected with V. harv https://www.frontiersin.org/files/Articles/1725327/fmars-12-1725327-HTML/image_m/fmars-12-1725327-g005.jpg Figure 5. Analysis of the changes in the Vibrio ASV and Photobacterium ASV. With a relative abundanc https://www.frontiersin.org/files/Articles/1725327/fmars-12-1725327-HTML/image_m/fmars-12-1725327-g006.jpg Figure 6. Microbial co-occurrence network analysis. (A) Co-occurrence networks of the intestinal mic https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1696194/full https://www.frontiersin.org/files/Articles/1696194/fpls-16-1696194-HTML/image_m/fpls-16-1696194-g001.jpg Figure 1. The effects of seed hulls on the seed germination of L. chinensis. (a) Total germination r https://www.frontiersin.org/files/Articles/1696194/fpls-16-1696194-HTML/image_m/fpls-16-1696194-g002.jpg Figure 2. Volcano plots of DEGs’ upregulation and downregulation. DH_CK_0, DH_CK_2, and DH_CK_7 repr https://www.frontiersin.org/files/Articles/1696194/fpls-16-1696194-HTML/image_m/fpls-16-1696194-g003.jpg Figure 3. Characteristics of DEGs related to seed germination. (a) The bar diagram represented the n https://www.frontiersin.org/files/Articles/1696194/fpls-16-1696194-HTML/image_m/fpls-16-1696194-g004.jpg Figure 4. GO and KEGG functional pathways enrichment analysis of DEGs. (a, d) The comparison group H https://www.frontiersin.org/files/Articles/1696194/fpls-16-1696194-HTML/image_m/fpls-16-1696194-g005.jpg Figure 5. Effect of seed hulls on physiological and biochemical indicators during seed germination. https://www.frontiersin.org/files/Articles/1696194/fpls-16-1696194-HTML/image_m/fpls-16-1696194-g006.jpg Figure 6. Effect of seed hulls on enzyme activity during seed germination. (a) α-amylase activity. ( https://www.frontiersin.org/files/Articles/1696194/fpls-16-1696194-HTML/image_m/fpls-16-1696194-g007.jpg Figure 7. Validation of the relative expression levels of 12 selected DEGs by RT-qPCR, which was cal https://www.frontiersin.org/files/Articles/1696194/fpls-16-1696194-HTML/image_m/fpls-16-1696194-g008.jpg Figure 8. Analysis results of WGCNA. (a) Hierarchical clustering tree showing co-expression modules https://www.frontiersin.org/files/Articles/1696194/fpls-16-1696194-HTML/image_m/fpls-16-1696194-g009.jpg Figure 9. The regulatory network diagram of the effect of seed hulls on the seed germination of L. c https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1739995/full https://www.frontiersin.org/files/Articles/1739995/fnut-13-1739995-HTML/image_m/fnut-13-1739995-g001.jpg Figure 1. Flowchart of the study participants. https://www.frontiersin.org/files/Articles/1739995/fnut-13-1739995-HTML/image_m/fnut-13-1739995-t001.jpg Table 1. Baseline demographic characteristics of participants. https://www.frontiersin.org/files/Articles/1739995/fnut-13-1739995-HTML/image_m/fnut-13-1739995-t002.jpg Table 2. Comparison of baseline outcome measures between the two groups. https://www.frontiersin.org/files/Articles/1739995/fnut-13-1739995-HTML/image_m/fnut-13-1739995-t003.jpg Table 3. Change in BMI, SBQ, ESS, sleep and quality of life by week 12. https://www.frontiersin.org/files/Articles/1739995/fnut-13-1739995-HTML/image_m/fnut-13-1739995-g002.jpg Figure 2. Change in BMI in the TRE and control group after 12 weeks. BMI, body mass index. TRE, time https://www.frontiersin.org/files/Articles/1739995/fnut-13-1739995-HTML/image_m/fnut-13-1739995-g003.jpg Figure 3. Change in sleep efficiency and daytime dysfunction in the TRE after 12 weeks and compariso https://www.frontiersin.org/files/Articles/1739995/fnut-13-1739995-HTML/image_m/fnut-13-1739995-g004.jpg Figure 4. Change in emotional functioning and the total SAQLI score between TRE and control group by https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1792698/full https://www.frontiersin.org/files/Articles/1792698/fpsyg-17-1792698-HTML/image_m/fpsyg-17-1792698-g001.jpg Figure 1. The proposed chain mediation model. https://www.frontiersin.org/files/Articles/1792698/fpsyg-17-1792698-HTML/image_m/fpsyg-17-1792698-t001.jpg Table 1. Descriptive statistics and correlations of depression, pss components, emotion regulation s https://www.frontiersin.org/files/Articles/1792698/fpsyg-17-1792698-HTML/image_m/fpsyg-17-1792698-t002.jpg Table 2. Multiple regression of the mediation effect (N = 998). https://www.frontiersin.org/files/Articles/1792698/fpsyg-17-1792698-HTML/image_m/fpsyg-17-1792698-t003.jpg Table 3. Decomposition of the effect of perceived social support on depression. https://www.frontiersin.org/files/Articles/1792698/fpsyg-17-1792698-HTML/image_m/fpsyg-17-1792698-g002.jpg Figure 2. Multiple mediation model of the impacts of perceived social support on depression among Ch https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1798277/full https://www.frontiersin.org/files/Articles/1798277/fpls-17-1798277-HTML/image_m/fpls-17-1798277-g001.jpg Figure 1. Phenotypic and physiological characterization of cucumber accessions C52 (tolerant) and C1 https://www.frontiersin.org/files/Articles/1798277/fpls-17-1798277-HTML/image_m/fpls-17-1798277-g002.jpg Figure 2. Comparative transcriptomic landscape and gene co-expression network analysis of C13 and C5 https://www.frontiersin.org/files/Articles/1798277/fpls-17-1798277-HTML/image_m/fpls-17-1798277-g003.jpg Figure 3. Functional characterization of distinct regulatory modules associated with N-tolerance (RE https://www.frontiersin.org/files/Articles/1798277/fpls-17-1798277-HTML/image_m/fpls-17-1798277-g004.jpg Figure 4. Integration of ATAC-seq and RNA-seq to decipher the core transcriptional regulatory networ https://www.frontiersin.org/files/Articles/1798277/fpls-17-1798277-HTML/image_m/fpls-17-1798277-g005.jpg Figure 5. Functional validation of CsTGA7 in regulating low-nitrogen tolerance and nitrate homeostas https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1682622/full https://www.frontiersin.org/files/Articles/1682622/fendo-16-1682622-HTML/image_m/fendo-16-1682622-g001.jpg Figure 1. Flowchart of study population enrollment. https://www.frontiersin.org/files/Articles/1682622/fendo-16-1682622-HTML/image_m/fendo-16-1682622-t001.jpg Table 1. Baseline characteristics of AF patients grouped by cardiometabolic index tertiles. https://www.frontiersin.org/files/Articles/1682622/fendo-16-1682622-HTML/image_m/fendo-16-1682622-t002.jpg Table 2. Three Cox regression models for the association between CMI and MACE. https://www.frontiersin.org/files/Articles/1682622/fendo-16-1682622-HTML/image_m/fendo-16-1682622-g002.jpg Figure 2. Forest plot of three Cox regression models. *The P-value show statistical significance. https://www.frontiersin.org/files/Articles/1682622/fendo-16-1682622-HTML/image_m/fendo-16-1682622-g003.jpg Figure 3. Comparison of C-index values for the three models. https://www.frontiersin.org/files/Articles/1682622/fendo-16-1682622-HTML/image_m/fendo-16-1682622-g004.jpg Figure 4. Kaplan–Meier analysis. https://www.frontiersin.org/files/Articles/1682622/fendo-16-1682622-HTML/image_m/fendo-16-1682622-g005.jpg Figure 5. Restricted Cubic Spline. https://www.frontiersin.org/files/Articles/1682622/fendo-16-1682622-HTML/image_m/fendo-16-1682622-g006.jpg Figure 6. Feature importance based on SHAP values for the predictive model. https://www.frontiersin.org/files/Articles/1682622/fendo-16-1682622-HTML/image_m/fendo-16-1682622-g007.jpg Figure 7. Association between CMI and impaired MACE by subgroup. *The P-value show statistical signi https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1775896/full https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g001.jpg Figure 1. Schematic diagram of the overall structure of the MSCPNN networks. https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g002.jpg Figure 2. Schematic architecture of the channel attention module (CAM). https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g003.jpg Figure 3. Schematic diagram of the spatial attention module (SAM). https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g004.jpg Figure 4. Spatial distribution of the annual mean correlation coefficients among salinity, currents, https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g005.jpg Figure 5. Spatial distribution of the annual mean correlation strength among salinity, currents, and https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g006.jpg Figure 6. Spatial distribution maps of the correlation coefficients between salinity, ocean currents https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g007.jpg Figure 7. Spatial distribution map of the correlation intensity between salinity, ocean currents, an https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g008.jpg Figure 8. Comparison of MSCPNN and other algorithm forecast results for all seasons. https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-t001.jpg Table 1. Comparison of the RMSE of temperature forecast results of each coupled forecast algorithm i https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-t002.jpg Table 2. Comparison of the correlation coefficients r for the temperature forecasts of each coupled https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-t003.jpg Table 3. Comparison of peak signal-to-noise ratio (PSNR) of the temperature forecast results for eac https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-t004.jpg Table 4. Comparison of the structural similarity index measure (SSIM) of temperature forecast result https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g009.jpg Figure 9. Comparison of evaluation indicators for annual average temperature forecasting algorithms https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-t005.jpg Table 5. Comparison of the uncertainty metrics for RMSE of the temperature forecast results by coupl https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-t006.jpg Table 6. Comparison of the computational efficiency and resource burden among the evaluated forecast https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g010.jpg Figure 10. Comparison of forecast results of MSCPNN ablation experiments for the temperatures of eac https://www.frontiersin.org/files/Articles/1775896/fmars-13-1775896-HTML-r1/image_m/fmars-13-1775896-g011.jpg Figure 11. Comparison of evaluation indicators for annual average temperature forecast in the meltin https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1793536/full https://www.frontiersin.org/files/Articles/1793536/fendo-17-1793536-HTML-r1/image_m/fendo-17-1793536-g001.jpg Figure 1. Insulin and IGF signaling: an integrative hub for synaptic and network homeostasis. This s https://www.frontiersin.org/files/Articles/1793536/fendo-17-1793536-HTML-r1/image_m/fendo-17-1793536-t001.jpg Table 1. Molecular and spatial specificity of insulin and IGF receptors in the brain. https://www.frontiersin.org/files/Articles/1793536/fendo-17-1793536-HTML-r1/image_m/fendo-17-1793536-g002.jpg Figure 2. Insulin/IGF-1 signaling as a regulator of synaptic homeostasis. Insulin and IGF-1 signalin https://www.frontiersin.org/files/Articles/1793536/fendo-17-1793536-HTML-r1/image_m/fendo-17-1793536-t002.jpg Table 2. Multilevel regulatory roles of insulin and IGF signaling in synaptic and circuit homeostasi https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1773193/full https://www.frontiersin.org/files/Articles/1773193/fonc-16-1773193-HTML/image_m/fonc-16-1773193-g001.jpg Figure 1. Screening of differential genes associated with prognosis in glioma. (A) The forest plot d https://www.frontiersin.org/files/Articles/1773193/fonc-16-1773193-HTML/image_m/fonc-16-1773193-g002.jpg Figure 2. The predictive value of GLA regarding the prognosis of glioma patients. (A) The relationsh https://www.frontiersin.org/files/Articles/1773193/fonc-16-1773193-HTML/image_m/fonc-16-1773193-g003.jpg Figure 3. The analysis of prognosis-related genes in conjunction with clinical indicators. (A-F) The https://www.frontiersin.org/files/Articles/1773193/fonc-16-1773193-HTML/image_m/fonc-16-1773193-g004.jpg Figure 4. CGGA data validation of GLA expression in glioma. (A-F) The expression levels of GLA in th https://www.frontiersin.org/files/Articles/1773193/fonc-16-1773193-HTML/image_m/fonc-16-1773193-g005.jpg Figure 5. The GLA expression in various glioma cell types. (A) The heatmap of marker genes from sing https://www.frontiersin.org/files/Articles/1773193/fonc-16-1773193-HTML/image_m/fonc-16-1773193-g006.jpg Figure 6. Analysis of GLA-related pathways. The scatter plots illustrating the correlation analysis https://www.frontiersin.org/files/Articles/1773193/fonc-16-1773193-HTML/image_m/fonc-16-1773193-g007.jpg Figure 7. Exploration of the specific mechanism of GLA regulating glioma cell proliferation and apop https://www.frontiersin.org/files/Articles/1773193/fonc-16-1773193-HTML/image_m/fonc-16-1773193-g008.jpg Figure 8. The knockdown of GLA inhibits the proliferation of glioma cells. (A, B) The mRNA expressio https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1698840/full https://www.frontiersin.org/files/Articles/1698840/fonc-16-1698840-HTML/image_m/fonc-16-1698840-g001.jpg Figure 1. Clinical photograph and preoperative non-contrast CT. (A) Clinical photograph showing the https://www.frontiersin.org/files/Articles/1698840/fonc-16-1698840-HTML/image_m/fonc-16-1698840-g002.jpg Figure 2. Preoperative post-contrast T1-weighted MRI in multiple planes. Representative post-gadolin https://www.frontiersin.org/files/Articles/1698840/fonc-16-1698840-HTML/image_m/fonc-16-1698840-g003.jpg Figure 3. Preoperative bone-window CT demonstrating inferred routes of extension. (A) Axial bone-win https://www.frontiersin.org/files/Articles/1698840/fonc-16-1698840-HTML/image_m/fonc-16-1698840-g004.jpg Figure 4. Intraoperative findings, postoperative CT, and histopathology. (A) Intraoperative view sho https://www.frontiersin.org/files/Articles/1698840/fonc-16-1698840-HTML/image_m/fonc-16-1698840-t001.jpg Table 1. Selected literature context for meningioma with extracranial extension to the temporal regi https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1701645/full https://www.frontiersin.org/files/Articles/1701645/fimmu-16-1701645-HTML/image_m/fimmu-16-1701645-g001.jpg Figure 1. Clinical and humoral response after experimental infection of lambs with different doses ( https://www.frontiersin.org/files/Articles/1701645/fimmu-16-1701645-HTML/image_m/fimmu-16-1701645-g002.jpg Figure 2. Profile of the humoral response against individual antigens. IgG reactivity to rGra4Gra7, https://www.frontiersin.org/files/Articles/1701645/fimmu-16-1701645-HTML/image_m/fimmu-16-1701645-g003.jpg Figure 3. Histopathological analysis of infected lambs. Brain regions were examined by hematoxylin a https://www.frontiersin.org/files/Articles/1701645/fimmu-16-1701645-HTML/image_m/fimmu-16-1701645-g004.jpg Figure 4. Humoral response of naturally infected sheep. (A) Sera from seronegative (n = 18) and sero https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1704739/full https://www.frontiersin.org/files/Articles/1704739/fimmu-16-1704739-HTML/image_m/fimmu-16-1704739-g001.jpg Figure 1. The overview of this study. MPP, Mycoplasma pneumoniae pneumonia; BALF, bronchoalveolar la https://www.frontiersin.org/files/Articles/1704739/fimmu-16-1704739-HTML/image_m/fimmu-16-1704739-t001.jpg Table 1. Comparison of clinical characteristics among children with RMPP at different altitudes. https://www.frontiersin.org/files/Articles/1704739/fimmu-16-1704739-HTML/image_m/fimmu-16-1704739-t002.jpg Table 2. Comparison of clinical characteristics among children with GMPP at different altitudes. https://www.frontiersin.org/files/Articles/1704739/fimmu-16-1704739-HTML/image_m/fimmu-16-1704739-g002.jpg Figure 2. Pulmonary immune responses of children with MPP in the plain region were excessively activ https://www.frontiersin.org/files/Articles/1704739/fimmu-16-1704739-HTML/image_m/fimmu-16-1704739-g003.jpg Figure 3. Children with RMPP in the plateau region exhibited increased M2 macrophages and CD8+ T cel https://www.frontiersin.org/files/Articles/1704739/fimmu-16-1704739-HTML/image_m/fimmu-16-1704739-g004.jpg Figure 4. The association between key genes most closely related to the high altitude and pulmonary https://www.frontiersin.org/files/Articles/1704739/fimmu-16-1704739-HTML/image_m/fimmu-16-1704739-g005.jpg Figure 5. The intersection of DEGs and genes in the WGCNA module most relevant to the high altitude https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1712093/full https://www.frontiersin.org/files/Articles/1712093/fonc-16-1712093-HTML/image_m/fonc-16-1712093-t001.jpg Table 1. Timetable of the treatment. https://www.frontiersin.org/files/Articles/1712093/fonc-16-1712093-HTML/image_m/fonc-16-1712093-g001.jpg Figure 1. MRI examinations. The images show the patient’s coronal MRI of the pituitary tumor in July https://www.frontiersin.org/files/Articles/1712093/fonc-16-1712093-HTML/image_m/fonc-16-1712093-t002.jpg Table 2. Descriptions of MRI examinations from 2008 to 2022. https://www.frontiersin.org/files/Articles/1712093/fonc-16-1712093-HTML/image_m/fonc-16-1712093-g002.jpg Figure 2. First paradoxical response: cortisol level (µg/dl), administration of pasireotide LAR (arr https://www.frontiersin.org/files/Articles/1712093/fonc-16-1712093-HTML/image_m/fonc-16-1712093-g003.jpg Figure 3. Second and third paradoxical responses: cortisol level and administration of pasireotide L https://www.frontiersin.org/files/Articles/1712093/fonc-16-1712093-HTML/image_m/fonc-16-1712093-g004.jpg Figure 4. Fourth paradoxical response: cortisol level and administration of pasireotide LAR (arrow), https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1755019/full https://www.frontiersin.org/files/Articles/1755019/fonc-16-1755019-HTML/image_m/fonc-16-1755019-g001.jpg Figure 1. MR enhancement scans obtained before each surgery: (A, B) right frontal lesion before the https://www.frontiersin.org/files/Articles/1755019/fonc-16-1755019-HTML/image_m/fonc-16-1755019-g002.jpg Figure 2. Histopathological features of MFS. [(A), ×100] Tumor cells show moderate cellularity and a https://www.frontiersin.org/files/Articles/1755019/fonc-16-1755019-HTML/image_m/fonc-16-1755019-t001.jpg Table 1. Immunohistochemical makers of low-, intermediate-, and high-grade MFS. https://www.frontiersin.org/files/Articles/1755019/fonc-16-1755019-HTML/image_m/fonc-16-1755019-g003.jpg Figure 3. Clinical timeline of the patient, illustrating the sequence of initial presentation, surgi https://www.frontiersin.org/files/Articles/1755019/fonc-16-1755019-HTML/image_m/fonc-16-1755019-t002.jpg Table 2. Summary of reported cases of primary intracranial myxofibrosarcoma(MFS). https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1736382/full https://www.frontiersin.org/files/Articles/1736382/fphar-17-1736382-HTML-r1/image_m/fphar-17-1736382-t001.jpg Table 1. The descriptive statistics of general data (n = 402). https://www.frontiersin.org/files/Articles/1736382/fphar-17-1736382-HTML-r1/image_m/fphar-17-1736382-t002.jpg Table 2. The frequency description of general data (n = 402). https://www.frontiersin.org/files/Articles/1736382/fphar-17-1736382-HTML-r1/image_m/fphar-17-1736382-t003.jpg Table 3. The comparison of IBL and IBT between the two groups. https://www.frontiersin.org/files/Articles/1736382/fphar-17-1736382-HTML-r1/image_m/fphar-17-1736382-t004.jpg Table 4. The univariate binary logistic regression analysis of IBT in short-segment PLIF. https://www.frontiersin.org/files/Articles/1736382/fphar-17-1736382-HTML-r1/image_m/fphar-17-1736382-t005.jpg Table 5. The multivariate binary logistic regression analysis of IBT in short-segment PLIF. https://www.frontiersin.org/files/Articles/1736382/fphar-17-1736382-HTML-r1/image_m/fphar-17-1736382-t006.jpg Table 6. The multivariate binary logistic regression analysis of IBT after excluding IBL in short-se https://www.frontiersin.org/files/Articles/1736382/fphar-17-1736382-HTML-r1/image_m/fphar-17-1736382-g001.jpg Figure 1. The nomogram model of IBT risk in short-segment PLIF. TXA: The decision of whether to rece https://www.frontiersin.org/files/Articles/1736382/fphar-17-1736382-HTML-r1/image_m/fphar-17-1736382-g002.jpg Figure 2. The calibration curve plot of IBT risk in short-segment PLIF. Ideal Curve: This dashed lin https://www.frontiersin.org/files/Articles/1736382/fphar-17-1736382-HTML-r1/image_m/fphar-17-1736382-g003.jpg Figure 3. The threshold graph of IBT risk in short-segment PLIF. The black line (model): it represen https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1767338/full https://www.frontiersin.org/files/Articles/1767338/fphar-17-1767338-HTML/image_m/fphar-17-1767338-g001.jpg Figure 1. Postoperative pain management before and after protocol implementation. https://www.frontiersin.org/files/Articles/1767338/fphar-17-1767338-HTML/image_m/fphar-17-1767338-t001.jpg Table 1. Baseline demographic and clinical characteristics of study participants, stratified by prot https://www.frontiersin.org/files/Articles/1767338/fphar-17-1767338-HTML/image_m/fphar-17-1767338-g002.jpg Figure 2. Pain intensity (NRS 0-10) before and after protocol implementation. https://www.frontiersin.org/files/Articles/1767338/fphar-17-1767338-HTML/image_m/fphar-17-1767338-t002.jpg Table 2. Distribution and intensity of postoperative pain scores (NRS 0–10) in pre- and post-protoco https://www.frontiersin.org/files/Articles/1767338/fphar-17-1767338-HTML/image_m/fphar-17-1767338-g003.jpg Figure 3. MME before and after protocol implementation. https://www.frontiersin.org/files/Articles/1767338/fphar-17-1767338-HTML/image_m/fphar-17-1767338-t003.jpg Table 3. Hospital duration for study participants before and after protocol implementation. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1752413/full https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-t001.jpg Table 1. Comparison of the existing literature. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g001.jpg Figure 1. Overview of the proposed automated deep learning framework. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g002.jpg Figure 2. Standard UNet architecture. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g003.jpg Figure 3. Proposed segmentation module using UNet architecture. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g004.jpg Figure 4. Preprocessing stage for conversion of 2D ultrasound images to 3D volumes. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g005.jpg Figure 5. Radiomic feature extraction framework for classification task. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-t002.jpg Table 2. Radiomics feature extraction workflow. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g006.jpg Figure 6. Proposed classification approach. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g007.jpg Figure 7. Typical CNN architecture for classification. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g008.jpg Figure 8. Proposed CNN classification models. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g009.jpg Figure 9. Radiomic features extracted from the BUSI dataset. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g010.jpg Figure 10. Comparison between the two approaches considered in the proposed work. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-t003.jpg Table 3. Performance of the machine learning algorithms for the breast cancer classification on the https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g011.jpg Figure 11. Confusion matrices for the bagged trees for (a) validation and (b) test set. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g012.jpg Figure 12. ROC-AUC curve for the bagged trees for (a) validation and (b) test set. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-t004.jpg Table 4. Training progress of UNet segmentation model. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g013.jpg Figure 13. A comparison between the original tumor image and its segments for both classes in the BU https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g014.jpg Figure 14. Some of the failure cases for both the classes. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-t005.jpg Table 5. Semantic segmentation performance metrics of UNet. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g015.jpg Figure 15. Radiomic features extracted from the BUSI dataset, using the UNet masks. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-t006.jpg Table 6. Performance of the machine learning algorithms for the breast cancer classification on the https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g016.jpg Figure 16. Confusion matrices for the Quadratic SVM for (a) validation and (b) test dataset. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g017.jpg Figure 17. ROC-AUC curve for the Quadratic SVM for (a) validation and (b) test set. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-t007.jpg Table 7. Performance of the CNN models for the breast cancer classification on the BUSI dataset. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g018.jpg Figure 18. Confusion matrices for the ResNet50 model for (a) training and (b) test dataset. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-g019.jpg Figure 19. ROC-AUC curve for the ResNet50 for training and test dataset. https://www.frontiersin.org/files/Articles/1752413/frai-09-1752413-HTML/image_m/frai-09-1752413-t008.jpg Table 8. Performance comparison of the proposed method with the existing state-of-the-art methods. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1711684/full https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-g001.jpg Figure 1. The detailed methodology of this research. https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-t001.jpg Table 1. Clinical characteristics and manifestations of 271 CMML patients. https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-t002.jpg Table 2. Laboratory findings of 271 CMML patients. https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-g002.jpg Figure 2. Common types of gene mutations in CMML patients. https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-g003.jpg Figure 3. Morphological characteristics of cells in patients with CMML. (A,B) Wright-Giemsa staining https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-g004.jpg Figure 4. Histopathological characteristics of patients with CMML. (A) Bone marrow hyperplasia is ma https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-g005.jpg Figure 5. Treatment regimens for CMML patients in this study. https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-t003.jpg Table 3. Clinical characteristics of patients with CMML and univariate analysis of OS. https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-g006.jpg Figure 6. Kaplan–Meier OS curves for CMML patients. (A) The overall survival curves of monocyte coun https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-t004.jpg Table 4. Multivariate analysis of OS in CMML patients with COX proportional hazards. https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-g007.jpg Figure 7. Kaplan–Meier OS and LFS curves for different treatments in CMML patients. (A) The overall https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-t005.jpg Table 5. Multivariate analysis of OS in CMML patients who received chemotherapy with COX proportiona https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-t006.jpg Table 6. Multivariate analysis of LFS in CMML patients who received chemotherapy with COX proportion https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-t007.jpg Table 7. Multivariate analysis of OS in CMML patients who received symptomatic and supportive care w https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-t008.jpg Table 8. Multivariate analysis of LFS in CMML patients who received symptomatic and supportive care https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-t009.jpg Table 9. Univariate analysis of risk factors for death in CMML patients. https://www.frontiersin.org/files/Articles/1711684/fmed-12-1711684-HTML/image_m/fmed-12-1711684-g008.jpg Figure 8. Multivariate logistic regression analysis of risk factors for mortality in CMML patients. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1734129/full https://www.frontiersin.org/files/Articles/1734129/fenvs-14-1734129-HTML/image_m/fenvs-14-1734129-g001.jpg Figure 1. Schematic diagram of needs constraint. https://www.frontiersin.org/files/Articles/1734129/fenvs-14-1734129-HTML/image_m/fenvs-14-1734129-g002.jpg Figure 2. Egworth box diagram that reasonably needs to be satisfied. https://www.frontiersin.org/files/Articles/1734129/fenvs-14-1734129-HTML/image_m/fenvs-14-1734129-g003.jpg Figure 3. general equilibrium of dynamic optimal allocation of resources and maximum satisfaction of https://www.frontiersin.org/files/Articles/1734129/fenvs-14-1734129-HTML/image_m/fenvs-14-1734129-g004.jpg Figure 4. Dynamic Edgeworth box diagram for optimal resource allocation with technological progress. https://www.frontiersin.org/files/Articles/1734129/fenvs-14-1734129-HTML/image_m/fenvs-14-1734129-g005.jpg Figure 5. Dynamic production possibilities curve with technological progress. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1695782/full https://www.frontiersin.org/files/Articles/1695782/fpubh-13-1695782-HTML/image_m/fpubh-13-1695782-t001.jpg Table 1. Summary of the number of martial arts halls in each province. https://www.frontiersin.org/files/Articles/1695782/fpubh-13-1695782-HTML/image_m/fpubh-13-1695782-t002.jpg Table 2. Summary of interview information of martial arts halls. https://www.frontiersin.org/files/Articles/1695782/fpubh-13-1695782-HTML/image_m/fpubh-13-1695782-g001.jpg Figure 1. Spatial distribution of contemporary martial arts halls (http://bzdt.ch.mnr.gov.cn/browse. https://www.frontiersin.org/files/Articles/1695782/fpubh-13-1695782-HTML/image_m/fpubh-13-1695782-g002.jpg Figure 2. Spatial distribution density of contemporary martial arts halls in provinces (http://bzdt. https://www.frontiersin.org/files/Articles/1695782/fpubh-13-1695782-HTML/image_m/fpubh-13-1695782-g003.jpg Figure 3. Lorentz curve of contemporary martial arts halls. https://www.frontiersin.org/files/Articles/1695782/fpubh-13-1695782-HTML/image_m/fpubh-13-1695782-g004.jpg Figure 4. Kernel density of martial arts halls. (http://bzdt.ch.mnr.gov.cn/browse.html?picId=%224o28 https://www.frontiersin.org/files/Articles/1695782/fpubh-13-1695782-HTML/image_m/fpubh-13-1695782-t003.jpg Table 3. Selection and meaning of influencing factors of spatial production representation of contem https://www.frontiersin.org/files/Articles/1695782/fpubh-13-1695782-HTML/image_m/fpubh-13-1695782-t004.jpg Table 4. OLS regression results of spatial production representation of martial arts halls. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1772195/full https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t001.jpg Table 1. Scale reliability testing results. https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t002.jpg Table 2. Demographic characteristics of respondents (N = 278). https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t003.jpg Table 3. Comparison of resident characteristics across institution types (N = 278). https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t004.jpg Table 4. Demand and satisfaction by service dimension (N = 278). https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t005.jpg Table 5. Supply-demand gap analysis. https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t006.jpg Table 6. Top 10 items by supply-demand gap. https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t007.jpg Table 7. Service accessibility by dimension (%). https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t008.jpg Table 8. Supply-demand gap distribution (N = 278). https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t009.jpg Table 9. Sports service supply-demand two-dimensional analysis matrix. https://www.frontiersin.org/files/Articles/1772195/fpubh-14-1772195-HTML/image_m/fpubh-14-1772195-t010.jpg Table 10. Logistic regression results for predictors of supply-demand matching (N = 278). https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1703523/full https://www.frontiersin.org/files/Articles/1703523/fmed-12-1703523-HTML/image_m/fmed-12-1703523-t001.jpg Table 1. Main areas and weight distribution of training needs for infection control liaison nurses ( https://www.frontiersin.org/files/Articles/1703523/fmed-12-1703523-HTML/image_m/fmed-12-1703523-t002.jpg Table 2. Framework for optimizing the training program for infection control liaison nurses (ICLNs). https://www.frontiersin.org/files/Articles/1703523/fmed-12-1703523-HTML/image_m/fmed-12-1703523-g001.jpg Figure 1. Proposed conceptual framework for a tiered and integrated training model for infection con https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1678095/full https://www.frontiersin.org/files/Articles/1678095/fped-13-1678095-HTML/image_m/fped-13-1678095-t001.jpg Table 1. Primer sequences of real-time PCR. https://www.frontiersin.org/files/Articles/1678095/fped-13-1678095-HTML/image_m/fped-13-1678095-t002.jpg Table 2. Demographic and clinical characteristics of KD patients and non-KD febrile controls cohorts https://www.frontiersin.org/files/Articles/1678095/fped-13-1678095-HTML/image_m/fped-13-1678095-g001.jpg Figure 1. Comparison of C-reactive protein (CRP) (A), platelet count (PLT) (B), miR-223-3p (C), miR- https://www.frontiersin.org/files/Articles/1678095/fped-13-1678095-HTML/image_m/fped-13-1678095-g002.jpg Figure 2. Receiver operating characteristic (ROC) curves illustrating the diagnostic performance of https://www.frontiersin.org/files/Articles/1678095/fped-13-1678095-HTML/image_m/fped-13-1678095-t003.jpg Table 3. Performance metrics for differentiating biomarkers' ROCs. https://www.frontiersin.org/files/Articles/1678095/fped-13-1678095-HTML/image_m/fped-13-1678095-g003.jpg Figure 3. Analysis of the correlation between the four potential biomarkers of KD. The positive pair https://www.frontiersin.org/files/Articles/1678095/fped-13-1678095-HTML/image_m/fped-13-1678095-g004.jpg Figure 4. MiRNA-gene/lncRNA interaction network showing the genes associated with three miRNAs, cons https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1791324/full https://www.frontiersin.org/files/Articles/1791324/fpubh-14-1791324-HTML/image_m/fpubh-14-1791324-t001.jpg Table 1. Descriptive statistics. https://www.frontiersin.org/files/Articles/1791324/fpubh-14-1791324-HTML/image_m/fpubh-14-1791324-t002.jpg Table 2. Correlation matrix. https://www.frontiersin.org/files/Articles/1791324/fpubh-14-1791324-HTML/image_m/fpubh-14-1791324-t003.jpg Table 3. Baseline results: public health credibility shocks and firm-level financial risk. https://www.frontiersin.org/files/Articles/1791324/fpubh-14-1791324-HTML/image_m/fpubh-14-1791324-t004.jpg Table 4. Mediation analysis of employee withdrawal. https://www.frontiersin.org/files/Articles/1791324/fpubh-14-1791324-HTML/image_m/fpubh-14-1791324-t005.jpg Table 5. Mediation analysis of consumer withdrawal. https://www.frontiersin.org/files/Articles/1791324/fpubh-14-1791324-HTML/image_m/fpubh-14-1791324-t006.jpg Table 6. Endogeneity checks. https://www.frontiersin.org/files/Articles/1791324/fpubh-14-1791324-HTML/image_m/fpubh-14-1791324-t007.jpg Table 7. Heterogeneity analysis. https://www.frontiersin.org/files/Articles/1791324/fpubh-14-1791324-HTML/image_m/fpubh-14-1791324-t008.jpg Table 8. Robustness check. https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2026.1809823/full https://www.frontiersin.org/files/Articles/1809823/fbuil-12-1809823-HTML/image_m/fbuil-12-1809823-g001.jpg Figure 1. Conceptual framework linking digitally constructed mobility infrastructure and population https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1789543/full https://www.frontiersin.org/files/Articles/1789543/fnut-13-1789543-HTML/image_m/fnut-13-1789543-g001.jpg Figure 1. Flow diagram of the study. https://www.frontiersin.org/files/Articles/1789543/fnut-13-1789543-HTML/image_m/fnut-13-1789543-g002.jpg Figure 2. Distribution of palatine tonsil grading in preschool children (n = 2,786). Bars represent https://www.frontiersin.org/files/Articles/1789543/fnut-13-1789543-HTML/image_m/fnut-13-1789543-t001.jpg Table 1. Baseline characteristics for the analytic sample and the entire cohort. https://www.frontiersin.org/files/Articles/1789543/fnut-13-1789543-HTML/image_m/fnut-13-1789543-g003.jpg Figure 3. Distribution of breastfeeding duration among study participants (n = 816). https://www.frontiersin.org/files/Articles/1789543/fnut-13-1789543-HTML/image_m/fnut-13-1789543-t002.jpg Table 2. Sex-stratified associations of feeding patterns from 0 to 24 months with palatine tonsil gr https://www.frontiersin.org/files/Articles/1789543/fnut-13-1789543-HTML/image_m/fnut-13-1789543-t003.jpg Table 3. Sex-stratified associations of early feeding durations with palatine tonsil grading. https://www.frontiersin.org/files/Articles/1789543/fnut-13-1789543-HTML/image_m/fnut-13-1789543-t004.jpg Table 4. Sex-stratified associations of feeding introduction timing with palatine tonsil grading. https://www.frontiersin.org/files/Articles/1789543/fnut-13-1789543-HTML/image_m/fnut-13-1789543-t005.jpg Table 5. Sex-stratified associations of perinatal and demographic factors with palatine tonsil gradi https://www.frontiersin.org/files/Articles/1789543/fnut-13-1789543-HTML/image_m/fnut-13-1789543-t006.jpg Table 6. Early feeding patterns and formula introduction timing with palatine tonsil grading in fema https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1714168/full https://www.frontiersin.org/files/Articles/1714168/fimmu-17-1714168-HTML/image_m/fimmu-17-1714168-g001.jpg Figure 1. TLR-driven induction of inflammatory signaling pathways in adipocytes. (A–C) Gene expressi https://www.frontiersin.org/files/Articles/1714168/fimmu-17-1714168-HTML/image_m/fimmu-17-1714168-g002.jpg Figure 2. Zinc pyrithione restricts IL-6 production in adipocytes. (A) Increased intracellular Zn le https://www.frontiersin.org/files/Articles/1714168/fimmu-17-1714168-HTML/image_m/fimmu-17-1714168-g003.jpg Figure 3. ZnPT alters the expression of genes involved in IL-6 production without inducing adipocyte https://www.frontiersin.org/files/Articles/1714168/fimmu-17-1714168-HTML/image_m/fimmu-17-1714168-g004.jpg Figure 4. ZnPT interferes with the activation of Erk1/2 and Stat3 signaling pathways. Proteins of in https://www.frontiersin.org/files/Articles/1714168/fimmu-17-1714168-HTML/image_m/fimmu-17-1714168-g005.jpg Figure 5. ZnPT restricts IL-6 induced activation of Stat3 signaling in adipocytes. (A) Protein level https://www.frontiersin.org/files/Articles/1714168/fimmu-17-1714168-HTML/image_m/fimmu-17-1714168-g006.jpg Figure 6. Schematic diagram illustrating the proposed mechanism of adipocyte IL-6 production (A) and https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1639375/full https://www.frontiersin.org/files/Articles/1639375/fpsyg-16-1639375-HTML/image_m/fpsyg-16-1639375-g001.jpg Figure 1. Hypothesized structural model. https://www.frontiersin.org/files/Articles/1639375/fpsyg-16-1639375-HTML/image_m/fpsyg-16-1639375-t001.jpg Table 1. Descriptive statistics and normality test of research variables. https://www.frontiersin.org/files/Articles/1639375/fpsyg-16-1639375-HTML/image_m/fpsyg-16-1639375-t002.jpg Table 2. Overall goodness of fit indicators for the intermediary structure model. https://www.frontiersin.org/files/Articles/1639375/fpsyg-16-1639375-HTML/image_m/fpsyg-16-1639375-t003.jpg Table 3. Standardized direct effects of predictors on GPA. https://www.frontiersin.org/files/Articles/1639375/fpsyg-16-1639375-HTML/image_m/fpsyg-16-1639375-t004.jpg Table 4. The standardized path coefficients of motivational variables on learning strategies and psy https://www.frontiersin.org/files/Articles/1639375/fpsyg-16-1639375-HTML/image_m/fpsyg-16-1639375-t005.jpg Table 5. GPA the direct effect, indirect effect and total effect of GPA. https://www.frontiersin.org/files/Articles/1639375/fpsyg-16-1639375-HTML/image_m/fpsyg-16-1639375-g002.jpg Figure 2. Path diagram of the structural model. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1742205/full https://www.frontiersin.org/files/Articles/1742205/fmed-12-1742205-HTML-r1/image_m/fmed-12-1742205-g001.jpg Figure 1. The integrated conceptual model derived from the mixed-methods analysis. Quantitative resu https://www.frontiersin.org/files/Articles/1742205/fmed-12-1742205-HTML-r1/image_m/fmed-12-1742205-t001.jpg Table 1. Sociodemographic characteristics of community pharmacists in Jordan (n = 348). https://www.frontiersin.org/files/Articles/1742205/fmed-12-1742205-HTML-r1/image_m/fmed-12-1742205-t002.jpg Table 2. Perceived barriers to restricting over-the-counter antibiotic sales among community pharmac https://www.frontiersin.org/files/Articles/1742205/fmed-12-1742205-HTML-r1/image_m/fmed-12-1742205-t003.jpg Table 3. Determinants and enabling factors influencing antimicrobial stewardship among community pha https://www.frontiersin.org/files/Articles/1742205/fmed-12-1742205-HTML-r1/image_m/fmed-12-1742205-t004.jpg Table 4. Associations between demographic variables and perceived barriers to restricting over-the-c https://www.frontiersin.org/files/Articles/1742205/fmed-12-1742205-HTML-r1/image_m/fmed-12-1742205-t005.jpg Table 5. Ordinal logistic regression model for predictors of high perceived barriers to restricting https://www.frontiersin.org/files/Articles/1742205/fmed-12-1742205-HTML-r1/image_m/fmed-12-1742205-t006.jpg Table 6. Summary of qualitative themes and representative quotations from interviews with community https://www.frontiersin.org/files/Articles/1742205/fmed-12-1742205-HTML-r1/image_m/fmed-12-1742205-t007.jpg Table 7. Joint display of quantitative and qualitative integration. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1674680/full https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-g001.jpg Figure 1. Flowchart of screening and propensity score matching. DGBI: disorders of gut–brain interac https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t001.jpg Table 1. Comparison of baseline characteristics between case and control groups before and after pro https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-g002.jpg Figure 2. Distribution of DGBI subtypes after matching. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-g003.jpg Figure 3. Distribution of clinical symptoms after matching. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t002.jpg Table 2. Comparison of fit indices between the six-factor model and the two-factor model. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-g004.jpg Figure 4. Panel (A) presents the six-factor model of the Chinese Highly Sensitive Person Scale (HSPS https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t003.jpg Table 3. Reliability indices of the HSPS-C. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t004.jpg Table 4. Model fit indices of HSPS-C. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t005.jpg Table 5. Results of convergent validity analysis. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t006.jpg Table 6. Results of discriminant validity analysis. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t007.jpg Table 7. Comparison of baseline characteristics between the groups post-matching. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t008.jpg Table 8. Post-matching comparative analysis of SPS between DGBI patients and HCs. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t009.jpg Table 9. Group comparison of HSP after matching. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t010.jpg Table 10. Univariate logistic regression analysis of risk factors for DGBI after matching. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t011.jpg Table 11. Multivariate logistic regression analysis of risk factors for DGBI after matching. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-g005.jpg Figure 5. ROC curves of SPS in predicting DGBI. https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t012.jpg Table 12. Comparison of HSPS-C total and subscale scores between upper and lower gastrointestinal DG https://www.frontiersin.org/files/Articles/1674680/fmed-13-1674680-HTML-r1/image_m/fmed-13-1674680-t013.jpg Table 13. Comparison of HSP proportions between upper and lower gastrointestinal DGBI patients. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1748584/full https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-g001.jpg Figure 1. Anterior rhinoscopy test. https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-g002.jpg Figure 2. Study flowchart. https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-t001.jpg Table 1. Volumes and related parameters of nasal vestibule. https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-g003.jpg Figure 3. CFD analysis of a three-dimensional reconstruction model and streamline illustrations: (A) https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-t002.jpg Table 2. Paired t test of CFD outcomes before and after stent placement. https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-g004.jpg Figure 4. NOSE score (Day 0 refers to pre-stent placement; days 1, 3, and 7 refer to the 1st, 3rd, a https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-t003.jpg Table 3. Paired t test and effect size of NOSE score before and after stent placement. https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-t004.jpg Table 4. Results of nasal resistance and acoustic rhinometry changes before and after stent placemen https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-g005.jpg Figure 5. Adverse event incidence. (A,B) Stent-wearing experience (assessed by the Western Nasal Dil https://www.frontiersin.org/files/Articles/1748584/fmed-12-1748584-HTML/image_m/fmed-12-1748584-g006.jpg Figure 6. Nasal vestibular stent and its placement within patient’s nasal vestibule. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1719884/full https://www.frontiersin.org/files/Articles/1719884/fsurg-12-1719884-HTML-r1/image_m/fsurg-12-1719884-t001.jpg Table 1. Abnormal test results. https://www.frontiersin.org/files/Articles/1719884/fsurg-12-1719884-HTML-r1/image_m/fsurg-12-1719884-g001.jpg Figure 1. Preoperative X-ray and MRI scans of the right knee joint. (A,B) Anteroposterior and latera https://www.frontiersin.org/files/Articles/1719884/fsurg-12-1719884-HTML-r1/image_m/fsurg-12-1719884-g002.jpg Figure 2. Intraoperative arthroscopic findings and immediate postoperative radiographs. (A) The meni https://www.frontiersin.org/files/Articles/1719884/fsurg-12-1719884-HTML-r1/image_m/fsurg-12-1719884-g003.jpg Figure 3. The imaging data were reviewed at each stage after operation. (A) Full-length radiographs https://www.frontiersin.org/files/Articles/1719884/fsurg-12-1719884-HTML-r1/image_m/fsurg-12-1719884-t002.jpg Table 2. Preoperative and postoperative KOOS scores. https://www.frontiersin.org/files/Articles/1719884/fsurg-12-1719884-HTML-r1/image_m/fsurg-12-1719884-g004.jpg Figure 4. Comparison of KOOS scores before surgery and 1 year after surgery. All five subscales of t https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2026.1766109/full https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-t001.jpg Table 1. Search string used for each database. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-g001.jpg Figure 1. Study flow diagram. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-t002.jpg Table 2. Summary of fall detection studies. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-g002.jpg Figure 2. The source of data set. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-g003.jpg Figure 3. Number of publications each year per number of sensor type. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-g004.jpg Figure 4. Number of publications each year per number of sensor locations. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-t003.jpg Table 3. Summary of device location of fall detection studies. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-g005.jpg Figure 5. Number of publications each year per type of algorithm. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-g006.jpg Figure 6. Number of publications each year per real-time evaluation. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-t004.jpg Table 4. Number of publications per type of machine learning algorithm. https://www.frontiersin.org/files/Articles/1766109/fnbot-20-1766109-HTML/image_m/fnbot-20-1766109-t005.jpg Table 5. Number of publications per type of outcome for each device combination. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1722108/full https://www.frontiersin.org/files/Articles/1722108/fphys-16-1722108-HTML/image_m/fphys-16-1722108-g001.jpg Figure 1. Study design and timeline. The protocol began with the administration of a self-report que https://www.frontiersin.org/files/Articles/1722108/fphys-16-1722108-HTML/image_m/fphys-16-1722108-g002.jpg Figure 2. TA MVC evaluation setting. The figure shows a study participant performing the MVC for TA. https://www.frontiersin.org/files/Articles/1722108/fphys-16-1722108-HTML/image_m/fphys-16-1722108-g003.jpg Figure 3. Schematic timeline of the dynamic warm-up protocol. The warm-up consisted of six exercises https://www.frontiersin.org/files/Articles/1722108/fphys-16-1722108-HTML/image_m/fphys-16-1722108-g004.jpg Figure 4. CONSORT flowchart of participant progression through the phases of the randomized controll https://www.frontiersin.org/files/Articles/1722108/fphys-16-1722108-HTML/image_m/fphys-16-1722108-t001.jpg Table 1. Descriptive statistics (mean ± standard deviation) for all measured variables across the th https://www.frontiersin.org/files/Articles/1722108/fphys-16-1722108-HTML/image_m/fphys-16-1722108-t002.jpg Table 2. Sport skill-related battery test score for T0 and T1. https://www.frontiersin.org/files/Articles/1722108/fphys-16-1722108-HTML/image_m/fphys-16-1722108-t003.jpg Table 3. Between-group post-test contrasts for all outcome measures. https://www.frontiersin.org/files/Articles/1722108/fphys-16-1722108-HTML/image_m/fphys-16-1722108-t004.jpg Table 4. EMG analysis percentage of muscle activation during the Sport Skill Test Battery. https://www.frontiersin.org/files/Articles/1722108/fphys-16-1722108-HTML/image_m/fphys-16-1722108-g005.jpg Figure 5. Interactions between group and time for (a) Tibialis Anterior (TA MVC %); (b) Gastrocnemiu https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1657966/full https://www.frontiersin.org/files/Articles/1657966/fphar-16-1657966-HTML/image_m/fphar-16-1657966-t001.jpg Table 1. Primer sequence. https://www.frontiersin.org/files/Articles/1657966/fphar-16-1657966-HTML/image_m/fphar-16-1657966-g001.jpg Figure 1. Network Pharmacology Analysis of Fraxin in NAFLD Management (A) Overlap between compound t https://www.frontiersin.org/files/Articles/1657966/fphar-16-1657966-HTML/image_m/fphar-16-1657966-t002.jpg Table 2. PDB IDs of potential core targets for fraxin treatment and corresponding docking scores. https://www.frontiersin.org/files/Articles/1657966/fphar-16-1657966-HTML/image_m/fphar-16-1657966-g002.jpg Figure 2. Molecular Docking of Fraxin with NAFLD-related Core Targets. Docking diagrams with (A) GAP https://www.frontiersin.org/files/Articles/1657966/fphar-16-1657966-HTML/image_m/fphar-16-1657966-g003.jpg Figure 3. Fraxin improves biochemical indicators and histopathology in MCD diet-induced NAFLD mice ( https://www.frontiersin.org/files/Articles/1657966/fphar-16-1657966-HTML/image_m/fphar-16-1657966-t003.jpg Table 3. Degree of hepatic steatosis in mice by H&E staining. https://www.frontiersin.org/files/Articles/1657966/fphar-16-1657966-HTML/image_m/fphar-16-1657966-g004.jpg Figure 4. Impact of Fraxin on Gene and Protein Expression (A–J) mRNA expression of FAS, ACC1, FAT/CD https://www.frontiersin.org/files/Articles/1657966/fphar-16-1657966-HTML/image_m/fphar-16-1657966-g005.jpg Figure 5. Fraxin’s effect on the Gut Flora (A) Alpha diversity analysis: Chao1 index before and afte https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1685333/full https://www.frontiersin.org/files/Articles/1685333/fpubh-14-1685333-HTML/image_m/fpubh-14-1685333-g001.jpg Figure 1. Flowchart illustrating the process of translation, cultural adaptation, and content valida https://www.frontiersin.org/files/Articles/1685333/fpubh-14-1685333-HTML/image_m/fpubh-14-1685333-t001.jpg Table 1. Relevance ratings and expert agreement for the calculation of Content Validity Indexes. https://www.frontiersin.org/files/Articles/1685333/fpubh-14-1685333-HTML/image_m/fpubh-14-1685333-t002.jpg Table 2. Item classification by modified kappa index across instruments and dimensions. https://www.frontiersin.org/files/Articles/1685333/fpubh-14-1685333-HTML/image_m/fpubh-14-1685333-t003.jpg Table 3. Low-rated items and expert recommendations for revision. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1752713/full https://www.frontiersin.org/files/Articles/1752713/fnut-13-1752713-HTML-r1/image_m/fnut-13-1752713-t001.jpg Table 1. Demographic and clinical characteristics of hemodialysis patients (n = 30). https://www.frontiersin.org/files/Articles/1752713/fnut-13-1752713-HTML-r1/image_m/fnut-13-1752713-t002.jpg Table 2. Comparative analysis of primary outcomes (serum potassium, IDWG, and quality of life) befor https://www.frontiersin.org/files/Articles/1752713/fnut-13-1752713-HTML-r1/image_m/fnut-13-1752713-t003.jpg Table 3. Efficacy of intervention on serum potassium levels at baseline (T0) and post-intervention ( https://www.frontiersin.org/files/Articles/1752713/fnut-13-1752713-HTML-r1/image_m/fnut-13-1752713-g001.jpg Figure 1. Related-samples Wilcoxon signed rank test (IDWG). Distribution of interdialytic weight gai https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1727740/full https://www.frontiersin.org/files/Articles/1727740/fcell-13-1727740-HTML-r1/image_m/fcell-13-1727740-g001.jpg Figure 1. Establishment of RpHluorin2-expressing cells and fluorescence detection using multiple det https://www.frontiersin.org/files/Articles/1727740/fcell-13-1727740-HTML-r1/image_m/fcell-13-1727740-g002.jpg Figure 2. Microplate reader and fluorescence microscopy analyses showed that JTC-801 induces dose-an https://www.frontiersin.org/files/Articles/1727740/fcell-13-1727740-HTML-r1/image_m/fcell-13-1727740-g003.jpg Figure 3. Flow cytometry analysis substantiated that JTC-801 induces dose-and time-dependent reduced https://www.frontiersin.org/files/Articles/1727740/fcell-13-1727740-HTML-r1/image_m/fcell-13-1727740-g004.jpg Figure 4. Small animal imaging system analysis confirmed that JTC-801 induces dose- and time-depende https://www.frontiersin.org/files/Articles/1727740/fcell-13-1727740-HTML-r1/image_m/fcell-13-1727740-g005.jpg Figure 5. Protein dot blot assay is utilized to detect the fluorescence changes of RpHluorin2 in alk https://www.frontiersin.org/files/Articles/1727740/fcell-13-1727740-HTML-r1/image_m/fcell-13-1727740-g006.jpg Figure 6. Schematic workflow for detecting alkaliptosis in RpHluorin2-expressing cells. This schemat https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1768231/full https://www.frontiersin.org/files/Articles/1768231/fmolb-13-1768231-HTML-r1/image_m/fmolb-13-1768231-g001.jpg Figure 1. Schematic ilustration of the protein dot blot detection workflow. https://www.frontiersin.org/files/Articles/1768231/fmolb-13-1768231-HTML-r1/image_m/fmolb-13-1768231-t001.jpg Table 1. Key technical considerations and critical precautions for Dot Blot Analysis. https://www.frontiersin.org/files/Articles/1768231/fmolb-13-1768231-HTML-r1/image_m/fmolb-13-1768231-g002.jpg Figure 2. Applications and future perspectives of protein dot blot. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1671839/full https://www.frontiersin.org/files/Articles/1671839/fimmu-16-1671839-HTML/image_m/fimmu-16-1671839-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1671839/fimmu-16-1671839-HTML/image_m/fimmu-16-1671839-g001.jpg Figure 1. Function and mechanism of B cell subsets in the TME. The main types of B cells in the TME https://www.frontiersin.org/files/Articles/1671839/fimmu-16-1671839-HTML/image_m/fimmu-16-1671839-t001.jpg Table 1. Roles of different types antibodies secreted by plasma cells in different diseases. https://www.frontiersin.org/files/Articles/1671839/fimmu-16-1671839-HTML/image_m/fimmu-16-1671839-g002.jpg Figure 2. Different roles of various immunoglobulins in tumor immunity and their pro/anti-tumor mech https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1794863/full https://www.frontiersin.org/files/Articles/1794863/feduc-11-1794863-HTML/image_m/feduc-11-1794863-t001.jpg Table 1. Summary of sample demographics for Study 1. https://www.frontiersin.org/files/Articles/1794863/feduc-11-1794863-HTML/image_m/feduc-11-1794863-t002.jpg Table 2. Modules for the online action-state orientation intervention. https://www.frontiersin.org/files/Articles/1794863/feduc-11-1794863-HTML/image_m/feduc-11-1794863-t003.jpg Table 3. Exercises for the face-to-face action-state orientation intervention. https://www.frontiersin.org/files/Articles/1794863/feduc-11-1794863-HTML/image_m/feduc-11-1794863-t004.jpg Table 4. Ten study strategies and frequency of use for students receiving the intervention for Study https://www.frontiersin.org/files/Articles/1794863/feduc-11-1794863-HTML/image_m/feduc-11-1794863-t005.jpg Table 5. Correlations among evaluation variables in Study 1. https://www.frontiersin.org/files/Articles/1794863/feduc-11-1794863-HTML/image_m/feduc-11-1794863-t006.jpg Table 6. Means for modalities of instruction delivery in Study 1. https://www.frontiersin.org/files/Articles/1794863/feduc-11-1794863-HTML/image_m/feduc-11-1794863-t007.jpg Table 7. Summary of sample demographics for Study 2. https://www.frontiersin.org/files/Articles/1794863/feduc-11-1794863-HTML/image_m/feduc-11-1794863-t008.jpg Table 8. Comparison of study strategies utilization for students who received versus did not receive https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1667882/full https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-t001.jpg Table 1. Brief overview of the study sites in the four countries. https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-t002.jpg Table 2. Elements of agroecology and their associated indicators used in the characterization of agr https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g001.jpg Figure 1. Results of TAPE Step 1, the characterization of agroecological transition (CAET) scores ac https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g002.jpg Figure 2. A cross-country comparison of the level of integration of each of the 10 elements of agroe https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g003.jpg Figure 3. Correlation among elements of agroecology in the study site. Values in each box represent https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g004.jpg Figure 4. Cluster analysis results displayed within scatter plots between efficiency and diversity ( https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g005.jpg Figure 5. Principal Component Analysis (PCA) results showing two optimal clusters identified from th https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-t003.jpg Table 3. Relationship between economic dimensions and selected elements of agroecology. https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-t004.jpg Table 4. ANOVA results for soil organic carbon (SOC) and total nitrogen (TN) by cluster. https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g006.jpg Figure 6. Correlation between degree of agroecological integration (CAET score) and composite agrobi https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g007.jpg Figure 7. Correlation between degree of agroecological integration (CAET score) and composite soil h https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g008.jpg Figure 8. Correlation of degree of agroecological integration (CAET scores) with soil organic carbon https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g009.jpg Figure 9. Correlation of degree of agroecological integration (CAET scores) with total soil nitrogen https://www.frontiersin.org/files/Articles/1667882/fsufs-09-1667882-HTML/image_m/fsufs-09-1667882-g010.jpg Figure 10. Correlation between degree of agroecological integration (CAET score) and composite dieta https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1776875/full https://www.frontiersin.org/files/Articles/1776875/fimmu-17-1776875-HTML/image_m/fimmu-17-1776875-g001.jpg Figure 1. Workflow summary. This figure illustrates the workflow of the study, investigating the rol https://www.frontiersin.org/files/Articles/1776875/fimmu-17-1776875-HTML/image_m/fimmu-17-1776875-g002.jpg Figure 2. Molecular characteristics of lactate levels and MRGs in BRCA. (A-C) Differential gene expr https://www.frontiersin.org/files/Articles/1776875/fimmu-17-1776875-HTML/image_m/fimmu-17-1776875-g003.jpg Figure 3. Consensus clustering of BRCA based on MRGs expression and functional enrichment analysis. https://www.frontiersin.org/files/Articles/1776875/fimmu-17-1776875-HTML/image_m/fimmu-17-1776875-g004.jpg Figure 4. Immune checkpoint expression and immune infiltration characteristics of BRCA subtypes. (A) https://www.frontiersin.org/files/Articles/1776875/fimmu-17-1776875-HTML/image_m/fimmu-17-1776875-g005.jpg Figure 5. Validation of the MRGs model’s prognostic and immunotherapy-predictive performance across https://www.frontiersin.org/files/Articles/1776875/fimmu-17-1776875-HTML/image_m/fimmu-17-1776875-g006.jpg Figure 6. Clinical relevance and pathway enrichment analysis of key MRGs in BRCA. (A) Oncoprint prof https://www.frontiersin.org/files/Articles/1776875/fimmu-17-1776875-HTML/image_m/fimmu-17-1776875-g007.jpg Figure 7. Single-cell RNA sequencing analysis of key MRGs expression patterns in BRCA. (A) t-SNE plo https://www.frontiersin.org/files/Articles/1776875/fimmu-17-1776875-HTML/image_m/fimmu-17-1776875-g008.jpg Figure 8. Cell-cell communication analysis between HAGHL-high Luminal HS-AV epithelial cells and oth https://www.frontiersin.org/files/Articles/1776875/fimmu-17-1776875-HTML/image_m/fimmu-17-1776875-g009.jpg Figure 9. HAGHL knockdown inhibits proliferation, and migration of BRCA cells. (A) Validation of HAG https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1784602/full https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-t001.jpg Table 1. Participant demographics. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-t002.jpg Table 2. Constructs and items of the applied questionnaire. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-t003.jpg Table 3. Cronbach's alpha of the constructs ME, A and MI. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-g001.jpg Figure 1. Architecture of the automated messaging system for educational support. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-t004.jpg Table 4. Acceptance level by construct. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-g002.jpg Figure 2. Level of acceptance by construct according to gender. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-g003.jpg Figure 3. ANOVA of EM, A, and IM by area of study. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-g004.jpg Figure 4. Pearson correlation matrix between EM, IM and A. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-t005.jpg Table 5. Multiple regression analysis predicting intrinsic motivation. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-t006.jpg Table 6. Motivational profiles identified through cluster analysis. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-g005.jpg Figure 5. Motivational profiles identified through cluster analysis based on EM, A, and IM. https://www.frontiersin.org/files/Articles/1784602/feduc-11-1784602-HTML/image_m/feduc-11-1784602-g006.jpg Figure 6. Three-dimensional visualization of motivational profiles based on EM, A, and IM. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1659937/full https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t001.jpg Table 1. Mean squares of the analysis of variance (ANOVA) for 15 yield-related agro-morphological ch https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t002.jpg Table 2. Mean performance of yield and yield related 15 agromorphological traits in 100 BWF RIL line https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t003.jpg Table 3. Mean performance of yield and yield related 15 agromorphological traits in 100 CWF RIL line https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-g001.jpg Figure 1. Scheme for recombinant inbred line (RIL) development in the pre-breeding program and the f https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t004.jpg Table 4. Descriptive statistics and broad-sense heritability (H%) for 15 traits in both the RIL popu https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-g002.jpg Figure 2. Pearson’s correlations, PCA bi-plot, and clustering dendrogram for both the RIL population https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t005.jpg Table 5. Cluster means for 15 traits among 100 BWF and 100 CWF RILs. https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t006.jpg Table 6. Average intercluster and intra-cluster distances (D2) among the seven clusters of BWF. https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t007.jpg Table 7. Cluster mean values estimated by Tocher’s method from 100 BWF RIL populations and the perce https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t008.jpg Table 8. Average intercluster and intra-cluster distances (D2) among the eleven clusters of CWF. https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t009.jpg Table 9. Cluster mean values estimated by Tocher’s method from 100 CWF RIL populations and the perce https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t010.jpg Table 10. Contribution of different traits toward the total variance in 100 BWF RIL populations (eig https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t011.jpg Table 11. Contribution of different traits toward the total variance in 100 CWF RIL populations (eig https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-g003.jpg Figure 3. Chromatogram of HR-LCMS-QTOF for the identification of the total free amino acids from two https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t012.jpg Table 12. Distinct types (46 components) of metabolites (anthocyanins and others) qualitatively dete https://www.frontiersin.org/files/Articles/1659937/fgene-16-1659937-HTML-r1/image_m/fgene-16-1659937-t013.jpg Table 13. Total amino acids in black rice breeding lines (mg/100 g dry weight basis) quantitatively https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1812244/full https://www.frontiersin.org/files/Articles/1812244/fspor-08-1812244-HTML/image_m/fspor-08-1812244-t001.jpg Table 1. Participant groups’ data. https://www.frontiersin.org/files/Articles/1812244/fspor-08-1812244-HTML/image_m/fspor-08-1812244-t002.jpg Table 2. Statistical results of preliminary performance between groups. https://www.frontiersin.org/files/Articles/1812244/fspor-08-1812244-HTML/image_m/fspor-08-1812244-t003.jpg Table 3. Statistical results of post-intervention performance between groups. https://www.frontiersin.org/files/Articles/1812244/fspor-08-1812244-HTML/image_m/fspor-08-1812244-t004.jpg Table 4. Results of the post-field tests and their within-group statistical comparison. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1787117/full https://www.frontiersin.org/files/Articles/1787117/fdgth-08-1787117-HTML/image_m/fdgth-08-1787117-t001.jpg Table 1. Demographic characteristics of study participants. https://www.frontiersin.org/files/Articles/1787117/fdgth-08-1787117-HTML/image_m/fdgth-08-1787117-t002.jpg Table 2. Knowledge and familiarity with AI in medicine (N = 587). https://www.frontiersin.org/files/Articles/1787117/fdgth-08-1787117-HTML/image_m/fdgth-08-1787117-t003.jpg Table 3. AI tools (instruments or applications) usage Among medical professionals in the last year b https://www.frontiersin.org/files/Articles/1787117/fdgth-08-1787117-HTML/image_m/fdgth-08-1787117-t004.jpg Table 4. Italian physicians' attitudes toward artificial intelligence in medicine n = 587. https://www.frontiersin.org/files/Articles/1787117/fdgth-08-1787117-HTML/image_m/fdgth-08-1787117-t005.jpg Table 5. Incentives and barriers to AI adoption Among Italian physicians. https://www.frontiersin.org/files/Articles/1787117/fdgth-08-1787117-HTML/image_m/fdgth-08-1787117-t006.jpg Table 6. Willingness and training preferences (n = 587). https://www.frontiersin.org/files/Articles/1787117/fdgth-08-1787117-HTML/image_m/fdgth-08-1787117-t007.jpg Table 7. Clinical agreement analysis between physicians and AI diagnoses for the universal scenario https://www.frontiersin.org/files/Articles/1787117/fdgth-08-1787117-HTML/image_m/fdgth-08-1787117-g001.jpg Figure 1. Clinical agreement distribution. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1716162/full https://www.frontiersin.org/files/Articles/1716162/fmars-13-1716162-HTML-r1/image_m/fmars-13-1716162-t001.jpg Table 1. Primers used for real-time quantitative PCR (qPCR). https://www.frontiersin.org/files/Articles/1716162/fmars-13-1716162-HTML-r1/image_m/fmars-13-1716162-g001.jpg Figure 1. Digestive enzyme activities measured in whole-body homogenates of Oryzias dancena reared u https://www.frontiersin.org/files/Articles/1716162/fmars-13-1716162-HTML-r1/image_m/fmars-13-1716162-g002.jpg Figure 2. Antioxidant enzyme activities measured in whole-body homogenates of Oryzias dancena reared https://www.frontiersin.org/files/Articles/1716162/fmars-13-1716162-HTML-r1/image_m/fmars-13-1716162-g003.jpg Figure 3. Metabolic enzyme activities measured in whole-body homogenates of Oryzias dancena reared u https://www.frontiersin.org/files/Articles/1716162/fmars-13-1716162-HTML-r1/image_m/fmars-13-1716162-g004.jpg Figure 4. A representative liver histological section of Oryzias dancena under different salinities https://www.frontiersin.org/files/Articles/1716162/fmars-13-1716162-HTML-r1/image_m/fmars-13-1716162-g005.jpg Figure 5. Hepatic mRNA expression of osmoregulatory and lipid metabolic genes in Oryzias dancena rea https://www.frontiersin.org/files/Articles/1716162/fmars-13-1716162-HTML-r1/image_m/fmars-13-1716162-t002.jpg Table 2. Fatty acid composition (percentage of total fatty acids) of the muscle of Oryzias dancena r https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1578265/full https://www.frontiersin.org/files/Articles/1578265/fpsyg-17-1578265-HTML-r1/image_m/fpsyg-17-1578265-t001.jpg Table 1. Sample description. https://www.frontiersin.org/files/Articles/1578265/fpsyg-17-1578265-HTML-r1/image_m/fpsyg-17-1578265-t002.jpg Table 2. Descriptive results for each variable of interest in the sample. https://www.frontiersin.org/files/Articles/1578265/fpsyg-17-1578265-HTML-r1/image_m/fpsyg-17-1578265-t003.jpg Table 3. Subject’s average count of the 51 behaviors for each intensity of approach and avoidance. https://www.frontiersin.org/files/Articles/1578265/fpsyg-17-1578265-HTML-r1/image_m/fpsyg-17-1578265-t004.jpg Table 4. Subject’s average count of the 51 behaviors for each dissonance level. https://www.frontiersin.org/files/Articles/1578265/fpsyg-17-1578265-HTML-r1/image_m/fpsyg-17-1578265-t005.jpg Table 5. Associations between dissonance levels and overall sexual life description variables. https://www.frontiersin.org/files/Articles/1578265/fpsyg-17-1578265-HTML-r1/image_m/fpsyg-17-1578265-g001.jpg Figure 1. Associations between traumatic event responses and dissonance level counts. Number of subj https://www.frontiersin.org/files/Articles/1578265/fpsyg-17-1578265-HTML-r1/image_m/fpsyg-17-1578265-t006.jpg Table 6. Results of the unsupervised variable selection analysis loading for 99% of the variability https://www.frontiersin.org/files/Articles/1578265/fpsyg-17-1578265-HTML-r1/image_m/fpsyg-17-1578265-g002.jpg Figure 2. Outcomes of approaching ED cluster analysis using variables resulted in unsupervised varia https://www.frontiersin.org/files/Articles/1578265/fpsyg-17-1578265-HTML-r1/image_m/fpsyg-17-1578265-g003.jpg Figure 3. Outcomes of avoiding ED cluster analysis using variables resulted in unsupervised variable https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1789367/full https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-g001.jpg Figure 1. Conceptual framework of land use change pathways to agrifood outcomes. https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-g002.jpg Figure 2. Map showing the study site with respect to Africa, Central Malawi, and study extension pla https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t001.jpg Table 1. Accuracy of the classified images. https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t002.jpg Table 2. Long-run land cover structural change in Central Malawi (2000–2022, % of total area). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-g003.jpg Figure 3. Long-run land cover trajectories in Central Malawi (2004–2024). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t003.jpg Table 3. Land use and land cover distribution in Mpingu EPA (2004–2024). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-g004.jpg Figure 4. Mpingu EPA LULC maps (2004, 2014, 2024). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t004.jpg Table 4. LULC change in Lilongwe District (2004–2024, km2). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-g005.jpg Figure 5. Land use and land cover classification maps of Lilongwe District (2004, 2014, 2024). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t005.jpg Table 5. Malingunde EPA LULC change (2004–2024, km2). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-g006.jpg Figure 6. Malingunde EPA LULC maps (2004, 2014, 2024). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t006.jpg Table 6. Household demographics and farm structure (n = 279). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t007.jpg Table 7. Integrated trends in maize productivity, soil fertility management, livestock systems, and https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t008.jpg Table 8. Regression-based evidence on the effects of land use change on maize yield, livestock holdi https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t009.jpg Table 9. Logistic regression of food insecurity determinants. https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t010.jpg Table 10. Household cumulative agrifood stress index (CASI) components. https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t011.jpg Table 11. Vulnerability regime clusters (K-means). https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-t012.jpg Table 12. Regime-specific agrifood outcomes. https://www.frontiersin.org/files/Articles/1789367/fsufs-10-1789367-HTML/image_m/fsufs-10-1789367-g007.jpg Figure 7. Conceptual model distinguishing underlying drivers (land fragmentation, forest loss, grazi https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1716076/full https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-t001.jpg Table 1. Demographic characteristics of the sample. https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-t002.jpg Table 2. Brain regions with significant differences in DMN/SMN/FPN gradient scores between the AD an https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-g001.jpg Figure 1. Brain regions with significant functional gradient differences in the DMN/VIS. (A) Signifi https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-g002.jpg Figure 2. Brain regions with significant functional gradient differences in the SMN. (A) Significant https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-g003.jpg Figure 3. Radar and ROC curves. (A) Network-level comparisons of the first three gradients between t https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-t003.jpg Table 3. Performance metrics of SVM. https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-g004.jpg Figure 4. Brain feature clustering in State 1, State 2, State 3, and State 4. (A-D) Brain feature cl https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-g005.jpg Figure 5. Dynamic functional connectivity patterns in State 1, State 2, State 3, and State 4. (A-D) https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-t004.jpg Table 4. Dynamic functional connectivity differences in state 2. https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-t005.jpg Table 5. Dynamic functional connectivity differences in state 3. https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-t006.jpg Table 6. Dynamic functional connectivity differences in state 4. https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-g006.jpg Figure 6. Between-group differences in temporal metrics across dynamic functional connectivity state https://www.frontiersin.org/files/Articles/1716076/fnagi-17-1716076-HTML/image_m/fnagi-17-1716076-g007.jpg Figure 7. Correlation between brain regions with functional gradient differences and clinical variab https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1759383/full https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g001.jpg Figure 1. YOLOv8-ADown model architecture. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g002.jpg Figure 2. ADown module schematic. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-t001.jpg Table 1. Comparison of model accuracy and parameters before and after improvement. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g003.jpg Figure 3. Comparison of normalized confusion matrices of the models. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g004.jpg Figure 4. Distribution and attribute analysis of training set data. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g005.jpg Figure 5. Correlation matrix of bounding box attributes. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g006.jpg Figure 6. Comparison of model loss and accuracy curves. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g007.jpg Figure 7. Comparison of model P curves. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g008.jpg Figure 8. Comparison of model R curves. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g009.jpg Figure 9. Comparison of model F1 curves. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g010.jpg Figure 10. Comparison of model PR curves. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g011.jpg Figure 11. Visualization comparison of model recognition results. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-t002.jpg Table 2. Training results of the models before and after improvement on the augmented dataset. https://www.frontiersin.org/files/Articles/1759383/fmed-13-1759383-HTML-r2/image_m/fmed-13-1759383-g012.jpg Figure 12. Comparison of accuracy curves between the improved and original models on the data augmen https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1658140/full https://www.frontiersin.org/files/Articles/1658140/fimmu-16-1658140-HTML/image_m/fimmu-16-1658140-t001.jpg Table 1. Summary of patient characteristics, therapy, and outcome. https://www.frontiersin.org/files/Articles/1658140/fimmu-16-1658140-HTML/image_m/fimmu-16-1658140-g001.jpg Figure 1. (A) Presentation of patients with each genetic defect in Tregopathy. (B) Polytherapy with https://www.frontiersin.org/files/Articles/1658140/fimmu-16-1658140-HTML/image_m/fimmu-16-1658140-t002.jpg Table 2. Summary of published information on Tregopathy. https://www.frontiersin.org/files/Articles/1658140/fimmu-16-1658140-HTML/image_m/fimmu-16-1658140-g002.jpg Figure 2. (A) Response to sirolimus with respect to patients and their autoimmunity. (B) Autoimmune https://www.frontiersin.org/files/Articles/1658140/fimmu-16-1658140-HTML/image_m/fimmu-16-1658140-g003.jpg Figure 3. Algorithmic approach for evaluating patients with immune dysregulation: Identifying possib https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2025.1674307/full https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g001.jpg Figure 1. (A) Knowledge extraction of Pachino’s square (1910) considering our multi-layer architectu https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g002.jpg Figure 2. ESBM: Agents and their neural structures as substrates of processes. https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g003.jpg Figure 3. ESBM: Sensorimotor responses elicited by environmental features. https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g004.jpg Figure 4. ESBM: The error-correction loop: predictions as frames, compared against sensory signals. https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g005.jpg Figure 5. ESBM: Agents and their neural structures as substrates of processes. https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g006.jpg Figure 6. BEL: Environmental experience situations, with affordances, rules, and social/behavioral p https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g007.jpg Figure 7. SIM: Agents’ interaction with their environment through ordered simulation states with soc https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g008.jpg Figure 8. BEACON: Prediction-error loops enact behavioral/social predictions from simulation states https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-t001.jpg Table 1. Detailed bridging across the seven layers by mapping between the high-level, symbolic conce https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-t002.jpg Table 2. Summary of key stratified concepts: the core conceptual pairs enabling the representation o https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g009.jpg Figure 9. A schematic representation of the integration between BEACON and wearable technologies. https://www.frontiersin.org/files/Articles/1674307/fbuil-11-1674307-HTML/image_m/fbuil-11-1674307-g010.jpg Figure 10. A practical application of BEACON through smart glasses technology. The red-highlighted a https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1764327/full https://www.frontiersin.org/files/Articles/1764327/feduc-11-1764327-HTML/image_m/feduc-11-1764327-t001.jpg Table 1. Comparison of New York City early childhood expansion and class size reduction initiatives. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1705607/full https://www.frontiersin.org/files/Articles/1705607/fneur-16-1705607-HTML/image_m/fneur-16-1705607-t001.jpg Table 1. Deaths and ASMRs with ADRD and hyperlipidemia by sex, race, region, and urbanization (1999– https://www.frontiersin.org/files/Articles/1705607/fneur-16-1705607-HTML/image_m/fneur-16-1705607-g001.jpg Figure 1. Sex-specific trends in ASMR due to ADRD and hyperlipidemia in the United States, 1999–2020 https://www.frontiersin.org/files/Articles/1705607/fneur-16-1705607-HTML/image_m/fneur-16-1705607-g002.jpg Figure 2. Race-specific trends in ASMR due to ADRD and hyperlipidemia in the United States, 1999–202 https://www.frontiersin.org/files/Articles/1705607/fneur-16-1705607-HTML/image_m/fneur-16-1705607-g003.jpg Figure 3. Census region trends in ASMR due to ADRD and hyperlipidemia in the United States, 1999–202 https://www.frontiersin.org/files/Articles/1705607/fneur-16-1705607-HTML/image_m/fneur-16-1705607-g004.jpg Figure 4. Urban–rural disparities in ASMR due to ADRD and hyperlipidemia in the United States, 1999– https://www.frontiersin.org/files/Articles/1705607/fneur-16-1705607-HTML/image_m/fneur-16-1705607-g005.jpg Figure 5. Trends in the proportion (A) and crude mortality rate (B) of ADRD-related deaths with hype https://www.frontiersin.org/files/Articles/1705607/fneur-16-1705607-HTML/image_m/fneur-16-1705607-g006.jpg Figure 6. Geographic region–specific trends in ASMR (A) and AAPC (B) due to ADRD and hyperlipidemia https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1783051/full https://www.frontiersin.org/files/Articles/1783051/fimmu-17-1783051-HTML/image_m/fimmu-17-1783051-g001.jpg Figure 1. Clinical course showing ALC, CRP, and absolute BCMA counts. https://www.frontiersin.org/files/Articles/1783051/fimmu-17-1783051-HTML/image_m/fimmu-17-1783051-g002.jpg Figure 2. MRI on post CAR-T day +21. MRI Brain with and without contrast showing T2 hyperintensity ( https://www.frontiersin.org/files/Articles/1783051/fimmu-17-1783051-HTML/image_m/fimmu-17-1783051-g003.jpg Figure 3. CT angiogram brain/neck. (A) Global vascular irregularity with segmental luminal narrowing https://www.frontiersin.org/files/Articles/1783051/fimmu-17-1783051-HTML/image_m/fimmu-17-1783051-g004.jpg Figure 4. Diagnostic cerebral angiogram showing multifocal stenosis in: (A) left middle cerebral art https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1771729/full https://www.frontiersin.org/files/Articles/1771729/fdgth-08-1771729-HTML/image_m/fdgth-08-1771729-g001.jpg Figure 1. Panelyze high-level workflow, architecture and outputs. Displays the 5-step workflow, key https://www.frontiersin.org/files/Articles/1771729/fdgth-08-1771729-HTML/image_m/fdgth-08-1771729-t001.jpg Table 1. Qualitative translation of proposal barriers by synthetic personas. Summarizes technical pr https://www.frontiersin.org/files/Articles/1771729/fdgth-08-1771729-HTML/image_m/fdgth-08-1771729-g002.jpg Figure 2. Panelyze Score radar chart for CAPRIE-2 research proposal. Displays the performance of the https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1816646/full https://www.frontiersin.org/files/Articles/1816646/fmars-13-1816646-HTML/image_m/fmars-13-1816646-t001.jpg Table 1. Comparison of LOEcrit values between T. septentrionalis and other fish. https://www.frontiersin.org/files/Articles/1816646/fmars-13-1816646-HTML/image_m/fmars-13-1816646-g001.jpg Figure 1. The domain architecture of TsHIF-αs. bHLH, basic helix-loop-helix; PAS, Per-Arnt-Sim motif https://www.frontiersin.org/files/Articles/1816646/fmars-13-1816646-HTML/image_m/fmars-13-1816646-g002.jpg Figure 2. Multiple sequence alignment of HIF-αs (A) HIF-1α; (B) HIF-2α; (C) HIF-3α; (D) TsHIF-αs. Th https://www.frontiersin.org/files/Articles/1816646/fmars-13-1816646-HTML/image_m/fmars-13-1816646-g003.jpg Figure 3. Phylogenetic tree comparing TsHIF-αs with other vertebrates and invertebrates. The neighbo https://www.frontiersin.org/files/Articles/1816646/fmars-13-1816646-HTML/image_m/fmars-13-1816646-g004.jpg Figure 4. Tertiary structure alignment of HIF-αs from T. septentrionalis, H. sapiens and N. ayraudi. https://www.frontiersin.org/files/Articles/1816646/fmars-13-1816646-HTML/image_m/fmars-13-1816646-g005.jpg Figure 5. The expression of hif-1α (A), hif-2α (B), and hif-3α (C) in different tissues of T. septen https://www.frontiersin.org/files/Articles/1816646/fmars-13-1816646-HTML/image_m/fmars-13-1816646-g006.jpg Figure 6. The effects of acute hypoxia on the hypoxia signaling pathway. The expression of hif-1α (A https://www.frontiersin.org/files/Articles/1816646/fmars-13-1816646-HTML/image_m/fmars-13-1816646-g007.jpg Figure 7. The effects of TsHIF-3α on the VEGFa promoter activity. (A) The promoter activity of VEGFa https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1607957/full https://www.frontiersin.org/files/Articles/1607957/fonc-15-1607957-HTML-r1/image_m/fonc-15-1607957-g001.jpg Figure 1. (A) 02–29 parathyroid ultrasound (B) 05-18 parathyroid ultrasound (C) 05–18 CT of the neck https://www.frontiersin.org/files/Articles/1607957/fonc-15-1607957-HTML-r1/image_m/fonc-15-1607957-g002.jpg Figure 2. (A) 05–07 the brain MRI (B) 06–03 the brain MRI (C) 05–14 cerebrospinal fluid biopsy. https://www.frontiersin.org/files/Articles/1607957/fonc-15-1607957-HTML-r1/image_m/fonc-15-1607957-t001.jpg Table 1. Treatment timeline chart. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1727768/full https://www.frontiersin.org/files/Articles/1727768/fmed-13-1727768-HTML-r1/image_m/fmed-13-1727768-t001.jpg Table 1. Baseline characteristics of patients with chemotherapy and non-chemotherapy. https://www.frontiersin.org/files/Articles/1727768/fmed-13-1727768-HTML-r1/image_m/fmed-13-1727768-t002.jpg Table 2. Univariate Cox proportional hazard model of disease-free survival (DFS) and overall surviva https://www.frontiersin.org/files/Articles/1727768/fmed-13-1727768-HTML-r1/image_m/fmed-13-1727768-t003.jpg Table 3. Multivariate Cox proportional hazard model of disease-free survival (DFS) and overall survi https://www.frontiersin.org/files/Articles/1727768/fmed-13-1727768-HTML-r1/image_m/fmed-13-1727768-t004.jpg Table 4. Baseline characteristics of patients with chemotherapy and no-chemotherapy in PSM group. https://www.frontiersin.org/files/Articles/1727768/fmed-13-1727768-HTML-r1/image_m/fmed-13-1727768-t005.jpg Table 5. Cox proportional hazard model of disease-free survival (DFS) in PSM group. https://www.frontiersin.org/files/Articles/1727768/fmed-13-1727768-HTML-r1/image_m/fmed-13-1727768-t006.jpg Table 6. Cox proportional hazard model of overall survival (OS) in PSM group. https://www.frontiersin.org/files/Articles/1727768/fmed-13-1727768-HTML-r1/image_m/fmed-13-1727768-g001.jpg Figure 1. Cox proportional hazards model for disease-free survival (DFS) in the PSM subgroup. https://www.frontiersin.org/files/Articles/1727768/fmed-13-1727768-HTML-r1/image_m/fmed-13-1727768-g002.jpg Figure 2. Kaplan–Meier curves and log-rank test results for disease-free survival in patients with ( https://www.frontiersin.org/files/Articles/1727768/fmed-13-1727768-HTML-r1/image_m/fmed-13-1727768-g003.jpg Figure 3. Kaplan–Meier survival curves for disease-free survival and overall survival, stratified by https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1750734/full https://www.frontiersin.org/files/Articles/1750734/fpsyg-17-1750734-HTML-r1/image_m/fpsyg-17-1750734-t001.jpg Table 1. Phases, specific objectives, and activities of the SLP 1. https://www.frontiersin.org/files/Articles/1750734/fpsyg-17-1750734-HTML-r1/image_m/fpsyg-17-1750734-t002.jpg Table 2. Psychosocial risk assessment using the TOCA-RR. https://www.frontiersin.org/files/Articles/1750734/fpsyg-17-1750734-HTML-r1/image_m/fpsyg-17-1750734-t003.jpg Table 3. Students with and without risk after the preventive workshop. https://www.frontiersin.org/files/Articles/1750734/fpsyg-17-1750734-HTML-r1/image_m/fpsyg-17-1750734-g001.jpg Figure 1. Effectiveness of preventive workshop. https://www.frontiersin.org/files/Articles/1750734/fpsyg-17-1750734-HTML-r1/image_m/fpsyg-17-1750734-t004.jpg Table 4. 2×2 contingency table in the McNemar test. https://www.frontiersin.org/files/Articles/1750734/fpsyg-17-1750734-HTML-r1/image_m/fpsyg-17-1750734-t005.jpg Table 5. Hypothesis testing summary. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1706012/full https://www.frontiersin.org/files/Articles/1706012/fimmu-17-1706012-HTML/image_m/fimmu-17-1706012-g001.jpg Figure 1. Neuroimaging findings of our case: In the first episode, MRI revealed a stroke-like lesion https://www.frontiersin.org/files/Articles/1706012/fimmu-17-1706012-HTML/image_m/fimmu-17-1706012-g002.jpg Figure 2. Immunofluorescence of anti-Caspr2 antibodies in the patient’s serum. https://www.frontiersin.org/files/Articles/1706012/fimmu-17-1706012-HTML/image_m/fimmu-17-1706012-g003.jpg Figure 3. The genetic analysis of the patient and his parents. A pathogenic m.3243A>G mutation in th https://www.frontiersin.org/files/Articles/1706012/fimmu-17-1706012-HTML/image_m/fimmu-17-1706012-t001.jpg Table 1. The clinical data of MELAS patients with cerebral artery vasodilation. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1681306/full https://www.frontiersin.org/files/Articles/1681306/fimmu-17-1681306-HTML/image_m/fimmu-17-1681306-g001.jpg Figure 1. Identification of 17 hub depression-related genes. (A) GO analysis revealed the gene ontol https://www.frontiersin.org/files/Articles/1681306/fimmu-17-1681306-HTML/image_m/fimmu-17-1681306-g002.jpg Figure 2. Classification of LUAD patients into two distinct clusters. (A) The consensus distribution https://www.frontiersin.org/files/Articles/1681306/fimmu-17-1681306-HTML/image_m/fimmu-17-1681306-g003.jpg Figure 3. Machine learning-based construction of depression-related signature. (A) C-index values we https://www.frontiersin.org/files/Articles/1681306/fimmu-17-1681306-HTML/image_m/fimmu-17-1681306-g004.jpg Figure 4. DRS acts as a robust prognostic indicator. (A–C) Heatmaps exhibit the expression profiles https://www.frontiersin.org/files/Articles/1681306/fimmu-17-1681306-HTML/image_m/fimmu-17-1681306-g005.jpg Figure 5. Integrated nomogram is developed for prognosis. (A) Univariate Cox regression was performe https://www.frontiersin.org/files/Articles/1681306/fimmu-17-1681306-HTML/image_m/fimmu-17-1681306-g006.jpg Figure 6. Unveil the involvement of DRS in tumor immune microenvironment (TME). (A) Patients in the https://www.frontiersin.org/files/Articles/1681306/fimmu-17-1681306-HTML/image_m/fimmu-17-1681306-g007.jpg Figure 7. DRS influences therapeutic responses and acts as a risk indicator in cellular level. (A–D) https://www.frontiersin.org/files/Articles/1681306/fimmu-17-1681306-HTML/image_m/fimmu-17-1681306-g008.jpg Figure 8. PSEN1 induced by depression promotes LUAD proliferation. (A) Process of the animal experim https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1807486/full https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-g001.jpg Figure 1. Graphical depiction of the experimental design of the study. For details, please see Parag https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-t001.jpg Table 1. Exposure times to cryoprotectant solutions in different protocols [standard (1 ×) vs. incre https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-g002.jpg Figure 2. Representative pictures of three-dimensional (3D) vitrification and in vitro maturation (I https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-g003.jpg Figure 3. Representative fluorescence micrographs of actin distribution patterns observed in this st https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-t002.jpg Table 2. Cumulus expansion, nuclear status and viability of fresh domestic cat oocytes following in https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-t003.jpg Table 3. Concentration of ethylene glycol (EG) and dimethyl sulfoxide (DMSO) in bovine cumulus-oocyt https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-t004.jpg Table 4. Viability and nuclear status of vitrified [standard protocol or three-dimensional (3D) vitr https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-t005.jpg Table 5. Actin distribution in vitrified [standard protocol or three-dimensional (3D) vitrification] https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-t006.jpg Table 6. Fertilization, embryonic development and degeneration rates of vitrified [standard protocol https://www.frontiersin.org/files/Articles/1807486/fvets-13-1807486-HTML/image_m/fvets-13-1807486-t007.jpg Table 7. Morphological quality classification of embryos obtained in vitro from vitrified [standard https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1633687/full https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g001.jpg Figure 1. Succession characteristics of soil physiochemical properties and enzyme activities of recl https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g002.jpg Figure 2. Variations in α diversity index of soil (a,b) bacterial and (c,d) fungal communities under https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g003.jpg Figure 3. Variations in the relative abundance of soil (a) bacterial and (b) fungal communities comp https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g004.jpg Figure 4. PCoA of (a) bacterial and (b) fungal communities under different reclamation durations and https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g005.jpg Figure 5. LEfSe analysis of soil (a) bacterial and (b) fungal communities under different reclamatio https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g006.jpg Figure 6. Co-occurrence networks of soil (a–e) bacterial and (f–j) fungal communities under differen https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g007.jpg Figure 7. Functional changes in key soil microbial communities predicted using (a) FAPROTAX and (b) https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g008.jpg Figure 8. Changes in (a,b) βNTI and (c,d) RCbray of soil bacteria and fungi under different reclamat https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g009.jpg Figure 9. Relative importance of ecological processes in soil (a) bacterial and (b) fungal community https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g010.jpg Figure 10. The Mantel tests were conducted to analyze the βNTI of bacterial and fungal communities b https://www.frontiersin.org/files/Articles/1633687/fmicb-16-1633687-HTML/image_m/fmicb-16-1633687-g011.jpg Figure 11. The PLS-PM (Partial least squares path modeling) was used to analyze the relationships am https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1758978/full https://www.frontiersin.org/files/Articles/1758978/fmicb-17-1758978-HTML/image_m/fmicb-17-1758978-g001.jpg Figure 1. Gujiao Tunlan mining area reclamation area (a), shows the interface between coal gangue an https://www.frontiersin.org/files/Articles/1758978/fmicb-17-1758978-HTML/image_m/fmicb-17-1758978-t001.jpg Table 1. Experimental treatments. https://www.frontiersin.org/files/Articles/1758978/fmicb-17-1758978-HTML/image_m/fmicb-17-1758978-g002.jpg Figure 2. Changes in the number of indigenous fungi (J-Z) under different treatments. The C treatmen https://www.frontiersin.org/files/Articles/1758978/fmicb-17-1758978-HTML/image_m/fmicb-17-1758978-g003.jpg Figure 3. pH changes after adding indigenous fungi solution under different treatments (a), Changes https://www.frontiersin.org/files/Articles/1758978/fmicb-17-1758978-HTML/image_m/fmicb-17-1758978-g004.jpg Figure 4. Changes in TOC after adding indigenous fungi solution, representing the C treatment of pur https://www.frontiersin.org/files/Articles/1758978/fmicb-17-1758978-HTML/image_m/fmicb-17-1758978-g005.jpg Figure 5. Changes in AP after adding indigenous fungi solution, represents the C treatment of pure c https://www.frontiersin.org/files/Articles/1758978/fmicb-17-1758978-HTML/image_m/fmicb-17-1758978-g006.jpg Figure 6. XRD spectra at different time intervals under M4 treatment (a), Normalized comparison char https://www.frontiersin.org/files/Articles/1758978/fmicb-17-1758978-HTML/image_m/fmicb-17-1758978-t002.jpg Table 2. Test results of organic acids in the indigenous fungus. https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2025.1681949/full https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-g001.jpg Figure 1. Summary of the research methodology steps. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-g002.jpg Figure 2. Publication trends by research focus (2015–2024). https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-g003.jpg Figure 3. Leading journals for fashion rental research. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-g004.jpg Figure 4. Geographical distribution of fashion rental research publications (2015–2024). https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-t001.jpg Table 1. Summarizes the theoretical frameworks. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-t002.jpg Table 2. Summary of research methods. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-g005.jpg Figure 5. Summary of research methods. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-t003.jpg Table 3. Summary of stimuli in fashion rental adoption. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-t004.jpg Table 4. Summary of Organism factors. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-t005.jpg Table 5. Summary of Response Factors. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-t006.jpg Table 6. Summary of moderating factors. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-t007.jpg Table 7. Comparison of stimuli across rental models (B2C vs. P2P). https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-g006.jpg Figure 6. The framework for fashion rental adoption. https://www.frontiersin.org/files/Articles/1681949/frsus-06-1681949-HTML/image_m/frsus-06-1681949-t008.jpg Table 8. Managerial levers derived from the S–O–R framework. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1700029/full https://www.frontiersin.org/files/Articles/1700029/fvets-12-1700029-HTML/image_m/fvets-12-1700029-t001.jpg Table 1. Subthemes and exemplar quotes for Theme 1. https://www.frontiersin.org/files/Articles/1700029/fvets-12-1700029-HTML/image_m/fvets-12-1700029-t002.jpg Table 2. Subthemes and exemplar quotes for Theme 2. https://www.frontiersin.org/files/Articles/1700029/fvets-12-1700029-HTML/image_m/fvets-12-1700029-t003.jpg Table 3. Subthemes and exemplar quotes for Theme 3. https://www.frontiersin.org/files/Articles/1700029/fvets-12-1700029-HTML/image_m/fvets-12-1700029-g001.jpg Figure 1. Formulated leadership skills and behavior values framework. Adapted from: Whetten and Came https://www.frontiersin.org/files/Articles/1700029/fvets-12-1700029-HTML/image_m/fvets-12-1700029-g002.jpg Figure 2. EALD experiential learning model. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1612739/full https://www.frontiersin.org/files/Articles/1612739/fendo-16-1612739-HTML/image_m/fendo-16-1612739-t001.jpg Table 1. PICOS criteria in this study. https://www.frontiersin.org/files/Articles/1612739/fendo-16-1612739-HTML/image_m/fendo-16-1612739-g001.jpg Figure 1. Flowchart of study identification and screening. https://www.frontiersin.org/files/Articles/1612739/fendo-16-1612739-HTML/image_m/fendo-16-1612739-t002.jpg Table 2. Basic characteristics of included studies. https://www.frontiersin.org/files/Articles/1612739/fendo-16-1612739-HTML/image_m/fendo-16-1612739-g002.jpg Figure 2. Results of quality assessment for (A) all the studies and (B) each study. https://www.frontiersin.org/files/Articles/1612739/fendo-16-1612739-HTML/image_m/fendo-16-1612739-g003.jpg Figure 3. Forest plot of comparing (A) total hip bone mineral density, (B) lumbar spine bone mineral https://www.frontiersin.org/files/Articles/1612739/fendo-16-1612739-HTML/image_m/fendo-16-1612739-t003.jpg Table 3. Level of evidence examining the effects of different non-pharmacological interventions. https://www.frontiersin.org/files/Articles/1612739/fendo-16-1612739-HTML/image_m/fendo-16-1612739-g004.jpg Figure 4. Summary of effect of non-pharmacological interventions on bone health among patients with https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1647882/full https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-t001.jpg Table 1. Baseline patient characteristics and x-ray image acquisition conditions. https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-g001.jpg Figure 1. Schematic diagram of the model design and laboratory workflow design processes. https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-g002.jpg Figure 2. Diagram of model construction for different tasks. (A) Part 1 of the model for lumbar segm https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-t002.jpg Table 2. Dataset partitioning for development and testing of AAC scoring regression model. https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-t003.jpg Table 3. Model performance metrics for accuracy and correlation. https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-g003.jpg Figure 3. Correlations between model predictions and manual standard scoring. (A) Internal validatio https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-t004.jpg Table 4. Comparison of ICC and Kappa values between manual standard scores (AAC1) vs. alternative ma https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-t005.jpg Table 5. Model performance metrics across categories of AAC severity. https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-g004.jpg Figure 4. Confusion matrices by AAC severity category (Pred: model prediction, label: manual standar https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-g005.jpg Figure 5. Visualization of neural network activation maps from the developed automated AAC scoring s https://www.frontiersin.org/files/Articles/1647882/fcvm-12-1647882-HTML/image_m/fcvm-12-1647882-t006.jpg Table 6. MACEs during follow-up according to the need for dialysis. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1620131/full https://www.frontiersin.org/files/Articles/1620131/fpsyt-16-1620131-HTML-r1/image_m/fpsyt-16-1620131-g001.jpg Figure 1. Procedure. SCZ, schizophrenia; PSM, propensity score matching. https://www.frontiersin.org/files/Articles/1620131/fpsyt-16-1620131-HTML-r1/image_m/fpsyt-16-1620131-g002.jpg Figure 2. Multivariate analysis forest plot. This forest plot shows multivariate-adjusted odds ratio https://www.frontiersin.org/files/Articles/1620131/fpsyt-16-1620131-HTML-r1/image_m/fpsyt-16-1620131-g003.jpg Figure 3. Subgroup multivariate analysis forest plot. Forest plots show multivariate-adjusted odds r https://www.frontiersin.org/files/Articles/1620131/fpsyt-16-1620131-HTML-r1/image_m/fpsyt-16-1620131-g004.jpg Figure 4. Overall time-trend analysis. Violin plots show longitudinal changes in the distribution of https://www.frontiersin.org/files/Articles/1620131/fpsyt-16-1620131-HTML-r1/image_m/fpsyt-16-1620131-g005.jpg Figure 5. Subgroup time-trend analysis. Violin plots illustrate longitudinal changes in biomarker le https://www.frontiersin.org/files/Articles/1620131/fpsyt-16-1620131-HTML-r1/image_m/fpsyt-16-1620131-t001.jpg Table 1. Model performance. https://www.frontiersin.org/files/Articles/1620131/fpsyt-16-1620131-HTML-r1/image_m/fpsyt-16-1620131-g006.jpg Figure 6. Clustered SHAP values heatmap and the confusion matrix. (a) Clustered SHAP (Shapley Additi https://www.frontiersin.org/files/Articles/1620131/fpsyt-16-1620131-HTML-r1/image_m/fpsyt-16-1620131-g007.jpg Figure 7. Added benefits of biomarker measurements and the receiver operating characteristic curves https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1787160/full https://www.frontiersin.org/files/Articles/1787160/fimmu-17-1787160-HTML/image_m/fimmu-17-1787160-g000.jpg Graphical Abstract. Schematic representation of study design and integrated metabolomic and cytokine https://www.frontiersin.org/files/Articles/1787160/fimmu-17-1787160-HTML/image_m/fimmu-17-1787160-t001.jpg Table 1. Demographic and clinical characteristics of the study population according to study groups. https://www.frontiersin.org/files/Articles/1787160/fimmu-17-1787160-HTML/image_m/fimmu-17-1787160-t002.jpg Table 2. Laboratory features according to each study group. https://www.frontiersin.org/files/Articles/1787160/fimmu-17-1787160-HTML/image_m/fimmu-17-1787160-g001.jpg Figure 1. Box plots of discriminatory metabolites and cytokines across study groups. Box plots illus https://www.frontiersin.org/files/Articles/1787160/fimmu-17-1787160-HTML/image_m/fimmu-17-1787160-t003.jpg Table 3. Multinomial logistic regression results for discriminative biomarkers among renal activity https://www.frontiersin.org/files/Articles/1787160/fimmu-17-1787160-HTML/image_m/fimmu-17-1787160-g002.jpg Figure 2. Modeled group probabilities based on urinary metabolites, serum glycerol, and urinary cyto https://www.frontiersin.org/files/Articles/1787160/fimmu-17-1787160-HTML/image_m/fimmu-17-1787160-g003.jpg Figure 3. Correlation matrix between serum/urinary metabolites and urinary cytokines. Spearman’s cor https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1711633/full https://www.frontiersin.org/files/Articles/1711633/fcvm-13-1711633-HTML/image_m/fcvm-13-1711633-t001.jpg Table 1. Baseline characteristics of study participants stratified by renal function. https://www.frontiersin.org/files/Articles/1711633/fcvm-13-1711633-HTML/image_m/fcvm-13-1711633-g001.jpg Figure 1. Correlation matrix heatmap. Red and blue colors indicate positive and negative correlation https://www.frontiersin.org/files/Articles/1711633/fcvm-13-1711633-HTML/image_m/fcvm-13-1711633-g002.jpg Figure 2. The heatmap illustrates the correlations among the TyG index, IMT, and ACR. Color—coding r https://www.frontiersin.org/files/Articles/1711633/fcvm-13-1711633-HTML/image_m/fcvm-13-1711633-t002.jpg Table 2. Association between TyG index and carotid IMT stratified by ACR group. https://www.frontiersin.org/files/Articles/1711633/fcvm-13-1711633-HTML/image_m/fcvm-13-1711633-g003.jpg Figure 3. Boxplots depict the distributions of IMT and TyG index by ACR group. Regarding IMT, the “A https://www.frontiersin.org/files/Articles/1711633/fcvm-13-1711633-HTML/image_m/fcvm-13-1711633-t003.jpg Table 3. Comprehensive multivariable regression analysis of factors associated with carotid IMT. https://www.frontiersin.org/files/Articles/1711633/fcvm-13-1711633-HTML/image_m/fcvm-13-1711633-g004.jpg Figure 4. Exploratory analysis of the TyG index for carotid plaque detection. The ROC curve illustra https://www.frontiersin.org/files/Articles/1711633/fcvm-13-1711633-HTML/image_m/fcvm-13-1711633-t004.jpg Table 4. Adjusted effects of TyG index on carotid IMT: stratified by age groups. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1782658/full https://www.frontiersin.org/files/Articles/1782658/fphar-17-1782658-HTML/image_m/fphar-17-1782658-t001.jpg Table 1. Baseline characteristics of study population. https://www.frontiersin.org/files/Articles/1782658/fphar-17-1782658-HTML/image_m/fphar-17-1782658-g001.jpg Figure 1. Comparative analysis of key clinical parameters before and after finerenone administration https://www.frontiersin.org/files/Articles/1782658/fphar-17-1782658-HTML/image_m/fphar-17-1782658-t002.jpg Table 2. Paired t-test Results (Baseline vs. Post-treatment). https://www.frontiersin.org/files/Articles/1782658/fphar-17-1782658-HTML/image_m/fphar-17-1782658-t003.jpg Table 3. Linear mixed model analysis of key outcomes before and after treatment. https://www.frontiersin.org/files/Articles/1782658/fphar-17-1782658-HTML/image_m/fphar-17-1782658-g002.jpg Figure 2. Pearson correlation coefficients between UA reduction and clinical variables. Note:This fi https://www.frontiersin.org/files/Articles/1782658/fphar-17-1782658-HTML/image_m/fphar-17-1782658-g003.jpg Figure 3. Lasso regression for predictor screening. Note:Ten-fold cross-validation is employed for L https://www.frontiersin.org/files/Articles/1782658/fphar-17-1782658-HTML/image_m/fphar-17-1782658-t004.jpg Table 4. LASSO regression coefficients for UA reduction predictors (S1). https://www.frontiersin.org/files/Articles/1782658/fphar-17-1782658-HTML/image_m/fphar-17-1782658-t005.jpg Table 5. Multivariable linear regression analysis for UA reduction predictors. https://www.frontiersin.org/files/Articles/1782658/fphar-17-1782658-HTML/image_m/fphar-17-1782658-g004.jpg Figure 4. Multivariable linear regression results for UA reduction predictors. Note:This figure pres https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1782916/full https://www.frontiersin.org/files/Articles/1782916/fmed-13-1782916-HTML/image_m/fmed-13-1782916-g001.jpg Figure 1. Flow diagram of the study population selection process. ELSA, English Longitudinal Study o https://www.frontiersin.org/files/Articles/1782916/fmed-13-1782916-HTML/image_m/fmed-13-1782916-t001.jpg Table 1. Baseline characteristics of participants for baseline frailty status analyses. https://www.frontiersin.org/files/Articles/1782916/fmed-13-1782916-HTML/image_m/fmed-13-1782916-t002.jpg Table 2. Baseline characteristics of participants for transitions in frailty status analyses. https://www.frontiersin.org/files/Articles/1782916/fmed-13-1782916-HTML/image_m/fmed-13-1782916-t003.jpg Table 3. Number and proportion of the transitions in frailty status. https://www.frontiersin.org/files/Articles/1782916/fmed-13-1782916-HTML/image_m/fmed-13-1782916-t004.jpg Table 4. Association of transitions in frailty status with risks of dementia. https://www.frontiersin.org/files/Articles/1782916/fmed-13-1782916-HTML/image_m/fmed-13-1782916-t005.jpg Table 5. Association of total frailty index score with risks of dementia. https://www.frontiersin.org/files/Articles/1782916/fmed-13-1782916-HTML/image_m/fmed-13-1782916-t006.jpg Table 6. Association of change in frailty index score with risks of dementia. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1667228/full https://www.frontiersin.org/files/Articles/1667228/fcell-13-1667228-HTML/image_m/fcell-13-1667228-g001.jpg Figure 1. Microglial activation following ICH primarily occurs through three main mechanisms: (1) He https://www.frontiersin.org/files/Articles/1667228/fcell-13-1667228-HTML/image_m/fcell-13-1667228-t001.jpg Table 1. Classification, functions, and characteristics of MMPs. https://www.frontiersin.org/files/Articles/1667228/fcell-13-1667228-HTML/image_m/fcell-13-1667228-g002.jpg Figure 2. The regulation of MMPs in the human body. https://www.frontiersin.org/files/Articles/1667228/fcell-13-1667228-HTML/image_m/fcell-13-1667228-t002.jpg Table 2. Expression changes of MMPs in patients with ICH. https://www.frontiersin.org/files/Articles/1667228/fcell-13-1667228-HTML/image_m/fcell-13-1667228-g003.jpg Figure 3. MMPs exert harmful effects following ICH primarily by promoting inflammatory responses, ex https://www.frontiersin.org/files/Articles/1667228/fcell-13-1667228-HTML/image_m/fcell-13-1667228-g004.jpg Figure 4. In the chronic stage of ICH, MMPs (mainly MMP-7 and MMP-9) promote the increase of VEGF an https://www.frontiersin.org/files/Articles/1667228/fcell-13-1667228-HTML/image_m/fcell-13-1667228-t003.jpg Table 3. The inhibitors of MMPs. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1741257/full https://www.frontiersin.org/files/Articles/1741257/fnins-20-1741257-HTML/image_m/fnins-20-1741257-g001.jpg Figure 1. Study design and analytic workflow. (A) Data acquisition and preprocessing. The cohort inc https://www.frontiersin.org/files/Articles/1741257/fnins-20-1741257-HTML/image_m/fnins-20-1741257-t001.jpg Table 1. Between-group differences for demographic and clinical characteristics, as well as magnetic https://www.frontiersin.org/files/Articles/1741257/fnins-20-1741257-HTML/image_m/fnins-20-1741257-g002.jpg Figure 2. Group differences in MRI-derived GS indexes between TLE-HS and HCs. (A) Total CPV/TIV was https://www.frontiersin.org/files/Articles/1741257/fnins-20-1741257-HTML/image_m/fnins-20-1741257-g003.jpg Figure 3. Associations between HPV/TIV and GS indexes in TLE-HS. (A) Scatterplot of DTI-ALPS versus https://www.frontiersin.org/files/Articles/1741257/fnins-20-1741257-HTML/image_m/fnins-20-1741257-g004.jpg Figure 4. Associations between disease duration and MRI-derived GS indexes in TLE-HS. (A) Longer dis https://www.frontiersin.org/files/Articles/1741257/fnins-20-1741257-HTML/image_m/fnins-20-1741257-g005.jpg Figure 5. Correlation matrix of MRI-derived glymphatic-related indices in TLE-HS. Upper-triangular h https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1669239/full https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-g001.jpg Figure 1. Flowchart. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-g002.jpg Figure 2. Distribution map of the 8 major syndrome types of CHD. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-t001.jpg Table 1. Baseline data analysis of patients with different TCM syndrome types of CHD. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-t002.jpg Table 2. Analysis of differences in myocardial injury markers among patients with different TCM synd https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-t003.jpg Table 3. Coefficients of discriminant analysis classification function. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-t004.jpg Table 4. Dialectical results of discriminant analysis. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-g003.jpg Figure 3. Overall classification diagram of canonical functions. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-t005.jpg Table 5. Analysis table of five model validation sets (baseline + TCM symptoms + myocardial injury m https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-g004.jpg Figure 4. Feature screening of boruta. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-g005.jpg Figure 5. Learning curves of the training set and validation set of the LGBM model. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-g006.jpg Figure 6. Heat map of the confusion matrix of the LGBM model training set. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-g007.jpg Figure 7. Heat map of the confusion matrix of the validation set for the LGBM model. https://www.frontiersin.org/files/Articles/1669239/fcvm-12-1669239-HTML/image_m/fcvm-12-1669239-g008.jpg Figure 8. Summary of the contribution of SHAP values in the LGBM model. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1676071/full https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g001.jpg Figure 1. Identification of Main Components (Positive Ions) in Bu Gan Jian Ji Fang. https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g002.jpg Figure 2. Identification of Main Components (Positive Ions) in Bu Gan Jian Ji Fang. https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g003.jpg Figure 3. Network pharmacology analysis of Bugan Jianxi Formula (A) Venn diagram of targets for BGJX https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g004.jpg Figure 4. Network and enrichment analyses of BGJXF targets in KOA pathology. (A) Target-Pathway Inte https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-t001.jpg Table 1. Binding energy of the molecular docking between key compounds and core targets (kJ·mol−1). https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g005.jpg Figure 5. Molecular docking pattern of active components to core targets. (A) IL-6- Dehydrocorydalin https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g006.jpg Figure 6. Dehydrocorydaline attenuates LPS-induced upregulation of p65 and TNF-α in human chondrocyt https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g007.jpg Figure 7. Effects of BGJXF on plasma IL-1β and IL-6 levels in the rat KOA model. (A) Plasma IL-1β le https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g008.jpg Figure 8. BGJXF ameliorates cartilage degeneration in a rat KOA model as assessed by H&E staining an https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g009.jpg Figure 9. BGJXF reduces IL-6 and p65 expression and co-localization in KOA cartilage. (A–C) Represen https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-t002.jpg Table 2. Quantitative analysis of IL-6 and NF-κB p65 immunofluorescence in rat cartilage from differ https://www.frontiersin.org/files/Articles/1676071/fmed-12-1676071-HTML/image_m/fmed-12-1676071-g010.jpg Figure 10. Schematic illustration of the multi-target mechanisms by which BGJXF and dehydrocorydalin https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1765643/full https://www.frontiersin.org/files/Articles/1765643/fphys-17-1765643-HTML-r1/image_m/fphys-17-1765643-g001.jpg Figure 1. Participants allocation. PLYOgen: Upper-Limb Plyometric Training; PLYObad: Technical Plyom https://www.frontiersin.org/files/Articles/1765643/fphys-17-1765643-HTML-r1/image_m/fphys-17-1765643-t001.jpg Table 1. Characteristics of participants. https://www.frontiersin.org/files/Articles/1765643/fphys-17-1765643-HTML-r1/image_m/fphys-17-1765643-t002.jpg Table 2. Description of the training interventions. https://www.frontiersin.org/files/Articles/1765643/fphys-17-1765643-HTML-r1/image_m/fphys-17-1765643-t003.jpg Table 3. Descriptive statistics and between-group comparisons for each time point. https://www.frontiersin.org/files/Articles/1765643/fphys-17-1765643-HTML-r1/image_m/fphys-17-1765643-g002.jpg Figure 2. Pre–post changes in Overhead Medicine Ball Throw performance (m) for PLYOgen, PLYObad, and https://www.frontiersin.org/files/Articles/1765643/fphys-17-1765643-HTML-r1/image_m/fphys-17-1765643-g003.jpg Figure 3. Pre–post changes in Seated Medicine Ball Chest Pass performance (m) for PLYOgen, PLYObad, https://www.frontiersin.org/files/Articles/1765643/fphys-17-1765643-HTML-r1/image_m/fphys-17-1765643-g004.jpg Figure 4. Pre–post changes in Push-Up Flight Height (cm) for PLYOgen, PLYObad, and Control groups. P https://www.frontiersin.org/files/Articles/1765643/fphys-17-1765643-HTML-r1/image_m/fphys-17-1765643-g005.jpg Figure 5. Pre–post changes in Badminton Smash Speed (km/h) for PLYOgen, PLYObad, and Control groups. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1728665/full https://www.frontiersin.org/files/Articles/1728665/fmed-13-1728665-HTML/image_m/fmed-13-1728665-t001.jpg Table 1. Socio—demographic characteristics of elderly patients with T2DM (N = 332). https://www.frontiersin.org/files/Articles/1728665/fmed-13-1728665-HTML/image_m/fmed-13-1728665-t002.jpg Table 2. The Prevalence and burden of elderly patients with T2DM related symptoms (N = 332). https://www.frontiersin.org/files/Articles/1728665/fmed-13-1728665-HTML/image_m/fmed-13-1728665-t003.jpg Table 3. The sentinel symptoms of each symptom cluster based on the Apriori algorithm. https://www.frontiersin.org/files/Articles/1728665/fmed-13-1728665-HTML/image_m/fmed-13-1728665-g001.jpg Figure 1. Symptom network of elderly patients with T2DM. For detailed explanations of symptom codes, https://www.frontiersin.org/files/Articles/1728665/fmed-13-1728665-HTML/image_m/fmed-13-1728665-g002.jpg Figure 2. Symptom network node centrality index. Idem. https://www.frontiersin.org/files/Articles/1728665/fmed-13-1728665-HTML/image_m/fmed-13-1728665-g003.jpg Figure 3. Stability analysis of the symptom network. https://www.frontiersin.org/files/Articles/1728665/fmed-13-1728665-HTML/image_m/fmed-13-1728665-g004.jpg Figure 4. Self-estimation analysis of network edge weights of symptoms. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1602921/full https://www.frontiersin.org/files/Articles/1602921/fcimb-15-1602921-HTML/image_m/fcimb-15-1602921-g001.jpg Figure 1. Flowchart of patient inclusion in this study. https://www.frontiersin.org/files/Articles/1602921/fcimb-15-1602921-HTML/image_m/fcimb-15-1602921-t001.jpg Table 1. Baseline characteristics and outcomes of participants stratified by CAR quartiles. https://www.frontiersin.org/files/Articles/1602921/fcimb-15-1602921-HTML/image_m/fcimb-15-1602921-g002.jpg Figure 2. Kaplan-Meier survival curves for 30-day mortality stratified by CAR quartiles. Panel (A) s https://www.frontiersin.org/files/Articles/1602921/fcimb-15-1602921-HTML/image_m/fcimb-15-1602921-t002.jpg Table 2. The association between the CAR groups and 30d-hospital and 30d-ICU mortality. https://www.frontiersin.org/files/Articles/1602921/fcimb-15-1602921-HTML/image_m/fcimb-15-1602921-g003.jpg Figure 3. Restricted cubic spline (RCS) plots illustrating the relationship between CAR and mortalit https://www.frontiersin.org/files/Articles/1602921/fcimb-15-1602921-HTML/image_m/fcimb-15-1602921-g004.jpg Figure 4. Subgroup analysis of the association between CAR and 30-day mortality outcomes. (A) shows https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1754982/full https://www.frontiersin.org/files/Articles/1754982/fpubh-14-1754982-HTML-r1/image_m/fpubh-14-1754982-g001.jpg Figure 1. Participant flow diagram. This diagram illustrates the screening, enrollment, and follow-u https://www.frontiersin.org/files/Articles/1754982/fpubh-14-1754982-HTML-r1/image_m/fpubh-14-1754982-t001.jpg Table 1. Demographic characteristics (N = 475). https://www.frontiersin.org/files/Articles/1754982/fpubh-14-1754982-HTML-r1/image_m/fpubh-14-1754982-t002.jpg Table 2. Anxiety scores and fear scores before and after skin grafting. https://www.frontiersin.org/files/Articles/1754982/fpubh-14-1754982-HTML-r1/image_m/fpubh-14-1754982-t003.jpg Table 3. Multidimensional perception assessment with effect sizes (N = 475). https://www.frontiersin.org/files/Articles/1754982/fpubh-14-1754982-HTML-r1/image_m/fpubh-14-1754982-t004.jpg Table 4. Multiple linear regression analysis of factors associated with postoperative anxiety (NRS). https://www.frontiersin.org/files/Articles/1754982/fpubh-14-1754982-HTML-r1/image_m/fpubh-14-1754982-g002.jpg Figure 2. Participants’ perceived side effects from skin grafting (N = 475). Data show the distribut https://www.frontiersin.org/files/Articles/1754982/fpubh-14-1754982-HTML-r1/image_m/fpubh-14-1754982-g003.jpg Figure 3. Interventions believed to improve comfort with skin grafting (N = 475). Patient preference https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1654489/full https://www.frontiersin.org/files/Articles/1654489/fspor-07-1654489-HTML/image_m/fspor-07-1654489-t002.jpg Table 2. Variables measured by the pressure sensing insole for each regions during running at each e https://www.frontiersin.org/files/Articles/1654489/fspor-07-1654489-HTML/image_m/fspor-07-1654489-g001.jpg Figure 1. The pressure measurement insoles array is divided into 3 regions: heel, midfoot, forefoot. https://www.frontiersin.org/files/Articles/1654489/fspor-07-1654489-HTML/image_m/fspor-07-1654489-g002.jpg Figure 2. Typical waveforms of plantar pressure (a) and plantar force (b), normalized to the percent https://www.frontiersin.org/files/Articles/1654489/fspor-07-1654489-HTML/image_m/fspor-07-1654489-t001.jpg Table 1. Variables measured by the pressure sensing insole for each regions during running at each e https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1787090/full https://www.frontiersin.org/files/Articles/1787090/fonc-16-1787090-HTML-r1/image_m/fonc-16-1787090-g001.jpg Figure 1. Flow chart of the study. https://www.frontiersin.org/files/Articles/1787090/fonc-16-1787090-HTML-r1/image_m/fonc-16-1787090-t001.jpg Table 1. Baseline Clinical Characteristics of the Study Cohort. https://www.frontiersin.org/files/Articles/1787090/fonc-16-1787090-HTML-r1/image_m/fonc-16-1787090-t002.jpg Table 2. Univariate and Multivariate Logistic Regression Analysis for Prostate Cancer Prediction (Tr https://www.frontiersin.org/files/Articles/1787090/fonc-16-1787090-HTML-r1/image_m/fonc-16-1787090-g002.jpg Figure 2. (A) The Nomogram based on the prediction model. (B) Case example: A 77-year-old male with https://www.frontiersin.org/files/Articles/1787090/fonc-16-1787090-HTML-r1/image_m/fonc-16-1787090-t003.jpg Table 3. Diagnostic Performance of the Nomogram Model and Individual Predictors. https://www.frontiersin.org/files/Articles/1787090/fonc-16-1787090-HTML-r1/image_m/fonc-16-1787090-g003.jpg Figure 3. The Receiving Operating Characteristic (ROC) curves of the prediction model based on the ( https://www.frontiersin.org/files/Articles/1787090/fonc-16-1787090-HTML-r1/image_m/fonc-16-1787090-g004.jpg Figure 4. The Precision-Recall (PR) curves of the prediction model based on the (A) training set, (B https://www.frontiersin.org/files/Articles/1787090/fonc-16-1787090-HTML-r1/image_m/fonc-16-1787090-g005.jpg Figure 5. The Calibration curves of the prediction model in the (A) training set, (B) internal valid https://www.frontiersin.org/files/Articles/1787090/fonc-16-1787090-HTML-r1/image_m/fonc-16-1787090-g006.jpg Figure 6. The Decision Curve Analysis (DCA) of the prediction model in the (A) training set, (B) int https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1761249/full https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-t001.jpg Table 1. Crop growth and cultivation system configurations. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-t002.jpg Table 2. Three-view imaging system configurations. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g001.jpg Figure 1. (A) Experimental scenario; (B) Cultivating sorghum seedlings; (C) Three-view imaging. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g002.jpg Figure 2. (A) Seed pretreatment; (B) Crop growth cultivation system; (C) Three-view imaging system; https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g003.jpg Figure 3. (A) The PTV2-Fr architecture; (B) The PG-InvFR architecture; (C) The MRDCA architecture. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-t003.jpg Table 3. Configuration of the experimental simulation system. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-t004.jpg Table 4. Data augmentation techniques used in training. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-t005.jpg Table 5. Ablation experiment results. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-t006.jpg Table 6. Training hyperparameters used for baseline models and the proposed PTV2-Fr. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g004.jpg Figure 4. The results of k-fold cross-validation. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-t007.jpg Table 7. Mean ± standard deviation of five-fold cross-validation results for the PTV2-Fr model. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g005.jpg Figure 5. Qualitative visual analysis of sorghum seedlings in ablation experiments. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-t008.jpg Table 8. Training hyperparameters used for baseline models and the proposed PTV2-Fr. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-t009.jpg Table 9. A comparative analysis of semantic segmentation performance across different deep learning https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g006.jpg Figure 6. Qualitative visual analysis of sorghum seedlings in different experiments. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g007.jpg Figure 7. Front-view captured images of sorghum seedlings during the growth process. https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g008.jpg Figure 8. Three-view point cloud reconstructed images of sorghum seedlings during the growth process https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g009.jpg Figure 9. Semantic segmentation of point cloud data images of sorghum seedlings using the PTV2-Fr mo https://www.frontiersin.org/files/Articles/1761249/fpls-17-1761249-HTML-r1/image_m/fpls-17-1761249-g010.jpg Figure 10. (A) The changes in the total number of leaves of sorghum seedlings over time in the CK an https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1786148/full https://www.frontiersin.org/files/Articles/1786148/fnut-13-1786148-HTML/image_m/fnut-13-1786148-g001.jpg Figure 1. Natural extracts characterization and effects on MCF 10A and MDA-MB-231 cell proliferation https://www.frontiersin.org/files/Articles/1786148/fnut-13-1786148-HTML/image_m/fnut-13-1786148-g002.jpg Figure 2. Effects of S. pratensis extract on cell viability, oxidative stress, cell cycle distributi https://www.frontiersin.org/files/Articles/1786148/fnut-13-1786148-HTML/image_m/fnut-13-1786148-g003.jpg Figure 3. Impact of S. pratensis on MDA-MB-231 gene expression, protein modulation, and miRNA analys https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1679586/full https://www.frontiersin.org/files/Articles/1679586/fped-14-1679586-HTML-r1/image_m/fped-14-1679586-g001.jpg Figure 1. Frequency of selected symptoms in children wearing masks as determined in scientific studi https://www.frontiersin.org/files/Articles/1679586/fped-14-1679586-HTML-r1/image_m/fped-14-1679586-g002.jpg Figure 2. The bar charts display excess CO2 in children's breathing air when wearing masks (mean/med https://www.frontiersin.org/files/Articles/1679586/fped-14-1679586-HTML-r1/image_m/fped-14-1679586-g003.jpg Figure 3. A graphical summary of studies showing that mask toxin content/release exceeds limits by u https://www.frontiersin.org/files/Articles/1679586/fped-14-1679586-HTML-r1/image_m/fped-14-1679586-g004.jpg Figure 4. Mask-induced exhaustion syndrome (MIES) involves adverse chemical, physical, biological, a https://www.frontiersin.org/files/Articles/1679586/fped-14-1679586-HTML-r1/image_m/fped-14-1679586-t001.jpg Table 1. Summary of key facts (scientific evidence) on the potential adverse effects associated with https://www.frontiersin.org/files/Articles/1679586/fped-14-1679586-HTML-r1/image_m/fped-14-1679586-t002.jpg Table 2. Summary of the most important facts on environmental influences and contextual factors rela https://www.frontiersin.org/files/Articles/1679586/fped-14-1679586-HTML-r1/image_m/fped-14-1679586-g005.jpg Figure 5. A graphical summary of mask risk assessment for children, incorporating key ethical princi https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1796936/full https://www.frontiersin.org/files/Articles/1796936/fmars-13-1796936-HTML/image_m/fmars-13-1796936-t001.jpg Table 1. Summary statistics. https://www.frontiersin.org/files/Articles/1796936/fmars-13-1796936-HTML/image_m/fmars-13-1796936-t002.jpg Table 2. Effects of AI on Res. https://www.frontiersin.org/files/Articles/1796936/fmars-13-1796936-HTML/image_m/fmars-13-1796936-t003.jpg Table 3. Endogeneity test. https://www.frontiersin.org/files/Articles/1796936/fmars-13-1796936-HTML/image_m/fmars-13-1796936-g001.jpg Figure 1. Placebo test results. https://www.frontiersin.org/files/Articles/1796936/fmars-13-1796936-HTML/image_m/fmars-13-1796936-t004.jpg Table 4. Robustness test. https://www.frontiersin.org/files/Articles/1796936/fmars-13-1796936-HTML/image_m/fmars-13-1796936-t005.jpg Table 5. Heterogeneity analysis. https://www.frontiersin.org/files/Articles/1796936/fmars-13-1796936-HTML/image_m/fmars-13-1796936-t006.jpg Table 6. Mechanism analysis. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1710083/full https://www.frontiersin.org/files/Articles/1710083/fpsyg-17-1710083-HTML/image_m/fpsyg-17-1710083-g001.jpg Figure 1. Theoretical model. https://www.frontiersin.org/files/Articles/1710083/fpsyg-17-1710083-HTML/image_m/fpsyg-17-1710083-t001.jpg Table 1. Confirmatory factor analysis results. https://www.frontiersin.org/files/Articles/1710083/fpsyg-17-1710083-HTML/image_m/fpsyg-17-1710083-t002.jpg Table 2. Descriptive statistics and correlation analysis results. https://www.frontiersin.org/files/Articles/1710083/fpsyg-17-1710083-HTML/image_m/fpsyg-17-1710083-t003.jpg Table 3. Regression analysis results for hypotheses 1–4. https://www.frontiersin.org/files/Articles/1710083/fpsyg-17-1710083-HTML/image_m/fpsyg-17-1710083-t004.jpg Table 4. Regression analysis results for hypotheses 5 and 6. https://www.frontiersin.org/files/Articles/1710083/fpsyg-17-1710083-HTML/image_m/fpsyg-17-1710083-g002.jpg Figure 2. Simple slope plot of the moderating effect of religiousness orientation on the relationshi https://www.frontiersin.org/files/Articles/1710083/fpsyg-17-1710083-HTML/image_m/fpsyg-17-1710083-g003.jpg Figure 3. Simple slope plot of the moderating effect of religiousness orientation on the relationshi https://www.frontiersin.org/files/Articles/1710083/fpsyg-17-1710083-HTML/image_m/fpsyg-17-1710083-t005.jpg Table 5. Sub-dimension correlation analysis. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1761007/full https://www.frontiersin.org/files/Articles/1761007/fimmu-17-1761007-HTML/image_m/fimmu-17-1761007-g001.jpg Figure 1. Partial body irradiation reduces the number of B-1a cells in the peritoneal cavity, spleen https://www.frontiersin.org/files/Articles/1761007/fimmu-17-1761007-HTML/image_m/fimmu-17-1761007-g002.jpg Figure 2. Radiation induces apoptosis and reduces B-1a cell numbers in vitro. Cells were collected f https://www.frontiersin.org/files/Articles/1761007/fimmu-17-1761007-HTML/image_m/fimmu-17-1761007-g003.jpg Figure 3. Adoptive transfer of B-1a cells ameliorates radiation-induced intestinal injury. Mice were https://www.frontiersin.org/files/Articles/1761007/fimmu-17-1761007-HTML/image_m/fimmu-17-1761007-g004.jpg Figure 4. Adoptive transfer of B-1a cells attenuates radiation-induced intestinal barrier dysfunctio https://www.frontiersin.org/files/Articles/1761007/fimmu-17-1761007-HTML/image_m/fimmu-17-1761007-g005.jpg Figure 5. Adoptive transfer of B-1a cells attenuates radiation-induced tissue injury and improves su https://www.frontiersin.org/files/Articles/1761007/fimmu-17-1761007-HTML/image_m/fimmu-17-1761007-g006.jpg Figure 6. Adoptive transfer of B-1a cells increases TGF-β in peritoneal cavity and small intestine. https://www.frontiersin.org/files/Articles/1761007/fimmu-17-1761007-HTML/image_m/fimmu-17-1761007-g007.jpg Figure 7. Summary of findings. Partial body irradiation depletes B-1a cells and causes intestinal in https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1769163/full https://www.frontiersin.org/files/Articles/1769163/fimmu-17-1769163-HTML/image_m/fimmu-17-1769163-t001.jpg Table 1. Description of Study COV-2118 and COV-2069. https://www.frontiersin.org/files/Articles/1769163/fimmu-17-1769163-HTML/image_m/fimmu-17-1769163-g001.jpg Figure 1. Compared to unvaccinated study participants in COV-2069, increased immunogenicity to the C https://www.frontiersin.org/files/Articles/1769163/fimmu-17-1769163-HTML/image_m/fimmu-17-1769163-g002.jpg Figure 2. Retrospective analysis of CAS+IMD prophylaxis study (COV-2069) revealed increased ADA inci https://www.frontiersin.org/files/Articles/1769163/fimmu-17-1769163-HTML/image_m/fimmu-17-1769163-g003.jpg Figure 3. Simultaneous incubation of CAS+IMD in molar excess with target (i.e., SARS-CoV-2 spike tri https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1722672/full https://www.frontiersin.org/files/Articles/1722672/fnut-13-1722672-HTML/image_m/fnut-13-1722672-g001.jpg Figure 1. Flow chart of the study population. https://www.frontiersin.org/files/Articles/1722672/fnut-13-1722672-HTML/image_m/fnut-13-1722672-t001.jpg Table 1. Basic characteristics of the study participants. https://www.frontiersin.org/files/Articles/1722672/fnut-13-1722672-HTML/image_m/fnut-13-1722672-t002.jpg Table 2. Concentrations of urinary elements corrected by creatinine among study participants. https://www.frontiersin.org/files/Articles/1722672/fnut-13-1722672-HTML/image_m/fnut-13-1722672-t003.jpg Table 3. Adjusted ORs and 95% CIs for the association between urinary element concentrations and bir https://www.frontiersin.org/files/Articles/1722672/fnut-13-1722672-HTML/image_m/fnut-13-1722672-g002.jpg Figure 2. The restricted cubic spline for the association between urinary elements and birth defects https://www.frontiersin.org/files/Articles/1722672/fnut-13-1722672-HTML/image_m/fnut-13-1722672-g003.jpg Figure 3. Element mixture effect on birth defects, comparing various percentiles of the mixture to t https://www.frontiersin.org/files/Articles/1722672/fnut-13-1722672-HTML/image_m/fnut-13-1722672-g004.jpg Figure 4. Exposure–response plots (95% CIs) for associations between log-transformed concentrations https://www.frontiersin.org/files/Articles/1722672/fnut-13-1722672-HTML/image_m/fnut-13-1722672-g005.jpg Figure 5. Bivariate exposure-response plots for log-transformed concentrations of individual element https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1749597/full https://www.frontiersin.org/files/Articles/1749597/fmicb-17-1749597-HTML/image_m/fmicb-17-1749597-t001.jpg Table 1. Antimicrobial susceptibility profiles of HVKP4 and 22ZR-42. https://www.frontiersin.org/files/Articles/1749597/fmicb-17-1749597-HTML/image_m/fmicb-17-1749597-g001.jpg Figure 1. Characterization of Fe3O4 nanoparticles and Fe/PPy nanocomposites. (A) Particle size distr https://www.frontiersin.org/files/Articles/1749597/fmicb-17-1749597-HTML/image_m/fmicb-17-1749597-g002.jpg Figure 2. Photothermal properties and reactive oxygen species generation analysis. (A) UV–Vis absorp https://www.frontiersin.org/files/Articles/1749597/fmicb-17-1749597-HTML/image_m/fmicb-17-1749597-g003.jpg Figure 3. Analysis of the antimicrobial performance of Fe/PPy nanocomposites. (A) Plate coating expe https://www.frontiersin.org/files/Articles/1749597/fmicb-17-1749597-HTML/image_m/fmicb-17-1749597-g004.jpg Figure 4. Antimicrobial mechanism analysis of HvKP4 and 22ZR-42 treated with Fe/PPy. (A) Quantitativ https://www.frontiersin.org/files/Articles/1749597/fmicb-17-1749597-HTML/image_m/fmicb-17-1749597-g005.jpg Figure 5. Biological safety evaluation of Fe/PPy nanomaterials. (A,B) Hemolysis assay results showin https://www.frontiersin.org/files/Articles/1749597/fmicb-17-1749597-HTML/image_m/fmicb-17-1749597-g006.jpg Figure 6. In vivo KP decolonization analysis. (A) Timeline depicting the establishment of a murine i https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1798540/full https://www.frontiersin.org/files/Articles/1798540/fmicb-17-1798540-HTML/image_m/fmicb-17-1798540-g001.jpg Figure 1. Multi-level characterization of the MRAY_PSEAE ligand binding pocket and metal site constr https://www.frontiersin.org/files/Articles/1798540/fmicb-17-1798540-HTML/image_m/fmicb-17-1798540-g002.jpg Figure 2. Candidate source similarity constraints and multi-objective selection signals. (A) Distrib https://www.frontiersin.org/files/Articles/1798540/fmicb-17-1798540-HTML/image_m/fmicb-17-1798540-g003.jpg Figure 3. Fingerprint similarity clustering and tiered docking energy screening of 270 candidates. ( https://www.frontiersin.org/files/Articles/1798540/fmicb-17-1798540-HTML/image_m/fmicb-17-1798540-g004.jpg Figure 4. Binding mode analysis of prioritized hits and pose retention in 1.0 μs MD. (A) 2D interact https://www.frontiersin.org/files/Articles/1798540/fmicb-17-1798540-HTML/image_m/fmicb-17-1798540-g005.jpg Figure 5. Trajectory-level interaction hotspots and stability of compound 5311309 versus the referen https://www.frontiersin.org/files/Articles/1798540/fmicb-17-1798540-HTML/image_m/fmicb-17-1798540-g006.jpg Figure 6. Dose–response growth inhibition of Pseudomonas aeruginosa by compound 5311309 and Tunicamy https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1630979/full https://www.frontiersin.org/files/Articles/1630979/fendo-16-1630979-HTML/image_m/fendo-16-1630979-g001.jpg Figure 1. Pdgfrα deficiency in pancreatic β-cells impairs metabolism in C57BL/6 mice fed with normal https://www.frontiersin.org/files/Articles/1630979/fendo-16-1630979-HTML/image_m/fendo-16-1630979-g002.jpg Figure 2. Exacerbated metabolic dysregulation in C57BL/6 mice with Pdgfrα-deficiency in β-cells fed https://www.frontiersin.org/files/Articles/1630979/fendo-16-1630979-HTML/image_m/fendo-16-1630979-g003.jpg Figure 3. The expression of inflammatory factors and chemokines increase in skeletal muscle of C57BL https://www.frontiersin.org/files/Articles/1630979/fendo-16-1630979-HTML/image_m/fendo-16-1630979-g004.jpg Figure 4. Pdgfrα deficiency decreases islet number and volume, increases β-cell apoptosis and alters https://www.frontiersin.org/files/Articles/1630979/fendo-16-1630979-HTML/image_m/fendo-16-1630979-g005.jpg Figure 5. The expression of atf5 and gadd45b in β-cells deficient in Pdgfrα. RNA sequencing analysis https://www.frontiersin.org/files/Articles/1630979/fendo-16-1630979-HTML/image_m/fendo-16-1630979-g006.jpg Figure 6. Pdgfrα inhibitor reduces the insulin secretion of NIT-1 cells, increases NIT-1 cell apopto https://www.frontiersin.org/files/Articles/1630979/fendo-16-1630979-HTML/image_m/fendo-16-1630979-g007.jpg Figure 7. Working hypothesis: Pdgfrα regulates apoptosis through Atf5 and Gadd45b in β-cells. Our fi https://www.frontiersin.org/journals/toxicology/articles/10.3389/ftox.2026.1818684/full https://www.frontiersin.org/files/Articles/1818684/ftox-08-1818684-HTML/image_m/ftox-08-1818684-t001.jpg Table 1. LD50: Dixon up-and-down dosing sequences and survival outcomes following single-dose intrap https://www.frontiersin.org/files/Articles/1818684/ftox-08-1818684-HTML/image_m/ftox-08-1818684-t002.jpg Table 2. RR: Dose-dependent respiratory rate (RR) changes in male and female Fischer 344 rats measur https://www.frontiersin.org/files/Articles/1818684/ftox-08-1818684-HTML/image_m/ftox-08-1818684-g001.jpg Figure 1. Sequential photographs of a rat obtained at 5, 7, and 10 min after intraperitoneal isotoni https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1793389/full https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g001.jpg Figure 1. PRISMA flow diagram of the study selection process. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-t002.jpg Table 2. Dose characteristics of exercise interventions. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g002.jpg Figure 2. Risk of bias summary graph. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g003.jpg Figure 3. Bar chart of risk of bias assessment results. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g004.jpg Figure 4. Network diagram of included studies for BBS scores. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g005.jpg Figure 5. Forest plot of relative effects for BBS scores. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g006.jpg Figure 6. Network diagram of included studies for TUG. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g007.jpg Figure 7. Forest plot of relative effects for TUG. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g008.jpg Figure 8. Network diagram of included studies for BMD. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g009.jpg Figure 9. Forest plot of relative effects for BMD. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g010.jpg Figure 10. Network diagram of included studies for OLS. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g011.jpg Figure 11. Forest plot of relative effects for OLS. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g012.jpg Figure 12. Network diagram of included studies for falls. https://www.frontiersin.org/files/Articles/1793389/fphys-17-1793389-HTML/image_m/fphys-17-1793389-g013.jpg Figure 13. Forest plot of relative effects for falls. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1727938/full https://www.frontiersin.org/files/Articles/1727938/feduc-11-1727938-HTML/image_m/feduc-11-1727938-t001.jpg Table 1. Gamification applications for sustainability education: methodological characteristics and https://www.frontiersin.org/files/Articles/1727938/feduc-11-1727938-HTML/image_m/feduc-11-1727938-g001.jpg Figure 1. Conceptual model articulating gamification, sustainability, artificial intelligence, and e https://www.frontiersin.org/files/Articles/1727938/feduc-11-1727938-HTML/image_m/feduc-11-1727938-t002.jpg Table 2. Design implications of the conceptual model across educational and corporate contexts. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1695962/full https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g001.jpg Figure 1. Geologic map of the study area with sampling locations. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g002.jpg Figure 2. (a) NDVI, (b) LULC (2000), and (c) LULC (2025) of the study area. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-t001.jpg Table 1. Areas of LULC in 2000 and 2025. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g003.jpg Figure 3. Digital elevation model (DEM) of the study area. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-t002.jpg Table 2. Statistics of HMs concentration within investigated area. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g004.jpg Figure 4. Interpolated maps of HMs concentration in mg Kg−1 within study area: (a) Co, (b) Cr, (c) C https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-t003.jpg Table 3. Comparison between heavy metals concentrations and other concentrations in the study area. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-t004.jpg Table 4. Shapiro–Wilk normality test of studied HMs. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g005.jpg Figure 5. Normality test distribution test of studied HMs. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g006.jpg Figure 6. Heat map correlation matrix of studied elements. * Correlation is significant at the 0.05 https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g007.jpg Figure 7. Non-linear relationships between major cations and heavy metals (HMs) in soil samples. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g008.jpg Figure 8. Eigenvalues and explained variance of PCs. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-t005.jpg Table 5. Prinicpls components (PC1, and PC2) of the studied elements. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g009.jpg Figure 9. Dendrogram of studied HMs. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-t006.jpg Table 6. Comparison between two studied clusters. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-t007.jpg Table 7. Statistics of contamination indices of investigated area. https://www.frontiersin.org/files/Articles/1695962/fsufs-09-1695962-HTML/image_m/fsufs-09-1695962-g010.jpg Figure 10. Interpolation maps of (a) NPI, and (b) PLI contamination indices of the study area. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1706260/full https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g001.jpg Figure 1. Location map of the experimental site (Nubaria region, Egypt). https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t001.jpg Table 1. Physical and chemical analysis of the experimental soil in 2023/24 and 2024/25 seasons. https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t002.jpg Table 2. Vegetation indices employed in this study, their mathematical formulations, and relevant re https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t003.jpg Table 3. Soil indices used in this study, their equations, references, and sensitivities. https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t004.jpg Table 4. Mean (±SD) of faba bean growth traits at 80 days after sowing (DAS) as affected by zinc con https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g002.jpg Figure 2. Spectral reflectance of faba bean plants under different ZnSO4 concentrations at 80 DAS. https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g003.jpg Figure 3. ANOVA of spectral reflectance bands (blue, green, red, NIR) under ZnSO4 treatments (p ≤ 0. https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t005.jpg Table 5. One-way ANOVA for growth traits of faba bean as affected by ZnSO4 concentrations (2023/24–2 https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t006.jpg Table 6. Vegetation indices of faba bean growth traits as influenced by foliar application of zinc s https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t007.jpg Table 7. Mean (±SD) of yield attributes of faba bean plant at harvest as affected by zinc concentrat https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g004.jpg Figure 4. Spectral reflectance of faba bean at harvest as influenced by foliar ZnSO4 concentrations. https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g005.jpg Figure 5. ANOVA of spectral reflectance bands (blue, green, red, NIR) at harvest under ZnSO4 treatme https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t008.jpg Table 8. One-way ANOVA for yield attributes of faba bean as affected by ZnSO4 concentrations (2023/2 https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t009.jpg Table 9. Vegetation indices of faba bean yield traits as influenced by foliar application of zinc su https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t010.jpg Table 10. Mean (±SD) of yield and chemical composition of faba bean plant as affected by zinc sulfat https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g006.jpg Figure 6. Spectral reflectance of faba bean related to yield and biochemical composition under ZnSO4 https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g007.jpg Figure 7. ANOVA of vegetation indices (NDVI, NDRE, PRI, SAVI) for faba bean yield and biochemical tr https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t011.jpg Table 11. One-way ANOVA for seed yield and chemical composition of faba bean as affected by ZnSO4 co https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t012.jpg Table 12. Vegetation indices of faba bean yield and biochemical traits as influenced by foliar appli https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g008.jpg Figure 8. Spectral reflectance curves of faba bean leaf samples under the optimal ZnSO4 concentratio https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g009.jpg Figure 9. ANOVA of spectral reflectance bands among faba bean leaf samples under the optimal ZnSO4 c https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t013.jpg Table 13. One-way ANOVA for spectral reflectance of faba bean leaf samples under optimal ZnSO4 treat https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t014.jpg Table 14. Vegetation indices of faba bean plants under different ZnSO4 concentrations (spectral band https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g010.jpg Figure 10. Spectral reflectance of soil samples under the optimal ZnSO4 concentration. https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-g011.jpg Figure 11. ANOVA of spectral reflectance bands among soil samples under the optimal ZnSO4 concentrat https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t015.jpg Table 15. One-way ANOVA for spectral reflectance of soil samples under optimal ZnSO4 treatment. https://www.frontiersin.org/files/Articles/1706260/fenvs-13-1706260-HTML/image_m/fenvs-13-1706260-t016.jpg Table 16. Soil spectral indices of faba bean soils under optimal foliar ZnSO4 concentration. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1726872/full https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g001.jpg Figure 1. Shows (a) location map of the study area, and (b) the spatial distribution of soil samplin https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g002.jpg Figure 2. Location of sampling sites and geological map of a research region. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g003.jpg Figure 3. (a) DEM and (b) NDVI of the study area. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-t001.jpg Table 1. The heavy metal concentrations in the Huraymla soil. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g004.jpg Figure 4. IDW of studied HMs: (a) As (mg kg-1), (b) Cu (mg Kg-1), (c) Fe (mg kg-1), (d) Ni (mg kg-1) https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-t002.jpg Table 2. Comparison of different local and global backdrops with the mean concentration of HMs in th https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g005.jpg Figure 5. Correlation matrix heatmap of the studied HMs. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-t003.jpg Table 3. Component matrix of studied elements. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g006.jpg Figure 6. Scree plot of PCA. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-t004.jpg Table 4. Mean concentration of HMs in different clusters. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g007.jpg Figure 7. Q-mode HCA of soil samples. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-t005.jpg Table 5. Minimum, maximum, and average levels of raw data on trace metal concentrations. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g008.jpg Figure 8. Distribution of EF and CF values for HMs per sample locations in Huraymla agricultural far https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g009.jpg Figure 9. (a) PLI and (b) RI of the investigated area. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-t006.jpg Table 6. PLI, and RI of the studied HMs. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g010.jpg Figure 10. Distance matrix heatmap of HCA. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g011.jpg Figure 11. Cluster visualization chart. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-t007.jpg Table 7. The HQ, HI, and CDI (in mg/kg/day) for non-carcinogenic hazards in adults and children. https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-t008.jpg Table 8. Carcinogenic hazards for Cr, Pb, and As, as well as the overall cancer risk (LCR) for both https://www.frontiersin.org/files/Articles/1726872/fsufs-09-1726872-HTML/image_m/fsufs-09-1726872-g012.jpg Figure 12. LCR averages in adults and children as a result of HMs. https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2026.1810631/full https://www.frontiersin.org/files/Articles/1810631/frhs-06-1810631-HTML/image_m/frhs-06-1810631-g001.jpg Figure 1. The continuum of professional exhaustion. The continuum represents a conceptual model of i https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1700742/full https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t001.jpg Table 1. Changes in pancreatic islet function parameters in patients. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-g001.jpg Figure 1. Continuous glucose monitoring (CGM) profile of the patient. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-g002.jpg Figure 2. Dynamic chart. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t002.jpg Table 2. General information. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t003.jpg Table 3. Sex distribution. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t004.jpg Table 4. Age distribution. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t005.jpg Table 5. Insulin antibodies. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t006.jpg Table 6. Diabetes classification, disease duration, and insulin usage duration. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t007.jpg Table 7. Body mass index. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t008.jpg Table 8. Types of insulin. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t009.jpg Table 9. Treatment approaches and symptom remission duration. https://www.frontiersin.org/files/Articles/1700742/fendo-16-1700742-HTML/image_m/fendo-16-1700742-t010.jpg Table 10. Treatment approaches and symptom remission duration. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1707117/full https://www.frontiersin.org/files/Articles/1707117/fcvm-13-1707117-HTML/image_m/fcvm-13-1707117-t001.jpg Table 1. Chronological timeline of patient presentation, management, and outcome. https://www.frontiersin.org/files/Articles/1707117/fcvm-13-1707117-HTML/image_m/fcvm-13-1707117-t002.jpg Table 2. Key laboratory parameters at critical time points. https://www.frontiersin.org/files/Articles/1707117/fcvm-13-1707117-HTML/image_m/fcvm-13-1707117-g001.jpg Figure 1. Preoperative transthoracic echocardiographic findings. (A) Parasternal short-axis and long https://www.frontiersin.org/files/Articles/1707117/fcvm-13-1707117-HTML/image_m/fcvm-13-1707117-g002.jpg Figure 2. Preoperative chest CT and intraoperative histopathology. (A) Chest computed tomography (CT https://www.frontiersin.org/files/Articles/1707117/fcvm-13-1707117-HTML/image_m/fcvm-13-1707117-g003.jpg Figure 3. Postoperative transthoracic echocardiographic findings. (A) Parasternal long-axis view dem https://www.frontiersin.org/files/Articles/1707117/fcvm-13-1707117-HTML/image_m/fcvm-13-1707117-g004.jpg Figure 4. Postoperative chest CT at 3-month follow-up. (A) Chest CT, lung window, demonstrating reso https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1747407/full https://www.frontiersin.org/files/Articles/1747407/fchem-13-1747407-HTML/image_m/fchem-13-1747407-t001.jpg Table 1. Modern AOPs and adsorption methods for water purification: a systematic review and comparis https://www.frontiersin.org/files/Articles/1747407/fchem-13-1747407-HTML/image_m/fchem-13-1747407-g001.jpg Figure 1. Comparison of water remediation adsorption-AOP integration diagrams with (A) Independent A https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1674858/full https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-g001.jpg Figure 1. Representation of the synthesis of CNSs from Vachellia nilotica wood biochar. https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-g002.jpg Figure 2. SEM-EDX analysis of CNSs (a,b), XRD pattern of CNSs (c), FT-IR spectra of CNSs (d), N2 ads https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-t001.jpg Table 1. Elemental composition of CNSs. https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-g003.jpg Figure 3. Optimization parameters for PND removal using CNSs under various initial concentrations an https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-g004.jpg Figure 4. Kinetic models for PND adsorption onto CNSs using linear and non-linear pseudo first-order https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-t002.jpg Table 2. Linear regression of kinetic models for the adsorption of PND onto CNSs. https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-t003.jpg Table 3. Non-linear regression of adsorption kinetics models for the adsorption of PND onto CNSs. https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-g005.jpg Figure 5. Linear and non-linear regression of adsorption isotherm of Langmuir (a,d), Freundlich (b,e https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-t004.jpg Table 4. Linear and non-linear isotherm model parameters. https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-t005.jpg Table 5. Comparison of the maximum adsorption capacity with other studies for PND removal. https://www.frontiersin.org/files/Articles/1674858/fenvs-13-1674858-HTML/image_m/fenvs-13-1674858-g006.jpg Figure 6. Effect of ionic strength on the percentage of PND removal (a) and adsorption and desorptio https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1720861/full https://www.frontiersin.org/files/Articles/1720861/fonc-15-1720861-HTML-r1/image_m/fonc-15-1720861-g001.jpg Figure 1. Receiver operating characteristic (ROC) curves for SUVmax and SUVratio in detecting clinic https://www.frontiersin.org/files/Articles/1720861/fonc-15-1720861-HTML-r1/image_m/fonc-15-1720861-t001.jpg Table 1. Summary of demographic and clinical characteristics for the study cohort (n = 96). https://www.frontiersin.org/files/Articles/1720861/fonc-15-1720861-HTML-r1/image_m/fonc-15-1720861-t002.jpg Table 2. Comparison of logistic regression results for csPCa prediction and PSMA PET positivity. https://www.frontiersin.org/files/Articles/1720861/fonc-15-1720861-HTML-r1/image_m/fonc-15-1720861-g002.jpg Figure 2. A 76-year-old man with a serum PSA of 3.7 ng/mL and a PSA density of 0.06 ng/mL/cc. BIOPST https://www.frontiersin.org/files/Articles/1720861/fonc-15-1720861-HTML-r1/image_m/fonc-15-1720861-g003.jpg Figure 3. A 72-year-old man with a serum PSA of 4.5 ng/mL and a PSA density of 0.10 ng/mL/cc. BIOPST https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1728797/full https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-g001.jpg Figure 1. Flowchart illustrating the exclusion criteria applied in the study selection process. Crit https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-g002.jpg Figure 2. The PRISMA strategy is illustrated here in its parts: screening, selection process and fin https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-t001.jpg Table 1. Overview of published studies on RLT in meningioma (word version). https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-t002.jpg Table 2. Overview of published studies on RLT in meningioma (excel version). https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-t003.jpg Table 3. Overview of published studies on RLT in gliomas (word version). https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-t004.jpg Table 4. Overview of published studies on RLT in gliomas (excel version). https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-t005.jpg Table 5. Published clinical experiences with RLT in meningiomas and gliomas. https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-t006.jpg Table 6. CASP analysis of published RLT studies on meningiomas. https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-g003.jpg Figure 3. In this figure CASP analysis on meningiomas studies is reported sinoptically. https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-t007.jpg Table 7. CASP analysis of published RLT studies on gliomas. https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-g004.jpg Figure 4. In this figure CASP analysis on gliomas studies is reported sinoptically. https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-g005.jpg Figure 5. In this figure the selection process for clinical trials is reported, from the first resea https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-t008.jpg Table 8. Overview of clinical trials of RLT in meningiomas. https://www.frontiersin.org/files/Articles/1728797/fmed-12-1728797-HTML-r1/image_m/fmed-12-1728797-t009.jpg Table 9. Overview of clinical trials of RLT in gliomas. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1758273/full https://www.frontiersin.org/files/Articles/1758273/fpls-17-1758273-HTML-r1/image_m/fpls-17-1758273-g001.jpg Figure 1. Identification and interaction mechanism analysis of the antagonistic fungus T19 and the g https://www.frontiersin.org/files/Articles/1758273/fpls-17-1758273-HTML-r1/image_m/fpls-17-1758273-g002.jpg Figure 2. Effects of the minimal SynCom (TB) on wheat growth, disease resistance, and yield traits. https://www.frontiersin.org/files/Articles/1758273/fpls-17-1758273-HTML-r1/image_m/fpls-17-1758273-g003.jpg Figure 3. Effects of different treatments on antioxidant enzyme activities in wheat and soil enzyme https://www.frontiersin.org/files/Articles/1758273/fpls-17-1758273-HTML-r1/image_m/fpls-17-1758273-g004.jpg Figure 4. Effects of different treatments on physicochemical properties and nutrient contents of whe https://www.frontiersin.org/files/Articles/1758273/fpls-17-1758273-HTML-r1/image_m/fpls-17-1758273-g005.jpg Figure 5. Rhizosphere microbial diversity and community composition under different treatments. (A) https://www.frontiersin.org/files/Articles/1758273/fpls-17-1758273-HTML-r1/image_m/fpls-17-1758273-g006.jpg Figure 6. Co-occurrence network structures of rhizosphere microbial communities under different trea https://www.frontiersin.org/files/Articles/1758273/fpls-17-1758273-HTML-r1/image_m/fpls-17-1758273-g007.jpg Figure 7. Metabolomic analysis of rhizosphere soil under different treatments. (A) Principal compone https://www.frontiersin.org/files/Articles/1758273/fpls-17-1758273-HTML-r1/image_m/fpls-17-1758273-g008.jpg Figure 8. Integrative analysis of soil–enzyme–microbiome pathways affecting wheat quality. (A) Mante https://www.frontiersin.org/files/Articles/1758273/fpls-17-1758273-HTML-r1/image_m/fpls-17-1758273-g009.jpg Figure 9. Proposed mechanistic model of the synthetic microbial consortium (TB) in wheat. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1673098/full https://www.frontiersin.org/files/Articles/1673098/fimmu-16-1673098-HTML/image_m/fimmu-16-1673098-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1673098/fimmu-16-1673098-HTML/image_m/fimmu-16-1673098-t002.jpg Table 2. Clinical efficacy. https://www.frontiersin.org/files/Articles/1673098/fimmu-16-1673098-HTML/image_m/fimmu-16-1673098-g001.jpg Figure 1. Kaplan–Meier estimates of progression-free survival. (A). Kaplan–Meier curves depicting th https://www.frontiersin.org/files/Articles/1673098/fimmu-16-1673098-HTML/image_m/fimmu-16-1673098-g002.jpg Figure 2. The immune phenotype of peripheral blood in PR and PD patients at baseline. (A). Percentag https://www.frontiersin.org/files/Articles/1673098/fimmu-16-1673098-HTML/image_m/fimmu-16-1673098-g003.jpg Figure 3. Multiplex immunohistochemistry of PDACs before immunotherapy. (A). Representative images o https://www.frontiersin.org/files/Articles/1673098/fimmu-16-1673098-HTML/image_m/fimmu-16-1673098-g004.jpg Figure 4. WTSS of DEGs between treatment responders and nonresponders. (A). Volcano plot of DEGs bet https://www.frontiersin.org/files/Articles/1673098/fimmu-16-1673098-HTML/image_m/fimmu-16-1673098-g005.jpg Figure 5. Transcriptomic features of representative immune-related genes. (A). Cluster analysis of t https://www.frontiersin.org/files/Articles/1673098/fimmu-16-1673098-HTML/image_m/fimmu-16-1673098-g006.jpg Figure 6. Differentially expressed lncRNA and circRNA between treatment responders and nonresponders https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1748541/full https://www.frontiersin.org/files/Articles/1748541/fcell-14-1748541-HTML/image_m/fcell-14-1748541-g001.jpg Figure 1. PRMT5 inhibition produces a universal ATM reduction across PDAC models, establishing a fun https://www.frontiersin.org/files/Articles/1748541/fcell-14-1748541-HTML/image_m/fcell-14-1748541-g002.jpg Figure 2. Dual PRMT5 and CHK1 inhibition synergistically impairs PDAC cell growth. L3.6 PL cells wer https://www.frontiersin.org/files/Articles/1748541/fcell-14-1748541-HTML/image_m/fcell-14-1748541-g003.jpg Figure 3. Co-inhibition of PRMT5 and CHK1 increases DSB accumulation and induces apoptotic cell deat https://www.frontiersin.org/files/Articles/1748541/fcell-14-1748541-HTML/image_m/fcell-14-1748541-g004.jpg Figure 4. RNA-seq reveals that combined PRMT5 and CHK1 inhibition reshapes the transcriptional lands https://www.frontiersin.org/files/Articles/1748541/fcell-14-1748541-HTML/image_m/fcell-14-1748541-g005.jpg Figure 5. PRMT5 and CHK1 co-inhibition significantly reduces tumor growth and prolongs survival in v https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1793228/full https://www.frontiersin.org/files/Articles/1793228/fimmu-17-1793228-HTML/image_m/fimmu-17-1793228-g001.jpg Figure 1. CARD15 Blau syndrome mutations, Yao syndrome mutations and Crohn’s disease variants. This https://www.frontiersin.org/files/Articles/1793228/fimmu-17-1793228-HTML/image_m/fimmu-17-1793228-g002.jpg Figure 2. Role of the TNF-α and IFN- γ signaling in Blau syndrome. (A) Takada et.al., and Kitagawa e https://www.frontiersin.org/files/Articles/1793228/fimmu-17-1793228-HTML/image_m/fimmu-17-1793228-g003.jpg Figure 3. Immunoregulatory pathways in Blau syndrome. (A) Possible faulty regulation of the T cell I https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2025.1746987/full https://www.frontiersin.org/files/Articles/1746987/frph-07-1746987-HTML/image_m/frph-07-1746987-g001.jpg Figure 1. Study flow diagram of patient inclusion and outcomes. https://www.frontiersin.org/files/Articles/1746987/frph-07-1746987-HTML/image_m/frph-07-1746987-t001.jpg Table 1. Demographic and baseline characteristics of the study population. https://www.frontiersin.org/files/Articles/1746987/frph-07-1746987-HTML/image_m/frph-07-1746987-t002.jpg Table 2. Hematologic and inflammatory indices measured on the ovulation trigger day. https://www.frontiersin.org/files/Articles/1746987/frph-07-1746987-HTML/image_m/frph-07-1746987-t003.jpg Table 3. Embryologic outcomes in pregnant and non-pregnant groups. https://www.frontiersin.org/files/Articles/1746987/frph-07-1746987-HTML/image_m/frph-07-1746987-g002.jpg Figure 2. ROC curve analysis of embryological variables for clinical pregnancy. https://www.frontiersin.org/files/Articles/1746987/frph-07-1746987-HTML/image_m/frph-07-1746987-t004.jpg Table 4. ROC analysis of embryologic parameters for prediction of clinical pregnancy. https://www.frontiersin.org/files/Articles/1746987/frph-07-1746987-HTML/image_m/frph-07-1746987-g003.jpg Figure 3. Logistic regression analysis of embryological parameters for prediction of clinical pregna https://www.frontiersin.org/files/Articles/1746987/frph-07-1746987-HTML/image_m/frph-07-1746987-t005.jpg Table 5. Multivariate logistic regression analysis of predictors for clinical pregnancy. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1692213/full https://www.frontiersin.org/files/Articles/1692213/fpsyt-17-1692213-HTML/image_m/fpsyt-17-1692213-g001.jpg Figure 1. MRI of the brain revealed a meningioma located in the antero-inferior region of the fronta https://www.frontiersin.org/files/Articles/1692213/fpsyt-17-1692213-HTML/image_m/fpsyt-17-1692213-g002.jpg Figure 2. The second meningioma measured 32x34x35 mm and was located at the anterior vertex of the f https://www.frontiersin.org/files/Articles/1692213/fpsyt-17-1692213-HTML/image_m/fpsyt-17-1692213-g003.jpg Figure 3. Timeline of the main clinical and legal events. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1759775/full https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-g001.jpg Figure 1. Attention measurement task protocols. (A) Attentional breadth task procedure; (B) attentio https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-g002.jpg Figure 2. Schematic of the Multirule Visual Monitoring Task. (A) Short-term conflict alert; (B) mini https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-g003.jpg Figure 3. Radar plots of difficulty levels in the Multirule Visual Monitoring Task (A) simple mode; https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-g004.jpg Figure 4. Workflow of the Multirule Visual Monitoring Task. https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-t001.jpg Table 1. Grouping criteria based on time variables. https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-g005.jpg Figure 5. Effects of attentional shifting, breadth, and time pressure on discrimination capacity as https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-t002.jpg Table 2. Binary logistic regression predictors of discriminability in the simple Multirule Visual Mo https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-t003.jpg Table 3. Binary logistic regression analysis of joint effects of vestibular function and attention o https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-t004.jpg Table 4. Binary logistic regression analysis of discriminability predictors in the difficult Multiru https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-t005.jpg Table 5. Binary logistic regression analysis of joint effects of vestibular function and attention o https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-t006.jpg Table 6. ROC analysis and area under the curve comparison of vestibular function and attention asses https://www.frontiersin.org/files/Articles/1759775/fpsyg-17-1759775-HTML/image_m/fpsyg-17-1759775-g006.jpg Figure 6. ROC curves for the individual and combined predictive value of vestibular function (VF), a https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1569615/full https://www.frontiersin.org/files/Articles/1569615/fmed-12-1569615-HTML-r3/image_m/fmed-12-1569615-t001.jpg Table 1. Tuberculosis diagnostic techniques. https://www.frontiersin.org/files/Articles/1569615/fmed-12-1569615-HTML-r3/image_m/fmed-12-1569615-t002.jpg Table 2. List of studies on machine learning (ML) methods and performance obtained based on transcri https://www.frontiersin.org/files/Articles/1569615/fmed-12-1569615-HTML-r3/image_m/fmed-12-1569615-g001.jpg Figure 1. Machine learning pipeline for efficient tuberculosis management and detection. https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2025.1647769/full https://www.frontiersin.org/files/Articles/1647769/fsoc-10-1647769-HTML/image_m/fsoc-10-1647769-t001.jpg Table 1. Two qualitative research projects as data basis. https://www.frontiersin.org/files/Articles/1647769/fsoc-10-1647769-HTML/image_m/fsoc-10-1647769-t002.jpg Table 2. The persons mentioned in this paper. https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2026.1642858/full https://www.frontiersin.org/files/Articles/1642858/fsoc-11-1642858-HTML/image_m/fsoc-11-1642858-g001.jpg Figure 1. Conceptualizing the menstrual cycle at work: the figure presents the study’s conceptual fr https://www.frontiersin.org/files/Articles/1642858/fsoc-11-1642858-HTML/image_m/fsoc-11-1642858-t001.jpg Table 1. Research methods, participants and time. https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2026.1774099/full https://www.frontiersin.org/files/Articles/1774099/frhs-06-1774099-HTML-r1/image_m/frhs-06-1774099-t001.jpg Table 1. Fit statistics for the latent profile analysis. https://www.frontiersin.org/files/Articles/1774099/frhs-06-1774099-HTML-r1/image_m/frhs-06-1774099-t002.jpg Table 2. Descriptive statistics for BSSQ variables that constituted the three profiles. https://www.frontiersin.org/files/Articles/1774099/frhs-06-1774099-HTML-r1/image_m/frhs-06-1774099-g001.jpg Figure 1. Latent profile for BSSQ mean values for the three-profile solution. https://www.frontiersin.org/files/Articles/1774099/frhs-06-1774099-HTML-r1/image_m/frhs-06-1774099-t003.jpg Table 3. Descriptive statistics in the full sample and each latent profile. https://www.frontiersin.org/files/Articles/1774099/frhs-06-1774099-HTML-r1/image_m/frhs-06-1774099-t004.jpg Table 4. Multivariate multinomial logistic regression results predicting profile membership. https://www.frontiersin.org/journals/urology/articles/10.3389/fruro.2026.1790745/full https://www.frontiersin.org/files/Articles/1790745/fruro-06-1790745-HTML/image_m/fruro-06-1790745-g001.jpg Figure 1. Evolution of the penile lesion surgical management. (A, B) Photographs showing the evoluti https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1683102/full https://www.frontiersin.org/files/Articles/1683102/fmicb-16-1683102-HTML/image_m/fmicb-16-1683102-t001.jpg Table 1. Antimicrobial susceptibility of the clinical A51998714 srain and its conjugant. https://www.frontiersin.org/files/Articles/1683102/fmicb-16-1683102-HTML/image_m/fmicb-16-1683102-g001.jpg Figure 1. Identification of the carbapenemase production with combined-disc test and disk diffusion https://www.frontiersin.org/files/Articles/1683102/fmicb-16-1683102-HTML/image_m/fmicb-16-1683102-g002.jpg Figure 2. Serum-killing assay of strain A51998714 and growth curves of transconjugants. (A) Serum ki https://www.frontiersin.org/files/Articles/1683102/fmicb-16-1683102-HTML/image_m/fmicb-16-1683102-t002.jpg Table 2. Genomic characteristics of the clinical A51998714 srain. https://www.frontiersin.org/files/Articles/1683102/fmicb-16-1683102-HTML/image_m/fmicb-16-1683102-t003.jpg Table 3. Virulence related genes in the clinical A51998714 srain. https://www.frontiersin.org/files/Articles/1683102/fmicb-16-1683102-HTML/image_m/fmicb-16-1683102-g003.jpg Figure 3. Genetic comparison of the virulence plasmid pA51998714-VIR. The plasmid pA51998714-VIR was https://www.frontiersin.org/files/Articles/1683102/fmicb-16-1683102-HTML/image_m/fmicb-16-1683102-g004.jpg Figure 4. Comparative analysis between the pA51998714-KPC plasmid and other similar plasmids. (A) Ci https://www.frontiersin.org/files/Articles/1683102/fmicb-16-1683102-HTML/image_m/fmicb-16-1683102-g005.jpg Figure 5. Comparative analysis between the pA51998714-NDM plasmid and other similar plasmids. (A) Ci https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1605078/full https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g001.jpg Figure 1. Flowchart of the present study. https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g002.jpg Figure 2. The expression distribution and genetic alteration of PRGs in ccRCC. (A) The expression of https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g003.jpg Figure 3. Survival and ROC analysis of the PRGs in ccRCC. (A–G) Survival analysis of PRGs (AIM2, GSD https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g004.jpg Figure 4. Identification of differential expression of PRGclusters and functional enrichment analysi https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g005.jpg Figure 5. Differential expression of PRG clusters and functional analysis. (A) Venn diagram of 289 D https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g006.jpg Figure 6. Construction of PANoptosis-related prognostic gene risk model. (A,B) LASSO regression iden https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g007.jpg Figure 7. Prognostic value of the risk model in All, training, and testing sets. (A–C) Heatmap of th https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g008.jpg Figure 8. Correlation between PANoptosis risk score and immune cells, CSC, TMB, and TME. (A) Correla https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g009.jpg Figure 9. Anticancer sensitivity analysis and RT-qPCR analysis of three risk genes. (A–H) Examples p https://www.frontiersin.org/files/Articles/1605078/fgene-16-1605078-HTML/image_m/fgene-16-1605078-g010.jpg Figure 10. External validation with GSE40435 dataset and RT-qPCR validation of hub genes. (A) Expres https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1665032/full https://www.frontiersin.org/files/Articles/1665032/fneur-16-1665032-HTML/image_m/fneur-16-1665032-g001.jpg Figure 1. Research workflow diagram. NIHSS, National Institutes of Health Stroke Scale; CT, computed https://www.frontiersin.org/files/Articles/1665032/fneur-16-1665032-HTML/image_m/fneur-16-1665032-g002.jpg Figure 2. Recruitment coverage for study participants. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1728521/full https://www.frontiersin.org/files/Articles/1728521/fimmu-17-1728521-HTML/image_m/fimmu-17-1728521-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1728521/fimmu-17-1728521-HTML/image_m/fimmu-17-1728521-t001.jpg Table 1. Baseline characteristics of included studies. https://www.frontiersin.org/files/Articles/1728521/fimmu-17-1728521-HTML/image_m/fimmu-17-1728521-g002.jpg Figure 2. Network plot of interventions included in the network meta-analysis. (A) OS (overall survi https://www.frontiersin.org/files/Articles/1728521/fimmu-17-1728521-HTML/image_m/fimmu-17-1728521-g003.jpg Figure 3. Forest plot for randomized controlled trials. (A) OS (overall survival); (B) ORR (objectiv https://www.frontiersin.org/files/Articles/1728521/fimmu-17-1728521-HTML/image_m/fimmu-17-1728521-g004.jpg Figure 4. Interventions based on P-values for each outcome. OS, overall survival; ORR, objective res https://www.frontiersin.org/files/Articles/1728521/fimmu-17-1728521-HTML/image_m/fimmu-17-1728521-t002.jpg Table 2. Pairwise comparisons results of network-meta analysis. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1801207/full https://www.frontiersin.org/files/Articles/1801207/fonc-16-1801207-HTML/image_m/fonc-16-1801207-g001.jpg Figure 1. Clinical images of melanoma dermal metastases centered around the left ankle (A) prior to https://www.frontiersin.org/files/Articles/1801207/fonc-16-1801207-HTML/image_m/fonc-16-1801207-g002.jpg Figure 2. CT chest appearances at (A) baseline prior to immunosuppression, (B) following 2 weeks of https://www.frontiersin.org/files/Articles/1801207/fonc-16-1801207-HTML/image_m/fonc-16-1801207-g003.jpg Figure 3. Timeline for IIP and response to treatment. https://www.frontiersin.org/files/Articles/1801207/fonc-16-1801207-HTML/image_m/fonc-16-1801207-g004.jpg Figure 4. CD2+/CD3+ therapeutic level correlation with eATG dose. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1771798/full https://www.frontiersin.org/files/Articles/1771798/fpsyt-17-1771798-HTML/image_m/fpsyt-17-1771798-g001.jpg Figure 1. CT Brain (14 September 2025): A 3 mm cortical calcification with perilesional edema in the https://www.frontiersin.org/files/Articles/1771798/fpsyt-17-1771798-HTML/image_m/fpsyt-17-1771798-g002.jpg Figure 2. MRI Brain (20 September 2025, performed in India): Axial T2-weighted MRI showing a solitar https://www.frontiersin.org/files/Articles/1771798/fpsyt-17-1771798-HTML/image_m/fpsyt-17-1771798-g003.jpg Figure 3. Timeline of key clinical events. Chronological summary of the patient’s major clinical eve https://www.frontiersin.org/files/Articles/1771798/fpsyt-17-1771798-HTML/image_m/fpsyt-17-1771798-g004.jpg Figure 4. Proposed multifactorial mechanisms of psychosis in neurocysticercosis. The diagram illustr https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1658029/full https://www.frontiersin.org/files/Articles/1658029/fmicb-16-1658029-HTML/image_m/fmicb-16-1658029-g001.jpg Figure 1. (A) Map of Northern India (Maps of India, 2025b), (B) District map of Himachal Pradesh (Ma https://www.frontiersin.org/files/Articles/1658029/fmicb-16-1658029-HTML/image_m/fmicb-16-1658029-g002.jpg Figure 2. Entry to the Baddi pharmaceutical hub on the banks of the Sirsa river (photo by Amishi Pan https://www.frontiersin.org/files/Articles/1658029/fmicb-16-1658029-HTML/image_m/fmicb-16-1658029-t001.jpg Table 1. Summary of participants and methods used in the study. https://www.frontiersin.org/files/Articles/1658029/fmicb-16-1658029-HTML/image_m/fmicb-16-1658029-g003.jpg Figure 3. Antibiotic sales data in Himachal Pradesh (2015–2022) with forecasted trends for 2023–2025 https://www.frontiersin.org/files/Articles/1658029/fmicb-16-1658029-HTML/image_m/fmicb-16-1658029-g004.jpg Figure 4. Antibiotic Sales Data in Baddi (2015–2022) with Forecasted Trends for 2023–2025. Actual sa https://www.frontiersin.org/files/Articles/1658029/fmicb-16-1658029-HTML/image_m/fmicb-16-1658029-g005.jpg Figure 5. Open medicine pudiya (photo by Amishi Panwar 2023). https://www.frontiersin.org/files/Articles/1658029/fmicb-16-1658029-HTML/image_m/fmicb-16-1658029-g006.jpg Figure 6. Monthly antibiotic sales data in Baddi (2015–2022), where a clear drop is observed in the https://www.frontiersin.org/files/Articles/1658029/fmicb-16-1658029-HTML/image_m/fmicb-16-1658029-g007.jpg Figure 7. Open drainage with wastewater from kitchens, toilets, and washing area in the juggi mergin https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1663094/full https://www.frontiersin.org/files/Articles/1663094/fcell-13-1663094-HTML/image_m/fcell-13-1663094-t001.jpg Table 1. Differences between 4-HNE, MDA and acrolein. https://www.frontiersin.org/files/Articles/1663094/fcell-13-1663094-HTML/image_m/fcell-13-1663094-g001.jpg Figure 1. 4-HNE causes organelle damage in DCM cardiomyocytes. https://www.frontiersin.org/files/Articles/1663094/fcell-13-1663094-HTML/image_m/fcell-13-1663094-g002.jpg Figure 2. Damage caused by 4-HNE in mitochondria. https://www.frontiersin.org/files/Articles/1663094/fcell-13-1663094-HTML/image_m/fcell-13-1663094-g003.jpg Figure 3. Damage caused by 4-HNE in the endoplasmic reticulum. https://www.frontiersin.org/files/Articles/1663094/fcell-13-1663094-HTML/image_m/fcell-13-1663094-g004.jpg Figure 4. Damage caused by 4-HNE in lysosomes. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1622677/full https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-g001.jpg Figure 1. The depicted screening study process. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-t001.jpg Table 1. Baseline characteristics of the included studies. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-t002.jpg Table 2. Cochrane collaboration’s tool for quality assessment in all included trials. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-t003.jpg Table 3. The outcomes of this meta-analysis. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-g002.jpg Figure 2. Forest plot for examining visual analogue scale and 5D-itch scale. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-g003.jpg Figure 3. Forest plot for examining effective rate and adverse drug reactions. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-g004.jpg Figure 4. Forest plot for examining serum calcium and serum parathyroid hormone. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-g005.jpg Figure 5. Forest plot for examining serum phosphorus and serum intact parathyroid hormone. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-g006.jpg Figure 6. Forest plot for examining serum creatinine and blood urea nitrogen. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-g007.jpg Figure 7. Forest plot for examining uric acid and β2-microglobulin. https://www.frontiersin.org/files/Articles/1622677/fmed-12-1622677-HTML/image_m/fmed-12-1622677-g008.jpg Figure 8. Forest plot for examining inflammatory markers. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1655213/full https://www.frontiersin.org/files/Articles/1655213/fonc-15-1655213-HTML/image_m/fonc-15-1655213-t001.jpg Table 1. Clinic characteristics of the screening set. https://www.frontiersin.org/files/Articles/1655213/fonc-15-1655213-HTML/image_m/fonc-15-1655213-t002.jpg Table 2. Reagents. https://www.frontiersin.org/files/Articles/1655213/fonc-15-1655213-HTML/image_m/fonc-15-1655213-t003.jpg Table 3. Instruments. https://www.frontiersin.org/files/Articles/1655213/fonc-15-1655213-HTML/image_m/fonc-15-1655213-t004.jpg Table 4. Data analysis software. https://www.frontiersin.org/files/Articles/1655213/fonc-15-1655213-HTML/image_m/fonc-15-1655213-g001.jpg Figure 1. Metabolomic analysis of saliva in the screening set. (A) PCA of BC group and NC group; (B) https://www.frontiersin.org/files/Articles/1655213/fonc-15-1655213-HTML/image_m/fonc-15-1655213-g002.jpg Figure 2. Differential metabolite pathway analysis. (A) KEGG Enrichment Analysis Plot. (B) KEGG Heat https://www.frontiersin.org/files/Articles/1655213/fonc-15-1655213-HTML/image_m/fonc-15-1655213-g003.jpg Figure 3. (A) represents the upregulated metabolites, while (B) represents the downregulated metabol https://www.frontiersin.org/files/Articles/1655213/fonc-15-1655213-HTML/image_m/fonc-15-1655213-g004.jpg Figure 4. ROC analysis and biomarker distribution in the validation set. (A) ROC curve illustrating https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1771440/full https://www.frontiersin.org/files/Articles/1771440/fpubh-14-1771440-HTML/image_m/fpubh-14-1771440-t001.jpg Table 1. Sociodemographic characteristics (N = 1,631). https://www.frontiersin.org/files/Articles/1771440/fpubh-14-1771440-HTML/image_m/fpubh-14-1771440-t002.jpg Table 2. Means and standard deviations of psychological distress scores by country of residence. https://www.frontiersin.org/files/Articles/1771440/fpubh-14-1771440-HTML/image_m/fpubh-14-1771440-t003.jpg Table 3. Resilience (RS-25) scores by country of residence. https://www.frontiersin.org/files/Articles/1771440/fpubh-14-1771440-HTML/image_m/fpubh-14-1771440-t004.jpg Table 4. Adaptive and maladaptive coping scores by country of residence. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1659939/full https://www.frontiersin.org/files/Articles/1659939/fimmu-16-1659939-HTML/image_m/fimmu-16-1659939-g001.jpg Figure 1. Glomerulitis (microvascular inflammation) – The images taken via light microscope both dem https://www.frontiersin.org/files/Articles/1659939/fimmu-16-1659939-HTML/image_m/fimmu-16-1659939-g002.jpg Figure 2. PTC-itis (microvascular inflammation) -The image taken via light microscope demonstrates a https://www.frontiersin.org/files/Articles/1659939/fimmu-16-1659939-HTML/image_m/fimmu-16-1659939-t001.jpg Table 1. Detailed demographic characterization of the study population. https://www.frontiersin.org/files/Articles/1659939/fimmu-16-1659939-HTML/image_m/fimmu-16-1659939-t002.jpg Table 2. Results – differences between analysed populations. https://www.frontiersin.org/files/Articles/1659939/fimmu-16-1659939-HTML/image_m/fimmu-16-1659939-g003.jpg Figure 3. Scatterplot demonstrating the relationship between AECA antibody status and microvascular https://www.frontiersin.org/files/Articles/1659939/fimmu-16-1659939-HTML/image_m/fimmu-16-1659939-g004.jpg Figure 4. Scatterplot demonstrating the relationship between ETAR antibody status and microvascular https://www.frontiersin.org/files/Articles/1659939/fimmu-16-1659939-HTML/image_m/fimmu-16-1659939-g005.jpg Figure 5. Scatterplot demonstrating the relationship between AT1R antibody levels and microvascular https://www.frontiersin.org/files/Articles/1659939/fimmu-16-1659939-HTML/image_m/fimmu-16-1659939-g006.jpg Figure 6. Receiver operating characteristic (ROC) curve illustrating the discriminatory performance https://www.frontiersin.org/files/Articles/1659939/fimmu-16-1659939-HTML/image_m/fimmu-16-1659939-t003.jpg Table 3. Illustration of association rules. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1776113/full https://www.frontiersin.org/files/Articles/1776113/fimmu-17-1776113-HTML/image_m/fimmu-17-1776113-t001.jpg Table 1. Main patients’ characteristics. Red p values indicate statistically significant association https://www.frontiersin.org/files/Articles/1776113/fimmu-17-1776113-HTML/image_m/fimmu-17-1776113-t002.jpg Table 2. Anti-AT1R antibody levels/OD depending on C4d score. https://www.frontiersin.org/files/Articles/1776113/fimmu-17-1776113-HTML/image_m/fimmu-17-1776113-t003.jpg Table 3. Anti-AT1R antibody levels/OD depending on anti-HLA2 status. https://www.frontiersin.org/files/Articles/1776113/fimmu-17-1776113-HTML/image_m/fimmu-17-1776113-g001.jpg Figure 1. Boxplot charts illustrating AT1R total, and AT1R-IgG3 profile across histopathological dia https://www.frontiersin.org/files/Articles/1776113/fimmu-17-1776113-HTML/image_m/fimmu-17-1776113-g002.jpg Figure 2. ROC AUC curves for predicting AMR based on total AT1R and AT1R-IgG3 subclass. https://www.frontiersin.org/files/Articles/1776113/fimmu-17-1776113-HTML/image_m/fimmu-17-1776113-g003.jpg Figure 3. AMR prevalence in particular AT1R-IgG (total) quartiles. https://www.frontiersin.org/files/Articles/1776113/fimmu-17-1776113-HTML/image_m/fimmu-17-1776113-g004.jpg Figure 4. AMR prevalence in particular AT1R-IgG3 quartiles. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1759219/full https://www.frontiersin.org/files/Articles/1759219/fonc-16-1759219-HTML/image_m/fonc-16-1759219-g001.jpg Figure 1. Summary of clinical events from diagnosis until disease progression following palliative c https://www.frontiersin.org/files/Articles/1759219/fonc-16-1759219-HTML/image_m/fonc-16-1759219-g002.jpg Figure 2. Preoperative imaging of the tumor. Sagittal and coronal CT scans demonstrating the extent https://www.frontiersin.org/files/Articles/1759219/fonc-16-1759219-HTML/image_m/fonc-16-1759219-g003.jpg Figure 3. Gross features of the hepatic cyst. The cut surface shows a unilocular cyst with a thicken https://www.frontiersin.org/files/Articles/1759219/fonc-16-1759219-HTML/image_m/fonc-16-1759219-g004.jpg Figure 4. (A) Low-power view showing two distinct components: sheets of blue cells with necrosis (up https://www.frontiersin.org/files/Articles/1759219/fonc-16-1759219-HTML/image_m/fonc-16-1759219-g005.jpg Figure 5. (A) High-power view showing the tumor cells with associated inflammatory cells (H&E x40). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1733310/full https://www.frontiersin.org/files/Articles/1733310/fpsyg-16-1733310-HTML/image_m/fpsyg-16-1733310-g001.jpg Figure 1. Neurocognitive benefits of martial arts in aging: enhanced BDNF, improved executive functi https://www.frontiersin.org/files/Articles/1733310/fpsyg-16-1733310-HTML/image_m/fpsyg-16-1733310-t001.jpg Table 1. Neural mechanisms underlying the promotion of mental health in older adults through martial https://www.frontiersin.org/files/Articles/1733310/fpsyg-16-1733310-HTML/image_m/fpsyg-16-1733310-t002.jpg Table 2. Martial arts benefits for health. https://www.frontiersin.org/files/Articles/1733310/fpsyg-16-1733310-HTML/image_m/fpsyg-16-1733310-g002.jpg Figure 2. Mental health benefits of martial arts training in older adults. https://www.frontiersin.org/files/Articles/1733310/fpsyg-16-1733310-HTML/image_m/fpsyg-16-1733310-g003.jpg Figure 3. Neural, psychological, and social mechanisms of martial arts benefits in older adults. https://www.frontiersin.org/files/Articles/1733310/fpsyg-16-1733310-HTML/image_m/fpsyg-16-1733310-g004.jpg Figure 4. Visual overview illustrates the six Olympic combat sports and hard martial arts and Tai Ch https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1821073/full https://www.frontiersin.org/files/Articles/1821073/fmicb-17-1821073-HTML/image_m/fmicb-17-1821073-g001.jpg Figure 1. Overall architecture of the hybrid expert-system. The workflow begins with alignment quali https://www.frontiersin.org/files/Articles/1821073/fmicb-17-1821073-HTML/image_m/fmicb-17-1821073-g002.jpg Figure 2. Distribution of retained high-confidence rare sequence signals across host-associated prox https://www.frontiersin.org/files/Articles/1821073/fmicb-17-1821073-HTML/image_m/fmicb-17-1821073-g003.jpg Figure 3. Comparison of sample-level rare-signal burden estimation accuracy between the proposed fra https://www.frontiersin.org/files/Articles/1821073/fmicb-17-1821073-HTML/image_m/fmicb-17-1821073-g004.jpg Figure 4. Heatmaps of classification outcomes for the proposed framework across six signal-abundance https://www.frontiersin.org/files/Articles/1821073/fmicb-17-1821073-HTML/image_m/fmicb-17-1821073-g005.jpg Figure 5. Precision-recall distribution of the proposed framework across six signal-abundance levels https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1666269/full https://www.frontiersin.org/files/Articles/1666269/fphar-17-1666269-HTML/image_m/fphar-17-1666269-t001.jpg Table 1. Analysis parameters and settings. https://www.frontiersin.org/files/Articles/1666269/fphar-17-1666269-HTML/image_m/fphar-17-1666269-g001.jpg Figure 1. Left panel: The number of proteins exhibiting significant stability differences following https://www.frontiersin.org/files/Articles/1666269/fphar-17-1666269-HTML/image_m/fphar-17-1666269-t002.jpg Table 2. The top 10 proteins with the most pronounced thermal stability differences and their annota https://www.frontiersin.org/files/Articles/1666269/fphar-17-1666269-HTML/image_m/fphar-17-1666269-g002.jpg Figure 2. (A) GO annotation statistics (A) are presented for proteins exhibiting significant thermal https://www.frontiersin.org/files/Articles/1666269/fphar-17-1666269-HTML/image_m/fphar-17-1666269-g003.jpg Figure 3. The signaling pathway diagram depicting the targeting of CASTOR1 by GAA, The red dashed li https://www.frontiersin.org/files/Articles/1666269/fphar-17-1666269-HTML/image_m/fphar-17-1666269-g004.jpg Figure 4. KEGG pathway enrichment analysis of binding proteins. https://www.frontiersin.org/files/Articles/1666269/fphar-17-1666269-HTML/image_m/fphar-17-1666269-g005.jpg Figure 5. Protein domain enrichment analysis. https://www.frontiersin.org/files/Articles/1666269/fphar-17-1666269-HTML/image_m/fphar-17-1666269-g006.jpg Figure 6. Active sites of ganoderic acid A (left); molecular docking simulation of ganoderic acid A https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1793570/full https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g001.jpg Figure 1. Chromosomal distribution of JmjC genes in Setaria italica and Setaria viridis. The genomic https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g002.jpg Figure 2. Phylogenetic relationship analysis of JmjC proteins in Setaria italica, Setaria viridis, O https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g003.jpg Figure 3. Phylogenetic relationships, conserved motifs, domain architectures, and gene structures of https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g004.jpg Figure 4. Synteny analysis and chromosomal duplication of JmjC genes. (A, B) Circos diagrams illustr https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g005.jpg Figure 5. Predicted cis-acting elements in the promoter regions of SiJMJ and SvJMJ genes. The phylog https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g006.jpg Figure 6. Statistical analysis of cis-acting elements in the promoter regions of SiJMJ and SvJMJ gen https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g007.jpg Figure 7. Prediction of transcription factors (TFs) targeting SiJMJ and SvJMJ promoters. (A, B) Freq https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g008.jpg Figure 8. Spatiotemporal expression patterns of SiJMJ and SvJMJ genes across various tissues and dev https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g009.jpg Figure 9. Expression profiles of SiJMJ genes under six different abiotic stresses. The heatmaps illu https://www.frontiersin.org/files/Articles/1793570/fpls-17-1793570-HTML/image_m/fpls-17-1793570-g010.jpg Figure 10. Expression profiles of SvJMJ genes under heat stress. The heatmap displays the expression https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2026.1782115/full https://www.frontiersin.org/files/Articles/1782115/fevo-14-1782115-HTML/image_m/fevo-14-1782115-g001.jpg Figure 1. Illustration of the study area and sampling locations in the Yellow River Wetlands of Baot https://www.frontiersin.org/files/Articles/1782115/fevo-14-1782115-HTML/image_m/fevo-14-1782115-t001.jpg Table 1. Above-ground biomass (AGB), below-ground biomass (BGB), root-to-shoot ratio (RSR), and domi https://www.frontiersin.org/files/Articles/1782115/fevo-14-1782115-HTML/image_m/fevo-14-1782115-g002.jpg Figure 2. Soil physicochemical and stoichiometric properties across different wetland types and soil https://www.frontiersin.org/files/Articles/1782115/fevo-14-1782115-HTML/image_m/fevo-14-1782115-g003.jpg Figure 3. Contents of (a) particulate organic carbon (POC), (b) mineral-associated organic carbon (M https://www.frontiersin.org/files/Articles/1782115/fevo-14-1782115-HTML/image_m/fevo-14-1782115-g004.jpg Figure 4. Proportions of (a) particulate organic carbon (POC/SOC) and (b) mineral–associated organic https://www.frontiersin.org/files/Articles/1782115/fevo-14-1782115-HTML/image_m/fevo-14-1782115-g005.jpg Figure 5. Relationships between environmental factors and soil organic carbon fractions in (a) topso https://www.frontiersin.org/files/Articles/1782115/fevo-14-1782115-HTML/image_m/fevo-14-1782115-g006.jpg Figure 6. Key environmental drivers of (a) particulate organic carbon (POC) and mineral-associated o https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1789519/full https://www.frontiersin.org/files/Articles/1789519/fpubh-14-1789519-HTML/image_m/fpubh-14-1789519-t001.jpg Table 1. SPIDER inclusion and exclusion criteria. https://www.frontiersin.org/files/Articles/1789519/fpubh-14-1789519-HTML/image_m/fpubh-14-1789519-g001.jpg Figure 1. Preferred reporting items for systematic reviews and meta-analyses flow diagram of study s https://www.frontiersin.org/files/Articles/1789519/fpubh-14-1789519-HTML/image_m/fpubh-14-1789519-t002.jpg Table 2. Summary of social isolation measurement tools in included studies. https://www.frontiersin.org/files/Articles/1789519/fpubh-14-1789519-HTML/image_m/fpubh-14-1789519-t003.jpg Table 3. Summary of included articles (n = 14), considering year and country of the study, aims, des https://www.frontiersin.org/files/Articles/1789519/fpubh-14-1789519-HTML/image_m/fpubh-14-1789519-t004.jpg Table 4. MMAT (2018) quality appraisal of the included studies. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1650828/full https://www.frontiersin.org/files/Articles/1650828/fsurg-12-1650828-HTML/image_m/fsurg-12-1650828-g001.jpg Figure 1. Illustration of SSJIBL (A) black, red, and blue boxes represent anastomosis, ligatures, an https://www.frontiersin.org/files/Articles/1650828/fsurg-12-1650828-HTML/image_m/fsurg-12-1650828-t001.jpg Table 1. Blauer's semi-quantitative grading method. https://www.frontiersin.org/files/Articles/1650828/fsurg-12-1650828-HTML/image_m/fsurg-12-1650828-g002.jpg Figure 2. Specimen processing and surgical results (A) tensile tester (B) group absorbable suture (C https://www.frontiersin.org/files/Articles/1650828/fsurg-12-1650828-HTML/image_m/fsurg-12-1650828-g003.jpg Figure 3. Histogram of surgical results (A) adhesion scores and (B) maximum tensile force for the 3 https://www.frontiersin.org/files/Articles/1650828/fsurg-12-1650828-HTML/image_m/fsurg-12-1650828-g004.jpg Figure 4. H&E staining (A) group NC (B) group absorbable suture (C) group non-absorbable suture (yel https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1773561/full https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-gr0001.jpg Graphical Abstract. The neuro-immuno-metabolic (NIM) axis framework. Schematic representation of the https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-t001.jpg Table 1. Core regulatory molecules in the neuro-immuno-metabolic (NIM) axis. https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-g001.jpg Figure 1. Metabolic-immune crosstalk in the NIM axis: exercise-induced reprogramming. Mechanistic mo https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-t002.jpg Table 2. Core signaling pathways in the neuro-immuno-metabolic (NIM) axis. https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-t003.jpg Table 3. Pathological vs. exercise-induced repair-oriented inflammation. https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-g002.jpg Figure 2. Exercise-induced resolving inflammation: a neuroprotective cascade. Schematic of the exerc https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-t004.jpg Table 4. Gut microbiota-metabolite-neural effect correlation. https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-g003.jpg Figure 3. Microbial metabolites as messengers in the gut-brain axis: exercise-modulated pathways. Me https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-g004.jpg Figure 4. Muscle-gut-brain axis: bidirectional communication in exercise-induced brain health. Schem https://www.frontiersin.org/files/Articles/1773561/fpsyg-17-1773561-HTML/image_m/fpsyg-17-1773561-t005.jpg Table 5. Mechanistic specificity of exercise modalities in the NIM axis. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1733974/full https://www.frontiersin.org/files/Articles/1733974/fmicb-16-1733974-HTML/image_m/fmicb-16-1733974-g001.jpg Figure 1. Microbial community structure and phylogenetic disparities across sample groups. (A) Relat https://www.frontiersin.org/files/Articles/1733974/fmicb-16-1733974-HTML/image_m/fmicb-16-1733974-g002.jpg Figure 2. Microbial community structure and phylogenetic disparities across sample groups at the phy https://www.frontiersin.org/files/Articles/1733974/fmicb-16-1733974-HTML/image_m/fmicb-16-1733974-g003.jpg Figure 3. Summary of unigenes matched to KEGG functional categories (level 1 and level 2) present in https://www.frontiersin.org/files/Articles/1733974/fmicb-16-1733974-HTML/image_m/fmicb-16-1733974-g004.jpg Figure 4. Comparison of KEGG functional profiles. (A) Relative abundance of KEGG Orthology (KO) cate https://www.frontiersin.org/files/Articles/1733974/fmicb-16-1733974-HTML/image_m/fmicb-16-1733974-g005.jpg Figure 5. Distribution and composition of ARG in the avian gut and environmental microbiota. (A) Rel https://www.frontiersin.org/files/Articles/1733974/fmicb-16-1733974-HTML/image_m/fmicb-16-1733974-g006.jpg Figure 6. Convergence in alpha and beta diversity of the antibiotic resistome in sympatric plateau b https://www.frontiersin.org/files/Articles/1733974/fmicb-16-1733974-HTML/image_m/fmicb-16-1733974-g007.jpg Figure 7. Circos plot illustrating the associations between bacterial taxa and antibiotic resistance https://www.frontiersin.org/files/Articles/1733974/fmicb-16-1733974-HTML/image_m/fmicb-16-1733974-g008.jpg Figure 8. (A–D) Circos plots representing the alignment of the proportions of different antibiotic r https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1606357/full https://www.frontiersin.org/files/Articles/1606357/fbioe-13-1606357-HTML-r1/image_m/fbioe-13-1606357-g001.jpg Figure 1. Serum DAO levels in individuals. Serum DAO levels were measured by ELISA. Data are present https://www.frontiersin.org/files/Articles/1606357/fbioe-13-1606357-HTML-r1/image_m/fbioe-13-1606357-g002.jpg Figure 2. DAO content and muscle strength and muscle fibre types in natural aged mice and rapidly ag https://www.frontiersin.org/files/Articles/1606357/fbioe-13-1606357-HTML-r1/image_m/fbioe-13-1606357-g003.jpg Figure 3. DAO content in acute damaged skeletal muscle model. (A) Experimental design for glycerol-i https://www.frontiersin.org/files/Articles/1606357/fbioe-13-1606357-HTML-r1/image_m/fbioe-13-1606357-g004.jpg Figure 4. AOC1’s impact on myoblast migration and fusion via the Fbln1/FAK pathway. (A) Functional e https://www.frontiersin.org/files/Articles/1606357/fbioe-13-1606357-HTML-r1/image_m/fbioe-13-1606357-g005.jpg Figure 5. The illustration diagram showing the role of DAO in muscle cell behavior and signaling pat https://www.frontiersin.org/files/Articles/1606357/fbioe-13-1606357-HTML-r1/image_m/fbioe-13-1606357-t001.jpg Table 1. Baseline characteristics of study participants stratified by grip strength. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1753451/full https://www.frontiersin.org/files/Articles/1753451/fsurg-12-1753451-HTML-r1/image_m/fsurg-12-1753451-g001.jpg Figure 1. Clinical photographs of the eyelids before and after treatment. (A) Preoperative appearanc https://www.frontiersin.org/files/Articles/1753451/fsurg-12-1753451-HTML-r1/image_m/fsurg-12-1753451-g002.jpg Figure 2. Radiological imaging of the orbits. (A) Preoperative axial computed tomography (CT) scan r https://www.frontiersin.org/files/Articles/1753451/fsurg-12-1753451-HTML-r1/image_m/fsurg-12-1753451-g003.jpg Figure 3. Pulmonary function test. (A) Flow-volume curve from the bronchial provocation test. (B) Ti https://www.frontiersin.org/files/Articles/1753451/fsurg-12-1753451-HTML-r1/image_m/fsurg-12-1753451-g004.jpg Figure 4. Intraoperative findings and pathological results. (A) Intraoperative view showing diffuse https://www.frontiersin.org/files/Articles/1753451/fsurg-12-1753451-HTML-r1/image_m/fsurg-12-1753451-t001.jpg Table 1. Postoperative glucocorticoid dosage and serum IgG4 levels. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1747222/full https://www.frontiersin.org/files/Articles/1747222/fpubh-14-1747222-HTML/image_m/fpubh-14-1747222-t001.jpg Table 1. Program participant characteristics (N = 33). https://www.frontiersin.org/files/Articles/1747222/fpubh-14-1747222-HTML/image_m/fpubh-14-1747222-t002.jpg Table 2. Family characteristics (N = 30). https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1762593/full https://www.frontiersin.org/files/Articles/1762593/fpubh-14-1762593-HTML/image_m/fpubh-14-1762593-t001.jpg Table 1. Characteristics of older patients referred (n = 370). https://www.frontiersin.org/files/Articles/1762593/fpubh-14-1762593-HTML/image_m/fpubh-14-1762593-t002.jpg Table 2. Service delivery characteristics. https://www.frontiersin.org/files/Articles/1762593/fpubh-14-1762593-HTML/image_m/fpubh-14-1762593-g001.jpg Figure 1. Outcomes of referrals to the Social Prescribing Programme (n = 370). Proportions of patien https://www.frontiersin.org/files/Articles/1762593/fpubh-14-1762593-HTML/image_m/fpubh-14-1762593-g002.jpg Figure 2. Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS) scores at baseline and post- completion https://www.frontiersin.org/files/Articles/1762593/fpubh-14-1762593-HTML/image_m/fpubh-14-1762593-g003.jpg Figure 3. WHO-5 well-being index before and after the social prescribing programme. Estimated margin https://www.frontiersin.org/files/Articles/1762593/fpubh-14-1762593-HTML/image_m/fpubh-14-1762593-g004.jpg Figure 4. Change in MYCaW wellbeing score at baseline and post-completion of programme. Estimated ma https://www.frontiersin.org/files/Articles/1762593/fpubh-14-1762593-HTML/image_m/fpubh-14-1762593-g005.jpg Figure 5. Change in MYCaW concern scores at baseline and post-completion of programme. (A) Concern/P https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1664093/full https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-t001.jpg Table 1. The coding scheme of classroom discourse analysis in KC. https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-t002.jpg Table 2. Classification of discourse threads by knowledge advances. https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-g001.jpg Figure 1. The procedures of discourse analysis. https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-t003.jpg Table 3. Descriptive statistics concerning acts of classroom discourse interaction in KC. https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-t004.jpg Table 4. Descriptive statistics concerning the levels of KC. https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-g002.jpg Figure 2. Discourse behavior sequence transformation for knowledge socialization. https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-t005.jpg https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-g003.jpg Figure 3. Sequential transformation map of discourse behaviors for externalization. https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-t006.jpg https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-g004.jpg Figure 4. Sequential transformation of discourse behaviors for combination. https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-t007.jpg https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-g005.jpg Figure 5. Thirty four sequential transformation of discourse behaviors for internalization. https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-t008.jpg https://www.frontiersin.org/files/Articles/1664093/feduc-10-1664093-HTML/image_m/feduc-10-1664093-g006.jpg Figure 6. Sequential classroom discourse interaction acts and its mechanism of KC. https://www.frontiersin.org/journals/language-sciences/articles/10.3389/flang.2026.1757671/full https://www.frontiersin.org/files/Articles/1757671/flang-05-1757671-HTML/image_m/flang-05-1757671-t001.jpg Table 1. Background information and word count of focal participants. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1800990/full https://www.frontiersin.org/files/Articles/1800990/fbioe-14-1800990-HTML/image_m/fbioe-14-1800990-g007.jpg Scheme 1. Schematic illustration of the AS-IV@HdECM for cardiac repair. https://www.frontiersin.org/files/Articles/1800990/fbioe-14-1800990-HTML/image_m/fbioe-14-1800990-g001.jpg Figure 1. Preparation and characterization of HdECM and AS-IV@HdECM hydrogels. (A) Schematic illustr https://www.frontiersin.org/files/Articles/1800990/fbioe-14-1800990-HTML/image_m/fbioe-14-1800990-g002.jpg Figure 2. Effects of AS-IV@HdECM hydrogel on HUVECs in vitro. (A,B) Viability of HUVECs treated with https://www.frontiersin.org/files/Articles/1800990/fbioe-14-1800990-HTML/image_m/fbioe-14-1800990-g003.jpg Figure 3. Morphological and histological evaluation of the therapeutic efficacy of AS-IV@HdECM hydro https://www.frontiersin.org/files/Articles/1800990/fbioe-14-1800990-HTML/image_m/fbioe-14-1800990-g004.jpg Figure 4. In vivo sustained release and cardiac function assessment. (A) Fluorescence images of HdEC https://www.frontiersin.org/files/Articles/1800990/fbioe-14-1800990-HTML/image_m/fbioe-14-1800990-g005.jpg Figure 5. Network pharmacology analysis of HdECM, AS-IV, and AS-IV@HdECM in the treatment of MI. (A) https://www.frontiersin.org/files/Articles/1800990/fbioe-14-1800990-HTML/image_m/fbioe-14-1800990-g006.jpg Figure 6. Effects of molecular docking and target expression analysis in vitro. (A) Visualization of https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1786502/full https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-g001.jpg Figure 1. The core structure of NRP1. NRP1 has a long extracellular domain, a transmembrane domain, https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-t001.jpg Table 1. NRP1 domains and their correlated ligands in cancer. https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-g002.jpg Figure 2. Pan-cancer expression analysis of NRP1 across TCGA and GTEx datasets. The horizontal axis https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-g003.jpg Figure 3. (A–F) Kaplan-Meier survival plots for six representative cancer types with significant NRP https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-g004.jpg Figure 4. NRP1 plays diverse roles in the TME. Immunosuppression: NRP1+ Tregs suppress IFN-γ secreti https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-g005.jpg Figure 5. (A) Classification of autophagy. (B) Molecular mechanisms of autophagy: The ULK1 complex ( https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-g006.jpg Figure 6. Autophagy-related signaling pathways in cancer. Multiple pathways (mTOR-AMPK, PI3K/AKT/mTO https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-t002.jpg Table 2. Autophagy regulation by core signaling pathways in different cancers. https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-g007.jpg Figure 7. (A) Bidirectional regulation between tumor cells and stromal cells: Autophagy in stromal c https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-g008.jpg Figure 8. (A, B) Venn diagrams of DEGs and ATGs. (A) For 10 cancer types with NRP1 upregulation, 340 https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-g009.jpg Figure 9. (A–D) Kaplan–Meier survival analysis curves of key ATGs from UALCAN platform: (A) CXCR4 hi https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-t003.jpg Table 3. ATGs significantly correlated with NRP1 prognosis in cancer samples. https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-t004.jpg Table 4. Evidence matrix of NRP1-autophagy axis in major cancer types. https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-t005.jpg Table 5. Research progress on NRP1 targeted therapy strategies. https://www.frontiersin.org/files/Articles/1786502/fimmu-17-1786502-HTML/image_m/fimmu-17-1786502-t006.jpg Table 6. Progress in clinical research of autophagy modulators in cancer treatment. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1788601/full https://www.frontiersin.org/files/Articles/1788601/fneur-17-1788601-HTML-r1/image_m/fneur-17-1788601-t001.jpg Table 1. Clinical characteristics of the 6 pediatric patients with PKD. https://www.frontiersin.org/files/Articles/1788601/fneur-17-1788601-HTML-r1/image_m/fneur-17-1788601-g001.jpg Figure 1. Genetic analysis and pedigree of Patient 1 (P1) with a PRRT2 mutation. Genetic sequencing https://www.frontiersin.org/files/Articles/1788601/fneur-17-1788601-HTML-r1/image_m/fneur-17-1788601-g002.jpg Figure 2. Genetic analysis and pedigree of Patient 2 (P2) with a PRRT2 mutation: Sequencing identifi https://www.frontiersin.org/files/Articles/1788601/fneur-17-1788601-HTML-r1/image_m/fneur-17-1788601-g003.jpg Figure 3. Genetic analysis of patient 3 (P3) with a PRRT2 mutation: Sequencing identified a c.1141de https://www.frontiersin.org/files/Articles/1788601/fneur-17-1788601-HTML-r1/image_m/fneur-17-1788601-g004.jpg Figure 4. Genetic analysis of patient 4 (P4) with a PRRT2 mutation a. Analysis identified a 0.62 Mb https://www.frontiersin.org/files/Articles/1788601/fneur-17-1788601-HTML-r1/image_m/fneur-17-1788601-g005.jpg Figure 5. Genetic analysis of Patient 6 (P6) with a KCNMA1 mutation. Sequencing identified a c.946G> https://www.frontiersin.org/files/Articles/1788601/fneur-17-1788601-HTML-r1/image_m/fneur-17-1788601-t002.jpg Table 2. Summary of drug treatment reactions. https://www.frontiersin.org/journals/neuroanatomy/articles/10.3389/fnana.2026.1801873/full https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g001.jpg Figure 1. Experimental design outlining antibiotic-induced maternal dysbiosis and vertical transmiss https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g002.jpg Figure 2. Antibiotic-induced maternal dysbiosis alters microbial diversity and composition. (A) Alph https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g003.jpg Figure 3. Maternal dysbiosis diminishes offspring microbial diversity and vertical transmission fide https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g004.jpg Figure 4. Maternal dysbiosis disrupts vertical microbial transmission between dams and offspring. (A https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g005.jpg Figure 5. Maternal dysbiosis disrupts offspring gut morphology, altered barrier integrity, and enter https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g006.jpg Figure 6. Effect of maternal dysbiosis on the morphology of interneurons in the SSC. (A) The box plo https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g007.jpg Figure 7. Effect of maternal dysbiosis on the morphology of interneurons in the MC. (A) The box plot https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g008.jpg Figure 8. Effect of maternal dysbiosis on the morphology of interneurons in the mEC. (A) The box plo https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g009.jpg Figure 9. Effect of maternal dysbiosis on the morphology of interneurons in the Hp. (A) The box plot https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g010.jpg Figure 10. Maternal dysbiosis selectively reduces GAD67-positive interneuron density in the layers I https://www.frontiersin.org/files/Articles/1801873/fnana-20-1801873-HTML/image_m/fnana-20-1801873-g011.jpg Figure 11. Gut microbial signatures are associated with enteric nervous system architecture and cort https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1604268/full https://www.frontiersin.org/files/Articles/1604268/fcvm-12-1604268-HTML/image_m/fcvm-12-1604268-g001.jpg Figure 1. Participant selection. Of the 290 patients who underwent the one-stop procedure (left atri https://www.frontiersin.org/files/Articles/1604268/fcvm-12-1604268-HTML/image_m/fcvm-12-1604268-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1604268/fcvm-12-1604268-HTML/image_m/fcvm-12-1604268-t002.jpg Table 2. Procedural characteristics. https://www.frontiersin.org/files/Articles/1604268/fcvm-12-1604268-HTML/image_m/fcvm-12-1604268-g002.jpg Figure 2. Kaplan–Meier survival curve for (A) safety and (B) efficacy Endpoints. https://www.frontiersin.org/files/Articles/1604268/fcvm-12-1604268-HTML/image_m/fcvm-12-1604268-t003.jpg Table 3. LAAO follow-up characteristics. https://www.frontiersin.org/files/Articles/1604268/fcvm-12-1604268-HTML/image_m/fcvm-12-1604268-t004.jpg Table 4. TEE evaluation on LAAO device pre- and post-DCCV. https://www.frontiersin.org/files/Articles/1604268/fcvm-12-1604268-HTML/image_m/fcvm-12-1604268-g003.jpg Central Illustration. Comprehensive images presenting the impact of DCCV on patients with the LAAO d https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1623506/full https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-g001.jpg Figure 1. Flow diagram of literature selection process. https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-t001.jpg Table 1. Characteristics of eligible studies. https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-g002.jpg Figure 2. Overall overview graph of bias risk in included studies. https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-g003.jpg Figure 3. Risk of bias evaluation graph for the included literature. https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-g004.jpg Figure 4. Forest plot of the overall effect of exercise intervention on sleep quality in adolescents https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-g005.jpg Figure 5. Forest plot of subgroup analysis by intervention frequency. https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-g006.jpg Figure 6. Forest plot of subgroup analysis by each intervention time. https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-g007.jpg Figure 7. Forest plot of subgroup analysis by total intervention duration. https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-g008.jpg Figure 8. Forest plot of subgroup analysis by exercise type. https://www.frontiersin.org/files/Articles/1623506/fpubh-13-1623506-HTML-r1/image_m/fpubh-13-1623506-g009.jpg Figure 9. Publication bias analysis results. https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2026.1804253/full https://www.frontiersin.org/files/Articles/1804253/frsus-07-1804253-HTML/image_m/frsus-07-1804253-g001.jpg Figure 1. Conceptual framework used to assess the mainstreaming of nature-based solutions (NbS) in d https://www.frontiersin.org/files/Articles/1804253/frsus-07-1804253-HTML/image_m/frsus-07-1804253-t001.jpg Table 1. Criteria for review of NbS policy review and stakeholder dialogue. https://www.frontiersin.org/files/Articles/1804253/frsus-07-1804253-HTML/image_m/frsus-07-1804253-t002.jpg Table 2. Sector-level summary of NbS policy evaluation scores. https://www.frontiersin.org/files/Articles/1804253/frsus-07-1804253-HTML/image_m/frsus-07-1804253-t003.jpg Table 3. Average scores across NbS evaluation criteria and cross-sector insights. https://www.frontiersin.org/files/Articles/1804253/frsus-07-1804253-HTML/image_m/frsus-07-1804253-g002.jpg Figure 2. Natural climate solutions offer 26 million metric tonnes CO2e/year mitigation potential in https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1671066/full https://www.frontiersin.org/files/Articles/1671066/fped-14-1671066-HTML/image_m/fped-14-1671066-t001.jpg Table 1. Molecular mechanisms and clinical phenotypic classification of ELN-related diseases. https://www.frontiersin.org/files/Articles/1671066/fped-14-1671066-HTML/image_m/fped-14-1671066-g001.jpg Figure 1. Family pedigree consists of one proband. I-1 represents the proband's father; I-2 represen https://www.frontiersin.org/files/Articles/1671066/fped-14-1671066-HTML/image_m/fped-14-1671066-g002.jpg Figure 2. The ELN mutation site. The red arrow indicates the site of the heterozygous 5-bp duplicati https://www.frontiersin.org/files/Articles/1671066/fped-14-1671066-HTML/image_m/fped-14-1671066-g003.jpg Figure 3. Conservation analysis of the ELN gene. The orange arrow highlights alanine 629 (p.Ala629), https://www.frontiersin.org/files/Articles/1671066/fped-14-1671066-HTML/image_m/fped-14-1671066-g004.jpg Figure 4. Elastin gene structure and pathogenic variants to date. Known exonic and intronic variants https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1713256/full https://www.frontiersin.org/files/Articles/1713256/fphar-16-1713256-HTML/image_m/fphar-16-1713256-g001.jpg Figure 1. Flow chart of study selection. https://www.frontiersin.org/files/Articles/1713256/fphar-16-1713256-HTML/image_m/fphar-16-1713256-t001.jpg Table 1. Characteristics of all the studies included in the meta-analysis. https://www.frontiersin.org/files/Articles/1713256/fphar-16-1713256-HTML/image_m/fphar-16-1713256-t002.jpg Table 2. Characteristics of all the studies included in the meta-analysis. https://www.frontiersin.org/files/Articles/1713256/fphar-16-1713256-HTML/image_m/fphar-16-1713256-g002.jpg Figure 2. Forest plot of the effect of proton pump inhibitor use on the incidence of pneumonia. https://www.frontiersin.org/files/Articles/1713256/fphar-16-1713256-HTML/image_m/fphar-16-1713256-t003.jpg Table 3. Subgroup analysis of the risk of pneumonia. https://www.frontiersin.org/files/Articles/1713256/fphar-16-1713256-HTML/image_m/fphar-16-1713256-g003.jpg Figure 3. Forest plot of the effect of PPI use on the incidence of pneumonia in Asian population. https://www.frontiersin.org/files/Articles/1713256/fphar-16-1713256-HTML/image_m/fphar-16-1713256-g004.jpg Figure 4. Forest plot of the effect of PPI use on the incidence of pneumonia in specific Asian count https://www.frontiersin.org/files/Articles/1713256/fphar-16-1713256-HTML/image_m/fphar-16-1713256-g005.jpg Figure 5. Forest plot of the effect of PPI use on the incidence of pneumonia in Asian population usi https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1797334/full https://www.frontiersin.org/files/Articles/1797334/fpubh-14-1797334-HTML-r1/image_m/fpubh-14-1797334-t001.jpg Table 1. Predefined illness symptoms based on descriptions in the KIDDI app (https://kiddi.rivm.nl/) https://www.frontiersin.org/files/Articles/1797334/fpubh-14-1797334-HTML-r1/image_m/fpubh-14-1797334-g001.jpg Figure 1. Flowchart of selection, inclusion, and participation of DCCs in the PHS region Zuid-Hollan https://www.frontiersin.org/files/Articles/1797334/fpubh-14-1797334-HTML-r1/image_m/fpubh-14-1797334-g002.jpg Figure 2. The mapped participating 30 DCCs. The color of the municipality indicates the number of pa https://www.frontiersin.org/files/Articles/1797334/fpubh-14-1797334-HTML-r1/image_m/fpubh-14-1797334-g003.jpg Figure 3. Median number of persons with symptoms per week per DCC in summer (A) and winter (B). https://www.frontiersin.org/files/Articles/1797334/fpubh-14-1797334-HTML-r1/image_m/fpubh-14-1797334-g004.jpg Figure 4. Incidence per 100 persons per week per DCC in summer (A) and winter (B). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1750523/full https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-g001.jpg Figure 1. Theoretical hypothesis model. https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-t001.jpg Table 1. Results of validation factor analysis. https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-t002.jpg Table 2. Demographic differences test. https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-t003.jpg Table 3. Descriptive statistics and correlation analysis. https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-t004.jpg Table 4. Main effects test. https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-t005.jpg Table 5. Path coefficients. https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-t006.jpg Table 6. Mediating effect test. https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-t007.jpg Table 7. Moderating effect of team atmosphere on the relationship between mission valence and volunt https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-g002.jpg Figure 2. The moderating effect of team atmosphere on the relationship between mission valence and v https://www.frontiersin.org/files/Articles/1750523/fpsyg-17-1750523-HTML/image_m/fpsyg-17-1750523-t008.jpg Table 8. Moderated mediation effect. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1791755/full https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-g001.jpg Figure 1. Flowchart of patient screening process. https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-t001.jpg Table 1. Comparison of baseline data between complete and incomplete datasets. https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-t002.jpg Table 2. Comparison of baseline data between GDM and non-GDM groups in complete data. https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-t003.jpg Table 3. Comparison of laboratory indicators and clinical variables between GDM and non-GDM groups. https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-t004.jpg Table 4. Multivariable logistic regression analysis of independent risk factors for GDM in advanced https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-g002.jpg Figure 2. Forest plot of risk factors for GDM in advanced maternal age nulliparous women. ART, assis https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-g003.jpg Figure 3. Assessment of predictor importance using SHAP values. Each point represents a single patie https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-g004.jpg Figure 4. Receiver operating characteristic (ROC) curve for the multivariable logistic regression mo https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-g005.jpg Figure 5. Calibration plot of predicted versus observed GDM risk. https://www.frontiersin.org/files/Articles/1791755/fendo-17-1791755-HTML/image_m/fendo-17-1791755-g006.jpg Figure 6. Decision curve analysis for the multivariable model. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1810397/full https://www.frontiersin.org/files/Articles/1810397/fnagi-18-1810397-HTML/image_m/fnagi-18-1810397-t001.jpg Table 1. Experimental evidence for the occurrence of excessive apoptosis in Parkinson’s disease. https://www.frontiersin.org/files/Articles/1810397/fnagi-18-1810397-HTML/image_m/fnagi-18-1810397-g001.jpg Figure 1. The main mechanism leading to excessive apoptosis of PD neurons. This diagram illustrates https://www.frontiersin.org/files/Articles/1810397/fnagi-18-1810397-HTML/image_m/fnagi-18-1810397-t002.jpg Table 2. Effects of exercise on apoptosis in patients or animal models of Parkinson’s disease. https://www.frontiersin.org/files/Articles/1810397/fnagi-18-1810397-HTML/image_m/fnagi-18-1810397-g002.jpg Figure 2. Flowchart of the experiment and alterations in the behavior and pathology of the mice. Exe https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2026.1792739/full https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-g001.jpg Figure 1. PRISMA-based literature screening and selection process. https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-t001.jpg Table 1. Inclusion and exclusion criteria applied during the PRISMA-based screening process for stud https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-t002.jpg Table 2. Quality appraisal framework used in this review. https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-t003.jpg Table 3. Appraisal outcomes across included studies (n = 120). https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-g002.jpg Figure 2. Geographic distribution of reviewed outdoor thermal comfort studies by country. https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-g003.jpg Figure 3. Distribution of reviewed outdoor thermal comfort studies by climate type (Köppen classific https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-g004.jpg Figure 4. Temporal distribution of outdoor thermal comfort (OTC) studies in hot arid and semi-arid M https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-t004.jpg Table 4. Synthesis of reviewed studies grouped by software and analytical tools used. https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-t005.jpg Table 5. Synthesis of reviewed studies grouped by common outdoor thermal comfort outcomes. https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-g005.jpg Figure 5. Conceptual multi-scale workflow illustrating integration of local climate zone (LCZ) class https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-t006.jpg Table 6. Integrated conceptual framework for outdoor thermal comfort (OTC) in hot arid and semi-arid https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-t007.jpg Table 7. Mapping of integrated outdoor thermal comfort framework components to supporting studies in https://www.frontiersin.org/files/Articles/1792739/frsc-08-1792739-HTML/image_m/frsc-08-1792739-t008.jpg Table 8. Policy highlights: outdoor thermal comfort in hot arid and semi-arid MENA cities. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1754873/full https://www.frontiersin.org/files/Articles/1754873/fonc-16-1754873-HTML/image_m/fonc-16-1754873-g001.jpg Figure 1. Increased dose exposure to olaparib induces acquired resistance in TNBC and HGSOC cell lin https://www.frontiersin.org/files/Articles/1754873/fonc-16-1754873-HTML/image_m/fonc-16-1754873-t001.jpg Table 1. Presence of TP53 mutations in HGSOC and TNBC olaparib-resistant cell lines makes them poten https://www.frontiersin.org/files/Articles/1754873/fonc-16-1754873-HTML/image_m/fonc-16-1754873-t002.jpg Table 2. NGS data of TP53 and BRCA2 mutations detected in HGSOC and TNBC olaparib-resistant cell lin https://www.frontiersin.org/files/Articles/1754873/fonc-16-1754873-HTML/image_m/fonc-16-1754873-g002.jpg Figure 2. Olaparib-resistant TNBC and HGSOC cell lines show cross-resistance to carboplatin. (A) Via https://www.frontiersin.org/files/Articles/1754873/fonc-16-1754873-HTML/image_m/fonc-16-1754873-g003.jpg Figure 3. Chou−Talalay analyses demonstrate a synergistic interaction (CI<1) of carboplatin + eprene https://www.frontiersin.org/files/Articles/1754873/fonc-16-1754873-HTML/image_m/fonc-16-1754873-g004.jpg Figure 4. Eprenetapopt and carboplatin do not produce a consistent increase in apoptosis levels of T https://www.frontiersin.org/files/Articles/1754873/fonc-16-1754873-HTML/image_m/fonc-16-1754873-g005.jpg Figure 5. Eprenetapopt + carboplatin or carboplatin monotherapy induce an arrest in S or G2/M phase https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1776298/full https://www.frontiersin.org/files/Articles/1776298/fpsyt-17-1776298-HTML/image_m/fpsyt-17-1776298-t001.jpg Table 1. General characteristics of MHD participants. https://www.frontiersin.org/files/Articles/1776298/fpsyt-17-1776298-HTML/image_m/fpsyt-17-1776298-t002.jpg Table 2. Logistic regression analysis. https://www.frontiersin.org/files/Articles/1776298/fpsyt-17-1776298-HTML/image_m/fpsyt-17-1776298-g001.jpg Figure 1. ROC curve of the training group of machine learning algorithms. https://www.frontiersin.org/files/Articles/1776298/fpsyt-17-1776298-HTML/image_m/fpsyt-17-1776298-g002.jpg Figure 2. ROC curve of test group of machine learning algorithms. https://www.frontiersin.org/files/Articles/1776298/fpsyt-17-1776298-HTML/image_m/fpsyt-17-1776298-t003.jpg Table 3. Comparison of prediction models. https://www.frontiersin.org/files/Articles/1776298/fpsyt-17-1776298-HTML/image_m/fpsyt-17-1776298-g003.jpg Figure 3. Feature importance ranking. https://www.frontiersin.org/files/Articles/1776298/fpsyt-17-1776298-HTML/image_m/fpsyt-17-1776298-g004.jpg Figure 4. SHAP value (impact on model output). https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1762144/full https://www.frontiersin.org/files/Articles/1762144/fimmu-17-1762144-HTML/image_m/fimmu-17-1762144-g001.jpg Figure 1. Patient selection flowchart. Flowchart illustrating the selection process of patients with https://www.frontiersin.org/files/Articles/1762144/fimmu-17-1762144-HTML/image_m/fimmu-17-1762144-t001.jpg Table 1. Baseline demographic and clinical characteristics of the ICI-associated myocarditis and con https://www.frontiersin.org/files/Articles/1762144/fimmu-17-1762144-HTML/image_m/fimmu-17-1762144-t002.jpg Table 2. Baseline predictors of ICI-associated myocarditis in multivariable analysis. https://www.frontiersin.org/files/Articles/1762144/fimmu-17-1762144-HTML/image_m/fimmu-17-1762144-g002.jpg Figure 2. Predictive performance of logistic regression models for ICI-associated myocarditis. (A) R https://www.frontiersin.org/files/Articles/1762144/fimmu-17-1762144-HTML/image_m/fimmu-17-1762144-g003.jpg Figure 3. Comparative clinical profiles of mild versus severe ICI-associated myocarditis. (A) Swimme https://www.frontiersin.org/files/Articles/1762144/fimmu-17-1762144-HTML/image_m/fimmu-17-1762144-t003.jpg Table 3. Predictors at onset for severe ICI-associated myocarditis in multivariable analysis. https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2026.1778336/full https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-g001.jpg Figure 1. System model. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-t001.jpg Table 1. Parameters and parameter descriptions involved in this article. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-t007.jpg Algorithm 1. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-g002.jpg Figure 2. Auction process. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-t008.jpg Algorithm 2. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-t009.jpg Algorithm 3. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-t002.jpg Table 2. Accuracy evaluation. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-t003.jpg Table 3. Training time evaluation. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-g003.jpg Figure 3. Training epochs for the specified accuracy on the MNIST dataset. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-g004.jpg Figure 4. Training epochs to specified accuracy on the CIFAR-10 dataset. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-t004.jpg Table 4. The quantity of training epochs needed to reach the given accuracy. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-g005.jpg Figure 5. Duration of a single iteration of the MNIST dataset. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-g006.jpg Figure 6. Duration for a single iteration of the CIFAR-10 dataset. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-g007.jpg Figure 7. The overall duration required to finish the training with the given accuracy. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-t005.jpg Table 5. Total training time evaluation. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-t006.jpg Table 6. Comparison of algorithms. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-g008.jpg Figure 8. Total training time comparison of proposed and existing algorithms with given accuracy. https://www.frontiersin.org/files/Articles/1778336/fphy-14-1778336-HTML-r1/image_m/fphy-14-1778336-g009.jpg Figure 9. Accuracy comparison of proposed and existing algorithms. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1756207/full https://www.frontiersin.org/files/Articles/1756207/fnins-20-1756207-HTML/image_m/fnins-20-1756207-g001.jpg Figure 1. Flowchart illustrating the study design and participant selection process. MRI, magnetic r https://www.frontiersin.org/files/Articles/1756207/fnins-20-1756207-HTML/image_m/fnins-20-1756207-g002.jpg Figure 2. (A) Locations of the predefined ROIs used for analysis. Thirty-three ROIs were placed in a https://www.frontiersin.org/files/Articles/1756207/fnins-20-1756207-HTML/image_m/fnins-20-1756207-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1756207/fnins-20-1756207-HTML/image_m/fnins-20-1756207-g003.jpg Figure 3. (A) Regions of interest with higher cerebrospinal fluid motion in patients with TBI. The f https://www.frontiersin.org/files/Articles/1756207/fnins-20-1756207-HTML/image_m/fnins-20-1756207-t002.jpg Table 2. Mean f-values (%) in ROIs in healthy controls and patients with TBI. https://www.frontiersin.org/files/Articles/1756207/fnins-20-1756207-HTML/image_m/fnins-20-1756207-t003.jpg Table 3. Regression analyses with group and age as independent variables. https://www.frontiersin.org/files/Articles/1756207/fnins-20-1756207-HTML/image_m/fnins-20-1756207-t004.jpg Table 4. Longitudinal change in f-values among patients with TBI. https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2026.1797993/full https://www.frontiersin.org/files/Articles/1797993/fresc-07-1797993-HTML/image_m/fresc-07-1797993-t001.jpg Table 1. Demographics and professional profile of study participants (N = 110). https://www.frontiersin.org/files/Articles/1797993/fresc-07-1797993-HTML/image_m/fresc-07-1797993-g001.jpg Figure 1. Perceived benefits of occupational therapy in wheelchair seating. https://www.frontiersin.org/files/Articles/1797993/fresc-07-1797993-HTML/image_m/fresc-07-1797993-g002.jpg Figure 2. Top perceived barriers to effective occupational therapy seating interventions in Bahrain. https://www.frontiersin.org/files/Articles/1797993/fresc-07-1797993-HTML/image_m/fresc-07-1797993-g003.jpg Figure 3. Binary logistic regression predicting occupational therapy referrals. https://www.frontiersin.org/files/Articles/1797993/fresc-07-1797993-HTML/image_m/fresc-07-1797993-t002.jpg Table 2. Full logistic regression model for predicting OT referrals (N = 110). https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1717967/full https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-t001.jpg Table 1. Sociodemographic characteristics of the study participants (n = 810). https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-t002.jpg Table 2. Participants’ knowledge and perceptions regarding Mpox disease (n = 810). https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-g001.jpg Figure 1. University student perception regarding the cause of Mpox disease. https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-g002.jpg Figure 2. Sources of information about Mpox disease among university students (n = 810). https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-g003.jpg Figure 3. Students’ perception and awareness regarding the clinical features of Mpox. *Percentages m https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-t003.jpg Table 3. Students’ perceptions, attitudes, and practices regarding Mpox prevention and vaccination ( https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-g004.jpg Figure 4. Spearman’s rank-order correlation heatmap of students’ perceptions, attitudes, and practic https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-t004.jpg Table 4. Knowledge and perceptions regarding Mpox across different age groups (n = 810). https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-t005.jpg Table 5. Relationship between sex and Mpox-related knowledge and perception (n = 810). https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-t006.jpg Table 6. College-wise comparison of Mpox knowledge, awareness, and perceptions among students (n = 8 https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-g005.jpg Figure 5. Forest plot curve for significant factors associated with the good and moderate knowledge https://www.frontiersin.org/files/Articles/1717967/fpubh-14-1717967-HTML/image_m/fpubh-14-1717967-g006.jpg Figure 6. Distribution of information sources and attitudes toward Mpox vaccination and preventive m https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1759971/full https://www.frontiersin.org/files/Articles/1759971/fphar-17-1759971-HTML-r1/image_m/fphar-17-1759971-g005.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1759971/fphar-17-1759971-HTML-r1/image_m/fphar-17-1759971-g001.jpg Figure 1. The flow diagram of the retrospective and pharmacovigilance analysis. The retrospective an https://www.frontiersin.org/files/Articles/1759971/fphar-17-1759971-HTML-r1/image_m/fphar-17-1759971-t001.jpg Table 1. Characteristics of patients on concurrent anti-VEGF agents and anticoagulants. https://www.frontiersin.org/files/Articles/1759971/fphar-17-1759971-HTML-r1/image_m/fphar-17-1759971-g002.jpg Figure 2. Bleeding sites, grades, and incidence in patients on concurrent anti-VEGF agents and antic https://www.frontiersin.org/files/Articles/1759971/fphar-17-1759971-HTML-r1/image_m/fphar-17-1759971-g003.jpg Figure 3. VTE outcomes in patients receiving concurrent anti-VEGF agents and anticoagulants in the r https://www.frontiersin.org/files/Articles/1759971/fphar-17-1759971-HTML-r1/image_m/fphar-17-1759971-t002.jpg Table 2. Characteristics of bleeding reports co-administrated with anti-VEGF agents and anticoagulan https://www.frontiersin.org/files/Articles/1759971/fphar-17-1759971-HTML-r1/image_m/fphar-17-1759971-g004.jpg Figure 4. Bleeding sites and incidence of concomitant anti-VEGF agent and anticoagulant use in the p https://www.frontiersin.org/files/Articles/1759971/fphar-17-1759971-HTML-r1/image_m/fphar-17-1759971-t003.jpg Table 3. Shrinkage analysis for anti-VEGF agents and anticoagulants interaction. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1773756/full https://www.frontiersin.org/files/Articles/1773756/fpsyg-17-1773756-HTML-r1/image_m/fpsyg-17-1773756-g001.jpg Figure 1. Theoretical framework. https://www.frontiersin.org/files/Articles/1773756/fpsyg-17-1773756-HTML-r1/image_m/fpsyg-17-1773756-t001.jpg Table 1. Demographics of participants. https://www.frontiersin.org/files/Articles/1773756/fpsyg-17-1773756-HTML-r1/image_m/fpsyg-17-1773756-t002.jpg Table 2. The components of the questionnaire. https://www.frontiersin.org/files/Articles/1773756/fpsyg-17-1773756-HTML-r1/image_m/fpsyg-17-1773756-t003.jpg Table 3. Overall confirmatory factor analysis results. https://www.frontiersin.org/files/Articles/1773756/fpsyg-17-1773756-HTML-r1/image_m/fpsyg-17-1773756-t004.jpg Table 4. Discriminant validity assessment (fornell-larcker criterion). https://www.frontiersin.org/files/Articles/1773756/fpsyg-17-1773756-HTML-r1/image_m/fpsyg-17-1773756-g002.jpg Figure 2. Structural equation model path analysis. https://www.frontiersin.org/files/Articles/1773756/fpsyg-17-1773756-HTML-r1/image_m/fpsyg-17-1773756-g003.jpg Figure 3. Mediation effect of SA. https://www.frontiersin.org/files/Articles/1773756/fpsyg-17-1773756-HTML-r1/image_m/fpsyg-17-1773756-t005.jpg Table 5. Bootstrap test results of the mediating effect of SA. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1693253/full https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-g001.jpg Figure 1. Mechanisms of PNS in the treatment of cerebrovascular neurological disorders. Partly repri https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-g002.jpg Figure 2. Chemical structure diagram of PNS. https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-g003.jpg Figure 3. Retrieval process. https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-g004.jpg Figure 4. Metabolic pathways of ginsenoside PNS. https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-t001.jpg Table 1. Comparison of formulation strategies and routes of administration. https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-g005.jpg Figure 5. The therapeutic mechanism and critical pathways of PNS against cerebrovascular diseases. https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-t002.jpg Table 2. Summary of the mechanisms and effects of panax notoginseng saponins on cerebrovascular dise https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-g006.jpg Figure 6. The therapeutic mechanism and critical pathways of PNS against neurological disorders. https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-t003.jpg Table 3. Summary of the mechanisms and effects of panax notoginseng saponins on neurological disorde https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-t004.jpg Table 4. Single-agent of panax notoginseng saponins clinical application in neurological disorders. https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-t005.jpg Table 5. The incidence of adverse reactions with PNS for cerebrovascular neurological disorders. https://www.frontiersin.org/files/Articles/1693253/fphar-16-1693253-HTML-r2/image_m/fphar-16-1693253-t006.jpg Table 6. Analysis of evidence integration and transformation potential. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1774298/full https://www.frontiersin.org/files/Articles/1774298/fbioe-14-1774298-HTML/image_m/fbioe-14-1774298-g001.jpg Figure 1. Examples of different drug delivery approaches. https://www.frontiersin.org/files/Articles/1774298/fbioe-14-1774298-HTML/image_m/fbioe-14-1774298-g002.jpg Figure 2. Evolution of drug delivery systems from traditional dosage forms to advanced targeted and https://www.frontiersin.org/files/Articles/1774298/fbioe-14-1774298-HTML/image_m/fbioe-14-1774298-t001.jpg Table 1. Functional roles and molecular mechanisms of major polysaccharides in food systems. https://www.frontiersin.org/files/Articles/1774298/fbioe-14-1774298-HTML/image_m/fbioe-14-1774298-g003.jpg Figure 3. Polysaccharide-based biodegradable coatings and films for food packaging applications. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1593206/full https://www.frontiersin.org/files/Articles/1593206/fpubh-13-1593206-HTML/image_m/fpubh-13-1593206-g001.jpg Figure 1. Temporal trends in childhood UTIs disease burden, 1990–2021. (A) Incidence rate. (B) Morta https://www.frontiersin.org/files/Articles/1593206/fpubh-13-1593206-HTML/image_m/fpubh-13-1593206-g002.jpg Figure 2. Global heterogeneity of childhood UTIs epidemiology: Incident cases, incidence rates, and https://www.frontiersin.org/files/Articles/1593206/fpubh-13-1593206-HTML/image_m/fpubh-13-1593206-t001.jpg Table 1. Incidence, mortality and DALYs of UTIs in children of different sexes in all regions of the https://www.frontiersin.org/files/Articles/1593206/fpubh-13-1593206-HTML/image_m/fpubh-13-1593206-g003.jpg Figure 3. Age-sex stratified trends in childhood UTIs disease burden, 1990–2021. (A) Incidence burde https://www.frontiersin.org/files/Articles/1593206/fpubh-13-1593206-HTML/image_m/fpubh-13-1593206-g004.jpg Figure 4. Age distribution of childhood UTIs disease burden by region, 2021. (A) Proportional attrib https://www.frontiersin.org/files/Articles/1593206/fpubh-13-1593206-HTML/image_m/fpubh-13-1593206-g005.jpg Figure 5. Temporal trends in global UTIs burden by SDI quintile, 1990–2021. (A) Incident cases and i https://www.frontiersin.org/files/Articles/1593206/fpubh-13-1593206-HTML/image_m/fpubh-13-1593206-g006.jpg Figure 6. EAPC in childhood UTIs burden metrics by development level and region, 1990–2021. (A) EAPC https://www.frontiersin.org/files/Articles/1593206/fpubh-13-1593206-HTML/image_m/fpubh-13-1593206-g007.jpg Figure 7. Association between childhood UTIs burden and regional SDI, 1990–2021. (A) Incidence rate. https://www.frontiersin.org/files/Articles/1593206/fpubh-13-1593206-HTML/image_m/fpubh-13-1593206-g008.jpg Figure 8. Frontier analysis exploring the relationship between SDI and incidence rate for 5 childhoo https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1752134/full https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-t001.jpg Table 1. Sociodemographic and clinical characteristics of the study participants. https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-g001.jpg Figure 1. Study design and participant flow. Flow diagram summarizing the cross-sectional design, pa https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-g002.jpg Figure 2. Diagnosis experience and family impact. This figure illustrates the proportion of mothers https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-t002.jpg Table 2. Maternal experience of Down syndrome diagnosis disclosure and initial reaction. https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-t003.jpg Table 3. Association between provision of information at diagnosis and subsequent maternal feelings https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-t004.jpg Table 4. Association between the informant of the diagnosis and key communication and support outcom https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-t005.jpg Table 5. Associations between maternal employment status and psychosocial outcomes. https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-t006.jpg Table 6. Association between maternal employment status and feelings towards the child with Down syn https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-t007.jpg Table 7. Association between partner’s education level and maternal employment outcomes. https://www.frontiersin.org/files/Articles/1752134/fpsyg-17-1752134-HTML-r1/image_m/fpsyg-17-1752134-g003.jpg Figure 3. Conceptual framework of maternal emotional impact following Down syndrome diagnosis. This https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1686493/full https://www.frontiersin.org/files/Articles/1686493/fpsyg-16-1686493-HTML/image_m/fpsyg-16-1686493-t001.jpg Table 1. Characteristics of the participants. https://www.frontiersin.org/files/Articles/1686493/fpsyg-16-1686493-HTML/image_m/fpsyg-16-1686493-t002.jpg Table 2. Tabular inter-relationship diagram in descending order – focus group 1. https://www.frontiersin.org/files/Articles/1686493/fpsyg-16-1686493-HTML/image_m/fpsyg-16-1686493-t003.jpg Table 3. Tabular inter-relationship diagram in descending order – focus group 2. https://www.frontiersin.org/files/Articles/1686493/fpsyg-16-1686493-HTML/image_m/fpsyg-16-1686493-t004.jpg Table 4. Tabular inter-relationship diagram and SID of focus group 3. https://www.frontiersin.org/files/Articles/1686493/fpsyg-16-1686493-HTML/image_m/fpsyg-16-1686493-g001.jpg Figure 1. Combined uncluttered system influence diagram across all three (3) focus groups. https://www.frontiersin.org/files/Articles/1686493/fpsyg-16-1686493-HTML/image_m/fpsyg-16-1686493-t005.jpg Table 5. Summary of the findings. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1680160/full https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-g001.jpg Figure 1. The workflow of the critical steps. Tumors are segmented manually on Two-dimensional ultra https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-g002.jpg Figure 2. Patient recruitment workflow. In total, 671 out of 4551 patients were included according t https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-t001.jpg Table 1. Clinicopathologic characteristics between axillary lymph node positive and negative groups https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-g003.jpg Figure 3. Overall distribution of key radiomic features from US image among patients with and withou https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-t002.jpg Table 2. The prediction of ALN status results (N0 v.s. N+(≥1)). https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-t003.jpg Table 3. Comparison of the performance for ALNM prediction between the RS and radiologists’ judgment https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-t004.jpg Table 4. Comparison of the performance for ALNM prediction between ALN RS and radiologists’ judgment https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-g004.jpg Figure 4. Comparison of receiver operating characteristic (ROC) curves between different models for https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-t005.jpg Table 5. The prediction of ALN status results (N+(1–2) v.s. N+(≥3)). https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-g005.jpg Figure 5. Comparison of receiver operating characteristic (ROC) curves between different models for https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-g006.jpg Figure 6. The confusion matrix of predicting ALNM among (N0), [low-load ALNM (N+ (1–2)] and heavy-lo https://www.frontiersin.org/files/Articles/1680160/fonc-15-1680160-HTML/image_m/fonc-15-1680160-t006.jpg Table 6. Subgroup analysis of the RS for ALNM prediction. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1788854/full https://www.frontiersin.org/files/Articles/1788854/fspor-08-1788854-HTML/image_m/fspor-08-1788854-g001.jpg Figure 1. Study design. https://www.frontiersin.org/files/Articles/1788854/fspor-08-1788854-HTML/image_m/fspor-08-1788854-t001.jpg Table 1. Sample operationalization and description criteria. https://www.frontiersin.org/files/Articles/1788854/fspor-08-1788854-HTML/image_m/fspor-08-1788854-g002.jpg Figure 2. Schematic representation of the LSPT, adapted from Ali et al. (34). https://www.frontiersin.org/files/Articles/1788854/fspor-08-1788854-HTML/image_m/fspor-08-1788854-g003.jpg Figure 3. Schematic representation of the Y-SART, adapted from Yuan et al. (2). https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2026.1742001/full https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g001.jpg Figure 1. Location map of the study area. (a) DEM of Qingyang City; (b) Satellite image of the study https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g002.jpg Figure 2. Geological profile derived from UAV data along the section line (Location as shown in Figu https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g003.jpg Figure 3. 3D model of the landslide area. (a) 3D model of the slope; (b) Local point cloud view. https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-t001.jpg Table 1. Details of datasets used in this study. https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g004.jpg Figure 4. Resistivity cross-section of the landslide area. (a) Field layout of the ERT survey; (b) E https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g005.jpg Figure 5. Flume experiment setup. (a) Side view; (b) Front view. https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g006.jpg Figure 6. Undisturbed cubic loess sample collected in the field. https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-t002.jpg Table 2. Basic physical properties of soil. https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g007.jpg Figure 7. Photographs of slopes with collecting instruments. (a,b) Test flume as a whole; (c) Camera https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g008.jpg Figure 8. Historical image of the northern terrace of Qingyang. (a) Image map taken in February 2014 https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-t003.jpg Table 3. The area and quantity of landslides changed between 2014 and 2022. https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g009.jpg Figure 9. The incipient loess falls processes. (a) After test water injection; (b) Fall initiation; https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g010.jpg Figure 10. Process of ultimate loess fall. (a) Onset stage; (b) Progressive stage; (c) Final failure https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g011.jpg Figure 11. Process of slope crack development. (a) Initial crack formation; (b,c) Localized falls; ( https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g012.jpg Figure 12. Destruction process of slope shoulder. https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g013.jpg Figure 13. Velocity vector diagrams of slope velocity. (a,b) Steady-state deformation stage; (c,d) A https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g014.jpg Figure 14. Slope velocity field maps. (a) Initial stage; (b–e) Deformation creep stage; (f) Final st https://www.frontiersin.org/files/Articles/1742001/feart-14-1742001-HTML-r1/image_m/feart-14-1742001-g015.jpg Figure 15. Failure model of loess falls induced by the persistent scouring of domestic sewage. (a) E https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1762735/full https://www.frontiersin.org/files/Articles/1762735/fmed-13-1762735-HTML/image_m/fmed-13-1762735-t001.jpg Table 1. Summary of previous studies on lesion segmentation performance in FFA images. https://www.frontiersin.org/files/Articles/1762735/fmed-13-1762735-HTML/image_m/fmed-13-1762735-g001.jpg Figure 1. Flow diagram of this study. https://www.frontiersin.org/files/Articles/1762735/fmed-13-1762735-HTML/image_m/fmed-13-1762735-t002.jpg Table 2. Distribution of label frequency and pixel counts in 55 ° and UWF FFA images. https://www.frontiersin.org/files/Articles/1762735/fmed-13-1762735-HTML/image_m/fmed-13-1762735-t003.jpg Table 3. Model performance metrics on 55 ° FFA, UWF FFA, and the overall dataset. https://www.frontiersin.org/files/Articles/1762735/fmed-13-1762735-HTML/image_m/fmed-13-1762735-t004.jpg Table 4. Model performance metrics by disease category. https://www.frontiersin.org/files/Articles/1762735/fmed-13-1762735-HTML/image_m/fmed-13-1762735-g002.jpg Figure 2. Comparative Dice scores for target labels across DR, RVO, and CNV. https://www.frontiersin.org/files/Articles/1762735/fmed-13-1762735-HTML/image_m/fmed-13-1762735-g003.jpg Figure 3. Comparative Dice scores for target labels across arteriovenous, venous, and late phases. https://www.frontiersin.org/files/Articles/1762735/fmed-13-1762735-HTML/image_m/fmed-13-1762735-t005.jpg Table 5. Model performance metrics by phase. https://www.frontiersin.org/files/Articles/1762735/fmed-13-1762735-HTML/image_m/fmed-13-1762735-g004.jpg Figure 4. Influence of training-set size on average Dice scores for lesion segmentation. https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2026.1799070/full https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g001.jpg Figure 1. Heat treatment process. https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-t001.jpg Table 1. Composition of experimental steels (wt%). https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g002.jpg Figure 2. Statistical results of the inclusions in H13 steels. (A) Average diameter of inclusions an https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g003.jpg Figure 3. Typical inclusions in H13 steel. (A) Al2O3; (B) Mg-Al-O; (C) Mg-Al-O-Mn-S. https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g004.jpg Figure 4. Typical inclusions in H13 steel after Mg-Ce treatment. (A) Ce2O3; (B) Ce-O-S; (C) Ce-P-As; https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g005.jpg Figure 5. SEM morphology of quenched H13 steel. (A) S0; (B) S1; (C) S2; (D) S3; (E) Area fraction of https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g006.jpg Figure 6. Austenite grains of the sample obtained via EBSD. (A) S0; (B) S1; (C) S2; (D) S3; (E) Stat https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g007.jpg Figure 7. SEM morphology of the sample after tempering. (A) S0; (B) S1; (C) S2; (D) S3. https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g008.jpg Figure 8. Variations in hardness and impact energy of H13 steel after Mg-Ce treatment. https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g009.jpg Figure 9. SEM micrographs of impact fractures for various samples. (A,B) S0; (C,D) S1; (E,F) S2; (G, https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g010.jpg Figure 10. Transformation of inclusions during solidification. (A) S0; (B) S1; (C) S2; (D) S3. https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g011.jpg Figure 11. Ce2O2S inclusions at the grain boundaries. (A) SEM morphology; (B) EDS. https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g012.jpg Figure 12. Changes in the MC phase diagram of H13 steel after Mg-Ce treatment. (A) Complete phase di https://www.frontiersin.org/files/Articles/1799070/fmats-13-1799070-HTML-r3/image_m/fmats-13-1799070-g013.jpg Figure 13. SEM images and elemental distribution in the crack initiation zone. (A) S0; (B) S2; (C) S https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1616579/full https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-g001.jpg Figure 1. Conceptual diagram. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t001.jpg Table 1. Variables description. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t002.jpg Table 2. Baseline regression results. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t003.jpg Table 3. Marginal effects. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t004.jpg Table 4. Robustness based on air pollution indicators. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t005.jpg Table 5. Robustness test based on air quality monitoring period. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t006.jpg Table 6. Heterogeneity analysis: gender and marital status. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t007.jpg Table 7. Heterogeneity analysis: age. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t008.jpg Table 8. Heterogeneity analysis: nature of employment. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t009.jpg Table 9. Heterogeneity analysis: income level. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-g002.jpg Figure 2. Heterogeneity analysis. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t010.jpg Table 10. KHB decomposition method test results. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t011.jpg Table 11. KHB decomposition method test results. https://www.frontiersin.org/files/Articles/1616579/fenvs-13-1616579-HTML-r1/image_m/fenvs-13-1616579-t012.jpg Table 12. Moderating effect analysis results. https://www.frontiersin.org/journals/signal-processing/articles/10.3389/frsip.2026.1680796/full https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-g001.jpg Figure 1. AD detection flow diagram. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-g002.jpg Figure 2. ViT architecture. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-g003.jpg Figure 3. Image fusion strategy adopted for AD detection. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-g004.jpg Figure 4. Augmented images of Alzheimer’s dataset. (a) Input image (b) Rotation (c) Reflection (d) T https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-t001.jpg Table 1. Hyper-parameters used in the proposed ViT. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-t002.jpg Table 2. Experimental results for AD detection using ViT and other state-of-art Models. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-g005.jpg Figure 5. ROC curve for ViT corresponding to experiment 1 in Table 2. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-g006.jpg Figure 6. Training and validation curve for ViT corresponding to experiment 1 in Table 2. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-t003.jpg Table 3. Statistical summary of classification accuracy across models. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-g007.jpg Figure 7. Performance analysis of ViT at different batch size. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-g008.jpg Figure 8. Performance analysis of vit at different patch size. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-t004.jpg Table 4. Ablation study of the proposed model for DWT based image fusion and ViT based classificatio https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-t005.jpg Table 5. Comparison of the proposed model with existing methods for AD detection. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-t006.jpg Table 6. Performance analysis of the proposed model for multi-class classification (MCI vs. AD vs. C https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-t007.jpg Table 7. Performance analysis of the proposed model under different classification scenarios. https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-t008.jpg Table 8. Comparative analysis of the proposed model with existing fusion-based methods for multi-cla https://www.frontiersin.org/files/Articles/1680796/frsip-06-1680796-HTML/image_m/frsip-06-1680796-t009.jpg Table 9. Ablation study: Result with and without DWT-based image fusion. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1664788/full https://www.frontiersin.org/files/Articles/1664788/fimmu-16-1664788-HTML/image_m/fimmu-16-1664788-t001.jpg Table 1. Characteristics of the cohort of CAR T-cell patients included. https://www.frontiersin.org/files/Articles/1664788/fimmu-16-1664788-HTML/image_m/fimmu-16-1664788-t002.jpg Table 2. Baseline characteristics of patients developing CRS in the CAR T-cell cohort and septic pat https://www.frontiersin.org/files/Articles/1664788/fimmu-16-1664788-HTML/image_m/fimmu-16-1664788-g001.jpg Figure 1. Correlation matrices between EASIX and m-EASIX scores and biomarkers of endotheliopathy an https://www.frontiersin.org/files/Articles/1664788/fimmu-16-1664788-HTML/image_m/fimmu-16-1664788-g002.jpg Figure 2. (A) Kinetics of EASIX and m-EASIX during the immunotherapy with CAR T-cells in patients wh https://www.frontiersin.org/files/Articles/1664788/fimmu-16-1664788-HTML/image_m/fimmu-16-1664788-g003.jpg Figure 3. ROC analysis for the performance of EASIX and m-EASIX for early prediction of any Grade 3– https://www.frontiersin.org/files/Articles/1664788/fimmu-16-1664788-HTML/image_m/fimmu-16-1664788-g004.jpg Figure 4. Survival plots in months by EASIX and m-EASIX groups defined by optimal cutoff point (LogR https://www.frontiersin.org/files/Articles/1664788/fimmu-16-1664788-HTML/image_m/fimmu-16-1664788-t003.jpg Table 3. Multivariable Cox proportional hazards model for overall survival. https://www.frontiersin.org/files/Articles/1664788/fimmu-16-1664788-HTML/image_m/fimmu-16-1664788-g005.jpg Figure 5. Performance of EASIX and m-EASIX in discriminating Sepsis from CRS at symptoms onset (feve https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1700907/full https://www.frontiersin.org/files/Articles/1700907/fimmu-16-1700907-HTML-r1/image_m/fimmu-16-1700907-t001.jpg Table 1. Clinical data. https://www.frontiersin.org/files/Articles/1700907/fimmu-16-1700907-HTML-r1/image_m/fimmu-16-1700907-t002.jpg Table 2. Laboratory data (n=55). https://www.frontiersin.org/files/Articles/1700907/fimmu-16-1700907-HTML-r1/image_m/fimmu-16-1700907-g001.jpg Figure 1. EASIX and ADAMTS13 activity dynamics at debut, clinical relapse, ADAMTS13 relapse, and iso https://www.frontiersin.org/files/Articles/1700907/fimmu-16-1700907-HTML-r1/image_m/fimmu-16-1700907-g002.jpg Figure 2. EASIX dynamics in non-refractory vs. refractory patients. https://www.frontiersin.org/files/Articles/1700907/fimmu-16-1700907-HTML-r1/image_m/fimmu-16-1700907-g003.jpg Figure 3. EASIX day 0 and day 1 ROC curves. https://www.frontiersin.org/files/Articles/1700907/fimmu-16-1700907-HTML-r1/image_m/fimmu-16-1700907-g004.jpg Figure 4. EASIX dynamics in survivors vs. non-survivors. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1800546/full https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-g001.jpg Figure 1. PRISMA flow diagram illustrating the study selection process for the systematic review and https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-g002.jpg Figure 2. Risk-of-bias assessment of included trials using the Cochrane RoB 2 tool. https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-g003.jpg Figure 3. Forest plot of the effects of creatine supplementation on maximal strength, power, anaerob https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-g004.jpg Figure 4. Forest plot of subgroup analyses stratified by RT and non-resistance training (non-RT). https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-g005.jpg Figure 5. Forest plot of subgroup analyses for CMJ. https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-g006.jpg Figure 6. Funnel plots assessing publication bias. https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-t002.jpg Table 2. Certainty of evidence (GRADE framework) for primary outcomes. https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-t003.jpg Table 3. Summary of outcome-specific effects by training context (RT vs. non-RT). https://www.frontiersin.org/files/Articles/1800546/fnut-13-1800546-HTML-r1/image_m/fnut-13-1800546-g007.jpg Figure A1. Outcome-specific forest plots for squat 1RM, leg press 1RM, CMJ, Wingate peak power, Wing https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1704240/full https://www.frontiersin.org/files/Articles/1704240/fpubh-14-1704240-HTML/image_m/fpubh-14-1704240-t001.jpg Table 1. Geo-demographic and socioeconomic information categorized by health status. https://www.frontiersin.org/files/Articles/1704240/fpubh-14-1704240-HTML/image_m/fpubh-14-1704240-g001.jpg Figure 1. Mean and standard deviations of psychological well-being indicators and lifestyle behavior https://www.frontiersin.org/files/Articles/1704240/fpubh-14-1704240-HTML/image_m/fpubh-14-1704240-t002.jpg Table 2. Potential predictive role of socio-demogarphic, psychological and behaviorals factors in he https://www.frontiersin.org/files/Articles/1704240/fpubh-14-1704240-HTML/image_m/fpubh-14-1704240-t003.jpg Table 3. Comprehensive predictive models of health status. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1775425/full https://www.frontiersin.org/files/Articles/1775425/fpsyg-17-1775425-HTML-r2/image_m/fpsyg-17-1775425-g001.jpg Figure 1. According to ISIPOR, the process of adapting the Digital Amnesia Scale (DAS) adult scale t https://www.frontiersin.org/files/Articles/1775425/fpsyg-17-1775425-HTML-r2/image_m/fpsyg-17-1775425-g002.jpg Figure 2. Confirmatory factor analysis (CFA) diagram for the Turkish version of the Digital Amnesia https://www.frontiersin.org/files/Articles/1775425/fpsyg-17-1775425-HTML-r2/image_m/fpsyg-17-1775425-t001.jpg Table 1. Statistical results for path coefficients of the Digital Amnesia Scale. https://www.frontiersin.org/files/Articles/1775425/fpsyg-17-1775425-HTML-r2/image_m/fpsyg-17-1775425-g003.jpg Figure 3. Distribution of responses according to Digital Amnesia dimensions. https://www.frontiersin.org/files/Articles/1775425/fpsyg-17-1775425-HTML-r2/image_m/fpsyg-17-1775425-t002.jpg Table 2. Reliability analysis results of the Turkish version of the Digital Amnesia Scale. https://www.frontiersin.org/files/Articles/1775425/fpsyg-17-1775425-HTML-r2/image_m/fpsyg-17-1775425-t003.jpg Table 3. Fit indices calculated for the Turkish version of the Digital Amnesia Scale. https://www.frontiersin.org/files/Articles/1775425/fpsyg-17-1775425-HTML-r2/image_m/fpsyg-17-1775425-g004.jpg Figure 4. Correlation graphs. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1703634/full https://www.frontiersin.org/files/Articles/1703634/fdgth-08-1703634-HTML-r1/image_m/fdgth-08-1703634-g001.jpg Figure 1. Illustrative examples of different types of brain hemorrhages in CT scans. The hemorrhagic https://www.frontiersin.org/files/Articles/1703634/fdgth-08-1703634-HTML-r1/image_m/fdgth-08-1703634-t001.jpg Table 1. The PRISMA 2020 flow summary describing the selection process. https://www.frontiersin.org/files/Articles/1703634/fdgth-08-1703634-HTML-r1/image_m/fdgth-08-1703634-t002.jpg Table 2. Survey 1 conducted for the study. https://www.frontiersin.org/files/Articles/1703634/fdgth-08-1703634-HTML-r1/image_m/fdgth-08-1703634-t003.jpg Table 3. Comparative analysis of selected deep learning models for intracerebral hemorrhage (ICH) cl https://www.frontiersin.org/files/Articles/1703634/fdgth-08-1703634-HTML-r1/image_m/fdgth-08-1703634-t004.jpg Table 4. Distribution of labeled samples for various hemorrhage types and non-hemorrhagic (normal) c https://www.frontiersin.org/files/Articles/1703634/fdgth-08-1703634-HTML-r1/image_m/fdgth-08-1703634-g002.jpg Figure 2. CT scan images under different window settings: default window, brain window, subdural win https://www.frontiersin.org/files/Articles/1703634/fdgth-08-1703634-HTML-r1/image_m/fdgth-08-1703634-t005.jpg Table 5. Comparison of Various hemorrhage detection techniques. https://www.frontiersin.org/files/Articles/1703634/fdgth-08-1703634-HTML-r1/image_m/fdgth-08-1703634-t006.jpg Table 6. Binary classification performance metrics (normal vs. subarachnoid hemorrhage) for differen https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1801981/full https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-g001.jpg Figure 1. Map location of the study locations. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t001.jpg Table 1. Descriptive statistics of the ground observed weather data for the three locations. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t002.jpg Table 2. Descriptive statistics of the NASA POWER data for the three locations. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-g002.jpg Figure 2. Monthly variation of the average reference evapotranspiration for Kano, Onne, and Ibadan. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t003.jpg Table 3. Relationships between the NASA POWER data and reference evapotranspiration. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t004.jpg Table 4. Models training results for the prediction of reference evapotranspiration. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t005.jpg Table 5. Model validation results for the prediction of the reference evapotranspiration. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t006.jpg Table 6. Prediction of reference evapotranspiration for the month of January at the different locati https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t007.jpg Table 7. Prediction of reference evapotranspiration for the month of February at the different locat https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t008.jpg Table 8. Prediction of reference evapotranspiration for the month of March at the different location https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t009.jpg Table 9. Prediction of reference evapotranspiration for the month of April at the different location https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t010.jpg Table 10. Prediction of reference evapotranspiration for the month of May at different locations. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t011.jpg Table 11. Prediction of reference evapotranspiration for the month of June at different locations. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t012.jpg Table 12. Prediction of reference evapotranspiration for the month of July at different locations. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t013.jpg Table 13. Prediction of reference evapotranspiration for the month of August at different locations. https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t014.jpg Table 14. Prediction of reference evapotranspiration for the month of September at different locatio https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t015.jpg Table 15. Prediction of reference evapotranspiration for the month of October at different locations https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t016.jpg Table 16. Prediction of reference evapotranspiration for the month of November at different location https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-t017.jpg Table 17. Prediction of reference evapotranspiration for the month of December at different location https://www.frontiersin.org/files/Articles/1801981/frai-09-1801981-HTML/image_m/frai-09-1801981-g003.jpg Figure 3. Relationships between observed ETo and the ML predicted ETo using FG SVM for Kano (a), Iba https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1735367/full https://www.frontiersin.org/files/Articles/1735367/fpubh-14-1735367-HTML-r1/image_m/fpubh-14-1735367-t001.jpg Table 1. Number of whole-body vibration measurements conducted during the study in Tanzania (N = 141 https://www.frontiersin.org/files/Articles/1735367/fpubh-14-1735367-HTML-r1/image_m/fpubh-14-1735367-t002.jpg Table 2. Demographic characteristics of truck drivers and heavy machine operators in the transportat https://www.frontiersin.org/files/Articles/1735367/fpubh-14-1735367-HTML-r1/image_m/fpubh-14-1735367-t003.jpg Table 3. Summary of whole-body vibration (WBV) exposure metrics [Aw, A(8) and crest factor] for work https://www.frontiersin.org/files/Articles/1735367/fpubh-14-1735367-HTML-r1/image_m/fpubh-14-1735367-g001.jpg Figure 1. Whole-body vibration exposure [A8, m/s2 A(8)] by equipment model in the mining (mines A an https://www.frontiersin.org/files/Articles/1735367/fpubh-14-1735367-HTML-r1/image_m/fpubh-14-1735367-t004.jpg Table 4. Factors associated with 12-month chronic low back pain among heavy mobile machine operators https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1731967/full https://www.frontiersin.org/files/Articles/1731967/fnut-13-1731967-HTML-r1/image_m/fnut-13-1731967-g001.jpg Figure 1. Conceptual model representing the relationships predicted to influence the intention of co https://www.frontiersin.org/files/Articles/1731967/fnut-13-1731967-HTML-r1/image_m/fnut-13-1731967-t001.jpg Table 1. Reliability (Cronbach alpha and composite reliability) and convergent validity (AVE) of eac https://www.frontiersin.org/files/Articles/1731967/fnut-13-1731967-HTML-r1/image_m/fnut-13-1731967-t002.jpg Table 2. Discriminant validity using the HTMT method. https://www.frontiersin.org/files/Articles/1731967/fnut-13-1731967-HTML-r1/image_m/fnut-13-1731967-t003.jpg Table 3. Results of the hypotheses testing (Gen X). https://www.frontiersin.org/files/Articles/1731967/fnut-13-1731967-HTML-r1/image_m/fnut-13-1731967-t004.jpg Table 4. Results of the hypotheses testing (Gen Y). https://www.frontiersin.org/journals/space-technologies/articles/10.3389/frspt.2026.1752453/full https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g001.jpg Figure 1. External view of the LunAres Research Station, a terrestrial analog habitat used for the 1 https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g002.jpg Figure 2. Internal layout of the LunAres Research Station, showing functional zones including living https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-t001.jpg Table 1. Descriptive statistics of physiological parameters measured during the 14-day isolation mis https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-t002.jpg Table 2. Pairwise comparisons of physiological parameters between mission days. https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g003.jpg Figure 3. Changes in crew members’ body weight. Black vertical lines indicate the mean ± standard de https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g004.jpg Figure 4. Q-Q plots for body weight during the 14-day isolation mission. https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g005.jpg Figure 5. Changes in crew members’ temperature. Black vertical lines indicate the mean ± standard de https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g006.jpg Figure 6. Q-Q plots for core body temperature during the 14-day isolation mission. https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g007.jpg Figure 7. Changes in crew members’ systolic blood pressure values. Black vertical lines indicate the https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g008.jpg Figure 8. Q-Q plots for systolic blood pressure during the 14-day isolation mission. https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g009.jpg Figure 9. Changes in crew members’ diastolic blood pressure values. Black vertical lines indicate th https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g010.jpg Figure 10. Q-Q plots for diastolic blood pressure during the 14-day isolation mission. https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g011.jpg Figure 11. Changes in crew members’ heart rate values. Black vertical lines indicate the mean ± stan https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g012.jpg Figure 12. Q-Q plots for heart rate during the 14-day isolation mission. https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g013.jpg Figure 13. Changes in crew members’ peripheral oxygen saturation values. Black vertical lines indica https://www.frontiersin.org/files/Articles/1752453/frspt-07-1752453-HTML/image_m/frspt-07-1752453-g014.jpg Figure 14. Q-Q plots for peripheral oxygen saturation (SpO2) during the 14-day isolation mission. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1767346/full https://www.frontiersin.org/files/Articles/1767346/fped-14-1767346-HTML/image_m/fped-14-1767346-i001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1767346/fped-14-1767346-HTML/image_m/fped-14-1767346-g001.jpg Figure 1. Study protocol and design, evaluation images (Gazefinder). (A) An outline of the study is https://www.frontiersin.org/files/Articles/1767346/fped-14-1767346-HTML/image_m/fped-14-1767346-t001.jpg Table 1. Demographic characteristics of study participants. https://www.frontiersin.org/files/Articles/1767346/fped-14-1767346-HTML/image_m/fped-14-1767346-g002.jpg Figure 2. Changes in the gut microbiota composition in children with ASD before and after SHIN-1 adm https://www.frontiersin.org/files/Articles/1767346/fped-14-1767346-HTML/image_m/fped-14-1767346-g003.jpg Figure 3. The effect of the new FMT method on reducing the severity of ASD. (A) The effect of a new https://www.frontiersin.org/files/Articles/1767346/fped-14-1767346-HTML/image_m/fped-14-1767346-g004.jpg Figure 4. The effect of the new FMT method on reducing the severity of SRS-2 sub-category. Upper: SC https://www.frontiersin.org/files/Articles/1767346/fped-14-1767346-HTML/image_m/fped-14-1767346-g005.jpg Figure 5. Effects of the novel FMT method on sensory processing disorder and ASD-related symptoms (g https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1720969/full https://www.frontiersin.org/files/Articles/1720969/fnhum-20-1720969-HTML/image_m/fnhum-20-1720969-g001.jpg Figure 1. Locations of the 32 electrodes with the default 16 (Huggins et al., 2016; Thompson et al., https://www.frontiersin.org/files/Articles/1720969/fnhum-20-1720969-HTML/image_m/fnhum-20-1720969-g002.jpg Figure 2. Custom subset locations, default and calibration accuracy for each participant with signif https://www.frontiersin.org/files/Articles/1720969/fnhum-20-1720969-HTML/image_m/fnhum-20-1720969-g003.jpg Figure 3. Screen layout for the BCI-presented vocabulary test format for Left: Protocol 1; Right: Pr https://www.frontiersin.org/files/Articles/1720969/fnhum-20-1720969-HTML/image_m/fnhum-20-1720969-t001.jpg Table 1. Distributions of participants by functional classification sytem in Protocol 1 and Protocol https://www.frontiersin.org/files/Articles/1720969/fnhum-20-1720969-HTML/image_m/fnhum-20-1720969-g004.jpg Figure 4. BCI calibration accuracy with different electrode subset types by participant group. The s https://www.frontiersin.org/files/Articles/1720969/fnhum-20-1720969-HTML/image_m/fnhum-20-1720969-g005.jpg Figure 5. BCI calibration accuracy across age. Experimental data is plotted as scatters and a fitted https://www.frontiersin.org/files/Articles/1720969/fnhum-20-1720969-HTML/image_m/fnhum-20-1720969-g006.jpg Figure 6. BCI testing accuracy with different electrode subset types. No significant improvement is https://www.frontiersin.org/files/Articles/1720969/fnhum-20-1720969-HTML/image_m/fnhum-20-1720969-g007.jpg Figure 7. Calibration accuracy for custom electrode subset sizes. (a) Each line is a participant. Ac https://www.frontiersin.org/journals/nephrology/articles/10.3389/fneph.2026.1608421/full https://www.frontiersin.org/files/Articles/1608421/fneph-06-1608421-HTML/image_m/fneph-06-1608421-t001.jpg Table 1. Demographic and cognitive characterization of the individuals included in the analyses. https://www.frontiersin.org/files/Articles/1608421/fneph-06-1608421-HTML/image_m/fneph-06-1608421-g001.jpg Figure 1. Average reaction time and proportion of accuracy per group and type of trial. https://www.frontiersin.org/files/Articles/1608421/fneph-06-1608421-HTML/image_m/fneph-06-1608421-g002.jpg Figure 2. Topographic maps per group and type of trial and topographic difference between incongruen https://www.frontiersin.org/files/Articles/1608421/fneph-06-1608421-HTML/image_m/fneph-06-1608421-g003.jpg Figure 3. (A) Averaged ERPs per group and trial type at Fz, FCz, and Cz. (B) Plots showing distribut https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1575315/full https://www.frontiersin.org/files/Articles/1575315/fonc-15-1575315-HTML/image_m/fonc-15-1575315-g001.jpg Figure 1. CT of the Abdomen and Pelvis revealed a large mixed-density mass (23.6 × 15.6 × 17.3 cm) i https://www.frontiersin.org/files/Articles/1575315/fonc-15-1575315-HTML/image_m/fonc-15-1575315-g002.jpg Figure 2. The tumor consists of bland spindle cells with abundant fibrotic stroma. Minimal mitotic a https://www.frontiersin.org/files/Articles/1575315/fonc-15-1575315-HTML/image_m/fonc-15-1575315-g003.jpg Figure 3. Intraoperative exploration revealed significant hemoperitoneum. A large intra-abdominal ma https://www.frontiersin.org/files/Articles/1575315/fonc-15-1575315-HTML/image_m/fonc-15-1575315-t001.jpg Table 1. Summary of pregnancy-associated intra-abdominal desmoid tumors. https://www.frontiersin.org/files/Articles/1575315/fonc-15-1575315-HTML/image_m/fonc-15-1575315-t002.jpg Table 2. Differential diagnosis of common abdominal tumors in pregnancy. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1741937/full https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-g001.jpg Figure 1. Experiential learning model. Source: Pamungkas et al. (2019). https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-t001.jpg Table 1. Biophilic design components. https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-t002.jpg Table 2. Variables for application of biophilic design concepts in architectural design. https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-t003.jpg Table 3. Profile of respondents. https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-g002.jpg Figure 2. Most prevalent biophilic elements. https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-g003.jpg Figure 3. Mean score of the biophilic design concepts. https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-t004.jpg Table 4. Application of the biophilic design concept in architecture design. https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-g004.jpg Figure 4. The correlation heatmap between biophilic design attributes and ELT stages. https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-t005.jpg Table 5. Reflective observation and abstract conceptualisation. https://www.frontiersin.org/files/Articles/1741937/feduc-11-1741937-HTML/image_m/feduc-11-1741937-t006.jpg Table 6. Active experimentation. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1772408/full https://www.frontiersin.org/files/Articles/1772408/fped-14-1772408-HTML-r1/image_m/fped-14-1772408-g001.jpg Figure 1. Pre-chemotherapy magnetic resonance imaging (MRI). (A) Axial T2-weighted image demonstrati https://www.frontiersin.org/files/Articles/1772408/fped-14-1772408-HTML-r1/image_m/fped-14-1772408-g002.jpg Figure 2. Follow-up computed tomography (CT) after 6 months of neoadjuvant chemotherapy. (A) Axial i https://www.frontiersin.org/files/Articles/1772408/fped-14-1772408-HTML-r1/image_m/fped-14-1772408-g003.jpg Figure 3. Intraoperative photographs of the bilateral nephron-sparing surgery: (A) the right kidney https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1711687/full https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-t001.jpg Table 1. Tukey’s multiple comparison test and significance analysis of treatments inoculated with Ba https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-t002.jpg Table 2. Tukey’s multiple comparison test and significance analysis of treatments inoculated with Ba https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-g001.jpg Figure 1. (A) Principal component analysis and (B) heatmap for the evaluation of agronomic traits in https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-g002.jpg Figure 2. Effect of Bacillus inoculation and Ascophyllum nodosum extract on maize growth under green https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-t003.jpg Table 3. Analysis of variance (ANOVA) for maize and soybean grain yield (t ha-¹) in multi-environmen https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-t004.jpg Table 4. Maize grain yield (t ha-¹) across six field environments and yield stability of biological https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-t005.jpg Table 5. Soybean grain yield (t ha-¹) across five field environments and yield stability of biologic https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-g003.jpg Figure 3. Database annotation for B. velezensis Ag129. (A) A circular genome map is presented, showi https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-g004.jpg Figure 4. Database annotation for B. velezensis Ag132. (A) A circular genome map is presented, showi https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-g005.jpg Figure 5. Phylogenetic dendrogram based on maximum likelihood analysis using ten available genome as https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-g006.jpg Figure 6. Heatmap of the distribution of genes related to traits that promote plant growth in the ge https://www.frontiersin.org/files/Articles/1711687/fpls-17-1711687-HTML/image_m/fpls-17-1711687-t006.jpg Table 6. Plant growth promotion and antagonism traits against phytopathogenic fungi of the strains B https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1702421/full https://www.frontiersin.org/files/Articles/1702421/fmicb-16-1702421-HTML-r3/image_m/fmicb-16-1702421-t001.jpg Table 1. Effect of different treatments on densities of eggs of Eugaster spinulosa under laboratory https://www.frontiersin.org/files/Articles/1702421/fmicb-16-1702421-HTML-r3/image_m/fmicb-16-1702421-t002.jpg Table 2. Effect of different treatments on motile stages (nymphs and adults) of Eugaster spinulosa u https://www.frontiersin.org/files/Articles/1702421/fmicb-16-1702421-HTML-r3/image_m/fmicb-16-1702421-g001.jpg Figure 1. Visual quality of O. ficus-indica plants after spray application of untreated control (T1) https://www.frontiersin.org/files/Articles/1702421/fmicb-16-1702421-HTML-r3/image_m/fmicb-16-1702421-t003.jpg Table 3. Effect of different treatments on densities of eggs of Eugaster spinulosa under screehouse https://www.frontiersin.org/files/Articles/1702421/fmicb-16-1702421-HTML-r3/image_m/fmicb-16-1702421-t004.jpg Table 4. Effect of different treatments on motile stages (nymphs and adults) of Eugaster spinulosa u https://www.frontiersin.org/files/Articles/1702421/fmicb-16-1702421-HTML-r3/image_m/fmicb-16-1702421-g002.jpg Figure 2. Visual quality of O. ficus-indica plants after spray application of untreated control (T1) https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1800956/full https://www.frontiersin.org/files/Articles/1800956/fnhum-20-1800956-HTML-r1/image_m/fnhum-20-1800956-g001.jpg Figure 1. Structural fingerprint matrix of mTBI derived from T1-weighted MRI. This matrix visualizat https://www.frontiersin.org/files/Articles/1800956/fnhum-20-1800956-HTML-r1/image_m/fnhum-20-1800956-g002.jpg Figure 2. (A) Network-level convergence (abnormal entries per ROI). Bar plot showing the ratio of ab https://www.frontiersin.org/files/Articles/1800956/fnhum-20-1800956-HTML-r1/image_m/fnhum-20-1800956-g003.jpg Figure 3. Hierarchical clustering of T1-weighted structural abnormalities in mTBI. Dendrogram showin https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2026.1790247/full https://www.frontiersin.org/files/Articles/1790247/fragi-07-1790247-HTML/image_m/fragi-07-1790247-t001.jpg Table 1. Therapeutic peptides - mechanisms, applications, and clinical evidence. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1765709/full https://www.frontiersin.org/files/Articles/1765709/fped-14-1765709-HTML/image_m/fped-14-1765709-g001.jpg Figure 1. Preoperative imaging findings. (A) Contrast-enhanced pelvic computed tomography demonstrat https://www.frontiersin.org/files/Articles/1765709/fped-14-1765709-HTML/image_m/fped-14-1765709-g002.jpg Figure 2. Laparoscopic fertility-preserving surgical management. (A) Laparoscopic view showing bilat https://www.frontiersin.org/files/Articles/1765709/fped-14-1765709-HTML/image_m/fped-14-1765709-g003.jpg Figure 3. Pathological findings of ovarian fibromas. (A) Gross specimen showing multiple nodular tum https://www.frontiersin.org/files/Articles/1765709/fped-14-1765709-HTML/image_m/fped-14-1765709-t001.jpg Table 1. Timeline of the patient's clinical course. https://www.frontiersin.org/files/Articles/1765709/fped-14-1765709-HTML/image_m/fped-14-1765709-t002.jpg Table 2. Genetic variants identified by trio whole-exome sequencing. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1756808/full https://www.frontiersin.org/files/Articles/1756808/fped-14-1756808-HTML-r1/image_m/fped-14-1756808-g001.jpg Figure 1. Participant selection flowchart (2020–2023 NSCH). Of 202,934 original participants, 74,125 https://www.frontiersin.org/files/Articles/1756808/fped-14-1756808-HTML-r1/image_m/fped-14-1756808-t001.jpg Table 1. Baseline characteristics of the study population. https://www.frontiersin.org/files/Articles/1756808/fped-14-1756808-HTML-r1/image_m/fped-14-1756808-t002.jpg Table 2. Weighted logistic regression analysis table. https://www.frontiersin.org/files/Articles/1756808/fped-14-1756808-HTML-r1/image_m/fped-14-1756808-g002.jpg Figure 2. Restricted cubic spline (RCS) of sleep duration vs. depression risk. Green squares = RCS n https://www.frontiersin.org/files/Articles/1756808/fped-14-1756808-HTML-r1/image_m/fped-14-1756808-g003.jpg Figure 3. Stratified RCS of sleep duration vs. depression risk (by age/gender). Green squares = RCS https://www.frontiersin.org/files/Articles/1756808/fped-14-1756808-HTML-r1/image_m/fped-14-1756808-t003.jpg Table 3. Subgroup analysis table. https://www.frontiersin.org/files/Articles/1756808/fped-14-1756808-HTML-r1/image_m/fped-14-1756808-g004.jpg Figure 4. Forest plot of subgroup analyses. Shows OR (95%CI) for sleep duration–depression associati https://www.frontiersin.org/files/Articles/1756808/fped-14-1756808-HTML-r1/image_m/fped-14-1756808-t004.jpg Table 4. Weighted mediation analysis. https://www.frontiersin.org/files/Articles/1756808/fped-14-1756808-HTML-r1/image_m/fped-14-1756808-g005.jpg Figure 5. Mediation analysis of sleep duration–depression association. Presents total/direct/indirec https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1636876/full https://www.frontiersin.org/files/Articles/1636876/fimmu-16-1636876-HTML/image_m/fimmu-16-1636876-g000.jpg Graphical Abstract. Regulatory Mechanisms and Therapeutic Strategies of the GLA in Pediatric Pulmona https://www.frontiersin.org/files/Articles/1636876/fimmu-16-1636876-HTML/image_m/fimmu-16-1636876-g001.jpg Figure 1. Gut microbiota modulates pathophysiological processes in pediatric pulmonary diseases via https://www.frontiersin.org/files/Articles/1636876/fimmu-16-1636876-HTML/image_m/fimmu-16-1636876-t001.jpg Table 1. Multi-omic insights into gut–lung axis mechanisms in respiratory diseases. https://www.frontiersin.org/files/Articles/1636876/fimmu-16-1636876-HTML/image_m/fimmu-16-1636876-g002.jpg Figure 2. Distinct features of gut and respiratory microbiota dysbiosis in pediatric pulmonary disea https://www.frontiersin.org/files/Articles/1636876/fimmu-16-1636876-HTML/image_m/fimmu-16-1636876-g003.jpg Figure 3. Gut dysbiosis drives disease-specific mechanisms in pediatric pulmonary disorders via meta https://www.frontiersin.org/files/Articles/1636876/fimmu-16-1636876-HTML/image_m/fimmu-16-1636876-g004.jpg Figure 4. Therapeutic strategies targeting the GLA for pediatric pulmonary diseases: mechanisms and https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1700499/full https://www.frontiersin.org/files/Articles/1700499/fnhum-20-1700499-HTML/image_m/fnhum-20-1700499-g001.jpg Figure 1. The process begins in the striatum when it receives integrated speech production signals f https://www.frontiersin.org/files/Articles/1700499/fnhum-20-1700499-HTML/image_m/fnhum-20-1700499-g002.jpg Figure 2. Dopamine as a mechanism accounting for all changes. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1759781/full https://www.frontiersin.org/files/Articles/1759781/fimmu-17-1759781-HTML/image_m/fimmu-17-1759781-g001.jpg Figure 1. Identification of differentially expressed genes and enriched pathways in the tubulointers https://www.frontiersin.org/files/Articles/1759781/fimmu-17-1759781-HTML/image_m/fimmu-17-1759781-g002.jpg Figure 2. PANoptosis is broadly present across diverse cell types in DKD. (A) GSEA analysis showing https://www.frontiersin.org/files/Articles/1759781/fimmu-17-1759781-HTML/image_m/fimmu-17-1759781-g003.jpg Figure 3. Construction of weighted gene co-expression network and identification of PANoptosis-relat https://www.frontiersin.org/files/Articles/1759781/fimmu-17-1759781-HTML/image_m/fimmu-17-1759781-g004.jpg Figure 4. Machine learning–based identification of hub PANoptosis-related genes in DKD. (A, B) LASSO https://www.frontiersin.org/files/Articles/1759781/fimmu-17-1759781-HTML/image_m/fimmu-17-1759781-g005.jpg Figure 5. Construction of PANoptosis-related risk score based on core genes. (A) Violin plots showin https://www.frontiersin.org/files/Articles/1759781/fimmu-17-1759781-HTML/image_m/fimmu-17-1759781-g006.jpg Figure 6. Functional enrichment analysis of DEGs between PANoptosis high- and low-risk groups. (A, B https://www.frontiersin.org/files/Articles/1759781/fimmu-17-1759781-HTML/image_m/fimmu-17-1759781-g007.jpg Figure 7. The landscape of immune cell infiltration and PANoptosis associations in DKD. (A) Heatmap https://www.frontiersin.org/files/Articles/1759781/fimmu-17-1759781-HTML/image_m/fimmu-17-1759781-g008.jpg Figure 8. Cell–cell communication between proximal tubule cells and immune populations in DKD. (A) G https://www.frontiersin.org/files/Articles/1759781/fimmu-17-1759781-HTML/image_m/fimmu-17-1759781-g009.jpg Figure 9. Validation of Hub PANoptosis-Related Genes in DKD Mouse Models. (A) Left, schematic illust https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1703916/full https://www.frontiersin.org/files/Articles/1703916/fpls-16-1703916-HTML/image_m/fpls-16-1703916-g001.jpg Figure 1. Mass loss dynamics of wheat straw (A), cellulose (B), hemicellulose (C), and lignin (D) un https://www.frontiersin.org/files/Articles/1703916/fpls-16-1703916-HTML/image_m/fpls-16-1703916-g002.jpg Figure 2. Temporal dynamics of extracellular enzyme activities (α-glucosidase: AG, β-glucosidase: BG https://www.frontiersin.org/files/Articles/1703916/fpls-16-1703916-HTML/image_m/fpls-16-1703916-t001.jpg Table 1. Temporal changes in bacterial and fungal α-diversity indices (Chao1 and Shannon) under N0 ( https://www.frontiersin.org/files/Articles/1703916/fpls-16-1703916-HTML/image_m/fpls-16-1703916-g003.jpg Figure 3. Relative abundance of bacterial (A) and fungal (B) communities at the phylum level across https://www.frontiersin.org/files/Articles/1703916/fpls-16-1703916-HTML/image_m/fpls-16-1703916-g004.jpg Figure 4. Top 20 bacterial (A) and fungal (B) genera identified as biomarkers of decomposition stage https://www.frontiersin.org/files/Articles/1703916/fpls-16-1703916-HTML/image_m/fpls-16-1703916-g005.jpg Figure 5. Hierarchical partitioning (HP) analysis quantifying the independent contributions of straw https://www.frontiersin.org/files/Articles/1703916/fpls-16-1703916-HTML/image_m/fpls-16-1703916-g006.jpg Figure 6. Partial least squares path model (PLS-PM) illustrating direct and indirect pathways linkin https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1772176/full https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-g001.jpg Figure 1. Comparison of Huimin Insurance and basic medical insurance. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-g002.jpg Figure 2. Scope of coverage for each medical insurance. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t001.jpg Table 1. Descriptive statistics. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t002.jpg Table 2. Benchmark regression results. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-g003.jpg Figure 3. Parallel trend test. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t003.jpg Table 3. Endogenous processing results. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-g004.jpg Figure 4. Placebo test. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t004.jpg Table 4. Robustness test 1. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t005.jpg Table 5. Robustness test 2. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t006.jpg Table 6. Robustness test 3. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t007.jpg Table 7. Guarantee level indicator system. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t008.jpg Table 8. Heterogeneity analysis 1. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t009.jpg Table 9. Heterogeneity analysis 2. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t010.jpg Table 10. Analysis of impact mechanism. https://www.frontiersin.org/files/Articles/1772176/fpubh-14-1772176-HTML-r1/image_m/fpubh-14-1772176-t011.jpg Table 11. Further analysis. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1646518/full https://www.frontiersin.org/files/Articles/1646518/fmicb-16-1646518-HTML/image_m/fmicb-16-1646518-g001.jpg Figure 1. (A) Optical density of culture of Bacillus licheniformis KNP after growth for 48 h in medi https://www.frontiersin.org/files/Articles/1646518/fmicb-16-1646518-HTML/image_m/fmicb-16-1646518-g002.jpg Figure 2. Effects of various temperatures and pH on bacterial growth and hexavalent chromium reducti https://www.frontiersin.org/files/Articles/1646518/fmicb-16-1646518-HTML/image_m/fmicb-16-1646518-g003.jpg Figure 3. (a) SEM analysis of chromium untreated (A) and chromium treated cells (B) of B. lichenifor https://www.frontiersin.org/files/Articles/1646518/fmicb-16-1646518-HTML/image_m/fmicb-16-1646518-g004.jpg Figure 4. FTIR analysis of cells of Bacillus licheniformis KNP (a) without Cr(VI) treatment and (b) https://www.frontiersin.org/files/Articles/1646518/fmicb-16-1646518-HTML/image_m/fmicb-16-1646518-t001.jpg Table 1. FTIR peak shifts in Bacillus licheniformis KNP under Cr(VI) stress. https://www.frontiersin.org/files/Articles/1646518/fmicb-16-1646518-HTML/image_m/fmicb-16-1646518-g005.jpg Figure 5. Chemotaxis away from chromium. (a) Drop plate assay showing accumulation of KNP cells towa https://www.frontiersin.org/files/Articles/1646518/fmicb-16-1646518-HTML/image_m/fmicb-16-1646518-t002.jpg Table 2. A list of annotated chemotactic genes in the genome of Bacillus licheniformis KNP. https://www.frontiersin.org/files/Articles/1646518/fmicb-16-1646518-HTML/image_m/fmicb-16-1646518-g006.jpg Figure 6. Mechanism of chemotaxis in Bacillus licheniformis strain KNP. Methyl-accepting chemotaxis https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1693596/full https://www.frontiersin.org/files/Articles/1693596/fmicb-16-1693596-HTML/image_m/fmicb-16-1693596-t001.jpg Table 1. PHA producing microorganisms obtained by natural screening. https://www.frontiersin.org/files/Articles/1693596/fmicb-16-1693596-HTML/image_m/fmicb-16-1693596-g001.jpg Figure 1. Main metabolic pathways of PHA generation (Adapted from Choi et al., 2020). https://www.frontiersin.org/files/Articles/1693596/fmicb-16-1693596-HTML/image_m/fmicb-16-1693596-g002.jpg Figure 2. Process of food waste-VFAs-PHA generation. https://www.frontiersin.org/files/Articles/1693596/fmicb-16-1693596-HTML/image_m/fmicb-16-1693596-g003.jpg Figure 3. Factors affecting the PHA production from food waste digestate. https://www.frontiersin.org/files/Articles/1693596/fmicb-16-1693596-HTML/image_m/fmicb-16-1693596-t002.jpg Table 2. Food waste-oriented VFAs for PHA generation using pure bacterial culture or mixed microbial https://www.frontiersin.org/files/Articles/1693596/fmicb-16-1693596-HTML/image_m/fmicb-16-1693596-t003.jpg Table 3. Techno-economic and environmental analysis of PHA production. https://www.frontiersin.org/files/Articles/1693596/fmicb-16-1693596-HTML/image_m/fmicb-16-1693596-g004.jpg Figure 4. Challenges in food waste-VFAs-PHA generation. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1795722/full https://www.frontiersin.org/files/Articles/1795722/fmed-13-1795722-HTML/image_m/fmed-13-1795722-t001.jpg Table 1. The description of attributes and levels. https://www.frontiersin.org/files/Articles/1795722/fmed-13-1795722-HTML/image_m/fmed-13-1795722-t002.jpg Table 2. An example of discrete choice experiment task. https://www.frontiersin.org/files/Articles/1795722/fmed-13-1795722-HTML/image_m/fmed-13-1795722-g001.jpg Figure 1. Questionnaire screening flow diagram. https://www.frontiersin.org/files/Articles/1795722/fmed-13-1795722-HTML/image_m/fmed-13-1795722-t003.jpg Table 3. Characteristics of respondents (n = 248). https://www.frontiersin.org/files/Articles/1795722/fmed-13-1795722-HTML/image_m/fmed-13-1795722-t004.jpg Table 4. Mixed logit model estimating PCPs’ preferences for attributes. https://www.frontiersin.org/files/Articles/1795722/fmed-13-1795722-HTML/image_m/fmed-13-1795722-g002.jpg Figure 2. PCPs’ preferences for implementing training programs. β, the average preferences of the st https://www.frontiersin.org/files/Articles/1795722/fmed-13-1795722-HTML/image_m/fmed-13-1795722-g003.jpg Figure 3. Changes in selection rates for training implementation. The baseline scenario is character https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1762807/full https://www.frontiersin.org/files/Articles/1762807/fmed-13-1762807-HTML/image_m/fmed-13-1762807-g001.jpg Figure 1. The flowchart of the study. https://www.frontiersin.org/files/Articles/1762807/fmed-13-1762807-HTML/image_m/fmed-13-1762807-t001.jpg Table 1. Basic characteristics of the included literature. https://www.frontiersin.org/files/Articles/1762807/fmed-13-1762807-HTML/image_m/fmed-13-1762807-g002.jpg Figure 2. Risk assessment of bias using the RoB2. Risk of bias items of all included studies are ind https://www.frontiersin.org/files/Articles/1762807/fmed-13-1762807-HTML/image_m/fmed-13-1762807-g003.jpg Figure 3. A network diagram of comparable studies for each outcome in the Bayesian network meta-anal https://www.frontiersin.org/files/Articles/1762807/fmed-13-1762807-HTML/image_m/fmed-13-1762807-g004.jpg Figure 4. Pooled estimates of the network meta-analysis. (A) Standard mean differences (SMDs) (95% C https://www.frontiersin.org/files/Articles/1762807/fmed-13-1762807-HTML/image_m/fmed-13-1762807-g005.jpg Figure 5. The results of Bayesian ranking for primary outcomes. The line graph presents the ranking https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1719310/full https://www.frontiersin.org/files/Articles/1719310/fnut-12-1719310-HTML/image_m/fnut-12-1719310-t001.jpg Table 1. Citation matrix of included meta-analyses. https://www.frontiersin.org/files/Articles/1719310/fnut-12-1719310-HTML/image_m/fnut-12-1719310-g001.jpg Figure 1. Flow diagram of the selection process. https://www.frontiersin.org/files/Articles/1719310/fnut-12-1719310-HTML/image_m/fnut-12-1719310-g002.jpg Figure 2. Significant benefits of probiotics versus control on clinical outcomes. https://www.frontiersin.org/files/Articles/1719310/fnut-12-1719310-HTML/image_m/fnut-12-1719310-t002.jpg Table 2. Study characteristics of the included studies. https://www.frontiersin.org/files/Articles/1719310/fnut-12-1719310-HTML/image_m/fnut-12-1719310-t003.jpg Table 3. AMSTAR 2 score for included articles. https://www.frontiersin.org/files/Articles/1719310/fnut-12-1719310-HTML/image_m/fnut-12-1719310-g003.jpg Figure 3. Forest plots indicating ESs and 95% CIs regarding the effects of probiotic supplementation https://www.frontiersin.org/files/Articles/1719310/fnut-12-1719310-HTML/image_m/fnut-12-1719310-g004.jpg Figure 4. Forest plots indicating ESs and 95% CIs regarding the effects of probiotic supplementation https://www.frontiersin.org/files/Articles/1719310/fnut-12-1719310-HTML/image_m/fnut-12-1719310-g005.jpg Figure 5. Forest plots indicating ESs and 95% CIs regarding the effects of probiotic supplementation https://www.frontiersin.org/files/Articles/1719310/fnut-12-1719310-HTML/image_m/fnut-12-1719310-g006.jpg Figure 6. Forest plots indicating ESs and 95% CIs regarding the effects of probiotic supplementation https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1788655/full https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g001.jpg Figure 1. RT-DETRv2 module structure. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g002.jpg Figure 2. Bidirectional cross gate module structure. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g003.jpg Figure 3. Dynamic channel shift module structure. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g004.jpg Figure 4. Structural comparison of different architectures. (a) RepVGG-style multi-branch structure. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-t001.jpg Table 1. Impact of different feature fusion structures on detection performance. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g005.jpg Figure 5. Sample images from the plant-disease dataset. (a-c) show three types of plant diseases, wh https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-t002.jpg Table 2. Software and hardware configuration for experiments. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-t003.jpg Table 3. Training hyperparameter settings. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-t004.jpg Table 4. Ablation study results of different modules on the plant-disease dataset. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-t005.jpg Table 5. Performance comparison with state-of-the-art object detection models on the plant-disease d https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-t006.jpg Table 6. Ablation study on the detecting rice crop diseases dataset. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-t007.jpg Table 7. Ablation study on the disease detection dataset. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g006.jpg Figure 6. Visualization of channel attention weights. The heatmap display range is normalized accord https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g007.jpg Figure 7. Comparison of effective receptive fields between the RepVGG block and the DCS block. (a) H https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g008.jpg Figure 8. Comparison of radial average response profiles between the RepVGG Block and the DCS Block. https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g009.jpg Figure 9. Visualization of decoder attention heatmaps. (a) Input image. (b) All-query attention of t https://www.frontiersin.org/files/Articles/1788655/fpls-17-1788655-HTML/image_m/fpls-17-1788655-g010.jpg Figure 10. Comparison of attention distribution in lesion regions. (a) Percentage of peak attention https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1662079/full https://www.frontiersin.org/files/Articles/1662079/fonc-15-1662079-HTML/image_m/fonc-15-1662079-t001.jpg Table 1. Patients and tumor characteristics. https://www.frontiersin.org/files/Articles/1662079/fonc-15-1662079-HTML/image_m/fonc-15-1662079-g001.jpg Figure 1. Representative immunohistochemical staining of key prognostic markers. (A) Positive nuclea https://www.frontiersin.org/files/Articles/1662079/fonc-15-1662079-HTML/image_m/fonc-15-1662079-t002.jpg Table 2. Multivariate Analysis of Factors Associated with Postoperative Recurrence. https://www.frontiersin.org/files/Articles/1662079/fonc-15-1662079-HTML/image_m/fonc-15-1662079-t003.jpg Table 3. Final Variables Incorporated into the Nomogram Model. https://www.frontiersin.org/files/Articles/1662079/fonc-15-1662079-HTML/image_m/fonc-15-1662079-g002.jpg Figure 2. Nomogram for predicting postoperative recurrence of glomus jugulare tumors. To use the nom https://www.frontiersin.org/files/Articles/1662079/fonc-15-1662079-HTML/image_m/fonc-15-1662079-g003.jpg Figure 3. Calibration curve of the nomogram. The 45-degree dotted line represents the ideal predicti https://www.frontiersin.org/files/Articles/1662079/fonc-15-1662079-HTML/image_m/fonc-15-1662079-t004.jpg Table 4. Number of Positive Cases or Mean Values of Predictive Factors in the Validation and Test Se https://www.frontiersin.org/files/Articles/1662079/fonc-15-1662079-HTML/image_m/fonc-15-1662079-g004.jpg Figure 4. Receiver operating characteristic (ROC) curves of the nomogram. (A) Training set (AUC = 0. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1748810/full https://www.frontiersin.org/files/Articles/1748810/fphys-17-1748810-HTML/image_m/fphys-17-1748810-g001.jpg Figure 1. Analytical framework for the study. NSEPs, National sports event policies; LGDGs, Local go https://www.frontiersin.org/files/Articles/1748810/fphys-17-1748810-HTML/image_m/fphys-17-1748810-t001.jpg Table 1. The assignment to the outcome variable and the condition variable. https://www.frontiersin.org/files/Articles/1748810/fphys-17-1748810-HTML/image_m/fphys-17-1748810-t002.jpg Table 2. Necessary analysis result. https://www.frontiersin.org/files/Articles/1748810/fphys-17-1748810-HTML/image_m/fphys-17-1748810-t003.jpg Table 3. Sufficiency analysis results. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1594751/full https://www.frontiersin.org/files/Articles/1594751/fmars-12-1594751-HTML/image_m/fmars-12-1594751-t001.jpg Table 1. The ingredients and biochemical analysis of the experimental groups. https://www.frontiersin.org/files/Articles/1594751/fmars-12-1594751-HTML/image_m/fmars-12-1594751-t002.jpg Table 2. Primers, accession numbers, sequences, and product sizes (bp) were used for the qPCR study. https://www.frontiersin.org/files/Articles/1594751/fmars-12-1594751-HTML/image_m/fmars-12-1594751-t003.jpg Table 3. Growth performance and nutrient utilization efficiency of Litopenaeus vannamei shrimp fed d https://www.frontiersin.org/files/Articles/1594751/fmars-12-1594751-HTML/image_m/fmars-12-1594751-t004.jpg Table 4. Biochemical composition analysis (%) of Litopenaeus vannamei shrimp fed diets supplemented https://www.frontiersin.org/files/Articles/1594751/fmars-12-1594751-HTML/image_m/fmars-12-1594751-t005.jpg Table 5. Immunological responses and antioxidant of Litopenaeus vannamei shrimp fed diets supplement https://www.frontiersin.org/files/Articles/1594751/fmars-12-1594751-HTML/image_m/fmars-12-1594751-g001.jpg Figure 1. Digestive enzyme activities (A amylase and B lipase) of L. vannamei shrimp fed diets conta https://www.frontiersin.org/files/Articles/1594751/fmars-12-1594751-HTML/image_m/fmars-12-1594751-g002.jpg Figure 2. Gene expression levels of growth (A) GH, (B) IGF-1, and (C) IGF-II, and immunity; (D) Prop https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1807720/full https://www.frontiersin.org/files/Articles/1807720/fmars-13-1807720-HTML/image_m/fmars-13-1807720-t001.jpg Table 1. Vibrio strains used in this study. https://www.frontiersin.org/files/Articles/1807720/fmars-13-1807720-HTML/image_m/fmars-13-1807720-g001.jpg Figure 1. Strain-specific virulence phenotypes correspond to V. mediterranei isolation source. Eleve https://www.frontiersin.org/files/Articles/1807720/fmars-13-1807720-HTML/image_m/fmars-13-1807720-g002.jpg Figure 2. Strain-specific pathogenicity of Vibrio species in oyster larvae. Eastern oyster larvae (C https://www.frontiersin.org/files/Articles/1807720/fmars-13-1807720-HTML/image_m/fmars-13-1807720-g003.jpg Figure 3. V. mediterranei Vm02 pre-colonization provides robust protection against pathogenic Vibrio https://www.frontiersin.org/files/Articles/1807720/fmars-13-1807720-HTML/image_m/fmars-13-1807720-g004.jpg Figure 4. Persistent association with oyster larvae by GFP-tagged V. mediterranei Vm02. Fluorescence https://www.frontiersin.org/files/Articles/1807720/fmars-13-1807720-HTML/image_m/fmars-13-1807720-g005.jpg Figure 5. Phylogenetic analysis reveals three monophyletic clades within the V. mediterranei species https://www.frontiersin.org/files/Articles/1807720/fmars-13-1807720-HTML/image_m/fmars-13-1807720-g006.jpg Figure 6. Proposed ecological strategies of V. mediterranei lineages. Schematic model illustrating d https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2026.1792340/full https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-g001.jpg Figure 1. Histogram of Drug Loading Capacity values. https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-g002.jpg Figure 2. Histogram of Cell Viability values. https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-g003.jpg Figure 3. The CO working Flowchart (Alqarni and Alqarni, 2024). https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-t001.jpg Table 1. Coefficient of determination values of models for drug loading capacity as output. https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-t002.jpg Table 2. Error rates of models for drug loading capacity as output. https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-t003.jpg Table 3. Coefficient of determination values of models for cell viability as output. https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-t004.jpg Table 4. Error rates of models for cell viability as output. https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-g004.jpg Figure 4. Comparison of predicted and actual values for Drug Loading Capacity using DGPR model as th https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-g005.jpg Figure 5. Comparison of predicted and actual values for Cell Viability using DGPR model as the best https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-g006.jpg Figure 6. SHAP Dependence Plot for MOFs (drug loading capacity as output). https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-g007.jpg Figure 7. SHAP Dependence Plot for MOFs (cell viability as output). https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-t005.jpg Table 5. Uncertainty analysis of probabilistic models on test dataset. https://www.frontiersin.org/files/Articles/1792340/fchem-14-1792340-HTML/image_m/fchem-14-1792340-t006.jpg Table 6. Ablation study evaluating contribution of model components. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1704525/full https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-g001.jpg Figure 1. Effects of basal fertilizer nitrogen reduction on soil physicochemical properties and enzy https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-t001.jpg Table 1. Alpha diversity indices of microbial communities at the OTU level in rhizosphere soils. https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-g002.jpg Figure 2. Beta diversity of soil microbial communities. Principal coordinate analysis (PCoA) based o https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-g003.jpg Figure 3. Composition of soil microbial communities. (A) Bacterial community composition at the phyl https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-g004.jpg Figure 4. Differential analysis of microbial communities in non-diseased and diseased soils. Taxa wi https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-g005.jpg Figure 5. Soil microbial co-occurrence network analysis. The top 100 genera in relative abundance we https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-t002.jpg Table 2. Overview of the topological properties of soil microbial networks. https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-t003.jpg Table 3. Correlation between bacterial genera and Ralstonia in healthy (non-diseased) soil. https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-t004.jpg Table 4. Correlation between bacterial genera and Ralstonia in diseased soil. https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-t005.jpg Table 5. Correlation between fungal genera and Ralstonia in healthy (non-diseased) soil. https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-t006.jpg Table 6. Correlation between fungal genera and Ralstonia in diseased soil. https://www.frontiersin.org/files/Articles/1704525/fmicb-16-1704525-HTML-r1/image_m/fmicb-16-1704525-g006.jpg Figure 6. Analysis of environmental factors and key microbial genera. Key microbial genera in bacter https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1713022/full https://www.frontiersin.org/files/Articles/1713022/fmed-12-1713022-HTML/image_m/fmed-12-1713022-g001.jpg Figure 1. Intraoperative view showing a sharp wooden foreign body perforating the terminal ileum. Th https://www.frontiersin.org/files/Articles/1713022/fmed-12-1713022-HTML/image_m/fmed-12-1713022-g002.jpg Figure 2. Fresh leaves of Gnetum gnemon var. tenerum (“Liang” in Thai) and a common local recipe of https://www.frontiersin.org/files/Articles/1713022/fmed-12-1713022-HTML/image_m/fmed-12-1713022-t001.jpg Table 1. Reported cases and series of plant-origin foreign bodies associated with bowel perforation. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1732979/full https://www.frontiersin.org/files/Articles/1732979/fpls-16-1732979-HTML/image_m/fpls-16-1732979-g001.jpg Figure 1. Conceptual framework of AI-enabled plant phenotyping and yield forecasting. The core struc https://www.frontiersin.org/files/Articles/1732979/fpls-16-1732979-HTML/image_m/fpls-16-1732979-g002.jpg Figure 2. Framework of AI modeling in plant phenotyping and remote sensing. Traditional ML methods, https://www.frontiersin.org/files/Articles/1732979/fpls-16-1732979-HTML/image_m/fpls-16-1732979-t001.jpg Table 1. Comparative summary of AI methods for plant phenotyping applications. https://www.frontiersin.org/files/Articles/1732979/fpls-16-1732979-HTML/image_m/fpls-16-1732979-g003.jpg Figure 3. Overview of image-based phenotyping from lab to field. The controlled-environment phenotyp https://www.frontiersin.org/files/Articles/1732979/fpls-16-1732979-HTML/image_m/fpls-16-1732979-t002.jpg Table 2. Representative AI-based image phenotyping applications. https://www.frontiersin.org/files/Articles/1732979/fpls-16-1732979-HTML/image_m/fpls-16-1732979-g004.jpg Figure 4. Controlled-environment Lab phenotyping. In controlled lab, acquired images are processed u https://www.frontiersin.org/files/Articles/1732979/fpls-16-1732979-HTML/image_m/fpls-16-1732979-g005.jpg Figure 5. Image-based phenotyping to real-world field conditions. Domain adaptation allows models to https://www.frontiersin.org/files/Articles/1732979/fpls-16-1732979-HTML/image_m/fpls-16-1732979-g006.jpg Figure 6. AI-enabled workflow for crop monitoring and decision support. Multisource environmental, p https://www.frontiersin.org/files/Articles/1732979/fpls-16-1732979-HTML/image_m/fpls-16-1732979-t003.jpg Table 3. Representative AI-based models for crop yield and trait forecasting. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1642956/full https://www.frontiersin.org/files/Articles/1642956/fimmu-16-1642956-HTML/image_m/fimmu-16-1642956-g001.jpg Figure 1. The role of chromosomes in cancer sexual dimorphism. (A) Loss of the Y chromosome in tumor https://www.frontiersin.org/files/Articles/1642956/fimmu-16-1642956-HTML/image_m/fimmu-16-1642956-g002.jpg Figure 2. Roles of sex hormones in cancer sexual dimorphism. (A) Androgen receptor (AR) modulates th https://www.frontiersin.org/files/Articles/1642956/fimmu-16-1642956-HTML/image_m/fimmu-16-1642956-t001.jpg Table 1. Summary of Major Risk Factors for Non-Reproductive Cancers*. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1661291/full https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-g001.jpg Figure 1. PRISMA flowchart. https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-t001.jpg Table 1. Characteristics of the included studies. https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-g002.jpg Figure 2. Risk of bias assessment proportion plot. https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-g003.jpg Figure 3. Trace plots and density plots for the Bayesian network meta-analysis models. Trace plots s https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-t002.jpg Table 2. Potential scale reduction factors (PSRFs) indicating satisfactory convergence of all Bayesi https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-g004.jpg Figure 4. Evidence network for each outcome indicator. Circles represent interventions, with the cir https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-g005.jpg Figure 5. Forest plot of the consistency tests. For each comparison, the three points represent the https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-g006.jpg Figure 6. Forest plots of various outcome measures. Each point represents the odds ratio (OR) or mea https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-g007.jpg Figure 7. Comparison of the effects of different interventions across outcome indicators. Letters on https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-t003.jpg Table 3. Comprehensive best probability ranking table. https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-t004.jpg Table 4. Summary of the reported adverse events across the included studies. https://www.frontiersin.org/files/Articles/1661291/fimmu-17-1661291-HTML-r1/image_m/fimmu-17-1661291-g008.jpg Figure 8. Corrected funnel plots for each outcome indicator. The x-axis represents the mean differen https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1693537/full https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-t001.jpg Table 1. PubMed search strategy. https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-g001.jpg Figure 1. Flowchart of study selection for inclusion. https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-t002.jpg Table 2. Basic characteristics of the included studies. https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-t003.jpg Table 3. rTMS parameters and treatment protocols in the included literature. https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-t004.jpg Table 4. Characteristics of tDCS studies in the included literature. https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-g002.jpg Figure 2. Risk of bias assessment of the included studies. https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-g003.jpg Figure 3. Trace plot and density plot for each outcome indicator. Trace Plots: The horizontal axis r https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-g004.jpg Figure 4. Evidence network for each outcome indicator. Circles represent different interventions; ci https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-t005.jpg Table 5. Goodness-of-fit and heterogeneity assessment results for the consistency models under diffe https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-g005.jpg Figure 5. Forest plots for each outcome indicator. Each point represents the OR or MD of the interve https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-g006.jpg Figure 6. Comparison of the effects of different interventions on different outcome indicators. Diff https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-t006.jpg Table 6. Network meta-analysis results of the top three ranked interventions for each outcome measur https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-g007.jpg Figure 7. Contour-enhanced funnel plots for each outcome indicator. The horizontal axis represents t https://www.frontiersin.org/files/Articles/1693537/fneur-16-1693537-HTML/image_m/fneur-16-1693537-g008.jpg Figure 8. Inconsistency test plots for each indicator. For each comparison, the three points represe https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1664776/full https://www.frontiersin.org/files/Articles/1664776/fneur-16-1664776-HTML/image_m/fneur-16-1664776-g001.jpg Figure 1. Schematic diagram of the molecular functions of CHAMP1 protein and pathogenic mechanisms o https://www.frontiersin.org/files/Articles/1664776/fneur-16-1664776-HTML/image_m/fneur-16-1664776-t001.jpg Table 1. Summary table of associations between different CHAMP1 gene variant types and clinical phen https://www.frontiersin.org/files/Articles/1664776/fneur-16-1664776-HTML/image_m/fneur-16-1664776-t002.jpg Table 2. Summary table of gastrointestinal symptoms reported in literature on CHAND/MRD40. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1640684/full https://www.frontiersin.org/files/Articles/1640684/feduc-10-1640684-HTML/image_m/feduc-10-1640684-t001.jpg Table 1. Differences between traditional paradigm and multimodal large models at the design thinking https://www.frontiersin.org/files/Articles/1640684/feduc-10-1640684-HTML/image_m/feduc-10-1640684-t002.jpg Table 2. Methods and objectives of cultivation of each subject in the definition and conceptualizati https://www.frontiersin.org/files/Articles/1640684/feduc-10-1640684-HTML/image_m/feduc-10-1640684-t003.jpg Table 3. Training methods for subjects and target student competencies at the prototype stage. https://www.frontiersin.org/files/Articles/1640684/feduc-10-1640684-HTML/image_m/feduc-10-1640684-t004.jpg Table 4. Methods of training and students' goal abilities in the testing stage. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1767604/full https://www.frontiersin.org/files/Articles/1767604/fpubh-14-1767604-HTML/image_m/fpubh-14-1767604-t001.jpg Table 1. Baseline demographic, socioeconomic, cultural, and disease-related characteristics of the s https://www.frontiersin.org/files/Articles/1767604/fpubh-14-1767604-HTML/image_m/fpubh-14-1767604-g001.jpg Figure 1. Domain-specific and total PedsQL™ scores reported by parents of children with congenital a https://www.frontiersin.org/files/Articles/1767604/fpubh-14-1767604-HTML/image_m/fpubh-14-1767604-t002.jpg Table 2. Univariable analysis of factors influencing the quality of life of parents of children with https://www.frontiersin.org/files/Articles/1767604/fpubh-14-1767604-HTML/image_m/fpubh-14-1767604-t003.jpg Table 3. Multivariable stepwise regression analysis of factors affecting parental quality of life. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1657892/full https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-t001.jpg Table 1. Summary of key characteristics of included studies. https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-g001.jpg Figure 1. Hemodynamic representation of typical changes in blood lactate, heart rate, and selected g https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-t002.jpg Table 2. Hypothetical model of various thresholds and phases selected characteristics during progres https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-g002.jpg Figure 2. Ventilation Volume-Intensity Curve. VT1, first ventilation threshold; VT2, second ventilat https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-t003.jpg Table 3. Intensity scale for elite endurance athletes. https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-t004.jpg Table 4. Intensity level and standard of Chinese kayak team water training load. https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-g003.jpg Figure 3. Comparative schematic of TID models and intensity zones. In this figure, threshold trainin https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-g004.jpg Figure 4. Evolution of Training Intensity Distribution (TID) Theory in Endurance Sports (1979–2025). https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-g005.jpg Figure 5. Training volume distribution chart for the three training models. https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-t005.jpg Table 5. Changes in physiological indicators before and after the intervention. https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-g006.jpg Figure 6. Training intensity distribution across different training phases in elite endurance athlet https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-t006.jpg Table 6. Training intensity distribution characteristics across training phases. https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-t007.jpg Table 7. Meta-analysis results of time-trial performance and maximal oxygen uptake in endurance athl https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-t008.jpg Table 8. Training load distribution in high-level endurance athletes (Guo et al., 2010). https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-g007.jpg Figure 7. Time/distance intensity distribution in endurance athletes (Burnley et al., 2022) https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-g008.jpg Figure 8. Training Intensity Distribution Structure of the Chinese Canoeing Team during the Olympic https://www.frontiersin.org/files/Articles/1657892/fphys-16-1657892-HTML/image_m/fphys-16-1657892-t009.jpg Table 9. Comparative analysis of TID models (PYR, THR, POL). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1702052/full https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-g001.jpg Figure 1. Conceptual model of this study. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t001.jpg Table 1. Variable measurement items and source. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-g002.jpg Figure 2. No. 481, West Street Zhuang Zheng's Former Residence. Exterior and Interior – Photographed https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-g003.jpg Figure 3. No. 116, West Street Song Residence, Exterior and Interior – Photographed by the author. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-g004.jpg Figure 4. No. 356, West Street, Lin Family Compound. Exterior and Interior – Photographed by the aut https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t002.jpg Table 2. Descriptive statistical analysis. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t003.jpg Table 3. Descriptive statistics and factor loading of items. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t004.jpg Table 4. Reliability and convergent validity test. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t005.jpg Table 5. Discriminant validity test. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t006.jpg Table 6. HTMT test. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t007.jpg Table 7. Collinearity assessment. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t008.jpg Table 8. PLS direct effect. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t009.jpg Table 9. PLS mediating effect. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t010.jpg Table 10. PLS moderating effect. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-g005.jpg Figure 5. Simple slope analysis (Identity Climate × Physical Engagement). https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-g006.jpg Figure 6. Simple slope analysis (Identity Climate × Cognitive Processing). https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-g007.jpg Figure 7. Results of the concept model verification. https://www.frontiersin.org/files/Articles/1702052/fpsyg-16-1702052-HTML/image_m/fpsyg-16-1702052-t011.jpg Table 11. R square and Q square. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1694110/full https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-g001.jpg Figure 1. PRISMA flowchart of literature selection. https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-t001.jpg Table 1. Characteristics of the included studies. https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-g002.jpg Figure 2. Risk of bias assessment for each study based on the Cochrane RoB 2.0 tool. https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-g003.jpg Figure 3. Forest plot depicting differences in SDNN between acupuncture treatment and control interv https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-g004.jpg Figure 4. Forest plot depicting differences in LF between acupuncture treatment and control interven https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-g005.jpg Figure 5. Forest plot depicts differences in HF between acupuncture treatment and control interventi https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-g006.jpg Figure 6. Forest plot depicting differences in the LF/HF ratio between acupuncture treatment and con https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-g007.jpg Figure 7. Forest plot comparing adverse events between acupuncture and control treatments for autono https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-t002.jpg Table 2. Sensitivity analyses of HRV parameters. https://www.frontiersin.org/files/Articles/1694110/fnins-19-1694110-HTML/image_m/fnins-19-1694110-g008.jpg Figure 8. Funnel plots assess potential publication bias for acupuncture’s modulation of autonomic n https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1656819/full https://www.frontiersin.org/files/Articles/1656819/fsurg-12-1656819-HTML/image_m/fsurg-12-1656819-g001.jpg Figure 1. Components of the novel vacuum suction semirigid ureteroscopy: (A) The rigid ureteral acce https://www.frontiersin.org/files/Articles/1656819/fsurg-12-1656819-HTML/image_m/fsurg-12-1656819-g002.jpg Figure 2. Negative pressure irrigation suction device. https://www.frontiersin.org/files/Articles/1656819/fsurg-12-1656819-HTML/image_m/fsurg-12-1656819-t001.jpg Table 1. Main group analysis: classifications of preoperative baseline data, intraoperative data, po https://www.frontiersin.org/files/Articles/1656819/fsurg-12-1656819-HTML/image_m/fsurg-12-1656819-t002.jpg Table 2. Subgroup analysis: preoperative calculus data and postoperative effect for impacted and non https://www.frontiersin.org/files/Articles/1656819/fsurg-12-1656819-HTML/image_m/fsurg-12-1656819-g003.jpg Figure 3. Comparison of patients' CT 3D reconstructed images: (A) pre-operation. (B) post-operation. https://www.frontiersin.org/files/Articles/1656819/fsurg-12-1656819-HTML/image_m/fsurg-12-1656819-g004.jpg Figure 4. CT images of patient with incomplete stone removal on the first day after surgery. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1735605/full https://www.frontiersin.org/files/Articles/1735605/fmicb-16-1735605-HTML-r1/image_m/fmicb-16-1735605-g001.jpg Figure 1. Lactobacillus helveticus WIS02 attenuates STZ-induced hyperglycemia and preserves pancreat https://www.frontiersin.org/files/Articles/1735605/fmicb-16-1735605-HTML-r1/image_m/fmicb-16-1735605-g002.jpg Figure 2. Lactobacillus helveticus WIS02 improves dyslipidemia in diabetic mice. (A) Changes in seru https://www.frontiersin.org/files/Articles/1735605/fmicb-16-1735605-HTML-r1/image_m/fmicb-16-1735605-g003.jpg Figure 3. Lactobacillus helveticus WIS02 regulates intestinal flora diversity. (A) Shannon curves. ( https://www.frontiersin.org/files/Articles/1735605/fmicb-16-1735605-HTML-r1/image_m/fmicb-16-1735605-g004.jpg Figure 4. Lactobacillus helveticus WIS02 tends to favor the gut flora of diabetic animals. (A) LEfSe https://www.frontiersin.org/files/Articles/1735605/fmicb-16-1735605-HTML-r1/image_m/fmicb-16-1735605-g005.jpg Figure 5. Correlation heatmap displaying relationships between various bacteria species and health i https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1665360/full https://www.frontiersin.org/files/Articles/1665360/fpubh-13-1665360-HTML/image_m/fpubh-13-1665360-t001.jpg Table 1. Results of binary logistic regression. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1645793/full https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-g001.jpg Figure 1. Flowchart of participants selection. https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-t001.jpg Table 1. Baseline characteristics of study participants. https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-t002.jpg Table 2. Association between MAR and other inflammatory biomarkers with CVD. https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-g002.jpg Figure 2. Smooth curve fitting for MAR with CVD. https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-t003.jpg Table 3. Threshold effect analysis of MAR on CVD utilizing a two-piecewise linear regression model i https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-g003.jpg Figure 3. Subgroup analysis for the associations of MAR with CVD. https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-g004.jpg Figure 4. ROC curves and the AUC values of the six inflammatory biomarkers in diagnosing CVD. https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-g005.jpg Figure 5. ROC curves and the AUC values of MAR in diagnosing CVD events. https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-t004.jpg Table 4. Cox regression of the link between MAR and all-cause mortality and CVD-cause mortality in t https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-g006.jpg Figure 6. Kaplan–meier survival analysis curves for all-cause and CVD-cause mortality. (A) Kaplan–Me https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-g007.jpg Figure 7. RCS curves of MAR impact on long-term ALL-cause and CVD-cause mortality in the general pop https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-g008.jpg Figure 8. Subgroup analysis of the links between MAR and ALL-cause mortality in general population. https://www.frontiersin.org/files/Articles/1645793/fcvm-12-1645793-HTML/image_m/fcvm-12-1645793-g009.jpg Figure 9. Subgroup analysis of the links between MAR and CVD-cause mortality in general population. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1571642/full https://www.frontiersin.org/files/Articles/1571642/fmed-12-1571642-HTML/image_m/fmed-12-1571642-g001.jpg Figure 1. Schematic representation of several common conjunctival incisions in strabismus surgery. https://www.frontiersin.org/files/Articles/1571642/fmed-12-1571642-HTML/image_m/fmed-12-1571642-g002.jpg Figure 2. Schematic diagram of the modified swan incision (MSI) combined with lateral rectus suspens https://www.frontiersin.org/files/Articles/1571642/fmed-12-1571642-HTML/image_m/fmed-12-1571642-g003.jpg Figure 3. Actual picture of the modified swan incision (MSI) combined with lateral rectus suspension https://www.frontiersin.org/files/Articles/1571642/fmed-12-1571642-HTML/image_m/fmed-12-1571642-g004.jpg Figure 4. Postoperative eye photo of the patient 1 week after undergoing MSI combined with bilateral https://www.frontiersin.org/files/Articles/1571642/fmed-12-1571642-HTML/image_m/fmed-12-1571642-g005.jpg Figure 5. Research flowchart: patient enrollment, exclusion, and grouping process. https://www.frontiersin.org/files/Articles/1571642/fmed-12-1571642-HTML/image_m/fmed-12-1571642-t001.jpg Table 1. Surgical procedures for patients with deviation greater than 40PD. https://www.frontiersin.org/files/Articles/1571642/fmed-12-1571642-HTML/image_m/fmed-12-1571642-t002.jpg Table 2. Comparison of baseline data between two groups. https://www.frontiersin.org/files/Articles/1571642/fmed-12-1571642-HTML/image_m/fmed-12-1571642-t003.jpg Table 3. Comparison of primary and secondary endpoints between the MSI and control groups. https://www.frontiersin.org/files/Articles/1571642/fmed-12-1571642-HTML/image_m/fmed-12-1571642-t004.jpg Table 4. Analysis of orthotropic rates using GEE. https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2025.1694630/full https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-g001.jpg Figure 1. PRISMA diagram showing the process of locating publications included in this meta– analysi https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-g002.jpg Figure 2. Distribution of all study sites included in this meta-analysis across China. https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-t001.jpg Table 1. Sensitivity analysis for the effects of mixed plantations on soil microbial diversity and e https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-t002.jpg Table 2. Results of publication bias. https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-g003.jpg Figure 3. Weighted effect sizes (A) of soil bacterial Shannon index and (B) of soil fungal Shannon i https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-g004.jpg Figure 4. Weighted effect sizes of individual ecosystem functions (LB, RB, SOC, TN, TP, AP, AK, NH4– https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-g005.jpg Figure 5. Effects of (A) N-fixing status (NF, nitrogen-fixing mixed plantations; NNF, non-nitrogen-f https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-g006.jpg Figure 6. Percent change in ecosystem multifunctionality (EMF) across different mixing strategies in https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-t003.jpg Table 3. Between-group heterogeneity (Qb and P-values) for each variable under different mixing stra https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-g007.jpg Figure 7. Relationships between the effect sizes (RR) of soil bacterial and fungal Shannon indices a https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-t004.jpg Table 4. Linear relationships between the effect sizes (RR) of soil microbial diversity and physical https://www.frontiersin.org/files/Articles/1694630/ffgc-08-1694630-HTML/image_m/ffgc-08-1694630-g008.jpg Figure 8. Relationships between the effect sizes (RR) of soil moisture content (SM, A) or soil bulk https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1775325/full https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-g001.jpg Figure 1. PRISMA flow chart. https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-g002.jpg Figure 2. Evidence network diagram: (A) excellent functional outcome; (B) good functional outcome; ( https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-g003.jpg Figure 3. Cochrane risk of bias tool. https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-t002.jpg Table 2. League diagram of excellent functional outcome at 90 days. https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-g004.jpg Figure 4. Surface under the cumulative ranking curve of excellent functional outcome at 90 days. (A) https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-t003.jpg Table 3. Rank of different doses of thrombolytics on excellent functional outcome at 90 days. https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-t004.jpg Table 4. League diagram of good functional outcome at 90 days. https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-g005.jpg Figure 5. Surface under the cumulative ranking curve of good functional outcome at 90 days. (A) Alte https://www.frontiersin.org/files/Articles/1775325/fneur-17-1775325-HTML/image_m/fneur-17-1775325-t005.jpg Table 5. Rank of different doses of thrombolytics on good functional outcome at 90 days. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1620766/full https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-g001.jpg Figure 1. Research model. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-t001.jpg Table 1. Sample description. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-t002.jpg Table 2. Model fit indicators and standard criteria. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-t003.jpg Table 3. Results of exploratory factor analysis during oblique rotations. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-g002.jpg Figure 2. Results of confirmatory factor analysis. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-t004.jpg Table 4. Reliability testing. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-t005.jpg Table 5. Correlation matrix and average variance extracted. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-t006.jpg Table 6. Structural equation modeling path test results. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-g003.jpg Figure 3. Structural equation model. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-t007.jpg Table 7. Bootstrap analysis of mediating effects. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-t008.jpg Table 8. Moderated mediation effects. https://www.frontiersin.org/files/Articles/1620766/fpsyg-16-1620766-HTML-r1/image_m/fpsyg-16-1620766-t009.jpg Table 9. Moderating effects between genders. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1708003/full https://www.frontiersin.org/files/Articles/1708003/fspor-08-1708003-HTML/image_m/fspor-08-1708003-t001.jpg Table 1. PICOS included in the study. https://www.frontiersin.org/files/Articles/1708003/fspor-08-1708003-HTML/image_m/fspor-08-1708003-g001.jpg Figure 1. Literature screening flowchart. https://www.frontiersin.org/files/Articles/1708003/fspor-08-1708003-HTML/image_m/fspor-08-1708003-t002.jpg Table 2. Study characteristics of included articles. https://www.frontiersin.org/files/Articles/1708003/fspor-08-1708003-HTML/image_m/fspor-08-1708003-g002.jpg Figure 2. Mechanism of replacing sedentary behavior with physical activity on cardiovascular health https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1750241/full https://www.frontiersin.org/files/Articles/1750241/fneur-16-1750241-HTML-r2/image_m/fneur-16-1750241-g001.jpg Figure 1. PRISMA flow diagram of study selection for the meta-analysis. # Wrong study design (includ https://www.frontiersin.org/files/Articles/1750241/fneur-16-1750241-HTML-r2/image_m/fneur-16-1750241-t001.jpg Table 1. Summary of the studies included in the meta-analysis of dual versus single antiplatelet the https://www.frontiersin.org/files/Articles/1750241/fneur-16-1750241-HTML-r2/image_m/fneur-16-1750241-g002.jpg Figure 2. Pooled risk of recurrent stroke with dual versus single antiplatelet therapy. ASA, aspirin https://www.frontiersin.org/files/Articles/1750241/fneur-16-1750241-HTML-r2/image_m/fneur-16-1750241-g003.jpg Figure 3. Subgroup analyses for the efficacy outcome (recurrent stroke prevention; Panel A) and the https://www.frontiersin.org/files/Articles/1750241/fneur-16-1750241-HTML-r2/image_m/fneur-16-1750241-g004.jpg Figure 4. Pooled risk of major bleeding with dual versus single antiplatelet therapy. ASA, aspirin; https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1753074/full https://www.frontiersin.org/files/Articles/1753074/fonc-16-1753074-HTML/image_m/fonc-16-1753074-g001.jpg Figure 1. Kaplan–Meier curves of 2-year DFS according to preoperative serum IgG levels (high vs low, https://www.frontiersin.org/files/Articles/1753074/fonc-16-1753074-HTML/image_m/fonc-16-1753074-t001.jpg Table 1. Demographics, comorbidities, functional and serological characteristics according to 2-year https://www.frontiersin.org/files/Articles/1753074/fonc-16-1753074-HTML/image_m/fonc-16-1753074-g002.jpg Figure 2. Flow diagram of patient selection and study cohort. *Patients receiving neoadjuvant therap https://www.frontiersin.org/files/Articles/1753074/fonc-16-1753074-HTML/image_m/fonc-16-1753074-t002.jpg Table 2. Tumour-related and treatment characteristics according to 2-year DFS status (n=192). https://www.frontiersin.org/files/Articles/1753074/fonc-16-1753074-HTML/image_m/fonc-16-1753074-t003.jpg Table 3. Univariate Cox regression analysis (selected variables with adequate completeness). https://www.frontiersin.org/files/Articles/1753074/fonc-16-1753074-HTML/image_m/fonc-16-1753074-t004.jpg Table 4. Multivariable Cox regression analysis of prognostic factors. https://www.frontiersin.org/files/Articles/1753074/fonc-16-1753074-HTML/image_m/fonc-16-1753074-g003.jpg Figure 3. Temporal trajectories of serum IgG and CEA following curative resection for high-risk stag https://www.frontiersin.org/files/Articles/1753074/fonc-16-1753074-HTML/image_m/fonc-16-1753074-g004.jpg Figure 4. Kaplan–Meier survival curves according to dynamic monitoring of IgG and CEA. (A) IgG traje https://www.frontiersin.org/files/Articles/1753074/fonc-16-1753074-HTML/image_m/fonc-16-1753074-t005.jpg Table 5. Multivariable Cox regression analysis of dynamic IgG and CEA trajectories. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1680998/full https://www.frontiersin.org/files/Articles/1680998/fphar-16-1680998-HTML/image_m/fphar-16-1680998-g007.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1680998/fphar-16-1680998-HTML/image_m/fphar-16-1680998-t001.jpg Table 1. Primer sequences used for RT-qPCR. https://www.frontiersin.org/files/Articles/1680998/fphar-16-1680998-HTML/image_m/fphar-16-1680998-g001.jpg Figure 1. Identification of five hub proteins shared between pulmonary fibrosis and aging. (A) Venn https://www.frontiersin.org/files/Articles/1680998/fphar-16-1680998-HTML/image_m/fphar-16-1680998-g002.jpg Figure 2. Drug screening based on shared senescence–PF targets and identification of resveratrol as https://www.frontiersin.org/files/Articles/1680998/fphar-16-1680998-HTML/image_m/fphar-16-1680998-g003.jpg Figure 3. Identification of seven hub proteins associated with epithelial cell senescence in PF. (A) https://www.frontiersin.org/files/Articles/1680998/fphar-16-1680998-HTML/image_m/fphar-16-1680998-g004.jpg Figure 4. Molecular docking analysis of Resveratrol with potential target proteins. Docking scores ( https://www.frontiersin.org/files/Articles/1680998/fphar-16-1680998-HTML/image_m/fphar-16-1680998-g005.jpg Figure 5. Resveratrol alleviates cellular senescence in A549 cells. (A) Cell viability was measured https://www.frontiersin.org/files/Articles/1680998/fphar-16-1680998-HTML/image_m/fphar-16-1680998-g006.jpg Figure 6. Resveratrol attenuates senescence markers and ameliorates pulmonary fibrosis in mice. (A) https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1644773/full https://www.frontiersin.org/files/Articles/1644773/fendo-16-1644773-HTML/image_m/fendo-16-1644773-g001.jpg Figure 1. Flowchart of the cycles’ selection. RPL, recurrent pregnancy loss; COS, controlled ovarian https://www.frontiersin.org/files/Articles/1644773/fendo-16-1644773-HTML/image_m/fendo-16-1644773-t001.jpg Table 1. Baseline characteristics of the PGT cycles in RPL patients with balanced chromosomal transl https://www.frontiersin.org/files/Articles/1644773/fendo-16-1644773-HTML/image_m/fendo-16-1644773-t002.jpg Table 2. Characteristics of frozen-thawed embryo transfer cycles in PRL patients with balanced chrom https://www.frontiersin.org/files/Articles/1644773/fendo-16-1644773-HTML/image_m/fendo-16-1644773-t003.jpg Table 3. The association between pregnancy outcomes, blastocyst developmental stage and morphologica https://www.frontiersin.org/files/Articles/1644773/fendo-16-1644773-HTML/image_m/fendo-16-1644773-t004.jpg Table 4. Baseline characteristics of the PGT cycles in normokaryotypic RPL patients. https://www.frontiersin.org/files/Articles/1644773/fendo-16-1644773-HTML/image_m/fendo-16-1644773-t005.jpg Table 5. Characteristics of frozen-thawed embryo transfer cycles in normokaryotypic RPL patients. https://www.frontiersin.org/files/Articles/1644773/fendo-16-1644773-HTML/image_m/fendo-16-1644773-t006.jpg Table 6. The association between pregnancy outcomes, blastocyst developmental stage and morphologica https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1754579/full https://www.frontiersin.org/files/Articles/1754579/fpls-17-1754579-HTML/image_m/fpls-17-1754579-g001.jpg Figure 1. Composition of the different nutrients in peel-pulp of mango. (A) Representative images of https://www.frontiersin.org/files/Articles/1754579/fpls-17-1754579-HTML/image_m/fpls-17-1754579-g002.jpg Figure 2. Principal component analysis (PCA) of mango fruit metabolites between cultivars. Pair-wise https://www.frontiersin.org/files/Articles/1754579/fpls-17-1754579-HTML/image_m/fpls-17-1754579-g003.jpg Figure 3. Pair-wise differentially abundant metabolites analysis across four mango cultivars. Volcan https://www.frontiersin.org/files/Articles/1754579/fpls-17-1754579-HTML/image_m/fpls-17-1754579-g004.jpg Figure 4. Dot plot visualization of KEGG pathway across four mango cultivars. Dot plots showing the https://www.frontiersin.org/files/Articles/1754579/fpls-17-1754579-HTML/image_m/fpls-17-1754579-g005.jpg Figure 5. Bacterial community diversity in mango fruit collected from different cultivars. (A) Raref https://www.frontiersin.org/files/Articles/1754579/fpls-17-1754579-HTML/image_m/fpls-17-1754579-g006.jpg Figure 6. Variation in bacterial community structure across mango cultivars. (A) Venn diagram showin https://www.frontiersin.org/files/Articles/1754579/fpls-17-1754579-HTML/image_m/fpls-17-1754579-g007.jpg Figure 7. The composition of bacterial communities in mango fruits. Relative abundance of fruit-asso https://www.frontiersin.org/files/Articles/1754579/fpls-17-1754579-HTML/image_m/fpls-17-1754579-g008.jpg Figure 8. Correlation heatmap of fruit-associated differentially abundant bacterial genera and top-f https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1597749/full https://www.frontiersin.org/files/Articles/1597749/fcvm-12-1597749-HTML-r2/image_m/fcvm-12-1597749-g001.jpg Figure 1. Flowchart and follow-up setting of this current study. https://www.frontiersin.org/files/Articles/1597749/fcvm-12-1597749-HTML-r2/image_m/fcvm-12-1597749-t001.jpg Table 1. Characteristics of 5,468 participants categorized by AIP and BMI levels. https://www.frontiersin.org/files/Articles/1597749/fcvm-12-1597749-HTML-r2/image_m/fcvm-12-1597749-t002.jpg Table 2. Association between AIP and BMI using multiple linear regression. https://www.frontiersin.org/files/Articles/1597749/fcvm-12-1597749-HTML-r2/image_m/fcvm-12-1597749-g002.jpg Figure 2. Dose-responsive relationship of the AIP and BMI levels with the risk of cardiovascular dis https://www.frontiersin.org/files/Articles/1597749/fcvm-12-1597749-HTML-r2/image_m/fcvm-12-1597749-g003.jpg Figure 3. K–M plot of cardiovascular diseases by AIP and BMI levels. (A) The cumulative hazard of ca https://www.frontiersin.org/files/Articles/1597749/fcvm-12-1597749-HTML-r2/image_m/fcvm-12-1597749-t003.jpg Table 3. Associations of AIP and BMI levels with risk of cardiovascular diseases. https://www.frontiersin.org/files/Articles/1597749/fcvm-12-1597749-HTML-r2/image_m/fcvm-12-1597749-g004.jpg Figure 4. Subgroups analysis of association between AIP and BMI levels with the risk of cardiovascul https://www.frontiersin.org/files/Articles/1597749/fcvm-12-1597749-HTML-r2/image_m/fcvm-12-1597749-g005.jpg Figure 5. Predictive performance of the combined AIP and BMI for cardiovascular diseases. (A) The re https://www.frontiersin.org/files/Articles/1597749/fcvm-12-1597749-HTML-r2/image_m/fcvm-12-1597749-g006.jpg Figure 6. Mediation effect of BMI between the AIP and cardiovascular diseases. (A) The unadjusted me https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1658245/full https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t001.jpg Table 1. Chemical properties of topsoil in the test field from 2018 to 2019. https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-g001.jpg Figure 1. Average temperature and rainfall during the rice growth period in the experimental area fr https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t002.jpg Table 2. Effects of N-K ratio on yield and yield components of mechanically inserted rice. https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t003.jpg Table 3. Effects of N-K management strategies on dry matter accumulation and translocation in machin https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t004.jpg Table 4. Effects of N-K management strategies on assimilating translocation from vegetative organs i https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t005.jpg Table 5. Effects of different N-K management strategies on nitrogen accumulation in organs of machin https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t006.jpg Table 6. Effects of N-K management strategies on nitrogen translocation in vegetative organs of mach https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t007.jpg Table 7. Effects of N-K management strategies on potassium uptake and utilization in machine-transpl https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t008.jpg Table 8. Effects of N-K management strategies on potassium translocation in machine-transplanted ric https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t009.jpg Table 9. Effects of N-K management strategies on culm physical traits in machine-transplanted rice. https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-t010.jpg Table 10. Effects of N-K management strategies on lodging index in machine-transplanted rice. https://www.frontiersin.org/files/Articles/1658245/fpls-16-1658245-HTML/image_m/fpls-16-1658245-g002.jpg Figure 2. Correlation analysis FHS - Full heading stage, MS - Maturity stages; S2 - penultimate inte https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1721332/full https://www.frontiersin.org/files/Articles/1721332/fvets-12-1721332-HTML/image_m/fvets-12-1721332-t001.jpg Table 1. Characteristics of participants stratified by sleep quality (n = 1,434). https://www.frontiersin.org/files/Articles/1721332/fvets-12-1721332-HTML/image_m/fvets-12-1721332-t002.jpg Table 2. Results of binary logistic regression models by sleep quality. https://www.frontiersin.org/files/Articles/1721332/fvets-12-1721332-HTML/image_m/fvets-12-1721332-t003.jpg Table 3. Characteristics of participants of pet ownership by sleep quality (N = 717). https://www.frontiersin.org/files/Articles/1721332/fvets-12-1721332-HTML/image_m/fvets-12-1721332-t004.jpg Table 4. Results of binary logistic regression models by sleep quality (N = 717). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1599442/full https://www.frontiersin.org/files/Articles/1599442/fpsyg-16-1599442-HTML/image_m/fpsyg-16-1599442-t001.jpg Table 1. Task and academic performance items to test chemistry disciplinary competence. https://www.frontiersin.org/files/Articles/1599442/fpsyg-16-1599442-HTML/image_m/fpsyg-16-1599442-g001.jpg Figure 1. Conceptual model. https://www.frontiersin.org/files/Articles/1599442/fpsyg-16-1599442-HTML/image_m/fpsyg-16-1599442-g002.jpg Figure 2. The chain mediating effect of Source beliefs and chemistry disciplinary competence. https://www.frontiersin.org/files/Articles/1599442/fpsyg-16-1599442-HTML/image_m/fpsyg-16-1599442-g003.jpg Figure 3. The chain mediating effect of Certainty beliefs and chemistry disciplinary competence. https://www.frontiersin.org/files/Articles/1599442/fpsyg-16-1599442-HTML/image_m/fpsyg-16-1599442-t002.jpg Table 2. Descriptive statistics and bivariate correlations. https://www.frontiersin.org/files/Articles/1599442/fpsyg-16-1599442-HTML/image_m/fpsyg-16-1599442-t003.jpg Table 3. Results of multiple linear regression. https://www.frontiersin.org/files/Articles/1599442/fpsyg-16-1599442-HTML/image_m/fpsyg-16-1599442-t004.jpg Table 4. Indirect effect of critical thinking disposition and chemistry learning approaches. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1733102/full https://www.frontiersin.org/files/Articles/1733102/fpls-16-1733102-HTML-r3/image_m/fpls-16-1733102-g001.jpg Figure 1. Statistical analysis and t tests (P<0.05) of the cold tolerance index for different phenot https://www.frontiersin.org/files/Articles/1733102/fpls-16-1733102-HTML-r3/image_m/fpls-16-1733102-g002.jpg Figure 2. RNA-seq analysis under cold stress at 0 h and 6 h (A) PCA based on gene expression of the https://www.frontiersin.org/files/Articles/1733102/fpls-16-1733102-HTML-r3/image_m/fpls-16-1733102-g003.jpg Figure 3. Untargeted metabolomics data analysis under cold stress at 0 h and 6 h (A) PCA based on me https://www.frontiersin.org/files/Articles/1733102/fpls-16-1733102-HTML-r3/image_m/fpls-16-1733102-g004.jpg Figure 4. Joint analysis of TFs and fatty acid metabolites on the basis of the association between t https://www.frontiersin.org/files/Articles/1733102/fpls-16-1733102-HTML-r3/image_m/fpls-16-1733102-g005.jpg Figure 5. ATAC-seq landscape. (A) Average plots and heatmaps showing the signals at the transcriptio https://www.frontiersin.org/files/Articles/1733102/fpls-16-1733102-HTML-r3/image_m/fpls-16-1733102-g006.jpg Figure 6. Correlation network of hub TFs, DEGs involved in fatty acid biosynthesis pathway, differen https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1564539/full https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-t001.jpg Table 1. Sample collection information of the genus Periploca from China. https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g001.jpg Figure 1. Field morphology of the six Periploca species from China. (A) P. forrestii. (B) P. tsiangi https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g002.jpg Figure 2. Morphometric analyses of six Periploca species from China based on 19 morphological charac https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-t002.jpg Table 2. Characteristics of the chloroplast genomes of six Periploca species from China. https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g003.jpg Figure 3. Long repeat sequences of the six Periploca species from China. (A) long repeat sequence. ( https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g004.jpg Figure 4. Simple sequence repeats of the six Periploca species from China. https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g005.jpg Figure 5. Comparison of the LSC, SSC and IR junction among the six Periploca species from China and https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g006.jpg Figure 6. mVISTA map of the chloroplast genome of the six Periploca species from China. The x-axis r https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g007.jpg Figure 7. Nucleotide diversity (Pi) of the chloroplast genome of the six Periploca species from Chin https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g008.jpg Figure 8. Gel electrophoresis of the amplification products using the designed primers (Supplementar https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g009.jpg Figure 9. Sequence alignment of PCR products for the five Periploca species of the ndhC-trnC-ACA reg https://www.frontiersin.org/files/Articles/1564539/fpls-16-1564539-HTML/image_m/fpls-16-1564539-g010.jpg Figure 10. Phylogenetic tree of 29 species including six species of the Periploca genus in the Apocy https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1710417/full https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-g001.jpg Figure 1. Assembly results of the J.spinosa. (A) Mitogenome (B) Chloroplast. Genes with different fu https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-t001.jpg Table 1. Gene composition in this mitochondrial genome of J. spinosa. https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-t002.jpg Table 2. Gene composition in this chloroplast genome of J. spinosa. https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-g002.jpg Figure 2. Repetitive sequences in the mitochondrial and chloroplast genomes of J. spinosa. (A, B) Di https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-g003.jpg Figure 3. Codon preference of J. spinosa. (A) Mitochondria genome. (B) Chloroplast genomes. The x-ax https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-g004.jpg Figure 4. Statistical analysis and validation of RNA editing sites in mitochondria and chloroplasts https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-g005.jpg Figure 5. Homologous fragment analysis based on different organelle genomes. The orange and green ou https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-g006.jpg Figure 6. The phylogenetic relationships between J. spinosa and 11 Brassicales species were inferred https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-g007.jpg Figure 7. Ka/Ks analysis of mitochondrial and chloroplast genes (A) Comparison of the Ka/Ks ratios o https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-g008.jpg Figure 8. Synteny analysis between J. spinosa and C. papaya using the Mauve software. The differentl https://www.frontiersin.org/files/Articles/1710417/fpls-16-1710417-HTML-r1/image_m/fpls-16-1710417-g009.jpg Figure 9. Sequence variation among J. spinosa, C. papaya, V. cundinamarcensis, and V. carvalhoae was https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1707203/full https://www.frontiersin.org/files/Articles/1707203/fimmu-17-1707203-HTML/image_m/fimmu-17-1707203-t001.jpg Table 1. Primer sequences for RT-qPCR. https://www.frontiersin.org/files/Articles/1707203/fimmu-17-1707203-HTML/image_m/fimmu-17-1707203-g001.jpg Figure 1. SLC31A1 is abundantly expressed in post-AMI HF mouse models, silencing of which mitigates https://www.frontiersin.org/files/Articles/1707203/fimmu-17-1707203-HTML/image_m/fimmu-17-1707203-g002.jpg Figure 2. SLC31A1 knockdown arrests cuproptosis and HMGB1 release by regulating copper metabolism im https://www.frontiersin.org/files/Articles/1707203/fimmu-17-1707203-HTML/image_m/fimmu-17-1707203-g003.jpg Figure 3. ATTM inhibits cuproptosis in macrophages, impedes cardiomyocyte apoptosis and relieves HF https://www.frontiersin.org/files/Articles/1707203/fimmu-17-1707203-HTML/image_m/fimmu-17-1707203-g004.jpg Figure 4. SLC31A1 silencing or ATTM suppresses cuproptosis and inflammatory responses in hypoxia-ind https://www.frontiersin.org/files/Articles/1707203/fimmu-17-1707203-HTML/image_m/fimmu-17-1707203-g005.jpg Figure 5. SLC31A1 is implicated in cuproptosis in macrophages through regulation of the NLRP3/HMGB1 https://www.frontiersin.org/files/Articles/1707203/fimmu-17-1707203-HTML/image_m/fimmu-17-1707203-g006.jpg Figure 6. SLC31A1 silencing hampers cardiomyocyte apoptosis via the NLRP3/HMGB1-dependent macrophage https://www.frontiersin.org/files/Articles/1707203/fimmu-17-1707203-HTML/image_m/fimmu-17-1707203-g007.jpg Figure 7. Activation of the NLRP3/HMGB1 pathway partly counteracts the protection against macrophage https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1607242/full https://www.frontiersin.org/files/Articles/1607242/fnins-19-1607242-HTML/image_m/fnins-19-1607242-g001.jpg Figure 1. Representation of the image processing pipeline. https://www.frontiersin.org/files/Articles/1607242/fnins-19-1607242-HTML/image_m/fnins-19-1607242-t001.jpg Table 1. Demographics of the participants and distribution of WMH volume. https://www.frontiersin.org/files/Articles/1607242/fnins-19-1607242-HTML/image_m/fnins-19-1607242-g002.jpg Figure 2. Multivariate analysis for metabolites. (A) Principal component analysis (PCA) score plots; https://www.frontiersin.org/files/Articles/1607242/fnins-19-1607242-HTML/image_m/fnins-19-1607242-g003.jpg Figure 3. Volcano plots (A) and heatmaps (B) illustrating the different metabolites. https://www.frontiersin.org/files/Articles/1607242/fnins-19-1607242-HTML/image_m/fnins-19-1607242-g004.jpg Figure 4. Weighted correlation network analysis and the selection of hub metabolites. (A) Scale-free https://www.frontiersin.org/files/Articles/1607242/fnins-19-1607242-HTML/image_m/fnins-19-1607242-g005.jpg Figure 5. The Venn diagram of differential and hub metabolites. https://www.frontiersin.org/files/Articles/1607242/fnins-19-1607242-HTML/image_m/fnins-19-1607242-g006.jpg Figure 6. Enrichment analyses of differential and key metabolites. (A) Enrichment analyses of differ https://www.frontiersin.org/files/Articles/1607242/fnins-19-1607242-HTML/image_m/fnins-19-1607242-t002.jpg Table 2. Linear regression models for WMH volume. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1739153/full https://www.frontiersin.org/files/Articles/1739153/fcvm-13-1739153-HTML/image_m/fcvm-13-1739153-t001.jpg Table 1. Changes in resting systolic blood pressure (SBP/mm Hg) (n = 8). https://www.frontiersin.org/files/Articles/1739153/fcvm-13-1739153-HTML/image_m/fcvm-13-1739153-t002.jpg Table 2. Changes in active systolic blood pressure (SBP/mm Hg) (n = 8). https://www.frontiersin.org/files/Articles/1739153/fcvm-13-1739153-HTML/image_m/fcvm-13-1739153-t003.jpg Table 3. Changes in blood pressure variability (%) (n = 8). https://www.frontiersin.org/files/Articles/1739153/fcvm-13-1739153-HTML/image_m/fcvm-13-1739153-g001.jpg Figure 1. The impact of renal denervation (RDN) on circadian differences in sympathetic nerve activi https://www.frontiersin.org/files/Articles/1739153/fcvm-13-1739153-HTML/image_m/fcvm-13-1739153-g002.jpg Figure 2. The impact of RDN on the circadian expression differences of renal and plasma Ang II and A https://www.frontiersin.org/files/Articles/1739153/fcvm-13-1739153-HTML/image_m/fcvm-13-1739153-g003.jpg Figure 3. The impact of RDN on the circadian expression of renal RAS components in various groups of https://www.frontiersin.org/files/Articles/1739153/fcvm-13-1739153-HTML/image_m/fcvm-13-1739153-g004.jpg Figure 4. The impact of RDN on the circadian expression differences of BMAL1 (n=8). Representative W https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1706432/full https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-g001.jpg Figure 1. Research workflow diagram. (A) Overview of the study, including the collection of lung mic https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-t001.jpg Table 1. Demographic characteristics, severity scores, comorbidities, and clinical outcomes of pneum https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-g002.jpg Figure 2. Comparison of pathogen identification by NGS and traditional bacterial culture (TBC) metho https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-g003.jpg Figure 3. Global microbiome composition and its associations with clinical indicators for corticoste https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-t002.jpg Table 2. Clinical characteristics and outcomes of “survivor” group (S) vs. “non- survivor” group (NS https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-g004.jpg Figure 4. Microbiome composition differences between Survivor (S) and Non-Survivor (NS) groups. (A) https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-g005.jpg Figure 5. LEfSe analysis of microbial trends in survivors (S) and non-survivors (NS) receiving corti https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-g006.jpg Figure 6. Volcano plots illustrating microbial differences between survivor (S) and non-survivor (NS https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-g007.jpg Figure 7. Temporal trends in relative abundance of microbial genera over seven days between Survivor https://www.frontiersin.org/files/Articles/1706432/fmicb-16-1706432-HTML/image_m/fmicb-16-1706432-g008.jpg Figure 8. Identification and validation of microbial class-level features predictive of corticostero https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1769781/full https://www.frontiersin.org/files/Articles/1769781/fpubh-14-1769781-HTML/image_m/fpubh-14-1769781-g001.jpg Figure 1. PRISMA flow diagram of literature screening. https://www.frontiersin.org/files/Articles/1769781/fpubh-14-1769781-HTML/image_m/fpubh-14-1769781-t001.jpg Table 1. Quality appraisal of the included studies. https://www.frontiersin.org/files/Articles/1769781/fpubh-14-1769781-HTML/image_m/fpubh-14-1769781-t002.jpg Table 2. Characteristics of incorporated studies. https://www.frontiersin.org/files/Articles/1769781/fpubh-14-1769781-HTML/image_m/fpubh-14-1769781-t003.jpg Table 3. Demographic information of the participants. https://www.frontiersin.org/files/Articles/1769781/fpubh-14-1769781-HTML/image_m/fpubh-14-1769781-t004.jpg Table 4. Synthesized research themes. https://www.frontiersin.org/files/Articles/1769781/fpubh-14-1769781-HTML/image_m/fpubh-14-1769781-g002.jpg Figure 2. TMD patient journey map. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1662745/full https://www.frontiersin.org/files/Articles/1662745/fpsyg-16-1662745-HTML-r1/image_m/fpsyg-16-1662745-g001.jpg Figure 1. Flowchart illustrating the literature selection and database construction process. https://www.frontiersin.org/files/Articles/1662745/fpsyg-16-1662745-HTML-r1/image_m/fpsyg-16-1662745-g002.jpg Figure 2. Evolution of environmental behavior-related publications in Web of Science and Scopus data https://www.frontiersin.org/files/Articles/1662745/fpsyg-16-1662745-HTML-r1/image_m/fpsyg-16-1662745-g003.jpg Figure 3. Evolution of published journals related to environmental behavior keywords in scientific n https://www.frontiersin.org/files/Articles/1662745/fpsyg-16-1662745-HTML-r1/image_m/fpsyg-16-1662745-g004.jpg Figure 4. (A) Total number of documents and percentage of documents of the top 15 disciplines in env https://www.frontiersin.org/files/Articles/1662745/fpsyg-16-1662745-HTML-r1/image_m/fpsyg-16-1662745-g005.jpg Figure 5. Author cooperation diagram. https://www.frontiersin.org/files/Articles/1662745/fpsyg-16-1662745-HTML-r1/image_m/fpsyg-16-1662745-g006.jpg Figure 6. Keywords co-occurrence time graph. https://www.frontiersin.org/files/Articles/1662745/fpsyg-16-1662745-HTML-r1/image_m/fpsyg-16-1662745-g007.jpg Figure 7. (A) Geographical distribution of studies on environmental behavior by country. (B) Trends https://www.frontiersin.org/files/Articles/1662745/fpsyg-16-1662745-HTML-r1/image_m/fpsyg-16-1662745-g008.jpg Figure 8. Evolution trend of the top 25 hot spots of environmental behavior in terms of the trend fa https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1695847/full https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-g001.jpg Figure 1. Map indicating the study area. Source: https://www.google.com/search?client=firefox-b-e&q= https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-t001.jpg Table 1. Age distribution of participants. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-t002.jpg Table 2. Gender distribution of participants and non-participants in the intervention programme. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-t003.jpg Table 3. Marital status distribution of respondents. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-t004.jpg Table 4. Distribution of educational levels among respondents. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-t005.jpg Table 5. Respondents’ distribution of household size. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-g002.jpg Figure 2. Distribution of the employment status of participants. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-t006.jpg Table 6. Respondents’ distribution of farming experience (years). https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-g003.jpg Figure 3. Distribution of the participants’ food security status. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-g004.jpg Figure 4. Respondents’ perception of the effectiveness of the programme. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-g005.jpg Figure 5. Respondents’ primary challenges faced in the intervention programme. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-g006.jpg Figure 6. Impact of the government intervention programme. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-g007.jpg Figure 7. Accessibility of the government food intervention programmes. https://www.frontiersin.org/files/Articles/1695847/fsufs-10-1695847-HTML/image_m/fsufs-10-1695847-t007.jpg Table 7. Distribution of the respondents’ satisfaction level with the programme. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1681420/full https://www.frontiersin.org/files/Articles/1681420/fonc-15-1681420-HTML/image_m/fonc-15-1681420-g001.jpg Figure 1. Study flow diagram illustrating patient enrollment, exclusions, and sample availability. https://www.frontiersin.org/files/Articles/1681420/fonc-15-1681420-HTML/image_m/fonc-15-1681420-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1681420/fonc-15-1681420-HTML/image_m/fonc-15-1681420-g002.jpg Figure 2. Oncoprint of the study population showing functionally relevant genomic alterations detect https://www.frontiersin.org/files/Articles/1681420/fonc-15-1681420-HTML/image_m/fonc-15-1681420-g003.jpg Figure 3. Association between blood tumor mutational burden (bTMB) and progression-free survival (PF https://www.frontiersin.org/files/Articles/1681420/fonc-15-1681420-HTML/image_m/fonc-15-1681420-g004.jpg Figure 4. (A) Kaplan-Meier curves for progression-free survival (PFS) according to PD-L1 status. (B) https://www.frontiersin.org/files/Articles/1681420/fonc-15-1681420-HTML/image_m/fonc-15-1681420-g005.jpg Figure 5. Forest plot for the multivariable analysis of factors associated with progression-free sur https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1763757/full https://www.frontiersin.org/files/Articles/1763757/fonc-16-1763757-HTML/image_m/fonc-16-1763757-g001.jpg Figure 1. Imaging findings. (A–C) Contrast-enhanced computed tomography (CT) images demonstrating a https://www.frontiersin.org/files/Articles/1763757/fonc-16-1763757-HTML/image_m/fonc-16-1763757-g002.jpg Figure 2. Endoscopic ultrasound–guided fine-needle aspiration (EUS-FNA). (A) EUS image demonstrating https://www.frontiersin.org/files/Articles/1763757/fonc-16-1763757-HTML/image_m/fonc-16-1763757-g003.jpg Figure 3. Cytological and histopathological findings from endoscopic ultrasound–guided fine-needle a https://www.frontiersin.org/files/Articles/1763757/fonc-16-1763757-HTML/image_m/fonc-16-1763757-g004.jpg Figure 4. Immunohistochemical characteristics of the tumor. (A) Complete loss of SMARCA4 expression, https://www.frontiersin.org/files/Articles/1763757/fonc-16-1763757-HTML/image_m/fonc-16-1763757-t001.jpg Table 1. Clinical and pathological characteristics reported in SMARCA4-deficient gastric carcinoma. https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2025.1700665/full https://www.frontiersin.org/files/Articles/1700665/fdmed-06-1700665-HTML/image_m/fdmed-06-1700665-g001.jpg Figure 1. (a) Structure of CTLA-4. (b) Conventional antigen presentation complex. (c) Co-inhibitory https://www.frontiersin.org/files/Articles/1700665/fdmed-06-1700665-HTML/image_m/fdmed-06-1700665-t001.jpg Table 1. Systemic irAEs pertaining to various types of immune checkpoint blockers. https://www.frontiersin.org/files/Articles/1700665/fdmed-06-1700665-HTML/image_m/fdmed-06-1700665-g002.jpg Figure 2. (a) Aberrant Wnt/β-catenin pathway induced by periodontitis and its plausible mechanisms i https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1731466/full https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-g001.jpg Figure 1. Study flowdiagram. https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-t001.jpg Table 1. Characteristics of included RCTs. https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-g002.jpg Figure 2. Risk of bias of RCTs: risk of bias graph. https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-g003.jpg Figure 3. Risk of bias of RCTs: risk of bias summary. https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-g004.jpg Figure 4. Forest plot of FBG according to the comparison of Baduanjin vs. control. https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-g005.jpg Figure 5. Forest plot of HbA1C according to the comparison of Baduanjin vs. control. https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-g006.jpg Figure 6. Forest plot of TG according to the comparison of Baduanjin vs. control. https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-g007.jpg Figure 7. Forest plot of TC according to the comparison of Baduanjin vs. control. https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-t002.jpg Table 2. Effect of different Baduanjin exercise parameters on the indicators of glucose and lipid me https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-t003.jpg Table 3. Evidence quality assessment according to GRADE. https://www.frontiersin.org/files/Articles/1731466/fendo-17-1731466-HTML/image_m/fendo-17-1731466-g008.jpg Figure 8. Publication bias: (A) Funnel Plot for FBG , (B) Funnel Plot for HBA1C, (C) Funnel Plot for https://www.frontiersin.org/journals/research-metrics-and-analytics/articles/10.3389/frma.2026.1778160/full https://www.frontiersin.org/files/Articles/1778160/frma-11-1778160-HTML/image_m/frma-11-1778160-t001.jpg Table 1. Data collection methods in qualitative research. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1709531/full https://www.frontiersin.org/files/Articles/1709531/fmed-12-1709531-HTML/image_m/fmed-12-1709531-g001.jpg Figure 1. Literature screening process. https://www.frontiersin.org/files/Articles/1709531/fmed-12-1709531-HTML/image_m/fmed-12-1709531-t001.jpg Table 1. Characteristics of the included literature. https://www.frontiersin.org/files/Articles/1709531/fmed-12-1709531-HTML/image_m/fmed-12-1709531-g002.jpg Figure 2. Radar chart of scores for each item of AMSTAR-2. https://www.frontiersin.org/files/Articles/1709531/fmed-12-1709531-HTML/image_m/fmed-12-1709531-g003.jpg Figure 3. Cartesian heatmap of the scores of each item in PRISMA 2020. https://www.frontiersin.org/files/Articles/1709531/fmed-12-1709531-HTML/image_m/fmed-12-1709531-t002.jpg Table 2. Evidence quality assessment. https://www.frontiersin.org/files/Articles/1709531/fmed-12-1709531-HTML/image_m/fmed-12-1709531-g004.jpg Figure 4. Meta-analysis of combined symptom and medication score. https://www.frontiersin.org/files/Articles/1709531/fmed-12-1709531-HTML/image_m/fmed-12-1709531-g005.jpg Figure 5. Meta-analysis of symptom score. https://www.frontiersin.org/files/Articles/1709531/fmed-12-1709531-HTML/image_m/fmed-12-1709531-g006.jpg Figure 6. Meta-analysis of serum-specific IgE. https://www.frontiersin.org/files/Articles/1709531/fmed-12-1709531-HTML/image_m/fmed-12-1709531-g007.jpg Figure 7. Meta-analysis of serum-specific IgG4. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1711096/full https://www.frontiersin.org/files/Articles/1711096/fmed-12-1711096-HTML/image_m/fmed-12-1711096-g001.jpg Figure 1. Literature screening process. https://www.frontiersin.org/files/Articles/1711096/fmed-12-1711096-HTML/image_m/fmed-12-1711096-t001.jpg Table 1. Characteristics of the included literature. https://www.frontiersin.org/files/Articles/1711096/fmed-12-1711096-HTML/image_m/fmed-12-1711096-g002.jpg Figure 2. Radar chart of scores for each item of AMSTAR-2. https://www.frontiersin.org/files/Articles/1711096/fmed-12-1711096-HTML/image_m/fmed-12-1711096-g003.jpg Figure 3. Cartesian heatmap of the scores of each item in PRISMA 2020. https://www.frontiersin.org/files/Articles/1711096/fmed-12-1711096-HTML/image_m/fmed-12-1711096-t002.jpg Table 2. Evidence quality assessment. https://www.frontiersin.org/files/Articles/1711096/fmed-12-1711096-HTML/image_m/fmed-12-1711096-g004.jpg Figure 4. Meta-analysis of total IgE. https://www.frontiersin.org/files/Articles/1711096/fmed-12-1711096-HTML/image_m/fmed-12-1711096-g005.jpg Figure 5. Meta-analysis of sIgE. https://www.frontiersin.org/files/Articles/1711096/fmed-12-1711096-HTML/image_m/fmed-12-1711096-g006.jpg Figure 6. Meta-analysis of eosinophil count. https://www.frontiersin.org/files/Articles/1711096/fmed-12-1711096-HTML/image_m/fmed-12-1711096-g007.jpg Figure 7. Meta-analysis of Th1/Th2 ratio. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1755023/full https://www.frontiersin.org/files/Articles/1755023/fmed-12-1755023-HTML/image_m/fmed-12-1755023-g001.jpg Figure 1. Literature screening process. https://www.frontiersin.org/files/Articles/1755023/fmed-12-1755023-HTML/image_m/fmed-12-1755023-t001.jpg Table 1. Characteristics of the included literature. https://www.frontiersin.org/files/Articles/1755023/fmed-12-1755023-HTML/image_m/fmed-12-1755023-t002.jpg Table 2. Scores for each item of AMSTAR-2. https://www.frontiersin.org/files/Articles/1755023/fmed-12-1755023-HTML/image_m/fmed-12-1755023-g002.jpg Figure 2. Cartesian heatmap of the scores of each item in PRISMA 2020. https://www.frontiersin.org/files/Articles/1755023/fmed-12-1755023-HTML/image_m/fmed-12-1755023-t003.jpg Table 3. Evidence quality assessment. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1791964/full https://www.frontiersin.org/files/Articles/1791964/fspor-08-1791964-HTML-r1/image_m/fspor-08-1791964-g001.jpg Figure 1. Hypothesized theoretical model. https://www.frontiersin.org/files/Articles/1791964/fspor-08-1791964-HTML-r1/image_m/fspor-08-1791964-t001.jpg Table 1. Correlation and reliability of all the scales. https://www.frontiersin.org/files/Articles/1791964/fspor-08-1791964-HTML-r1/image_m/fspor-08-1791964-t002.jpg Table 2. Invariance analysis of moral disengagement, moral identity, self-regulatory efficacy. https://www.frontiersin.org/files/Articles/1791964/fspor-08-1791964-HTML-r1/image_m/fspor-08-1791964-g002.jpg Figure 2. SEM path diagram. Results of the overall model. https://www.frontiersin.org/files/Articles/1791964/fspor-08-1791964-HTML-r1/image_m/fspor-08-1791964-t003.jpg Table 3. Statistically significant differences in direct effects of multigroup analysis. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1778957/full https://www.frontiersin.org/files/Articles/1778957/fmicb-17-1778957-HTML/image_m/fmicb-17-1778957-g001.jpg Figure 1. Comparison of α-diversity and coefficient of variation (CV%) of the bacterial community in https://www.frontiersin.org/files/Articles/1778957/fmicb-17-1778957-HTML/image_m/fmicb-17-1778957-g002.jpg Figure 2. Relative abundances of dominant bacterial phyla/Pseudomonadota classes (top) and families https://www.frontiersin.org/files/Articles/1778957/fmicb-17-1778957-HTML/image_m/fmicb-17-1778957-g003.jpg Figure 3. Redundancy analysis (RDA) illustrating relationships between bacterial community compositi https://www.frontiersin.org/files/Articles/1778957/fmicb-17-1778957-HTML/image_m/fmicb-17-1778957-g004.jpg Figure 4. Abundance patterns of dominant zOTUs and their correlations with environmental parameters. https://www.frontiersin.org/files/Articles/1778957/fmicb-17-1778957-HTML/image_m/fmicb-17-1778957-g005.jpg Figure 5. Differential functional profiles of the bacterial community in response to peanut-shell ad https://www.frontiersin.org/files/Articles/1778957/fmicb-17-1778957-HTML/image_m/fmicb-17-1778957-g006.jpg Figure 6. Neutral model (NCM) fit for control (A–C) and treated (D–F) groups at 1, 14, and 28 days. https://www.frontiersin.org/files/Articles/1778957/fmicb-17-1778957-HTML/image_m/fmicb-17-1778957-g007.jpg Figure 7. Distribution of taxa under the neutral model in control and treatment groups. (A–C) Contro https://www.frontiersin.org/files/Articles/1778957/fmicb-17-1778957-HTML/image_m/fmicb-17-1778957-t001.jpg Table 1. Results of the redundancy analysis (RDA) showing the explanatory power and statistical sign https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1752073/full https://www.frontiersin.org/files/Articles/1752073/fneur-17-1752073-HTML/image_m/fneur-17-1752073-g001.jpg Figure 1. Logic model SENSS. https://www.frontiersin.org/files/Articles/1752073/fneur-17-1752073-HTML/image_m/fneur-17-1752073-g002.jpg Figure 2. Overview of study design. https://www.frontiersin.org/files/Articles/1752073/fneur-17-1752073-HTML/image_m/fneur-17-1752073-g003.jpg Figure 3. The stepped care model. https://www.frontiersin.org/files/Articles/1752073/fneur-17-1752073-HTML/image_m/fneur-17-1752073-t001.jpg Table 1. Overview primary and secondary outcome measures. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1716843/full https://www.frontiersin.org/files/Articles/1716843/fendo-17-1716843-HTML/image_m/fendo-17-1716843-t001.jpg Table 1. Comparison of basic characteristics of study subjects. https://www.frontiersin.org/files/Articles/1716843/fendo-17-1716843-HTML/image_m/fendo-17-1716843-g001.jpg Figure 1. Comparison of serum TNFR1 and TNFR2 levels across study groups. Serum concentrations of (A https://www.frontiersin.org/files/Articles/1716843/fendo-17-1716843-HTML/image_m/fendo-17-1716843-t002.jpg Table 2. Correlation analysis of TNFR1 and TNFR2 with metabolic risk factors in the DM subjects. https://www.frontiersin.org/files/Articles/1716843/fendo-17-1716843-HTML/image_m/fendo-17-1716843-t003.jpg Table 3. Multivariable regression analysis of TNFR1 and TNFR2 with metabolic risk factors in the DM https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1676689/full https://www.frontiersin.org/files/Articles/1676689/fped-14-1676689-HTML/image_m/fped-14-1676689-g001.jpg Figure 1. Number of cases of bronchiolitis in the study period. Data regarding RSV molecular testing https://www.frontiersin.org/files/Articles/1676689/fped-14-1676689-HTML/image_m/fped-14-1676689-t001.jpg Table 1. Main study results. https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1613828/full https://www.frontiersin.org/files/Articles/1613828/fchem-13-1613828-HTML-r1/image_m/fchem-13-1613828-g001.jpg Figure 1. Bar stacking plots of eight types of volatile compounds from different origins. https://www.frontiersin.org/files/Articles/1613828/fchem-13-1613828-HTML-r1/image_m/fchem-13-1613828-g002.jpg Figure 2. Bar stacking plots of eight types of volatile compounds from different origins (Exclusion https://www.frontiersin.org/files/Articles/1613828/fchem-13-1613828-HTML-r1/image_m/fchem-13-1613828-g003.jpg Figure 3. Systematic clustering of different origins. https://www.frontiersin.org/files/Articles/1613828/fchem-13-1613828-HTML-r1/image_m/fchem-13-1613828-g004.jpg Figure 4. Clustered heat map of the content of all volatiles from different origins. https://www.frontiersin.org/files/Articles/1613828/fchem-13-1613828-HTML-r1/image_m/fchem-13-1613828-g005.jpg Figure 5. Clustered heat map of the content of all volatiles from different origins with standardize https://www.frontiersin.org/files/Articles/1613828/fchem-13-1613828-HTML-r1/image_m/fchem-13-1613828-g006.jpg Figure 6. PCA based on volatile of FCT from different origins. https://www.frontiersin.org/files/Articles/1613828/fchem-13-1613828-HTML-r1/image_m/fchem-13-1613828-g007.jpg Figure 7. OPLS-DA based on volatile of FCT from different origins. https://www.frontiersin.org/files/Articles/1613828/fchem-13-1613828-HTML-r1/image_m/fchem-13-1613828-g008.jpg Figure 8. VIP based on volatile of FCT from different origins. https://www.frontiersin.org/files/Articles/1613828/fchem-13-1613828-HTML-r1/image_m/fchem-13-1613828-g009.jpg Figure 9. CWC content of FCT from different origins. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1667477/full https://www.frontiersin.org/files/Articles/1667477/fped-13-1667477-HTML-r1/image_m/fped-13-1667477-g001.jpg Figure 1. Typical features and brain MRI in 2 cases with HPIDS2. (A) Patient 1 (9 years 3 months) fa https://www.frontiersin.org/files/Articles/1667477/fped-13-1667477-HTML-r1/image_m/fped-13-1667477-g002.jpg Figure 2. (A) Protein tertiary structure changes. Blue: Hydrogen bonds; Purple: Interacting forces ( https://www.frontiersin.org/files/Articles/1667477/fped-13-1667477-HTML-r1/image_m/fped-13-1667477-t001.jpg Table 1. ACMG rating information. https://www.frontiersin.org/files/Articles/1667477/fped-13-1667477-HTML-r1/image_m/fped-13-1667477-t002.jpg Table 2. Genotypes and clinical manifestations in children with HPIDS2. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1801722/full https://www.frontiersin.org/files/Articles/1801722/fimmu-17-1801722-HTML/image_m/fimmu-17-1801722-t001.jpg Table 1. Major TIL constructs in therapeutic pipeline for metastatic melanoma. https://www.frontiersin.org/files/Articles/1801722/fimmu-17-1801722-HTML/image_m/fimmu-17-1801722-g001.jpg Figure 1. Schematic of the tumor-infiltrating lymphocyte (TIL) therapy process. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1758541/full https://www.frontiersin.org/files/Articles/1758541/fnagi-18-1758541-HTML/image_m/fnagi-18-1758541-g001.jpg Figure 1. Results of a meta-analysis conducted with neuroimaging studies on handwriting. Brain regio https://www.frontiersin.org/files/Articles/1758541/fnagi-18-1758541-HTML/image_m/fnagi-18-1758541-t001.jpg Table 1. Age-related handwriting changes and associated kinematic, sensory, and cognitive factors. https://www.frontiersin.org/files/Articles/1758541/fnagi-18-1758541-HTML/image_m/fnagi-18-1758541-t002.jpg Table 2. Disease-specific handwriting characteristics across major neurodegenerative conditions. https://www.frontiersin.org/files/Articles/1758541/fnagi-18-1758541-HTML/image_m/fnagi-18-1758541-g002.jpg Figure 2. Handwriting data acquisition using a tablet-based system. As an example, the word “les” is https://www.frontiersin.org/files/Articles/1758541/fnagi-18-1758541-HTML/image_m/fnagi-18-1758541-g003.jpg Figure 3. Automated segmentation of upstrokes and downstrokes. The trajectory and velocity profile o https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1772606/full https://www.frontiersin.org/files/Articles/1772606/fped-14-1772606-HTML/image_m/fped-14-1772606-g001.jpg Figure 1. Primary and secondary pathophysiological defects within the nephron in Bartter syndrome. https://www.frontiersin.org/files/Articles/1772606/fped-14-1772606-HTML/image_m/fped-14-1772606-t001.jpg Table 1. 30 months follow-up—treatment, diet, development and other problems. https://www.frontiersin.org/files/Articles/1772606/fped-14-1772606-HTML/image_m/fped-14-1772606-t002.jpg Table 2. 30 months follow-up—blood and urine samples results. https://www.frontiersin.org/files/Articles/1772606/fped-14-1772606-HTML/image_m/fped-14-1772606-g002.jpg Figure 2. Left and right kidney: grade Ib nephrocalcinosis according to Hoyer, with hyperechoic marg https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2026.1802261/full https://www.frontiersin.org/files/Articles/1802261/frwa-08-1802261-HTML/image_m/frwa-08-1802261-g001.jpg Figure 1. Methodology overview. https://www.frontiersin.org/files/Articles/1802261/frwa-08-1802261-HTML/image_m/frwa-08-1802261-g002.jpg Figure 2. Study area with Tuscany region divided in its 48 local labor systems including topography, https://www.frontiersin.org/files/Articles/1802261/frwa-08-1802261-HTML/image_m/frwa-08-1802261-g003.jpg Figure 3. Net flow of virtual water in Tuscany LLSs (red = net importers; blue = net exporters). https://www.frontiersin.org/files/Articles/1802261/frwa-08-1802261-HTML/image_m/frwa-08-1802261-g004.jpg Figure 4. Volumetric water demand (right axis), scarcity-weighted water footprints (SWF), and social https://www.frontiersin.org/files/Articles/1802261/frwa-08-1802261-HTML/image_m/frwa-08-1802261-g005.jpg Figure 5. Prato LLS consumption-driven virtual water exports. https://www.frontiersin.org/files/Articles/1802261/frwa-08-1802261-HTML/image_m/frwa-08-1802261-g006.jpg Figure 6. Analysis of the WSI and SV in the LLS of origin of the water consumed in Florence. The rig https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1790430/full https://www.frontiersin.org/files/Articles/1790430/fcimb-16-1790430-HTML/image_m/fcimb-16-1790430-g000.jpg Graphical Abstract. Shao, Y. (2026) https://BioRender.com/7rhrtvg. https://www.frontiersin.org/files/Articles/1790430/fcimb-16-1790430-HTML/image_m/fcimb-16-1790430-g001.jpg Figure 1. Isolation of phage-resistant bacterial strains. (A–D) Phage-resistant bacterial strains we https://www.frontiersin.org/files/Articles/1790430/fcimb-16-1790430-HTML/image_m/fcimb-16-1790430-t001.jpg Table 1. Genetic changes potentially associated with bacteriophage resistance. https://www.frontiersin.org/files/Articles/1790430/fcimb-16-1790430-HTML/image_m/fcimb-16-1790430-g002.jpg Figure 2. Abnormal capsular synthesis in MRABP9-resistant strains. (A) Schematic representation of t https://www.frontiersin.org/files/Articles/1790430/fcimb-16-1790430-HTML/image_m/fcimb-16-1790430-g003.jpg Figure 3. The bacteriophage MRABphi22 effectively targets MRABP9-resistant isolates. (A) Schematic i https://www.frontiersin.org/files/Articles/1790430/fcimb-16-1790430-HTML/image_m/fcimb-16-1790430-g004.jpg Figure 4. The dual-phage cocktail demonstrates high efficacy in controlling bacterial growth and pha https://www.frontiersin.org/files/Articles/1790430/fcimb-16-1790430-HTML/image_m/fcimb-16-1790430-g005.jpg Figure 5. Fitness trade-offs occurred in phage-resistant strains, with the DPR strains bearing a hig https://www.frontiersin.org/files/Articles/1790430/fcimb-16-1790430-HTML/image_m/fcimb-16-1790430-g006.jpg Figure 6. The dual-phage cocktail effectively controlled the growth and phage resistance development https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1752250/full https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-g001.jpg Figure 1. Flowchart illustrating the construction of the NRS2002 classification and prediction dual- https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-g002.jpg Figure 2. Tongue image dataset distribution and comparison of different segmentation methods. https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-g003.jpg Figure 3. Schematic of the CLES strategy. https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-g004.jpg Figure 4. SelectNet framework. https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-g005.jpg Figure 5. Shuttle attention mechanism classification model. https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-t001.jpg Table 1. Test results of different feature selection methods. https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-g006.jpg Figure 6. ROC curve and confusion matrix of different feature selection methods. https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-t002.jpg Table 2. Test results of different data balancing methods. https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-t003.jpg Table 3. Test results of different feature selection methods. https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-t004.jpg Table 4. Results of fusion strategy. https://www.frontiersin.org/files/Articles/1752250/fnut-12-1752250-HTML-r1/image_m/fnut-12-1752250-g007.jpg Figure 7. Model explainability analysis. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1714530/full https://www.frontiersin.org/files/Articles/1714530/fphar-17-1714530-HTML-r1/image_m/fphar-17-1714530-g001.jpg Figure 1. Study design. Flow diagram of the retrospective cohort study conducted at Hospital Dr. Gus https://www.frontiersin.org/files/Articles/1714530/fphar-17-1714530-HTML-r1/image_m/fphar-17-1714530-t001.jpg Table 1. Characteristics of patients by blood bicarbonate levels. https://www.frontiersin.org/files/Articles/1714530/fphar-17-1714530-HTML-r1/image_m/fphar-17-1714530-t002.jpg Table 2. Mortality rate in patients positive for COVID-19 with or without ACEI and ARB drugs. https://www.frontiersin.org/files/Articles/1714530/fphar-17-1714530-HTML-r1/image_m/fphar-17-1714530-g002.jpg Figure 2. Relationship between HCO3− levels and clinical outcomes. Boxplots show intensive care unit https://www.frontiersin.org/files/Articles/1714530/fphar-17-1714530-HTML-r1/image_m/fphar-17-1714530-t003.jpg Table 3. Impact of RAAS blockers and bicarbonate status by multivariable logistic regression analysi https://www.frontiersin.org/files/Articles/1714530/fphar-17-1714530-HTML-r1/image_m/fphar-17-1714530-g003.jpg Figure 3. Comparison of HCO3− levels in patients with low, normal, and high bicarbonate across diffe https://www.frontiersin.org/files/Articles/1714530/fphar-17-1714530-HTML-r1/image_m/fphar-17-1714530-g004.jpg Figure 4. Relationship between changes in HCO3− levels and mortality on the second day of ICU admiss https://www.frontiersin.org/files/Articles/1714530/fphar-17-1714530-HTML-r1/image_m/fphar-17-1714530-g005.jpg Figure 5. Schematic representation of the renin-angiotensin-aldosterone system and its effects on lu https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1793843/full https://www.frontiersin.org/files/Articles/1793843/fpsyg-17-1793843-HTML/image_m/fpsyg-17-1793843-g001.jpg Figure 1. Conceptual model depicting the hypothesized pathways linking exercise intensity, duration, https://www.frontiersin.org/files/Articles/1793843/fpsyg-17-1793843-HTML/image_m/fpsyg-17-1793843-t001.jpg Table 1. Results of descriptive statistical analysis and correlation analysis of variables (N = 1,67 https://www.frontiersin.org/files/Articles/1793843/fpsyg-17-1793843-HTML/image_m/fpsyg-17-1793843-t002.jpg Table 2. Multiple regression diagnostics for smartphone addiction. https://www.frontiersin.org/files/Articles/1793843/fpsyg-17-1793843-HTML/image_m/fpsyg-17-1793843-t003.jpg Table 3. Residual statistics of the regression model (N = 1,670). https://www.frontiersin.org/files/Articles/1793843/fpsyg-17-1793843-HTML/image_m/fpsyg-17-1793843-g002.jpg Figure 2. Path analysis diagram of the theoretical hypothesized model. https://www.frontiersin.org/files/Articles/1793843/fpsyg-17-1793843-HTML/image_m/fpsyg-17-1793843-t004.jpg Table 4. Mediation analysis of exercise intensity on smartphone addiction. https://www.frontiersin.org/files/Articles/1793843/fpsyg-17-1793843-HTML/image_m/fpsyg-17-1793843-t005.jpg Table 5. Mediation analysis of exercise duration on smartphone addiction. https://www.frontiersin.org/files/Articles/1793843/fpsyg-17-1793843-HTML/image_m/fpsyg-17-1793843-t006.jpg Table 6. Mediation analysis of exercise frequency on smartphone addiction. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1709355/full https://www.frontiersin.org/files/Articles/1709355/fnagi-18-1709355-HTML/image_m/fnagi-18-1709355-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1709355/fnagi-18-1709355-HTML/image_m/fnagi-18-1709355-t002.jpg Table 2. EFS item-level comparisons. https://www.frontiersin.org/files/Articles/1709355/fnagi-18-1709355-HTML/image_m/fnagi-18-1709355-t003.jpg Table 3. Spearman correlations between EFS total score and MoCA domains. https://www.frontiersin.org/files/Articles/1709355/fnagi-18-1709355-HTML/image_m/fnagi-18-1709355-t004.jpg Table 4. Multivariable logistic regression for cognitive impairment. https://www.frontiersin.org/files/Articles/1709355/fnagi-18-1709355-HTML/image_m/fnagi-18-1709355-t005.jpg Table 5. Sensitivity analysis (EFS total minus cognition item.). https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1746202/full https://www.frontiersin.org/files/Articles/1746202/fonc-16-1746202-HTML/image_m/fonc-16-1746202-g001.jpg Figure 1. The hypothesized moderated mediation model. https://www.frontiersin.org/files/Articles/1746202/fonc-16-1746202-HTML/image_m/fonc-16-1746202-t001.jpg Table 1. Univariate analysis of depression in colorectal cancer patients based on different characte https://www.frontiersin.org/files/Articles/1746202/fonc-16-1746202-HTML/image_m/fonc-16-1746202-t002.jpg Table 2. Correlation analysis. https://www.frontiersin.org/files/Articles/1746202/fonc-16-1746202-HTML/image_m/fonc-16-1746202-t003.jpg Table 3. Mediation analysis. https://www.frontiersin.org/files/Articles/1746202/fonc-16-1746202-HTML/image_m/fonc-16-1746202-t004.jpg Table 4. Tests for mediating effect. https://www.frontiersin.org/files/Articles/1746202/fonc-16-1746202-HTML/image_m/fonc-16-1746202-t005.jpg Table 5. Moderated mediation analysis. https://www.frontiersin.org/files/Articles/1746202/fonc-16-1746202-HTML/image_m/fonc-16-1746202-t006.jpg Table 6. Moderating effects of different levels of self-esteem. https://www.frontiersin.org/files/Articles/1746202/fonc-16-1746202-HTML/image_m/fonc-16-1746202-g002.jpg Figure 2. Plot of the relationship between perceived stress and illness perception at two levels of https://www.frontiersin.org/files/Articles/1746202/fonc-16-1746202-HTML/image_m/fonc-16-1746202-g003.jpg Figure 3. Moderated mediation model. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1802072/full https://www.frontiersin.org/files/Articles/1802072/fonc-16-1802072-HTML/image_m/fonc-16-1802072-t001.jpg Table 1. Descriptive statistics of public, private and mixed radiotherapy facilities. https://www.frontiersin.org/files/Articles/1802072/fonc-16-1802072-HTML/image_m/fonc-16-1802072-t002.jpg Table 2. Linear accelerators (LINACs) utilization rates in different radiotherapy centers. https://www.frontiersin.org/files/Articles/1802072/fonc-16-1802072-HTML/image_m/fonc-16-1802072-t003.jpg Table 3. Normalized linear accelerator (LINAC) availability in different sectors (LINACs per 1,000 p https://www.frontiersin.org/files/Articles/1802072/fonc-16-1802072-HTML/image_m/fonc-16-1802072-t004.jpg Table 4. Number of linear accelerators (LINACs) and CT simulation machines in different radiotherapy https://www.frontiersin.org/files/Articles/1802072/fonc-16-1802072-HTML/image_m/fonc-16-1802072-t005.jpg Table 5. Availability of different radiotherapy technologies in the facilities. https://www.frontiersin.org/files/Articles/1802072/fonc-16-1802072-HTML/image_m/fonc-16-1802072-t006.jpg Table 6. Workload ratio for staff in different radiotherapy facilities. https://www.frontiersin.org/files/Articles/1802072/fonc-16-1802072-HTML/image_m/fonc-16-1802072-g001.jpg Figure 1. Training provided within departments from different sectors. https://www.frontiersin.org/files/Articles/1802072/fonc-16-1802072-HTML/image_m/fonc-16-1802072-g002.jpg Figure 2. Waiting times for patients from radiotherapy referral to start of treatment for Radical ca https://www.frontiersin.org/files/Articles/1802072/fonc-16-1802072-HTML/image_m/fonc-16-1802072-g003.jpg Figure 3. Perceived ratings of resource adequacy and effects of procurement delays in different sect https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1629251/full https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-t001.jpg Table 1. Inclusion and exclusion criteria. https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-g001.jpg Figure 1. PRISM flow diagram. https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-t002.jpg Table 2. Detailed characteristics of the included studies. https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-g002.jpg Figure 2. Summary of risk of bias. https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-g003.jpg Figure 3. Impact of interventions on Mobile phone addiction. https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-t003.jpg Table 3. Subgroup analysis to assess the effect of interventions on adolescents’ intervention addict https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-g004.jpg Figure 4. Funnel plot for the publication bias of adolescents’ Mobile phone addiction. https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-g005.jpg Figure 5. Network diagram of mobile phone addiction. Aerobic Aerobics (AA), Tai Chi (TC), Table tenn https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-g006.jpg Figure 6. Results of network meta-analysis. https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-g007.jpg Figure 7. Sucra graph of effectiveness among interventions. https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-t004.jpg Table 4. The SUCRA values of the interventions. https://www.frontiersin.org/files/Articles/1629251/fpsyt-16-1629251-HTML/image_m/fpsyt-16-1629251-g008.jpg Figure 8. Comparison-adjusted funnel plot of adolescent Internet addiction scores. (A) = Aerobic Aer https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1738521/full https://www.frontiersin.org/files/Articles/1738521/fpsyg-17-1738521-HTML/image_m/fpsyg-17-1738521-g001.jpg Figure 1. Hypothetical mediation model. https://www.frontiersin.org/files/Articles/1738521/fpsyg-17-1738521-HTML/image_m/fpsyg-17-1738521-t001.jpg Table 1. Descriptive analysis of childhood emotional abuse, depression, subjective well-being, and h https://www.frontiersin.org/files/Articles/1738521/fpsyg-17-1738521-HTML/image_m/fpsyg-17-1738521-t002.jpg Table 2. Pearson correlation matrix between relevant variables. https://www.frontiersin.org/files/Articles/1738521/fpsyg-17-1738521-HTML/image_m/fpsyg-17-1738521-t003.jpg Table 3. Mediation model test. https://www.frontiersin.org/files/Articles/1738521/fpsyg-17-1738521-HTML/image_m/fpsyg-17-1738521-g002.jpg Figure 2. Chain mediation model of childhood emotional abuse and healthy eating in university studen https://www.frontiersin.org/files/Articles/1738521/fpsyg-17-1738521-HTML/image_m/fpsyg-17-1738521-t004.jpg Table 4. Mediation model path analysis of childhood emotional abuse and healthy eating in university https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1753999/full https://www.frontiersin.org/files/Articles/1753999/fmicb-17-1753999-HTML/image_m/fmicb-17-1753999-g001.jpg Figure 1. Electroactive microorganisms in soil: electrical current generation, nutrient cycling, bio https://www.frontiersin.org/files/Articles/1753999/fmicb-17-1753999-HTML/image_m/fmicb-17-1753999-g002.jpg Figure 2. Gardening microorganisms for sustainable systems: microorganisms are cultivated and harnes https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1685297/full https://www.frontiersin.org/files/Articles/1685297/fpubh-14-1685297-HTML/image_m/fpubh-14-1685297-t001.jpg Table 1. Basic information of participants and KAP score. https://www.frontiersin.org/files/Articles/1685297/fpubh-14-1685297-HTML/image_m/fpubh-14-1685297-t002.jpg Table 2. Knowledge dimension of the participants. https://www.frontiersin.org/files/Articles/1685297/fpubh-14-1685297-HTML/image_m/fpubh-14-1685297-t003.jpg Table 3. Attitude dimension of the participants. https://www.frontiersin.org/files/Articles/1685297/fpubh-14-1685297-HTML/image_m/fpubh-14-1685297-t004.jpg Table 4. Practice dimension of the participants. https://www.frontiersin.org/files/Articles/1685297/fpubh-14-1685297-HTML/image_m/fpubh-14-1685297-t005.jpg Table 5. Correlation analysis of KAP scores. https://www.frontiersin.org/files/Articles/1685297/fpubh-14-1685297-HTML/image_m/fpubh-14-1685297-t006.jpg Table 6. Factors of practice based univariable and multivariable logistic regression. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1744348/full https://www.frontiersin.org/files/Articles/1744348/fphar-17-1744348-HTML-r2/image_m/fphar-17-1744348-t001.jpg Table 1. Epidemiology and risk factors associated with CL across endemic regions. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1748002/full https://www.frontiersin.org/files/Articles/1748002/fphar-17-1748002-HTML/image_m/fphar-17-1748002-g001.jpg Figure 1. Schematic representation of the life cycle of neurocysticercosis. https://www.frontiersin.org/files/Articles/1748002/fphar-17-1748002-HTML/image_m/fphar-17-1748002-t001.jpg Table 1. Pathophysiology, stages and clinical presentation of neurocysticercosis. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1748819/full https://www.frontiersin.org/files/Articles/1748819/fmars-13-1748819-HTML/image_m/fmars-13-1748819-g001.jpg Figure 1. Proposed model for the rationale of the success of C. sapidus invasion. https://www.frontiersin.org/files/Articles/1748819/fmars-13-1748819-HTML/image_m/fmars-13-1748819-g002.jpg Figure 2. Graphical depiction of the need for waste management and the benefits of bio-active compou https://www.frontiersin.org/files/Articles/1748819/fmars-13-1748819-HTML/image_m/fmars-13-1748819-t001.jpg Table 1. Fields where chitin can be used and its applications. https://www.frontiersin.org/files/Articles/1748819/fmars-13-1748819-HTML/image_m/fmars-13-1748819-t002.jpg Table 2. Principal applications of blue crab exoskeleton as carbonaceous materials. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1704392/full https://www.frontiersin.org/files/Articles/1704392/fmicb-16-1704392-HTML/image_m/fmicb-16-1704392-t001.jpg Table 1. Taxonomic composition of the CSF virome according to reads. https://www.frontiersin.org/files/Articles/1704392/fmicb-16-1704392-HTML/image_m/fmicb-16-1704392-g001.jpg Figure 1. Prevalence and relative abundance of CSF viral families by HIV status. Panels (A,B) show t https://www.frontiersin.org/files/Articles/1704392/fmicb-16-1704392-HTML/image_m/fmicb-16-1704392-g002.jpg Figure 2. α and β diversity and CSF viral metrics by HIV status. Panel (A) shows the number of obser https://www.frontiersin.org/files/Articles/1704392/fmicb-16-1704392-HTML/image_m/fmicb-16-1704392-g003.jpg Figure 3. Heatmap of the genome coverage of CSF viruses detected after contig assembly. Each column https://www.frontiersin.org/files/Articles/1704392/fmicb-16-1704392-HTML/image_m/fmicb-16-1704392-g004.jpg Figure 4. Heatmap of the correlations between relative abundance of CSF viral categories and neuroco https://www.frontiersin.org/files/Articles/1704392/fmicb-16-1704392-HTML/image_m/fmicb-16-1704392-g005.jpg Figure 5. Clusters based on relative abundance of human and non-human CSF viruses and blood–brain ba https://www.frontiersin.org/files/Articles/1704392/fmicb-16-1704392-HTML/image_m/fmicb-16-1704392-g006.jpg Figure 6. Significant differences between CSF clusters and Cognitive phenotypes of CSF clusters. Pan https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2026.1750121/full https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-t001.jpg Table 1. Baseline characteristics of the full cohort (PCS vs. COVID-19 recovered controls). https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-g001.jpg Figure 1. NfL and GFAP concentrations in PCS and recovered controls. Boxplots display medians and in https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-g002.jpg Figure 2. Scatterplots of serum NfL and GFAP in relation to kidney function (eGFR) across the entire https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-t002.jpg Table 2. Univariate group comparisons of serum NfL and GFAP between patients with PCS and recovered https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-t003.jpg Table 3. Multivariable linear regression models of log-transformed NfL and GFAP. https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-t004.jpg Table 4. ANCOVA-style linear regression model for log-transformed NfL, adjusted for group (PCS vs. H https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-t005.jpg Table 5. ANCOVA-style linear regression model for log-transformed GFAP, adjusted for group (PCS vs. https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-g003.jpg Figure 3. Forest plot of unadjusted and adjusted geometric mean ratios (PCS vs. COVID-19 recovered c https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-t006.jpg Table 6. Baseline characteristics of PCS patients and COVID-19 recovered controls after age- and sex https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-g004.jpg Figure 4. Serum NfL and GFAP concentrations in patients with PCS and recovered controls after age- a https://www.frontiersin.org/files/Articles/1750121/fncel-20-1750121-HTML-r1/image_m/fncel-20-1750121-t007.jpg Table 7. ANCOVA-style linear regression model after age- and sex-matching for log-transformed NfL an https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2026.1754667/full https://www.frontiersin.org/files/Articles/1754667/fbuil-12-1754667-HTML/image_m/fbuil-12-1754667-g001.jpg Figure 1. Preparation of the portable fly ash bricks. https://www.frontiersin.org/files/Articles/1754667/fbuil-12-1754667-HTML/image_m/fbuil-12-1754667-g002.jpg Figure 2. Brick masonry pillar types evaluated in this work. https://www.frontiersin.org/files/Articles/1754667/fbuil-12-1754667-HTML/image_m/fbuil-12-1754667-t001.jpg Table 1. Dimensions and properties of the fly ash bricks. https://www.frontiersin.org/files/Articles/1754667/fbuil-12-1754667-HTML/image_m/fbuil-12-1754667-t002.jpg Table 2. Details of the brick masonry pillar specimens. https://www.frontiersin.org/files/Articles/1754667/fbuil-12-1754667-HTML/image_m/fbuil-12-1754667-g003.jpg Figure 3. Failure patterns of brick columns with varying heights: (a) short unconfined pillar (S-pil https://www.frontiersin.org/files/Articles/1754667/fbuil-12-1754667-HTML/image_m/fbuil-12-1754667-g004.jpg Figure 4. Failure patterns of unconfined brick masonry pillars with axial reinforcement: (a) short r https://www.frontiersin.org/files/Articles/1754667/fbuil-12-1754667-HTML/image_m/fbuil-12-1754667-g005.jpg Figure 5. Failure patterns of confined brick masonry pillars: (a) short confined pillar (SWM-pillar) https://www.frontiersin.org/files/Articles/1754667/fbuil-12-1754667-HTML/image_m/fbuil-12-1754667-g006.jpg Figure 6. Load–deflection curves of the (a) short, (b) medium, and (c) long unconfined, axially rein https://www.frontiersin.org/files/Articles/1754667/fbuil-12-1754667-HTML/image_m/fbuil-12-1754667-t003.jpg Table 3. Compressive stress and ductility of the brick masonry pillars. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1733391/full https://www.frontiersin.org/files/Articles/1733391/fonc-16-1733391-HTML/image_m/fonc-16-1733391-g001.jpg Figure 1. Screening of novel FGFR ligands. (a) Molecular docking for new small molecule ligands for https://www.frontiersin.org/files/Articles/1733391/fonc-16-1733391-HTML/image_m/fonc-16-1733391-g002.jpg Figure 2. Docked pose of FGFR2 with ZINC000019528308 (a), ZINC000101867325 (b), and ZINC000101881326 https://www.frontiersin.org/files/Articles/1733391/fonc-16-1733391-HTML/image_m/fonc-16-1733391-g003.jpg Figure 3. Z325 inhibits the FGFR phosphorylation in CRC cell lines. (a) IC50 value of ZINC0001018673 https://www.frontiersin.org/files/Articles/1733391/fonc-16-1733391-HTML/image_m/fonc-16-1733391-g004.jpg Figure 4. Z325 promotes apoptosis in CRC cell lines. (a) Volcano plot displaying the impact of ZINC0 https://www.frontiersin.org/files/Articles/1733391/fonc-16-1733391-HTML/image_m/fonc-16-1733391-g005.jpg Figure 5. Molecular dynamics simulation of ZINC000101867325 on FGFR mutants. (a) Mutation frequency https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1786925/full https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-g001.jpg Figure 1. Patient selection flowchart demonstrating the derivation of the analytical cohort through https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-t001.jpg Table 1. Baseline characteristics of the study population stratified by SAP and non-SAP groups. https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-t002.jpg Table 2. Baseline characteristics of patients stratified by log₂-CAR quartiles. https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-t003.jpg Table 3. Multivariable logistic regression analysis of the association between log₂-transformed CAR https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-t004.jpg Table 4. Variance inflation factors (VIFs) for covariates included in the multivariable logistic reg https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-g002.jpg Figure 2. Subgroup analysis demonstrating the robust consistency of the CAR-SAP association across d https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-t005.jpg Table 5. Comparison of diagnostic performance of CAR, CRP, WBC NTC and A2DS2 for predicting stroke-a https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-t006.jpg Table 6. Results of statistical tests for pairwise comparisons. https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-g003.jpg Figure 3. ROC curve comparison of CAR versus alternative predictors for SAP. Pairwise ROC curve comp https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-g004.jpg Figure 4. Incremental predictive value of CAR when added to established baseline models for SAP pred https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-t007.jpg Table 7. Performance and incremental value of models with and without CAR for SAP prediction. https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-g005.jpg Figure 5. Non-linear relationship between log2-transformed CAR and predicted probability of SAP, rev https://www.frontiersin.org/files/Articles/1786925/fneur-17-1786925-HTML/image_m/fneur-17-1786925-t008.jpg Table 8. Multivariable logistic regression models evaluating the linear and two-piecewise associatio https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2026.1783641/full https://www.frontiersin.org/files/Articles/1783641/fenrg-14-1783641-HTML/image_m/fenrg-14-1783641-g001.jpg Figure 1. Selected country shares of variable and dispatchable renewable electricity generation, 202 https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1653053/full https://www.frontiersin.org/files/Articles/1653053/fcell-13-1653053-HTML/image_m/fcell-13-1653053-g001.jpg Figure 1. Visualization of RAB2A protein in boar spermatozoa and reproductive tissue sections. (A) F https://www.frontiersin.org/files/Articles/1653053/fcell-13-1653053-HTML/image_m/fcell-13-1653053-g002.jpg Figure 2. Detection and localization of RAB2A protein in boar spermatozoa. (A) RAB2A (5C5) antibody https://www.frontiersin.org/files/Articles/1653053/fcell-13-1653053-HTML/image_m/fcell-13-1653053-g003.jpg Figure 3. Visualization of lactadherin/MFGE8 in boar spermatozoa and reproductive tissue sections. ( https://www.frontiersin.org/files/Articles/1653053/fcell-13-1653053-HTML/image_m/fcell-13-1653053-g004.jpg Figure 4. Detection and localization of lactadherin/MFGE8 in boar spermatozoa. (A) Antibody against https://www.frontiersin.org/files/Articles/1653053/fcell-13-1653053-HTML/image_m/fcell-13-1653053-g005.jpg Figure 5. Evaluation of the number of spermatozoa bound to the ZP-intact oocytes in the in vitro bin https://www.frontiersin.org/files/Articles/1653053/fcell-13-1653053-HTML/image_m/fcell-13-1653053-g006.jpg Figure 6. Visualization of RAB2A in non-permeabilized boar spermatozoa. (A) Fluorescent signal of th https://www.frontiersin.org/files/Articles/1653053/fcell-13-1653053-HTML/image_m/fcell-13-1653053-g007.jpg Figure 7. Visualization of lactadherin/MFGE8 in non-permeabilized boar spermatozoa. (A) Fluorescent https://www.frontiersin.org/files/Articles/1653053/fcell-13-1653053-HTML/image_m/fcell-13-1653053-g008.jpg Figure 8. Localization and detection of RAB2A in human spermatozoa. (A) Fluorescent signal of the an https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1561573/full https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g001.jpg Figure 1. Cross-sectional schematic structure of AlGaN/GaN HEMT (a) with GaN cap layer (b) and witho https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g002.jpg Figure 2. Calibration of the silvaco ATLAS TCAD tool. Output characteristics compared with reported https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g003.jpg Figure 3. Variation of IDS as a function of VDS at VGS = 0 V and VGS = 2 V for AlGaN/GaN HEMTs with https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g004.jpg Figure 4. Transfer characteristics at VDS = 1 V and VDS = 4 V for GaN HEMTs with and without GaN cap https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g005.jpg Figure 5. Transconductance (gm) characteristics of GaN HEMTs with and without GaN cap layer at VDS = https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g006.jpg Figure 6. fT Vs IDS of GaN HEMTs with and without GaN cap layer at VDS = 3 V. https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g007.jpg Figure 7. Variation of output characteristics for different drain doping concentrations at VGS = 1 V https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g008.jpg Figure 8. Variation of Transfer characteristics for different drain doping concentrations at VDS = 1 https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g009.jpg Figure 9. Variation of Transconductance (gm) characteristics for different drain doping concentratio https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g010.jpg Figure 10. Variation of threshold voltage (VTH) for different drain doping concentrations at VDS = 3 https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-g011.jpg Figure 11. Variation of cut-off frequency (fT) for different drain doping concentrations at VDS = 3 https://www.frontiersin.org/files/Articles/1561573/fphy-13-1561573-HTML/image_m/fphy-13-1561573-t001.jpg Table 1. Comparison between GaN HEMT without cap layer and state-of-the-art GaN HEMTs with cap layer https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1743621/full https://www.frontiersin.org/files/Articles/1743621/fpsyg-17-1743621-HTML-r1/image_m/fpsyg-17-1743621-g001.jpg Figure 1. Braun and Clarke’s (2006) six-phase framework. https://www.frontiersin.org/files/Articles/1743621/fpsyg-17-1743621-HTML-r1/image_m/fpsyg-17-1743621-t001.jpg Table 1. Demographic and academic characteristics of participants (n = 18). https://www.frontiersin.org/files/Articles/1743621/fpsyg-17-1743621-HTML-r1/image_m/fpsyg-17-1743621-t002.jpg Table 2. Themes, sub-themes, and descriptions. https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2026.1713754/full https://www.frontiersin.org/files/Articles/1713754/frsc-08-1713754-HTML/image_m/frsc-08-1713754-g001.jpg Figure 1. PRISMA 2020 flow diagram: screening and selection of studies on implementation of digital https://www.frontiersin.org/files/Articles/1713754/frsc-08-1713754-HTML/image_m/frsc-08-1713754-t001.jpg Table 1. Inclusion and exclusion criteria. https://www.frontiersin.org/files/Articles/1713754/frsc-08-1713754-HTML/image_m/frsc-08-1713754-t002.jpg Table 2. Characteristics of included studies (2015–2025). https://www.frontiersin.org/files/Articles/1713754/frsc-08-1713754-HTML/image_m/frsc-08-1713754-t003.jpg Table 3. Quality appraisal tools and assessment summary. https://www.frontiersin.org/files/Articles/1713754/frsc-08-1713754-HTML/image_m/frsc-08-1713754-t004.jpg Table 4. Key variables and outcomes. https://www.frontiersin.org/files/Articles/1713754/frsc-08-1713754-HTML/image_m/frsc-08-1713754-t005.jpg Table 5. Principal barriers and matching facilitators influencing digital oversight. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1730277/full https://www.frontiersin.org/files/Articles/1730277/fonc-16-1730277-HTML/image_m/fonc-16-1730277-g001.jpg Figure 1. Flow diagram of this meta-analysis. https://www.frontiersin.org/files/Articles/1730277/fonc-16-1730277-HTML/image_m/fonc-16-1730277-g002.jpg Figure 2. Results of risk bias of included randomized controlled trials. A. Risk of bias summary; B. https://www.frontiersin.org/files/Articles/1730277/fonc-16-1730277-HTML/image_m/fonc-16-1730277-t001.jpg Table 1. Basic characteristics of included studies. https://www.frontiersin.org/files/Articles/1730277/fonc-16-1730277-HTML/image_m/fonc-16-1730277-g003.jpg Figure 3. Effect of sugammadex use on the risk of postoperative pulmonary complication among patient https://www.frontiersin.org/files/Articles/1730277/fonc-16-1730277-HTML/image_m/fonc-16-1730277-t002.jpg Table 2. Results of meta-analysis for the effect of sugammadex use on the risk of postoperative pulm https://www.frontiersin.org/files/Articles/1730277/fonc-16-1730277-HTML/image_m/fonc-16-1730277-g004.jpg Figure 4. Effect of sugammadex use on the risk of atelectasis among patients undergoing video-assist https://www.frontiersin.org/files/Articles/1730277/fonc-16-1730277-HTML/image_m/fonc-16-1730277-g005.jpg Figure 5. Sensitivity analysis for the effect of sugammadex use on the risk of postoperative pulmona https://www.frontiersin.org/files/Articles/1730277/fonc-16-1730277-HTML/image_m/fonc-16-1730277-g006.jpg Figure 6. Begg’s (A) and filled (B) funnel plots for the effect of sugammadex use on the risk of pos https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1783284/full https://www.frontiersin.org/files/Articles/1783284/fendo-17-1783284-HTML/image_m/fendo-17-1783284-t001.jpg Table 1. Four-grid table of disproportionality analysis method. https://www.frontiersin.org/files/Articles/1783284/fendo-17-1783284-HTML/image_m/fendo-17-1783284-t002.jpg Table 2. Principle of disproportionality analysis and standard of signal detection. https://www.frontiersin.org/files/Articles/1783284/fendo-17-1783284-HTML/image_m/fendo-17-1783284-g001.jpg Figure 1. Overall reporting characteristics of drug-associated osteoporosis adverse events in the FA https://www.frontiersin.org/files/Articles/1783284/fendo-17-1783284-HTML/image_m/fendo-17-1783284-g002.jpg Figure 2. Drug screening and classification results for osteoporosis-related adverse reactions. (A) https://www.frontiersin.org/files/Articles/1783284/fendo-17-1783284-HTML/image_m/fendo-17-1783284-g003.jpg Figure 3. Forest plot of odds ratios for drugs associated with osteoporosis. https://www.frontiersin.org/files/Articles/1783284/fendo-17-1783284-HTML/image_m/fendo-17-1783284-g004.jpg Figure 4. Results of the multivariable logistic regression analysis. https://www.frontiersin.org/files/Articles/1783284/fendo-17-1783284-HTML/image_m/fendo-17-1783284-g005.jpg Figure 5. Temporal characteristics of drug-induced osteoporosis adverse events. (A) Violin plots. (B https://www.frontiersin.org/files/Articles/1783284/fendo-17-1783284-HTML/image_m/fendo-17-1783284-t003.jpg Table 3. External validation of osteoporosis-related drug signals in the WHO Vigibase database. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1788432/full https://www.frontiersin.org/files/Articles/1788432/fendo-17-1788432-HTML/image_m/fendo-17-1788432-g001.jpg Figure 1. Workflow for quantitative comparison of mRNA copy numbers in identified human neuron types https://www.frontiersin.org/files/Articles/1788432/fendo-17-1788432-HTML/image_m/fendo-17-1788432-g002.jpg Figure 2. Digital PCR analysis of identified human neurons. Detection of INS (top) and GLP1R (bottom https://www.frontiersin.org/files/Articles/1788432/fendo-17-1788432-HTML/image_m/fendo-17-1788432-g003.jpg Figure 3. Representative image of LAMP5 (blue), INS (magenta) and GLP1R (green) expression and co-lo https://www.frontiersin.org/files/Articles/1788432/fendo-17-1788432-HTML/image_m/fendo-17-1788432-g004.jpg Figure 4. (A), Comparison of INS (bottom) and GLP1R (top) mRNA expression in cells collected from pa https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1694207/full https://www.frontiersin.org/files/Articles/1694207/fonc-16-1694207-HTML/image_m/fonc-16-1694207-t001.jpg Table 1. The baseline characteristics of patients. https://www.frontiersin.org/files/Articles/1694207/fonc-16-1694207-HTML/image_m/fonc-16-1694207-g001.jpg Figure 1. (A–D) Depict the clinical parameters that affect Progression-Free survival (PFS) in univar https://www.frontiersin.org/files/Articles/1694207/fonc-16-1694207-HTML/image_m/fonc-16-1694207-g002.jpg Figure 2. (A–D) depict the clinical parameters that affect OS in univariate analysis [(A) Treatment; https://www.frontiersin.org/files/Articles/1694207/fonc-16-1694207-HTML/image_m/fonc-16-1694207-t002.jpg Table 2. Single factor and multi factor Cox regression analysis of progression-free survival. https://www.frontiersin.org/files/Articles/1694207/fonc-16-1694207-HTML/image_m/fonc-16-1694207-t003.jpg Table 3. Single factor and multi factor Cox regression analysis of overall survival. https://www.frontiersin.org/files/Articles/1694207/fonc-16-1694207-HTML/image_m/fonc-16-1694207-g003.jpg Figure 3. (A) is the forest plot of PFS multi factor analysis, (B) is the forest plot of OS multi fa https://www.frontiersin.org/files/Articles/1694207/fonc-16-1694207-HTML/image_m/fonc-16-1694207-t004.jpg Table 4. Adverse events. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1704297/full https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-t001.jpg Table 1. Primers and probe used for ddPCR and qPCR assays. https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-g001.jpg Figure 1. ddPCR optimization of the annealing temperature using a thermal gradient ranging from 65 ° https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-g002.jpg Figure 2. ddPCR primers and probe optimization on 104 and 103 copies/μl of p72 plasmid. P1 assay (a) https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-g003.jpg Figure 3. Linearity of the ddPCR P1 (a) and P2 (b) on serial p72 plasmid dilutions from 104 to 1 cop https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-g004.jpg Figure 4. Linearity of the ddPCR P1 (a) and P2 (b) on serial p72 plasmid dilutions from 104 to 1 cop https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-t002.jpg Table 2. p72 plasmid detection of P1 and P2 ddPCRs. https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-t003.jpg Table 3. The repeatability of both P1 and P2 ddPCR assays. https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-t004.jpg Table 4. Evaluation of the limit of detection (LOD) of ddPCR and qPCR assays. https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-t005.jpg Table 5. Evaluation of the limit of quantification (LOQ) of the ddPCR and qPCR assays. https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-g005.jpg Figure 5. Comparison between both qPCR and ddPCR for the P1 (a) and the P2 (b) assays using the Blan https://www.frontiersin.org/files/Articles/1704297/fvets-12-1704297-HTML/image_m/fvets-12-1704297-g006.jpg Figure 6. Comparison between both P1 and P2 ddPCRs (a) and between the P1 and P2 qPCRs (b) using the https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1711701/full https://www.frontiersin.org/files/Articles/1711701/fonc-15-1711701-HTML-r4/image_m/fonc-15-1711701-g001.jpg Figure 1. Research procedures. https://www.frontiersin.org/files/Articles/1711701/fonc-15-1711701-HTML-r4/image_m/fonc-15-1711701-t001.jpg Table 1. Demographic and clinical characteristics of the study patients. https://www.frontiersin.org/files/Articles/1711701/fonc-15-1711701-HTML-r4/image_m/fonc-15-1711701-g002.jpg Figure 2. Proportion of hemostatic effectiveness. https://www.frontiersin.org/files/Articles/1711701/fonc-15-1711701-HTML-r4/image_m/fonc-15-1711701-t002.jpg Table 2. Results of the multivariable logistic regression adjusted for confounders. https://www.frontiersin.org/files/Articles/1711701/fonc-15-1711701-HTML-r4/image_m/fonc-15-1711701-t003.jpg Table 3. Treatment outcomes and adverse reactions in the two groups. https://www.frontiersin.org/files/Articles/1711701/fonc-15-1711701-HTML-r4/image_m/fonc-15-1711701-t004.jpg Table 4. Univariate and multivariate analysis of factors influencing hemostatic effectiveness. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1581286/full https://www.frontiersin.org/files/Articles/1581286/fimmu-16-1581286-HTML/image_m/fimmu-16-1581286-g001.jpg Figure 1. IL-9 expression is associated with BCG vaccine- and Mtb infection-induced TB immunity. (A) https://www.frontiersin.org/files/Articles/1581286/fimmu-16-1581286-HTML/image_m/fimmu-16-1581286-g002.jpg Figure 2. Protective in vitro effects of human and murine Th9 cells. Memory CD4+ T cells from PPD+ v https://www.frontiersin.org/files/Articles/1581286/fimmu-16-1581286-HTML/image_m/fimmu-16-1581286-g003.jpg Figure 3. Adoptive transfers of Th9 cells provide in vivo protection against aerosol Mtb challenge. https://www.frontiersin.org/files/Articles/1581286/fimmu-16-1581286-HTML/image_m/fimmu-16-1581286-g004.jpg Figure 4. Distinct in vivo inflammatory signatures are associated with Th9- vs Th1- mediated protect https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1702440/full https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g001.jpg Figure 1. BPI3Vc expression constructs and temperature sensitivity.(A) Design of the BPI3Vc construc https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g002.jpg Figure 2. Design of consensus Fusion (F2) and Hemagglutinin-Neuraminidase (HN2) antigens. Clustering https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g003.jpg Figure 3. BPI3Vc construct expressing F2-HN2. (A) Design of the BPI3Vc construct encoding F2-HN2 gen https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-t001.jpg Table 1. Primers used to amplify genes of interest. https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-t002.jpg Table 2. BPI3V calf immunization protocol. https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g004.jpg Figure 4. Surface display of F2. Display of F2 on the surface of MDBK cells infected with the rescue https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g005.jpg Figure 5. Characterization of the rBPI3VcmutF2-HN2 virus. Multicycle replication growth curve of the https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-t003.jpg Table 3A. rBPI3VcmutF2-HN2 viral RNA sequence at Passage 2. https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-t004.jpg Table 3B. rBPI3VcmutF2-HN2 viral RNA sequence at Passage 9. https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g006.jpg Figure 6. Animal study. (A) Immunization design and timeline. Calves were assigned to three treatmen https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g007.jpg Figure 7. Antibody response. Recognition of inactivated wild-type BPI3Va, b, and c virus by IgGs in https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g008.jpg Figure 8. Virus neutralization. Titers of virus neutralization antibodies in the sera from rBPI3Vcmu https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g009.jpg Figure 9. The F2-HN2 antigens were responsible for the robust broadly neutralizing antibodies. Virus https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g010.jpg Figure 10. Clinical outcomes post-challenge. (A) Mean rectal temperature change for each group recor https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g011.jpg Figure 11. Post-challenge Viremia. (A) Nasal shedding was determined in nasal swabs collected from t https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g012.jpg Figure 12. Gross lung lesions. Pulmonary atelectasis was the most common gross lesion observed consi https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g013.jpg Figure 13. Histologic lesions. Histologic lesions observed in calf 5669 lung [sham negative control https://www.frontiersin.org/files/Articles/1702440/fimmu-16-1702440-HTML-r1/image_m/fimmu-16-1702440-g014.jpg Figure 14. Histopathologic lung lesions. Lung lesions were scored from sections of the cranial, midd https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1661318/full https://www.frontiersin.org/files/Articles/1661318/frai-08-1661318-HTML/image_m/frai-08-1661318-g001.jpg Figure 1. Schematic diagram of the reformer-based cell type classification framework. https://www.frontiersin.org/files/Articles/1661318/frai-08-1661318-HTML/image_m/frai-08-1661318-t001.jpg Table 1. Computational resource consumption and parameters. https://www.frontiersin.org/files/Articles/1661318/frai-08-1661318-HTML/image_m/frai-08-1661318-g002.jpg Figure 2. Schematic illustration of cardiac uHAF. (A) The diagram of the progression from broad cell https://www.frontiersin.org/files/Articles/1661318/frai-08-1661318-HTML/image_m/frai-08-1661318-g003.jpg Figure 3. Model evaluation on the heart cell test set. (A) Confusion matrix of scReformer-BERT on ni https://www.frontiersin.org/files/Articles/1661318/frai-08-1661318-HTML/image_m/frai-08-1661318-t002.jpg Table 2. Ablation study results on the heart dataset. https://www.frontiersin.org/files/Articles/1661318/frai-08-1661318-HTML/image_m/frai-08-1661318-g004.jpg Figure 4. Comparison of model performance. Comparison of classification accuracy for different model https://www.frontiersin.org/files/Articles/1661318/frai-08-1661318-HTML/image_m/frai-08-1661318-t003.jpg Table 3. Generalization performance on independent datasets (mean ± SD via five-fold cross-validatio https://www.frontiersin.org/files/Articles/1661318/frai-08-1661318-HTML/image_m/frai-08-1661318-g005.jpg Figure 5. SHAP analysis for T-cell classification. (A) SHAP summary plot. (B) Bar chart of mean abso https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1700809/full https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-t001.jpg Table 1. Baseline characteristics of participants by K-means clustering analysis. https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-g001.jpg Figure 1. Flow chart of the study population. https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-g002.jpg Figure 2. K-means clustering results. (A, B) Optimal classification K value, (C) Clustering diagrams https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-t002.jpg Table 2. Different cluster of TyG-SII, baseline TyG-SII and risk of DVT. https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-g003.jpg Figure 3. RCS plots depicting the nonlinear association between baseline TyG-SII and the risk of (A) https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-t003.jpg Table 3. Different cluster of TyG-SII, baseline TyG-SII and risk of MCVT. https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-g004.jpg Figure 4. Receiver operating characteristic curves for (A) baseline TyG-SII, TyG, and SII predicting https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-t004.jpg Table 4. Subgroup analysis for the association (OR, 95% CI) between TyG-SII and risk for DVT. https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-g005.jpg Figure 5. Interaction effect model of the association between baseline TyG-SII predicted risk for DV https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-t005.jpg Table 5. Subgroup analysis for the association (OR, 95% CI) between TyG-SII and risk for MCVT. https://www.frontiersin.org/files/Articles/1700809/fendo-17-1700809-HTML-r1/image_m/fendo-17-1700809-g006.jpg Figure 6. Interaction effect model of the association between baseline TyG-SII predicted risk for DV https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1686133/full https://www.frontiersin.org/files/Articles/1686133/fsufs-09-1686133-HTML/image_m/fsufs-09-1686133-g001.jpg Figure 1. Key factors, challenges, and research gaps in pasture establishment in Aotearoa New Zealan https://www.frontiersin.org/files/Articles/1686133/fsufs-09-1686133-HTML/image_m/fsufs-09-1686133-t001.jpg Table 1. Summary of key synergies and trade-offs for pasture establishment and management in Aotearo https://www.frontiersin.org/files/Articles/1686133/fsufs-09-1686133-HTML/image_m/fsufs-09-1686133-t002.jpg Table 2. Strategic priorities for a future-proof Aotearoa New Zealand dairy pasture systems. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1753597/full https://www.frontiersin.org/files/Articles/1753597/fped-14-1753597-HTML/image_m/fped-14-1753597-g001.jpg Figure 1. (A, B) Cyst index in proximal humerus and femur. https://www.frontiersin.org/files/Articles/1753597/fped-14-1753597-HTML/image_m/fped-14-1753597-t001.jpg Table 1. Conservative and minimally invasive injection strategies for UBCs. https://www.frontiersin.org/files/Articles/1753597/fped-14-1753597-HTML/image_m/fped-14-1753597-t002.jpg Table 2. Surgical management strategies for UBCs. https://www.frontiersin.org/files/Articles/1753597/fped-14-1753597-HTML/image_m/fped-14-1753597-g002.jpg Figure 2. UBCs treatment suggestion. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1707304/full https://www.frontiersin.org/files/Articles/1707304/fimmu-17-1707304-HTML/image_m/fimmu-17-1707304-g001.jpg Figure 1. Workflow of transcriptome sequencing and analysis of Mtb-HAg regulation of γδ T cell funct https://www.frontiersin.org/files/Articles/1707304/fimmu-17-1707304-HTML/image_m/fimmu-17-1707304-g002.jpg Figure 2. Transcriptome profile and identification of differentially expressed genes. (A) Principal https://www.frontiersin.org/files/Articles/1707304/fimmu-17-1707304-HTML/image_m/fimmu-17-1707304-g003.jpg Figure 3. GO, KEGG, and GSEA of the differentially expressed genes. (A) GO term enrichment analysis https://www.frontiersin.org/files/Articles/1707304/fimmu-17-1707304-HTML/image_m/fimmu-17-1707304-g004.jpg Figure 4. Analysis of differentially expressed immune-related genes (DEIGs). (A) Venn diagram DEIGs https://www.frontiersin.org/files/Articles/1707304/fimmu-17-1707304-HTML/image_m/fimmu-17-1707304-g005.jpg Figure 5. Expression levels of the cytotoxic molecule genes granzyme B (GzmB) and perforin (PFP). https://www.frontiersin.org/files/Articles/1707304/fimmu-17-1707304-HTML/image_m/fimmu-17-1707304-g006.jpg Figure 6. Mtb-HAg amplified γδ T cell lytic activity and inhibited the growth of intracellular mycob https://www.frontiersin.org/files/Articles/1707304/fimmu-17-1707304-HTML/image_m/fimmu-17-1707304-g007.jpg Figure 7. The cytotoxic effect of Mtb-HAg-activated γδ T cells was inhibited by treatment with a BTN https://www.frontiersin.org/files/Articles/1707304/fimmu-17-1707304-HTML/image_m/fimmu-17-1707304-g008.jpg Figure 8. Flow cytometric analysis of Mtb-HAg-induced CD69 expression on γδ T cells. (A) The express https://www.frontiersin.org/files/Articles/1707304/fimmu-17-1707304-HTML/image_m/fimmu-17-1707304-g009.jpg Figure 9. A BTN3A1 antagonist impaired intracellular Mtb killing by Mtb-HAg-activated γδ T cells. (A https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1656247/full https://www.frontiersin.org/files/Articles/1656247/fpls-16-1656247-HTML/image_m/fpls-16-1656247-g001.jpg Figure 1. Proteomics: types, methods, steps. https://www.frontiersin.org/files/Articles/1656247/fpls-16-1656247-HTML/image_m/fpls-16-1656247-g002.jpg Figure 2. The most used high-throughput proteomics technologies for investigating medicinal plants. https://www.frontiersin.org/files/Articles/1656247/fpls-16-1656247-HTML/image_m/fpls-16-1656247-t001.jpg Table 1. High-throughput proteomics technologies. https://www.frontiersin.org/files/Articles/1656247/fpls-16-1656247-HTML/image_m/fpls-16-1656247-t002.jpg Table 2. Different proteomic techniques, targets, and identified proteins in medicinal plants studie https://www.frontiersin.org/files/Articles/1656247/fpls-16-1656247-HTML/image_m/fpls-16-1656247-t003.jpg Table 3. Different proteomic techniques, targets, and identified proteins of various medicinal plant https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1740623/full https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-g001.jpg Figure 1. Flowchart of study selection strategy. https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-t001.jpg Table 1. Baseline characteristics for inclusion. https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-g002.jpg Figure 2. (A) Risk of bias graph. (B) Risk of bias summary. https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-g003.jpg Figure 3. Forest plot of the Clinical effectiveness rate. https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-g004.jpg Figure 4. Forest plot for Serum creatinine levels. https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-g005.jpg Figure 5. Forest plot for Blood urea nitrogen levels. https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-g006.jpg Figure 6. Forest plot for 24-hour urine protein quantification. https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-t002.jpg Table 2. Secondary outcomes and safety outcomes. https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-t003.jpg Table 3. Detailed summary table of adverse reactions. https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-g007.jpg Figure 7. Funnel plots for (A) Clinical effectiveness rate, (B) Serum creatinine, (C) Blood urea nit https://www.frontiersin.org/files/Articles/1740623/fendo-17-1740623-HTML-r1/image_m/fendo-17-1740623-t004.jpg Table 4. The GRADE evidence of the main outcome indicators. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1678627/full https://www.frontiersin.org/files/Articles/1678627/fmars-12-1678627-HTML-r2/image_m/fmars-12-1678627-g001.jpg Figure 1. The content change of Chlorophyll a of M. aeruginosa during PS-NPs exposure and BG11 recov https://www.frontiersin.org/files/Articles/1678627/fmars-12-1678627-HTML-r2/image_m/fmars-12-1678627-g002.jpg Figure 2. The content of intracellular MCs, extracellular MCs and the total of MCs released from M. https://www.frontiersin.org/files/Articles/1678627/fmars-12-1678627-HTML-r2/image_m/fmars-12-1678627-g003.jpg Figure 3. Gene Ontology (GO) term analysis of DEGs pathways during 5 mg/L PS-NPs exposure (A) and po https://www.frontiersin.org/files/Articles/1678627/fmars-12-1678627-HTML-r2/image_m/fmars-12-1678627-g004.jpg Figure 4. Cluster analysis of significantly enriched GO terms (p<0.05) in M. aeruginosa following 5 https://www.frontiersin.org/files/Articles/1678627/fmars-12-1678627-HTML-r2/image_m/fmars-12-1678627-t001.jpg Table 1. Comparison of significant KEGG metabolic pathways of the M. aeruginosa transcriptomes betwe https://www.frontiersin.org/files/Articles/1678627/fmars-12-1678627-HTML-r2/image_m/fmars-12-1678627-g005.jpg Figure 5. GSEA plot showing normalized enrichment scores (NESs) for oxidative phosphorylation pathwa https://www.frontiersin.org/files/Articles/1678627/fmars-12-1678627-HTML-r2/image_m/fmars-12-1678627-t002.jpg Table 2. Differently expressed genes related to glycolysis/gluconeogenesis and TCA cycle. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1751311/full https://www.frontiersin.org/files/Articles/1751311/fmed-13-1751311-HTML/image_m/fmed-13-1751311-t001.jpg Table 1. Demographic and clinical characteristics of the cohort. https://www.frontiersin.org/files/Articles/1751311/fmed-13-1751311-HTML/image_m/fmed-13-1751311-g001.jpg Figure 1. Adjusted odds ratios of independent risk factors for in-hospital mortality in cancer patie https://www.frontiersin.org/files/Articles/1751311/fmed-13-1751311-HTML/image_m/fmed-13-1751311-g002.jpg Figure 2. Feature importance ranking based on Recursive Feature Elimination (RFE). This bar chart il https://www.frontiersin.org/files/Articles/1751311/fmed-13-1751311-HTML/image_m/fmed-13-1751311-t002.jpg Table 2. Model validation accuracy. https://www.frontiersin.org/files/Articles/1751311/fmed-13-1751311-HTML/image_m/fmed-13-1751311-g003.jpg Figure 3. (A) Receiver Operating Characteristic (ROC) Curve and Area Under the Curve (AUC) of the ra https://www.frontiersin.org/files/Articles/1751311/fmed-13-1751311-HTML/image_m/fmed-13-1751311-g004.jpg Figure 4. Confusion matrix with model sensitivity and specificity. This figure presents the confusio https://www.frontiersin.org/files/Articles/1751311/fmed-13-1751311-HTML/image_m/fmed-13-1751311-g005.jpg Figure 5. (A) Calibration curve of the Random Forest model vs. actual probability. This figure illus https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2025.1693701/full https://www.frontiersin.org/files/Articles/1693701/fragi-06-1693701-HTML-r1/image_m/fragi-06-1693701-t001.jpg Table 1. Target population and sample size distribution. https://www.frontiersin.org/files/Articles/1693701/fragi-06-1693701-HTML-r1/image_m/fragi-06-1693701-t002.jpg Table 2. Sociodemographic, self-reported, functional, and objective health Measures. https://www.frontiersin.org/files/Articles/1693701/fragi-06-1693701-HTML-r1/image_m/fragi-06-1693701-t003.jpg Table 3. Sociodemographic characteristics and their association with self-reported health among olde https://www.frontiersin.org/files/Articles/1693701/fragi-06-1693701-HTML-r1/image_m/fragi-06-1693701-t004.jpg Table 4. Association between self-reported and functional health among older adults in Kilifi County https://www.frontiersin.org/files/Articles/1693701/fragi-06-1693701-HTML-r1/image_m/fragi-06-1693701-t005.jpg Table 5. Association between self-reported and objective health status among older adults in Kilifi https://www.frontiersin.org/files/Articles/1693701/fragi-06-1693701-HTML-r1/image_m/fragi-06-1693701-g001.jpg Figure 1. Handgrip strength by gender and age group. https://www.frontiersin.org/files/Articles/1693701/fragi-06-1693701-HTML-r1/image_m/fragi-06-1693701-g002.jpg Figure 2. Ordinal logistic regression parameters estimate of the risk factors associated with self-r https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1743777/full https://www.frontiersin.org/files/Articles/1743777/fpsyg-17-1743777-HTML/image_m/fpsyg-17-1743777-g001.jpg Figure 1. Proposed moderated mediation model. https://www.frontiersin.org/files/Articles/1743777/fpsyg-17-1743777-HTML/image_m/fpsyg-17-1743777-t001.jpg Table 1. Descriptive statistics and correlations among study variables (N = 516). https://www.frontiersin.org/files/Articles/1743777/fpsyg-17-1743777-HTML/image_m/fpsyg-17-1743777-t002.jpg Table 2. Regression results for the mediation model. https://www.frontiersin.org/files/Articles/1743777/fpsyg-17-1743777-HTML/image_m/fpsyg-17-1743777-t003.jpg Table 3. Decomposition of total, direct, and mediating effects. https://www.frontiersin.org/files/Articles/1743777/fpsyg-17-1743777-HTML/image_m/fpsyg-17-1743777-t004.jpg Table 4. Moderating effect of social support on the relationship between smooth experience and re-pa https://www.frontiersin.org/files/Articles/1743777/fpsyg-17-1743777-HTML/image_m/fpsyg-17-1743777-g002.jpg Figure 2. Simple slope plot of the moderating effect of social support on the relationship between s https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1757853/full https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-g001.jpg Figure 1. Spatial trends and significance tests (p < 0.05, dotted areas) of ET in the ACA from 1985 https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-g002.jpg Figure 2. Changes in annual ET in the ACA in the near, mid, long and late term relative to the histo https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-g003.jpg Figure 3. The trends of ET in the ACA from 2021 to 2,100 under different SSP scenarios. (A–F) show t https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-t001.jpg Table 1. The rates and significance of ET in the ACA under different scenarios from 2021 to 2,100. https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-g004.jpg Figure 4. Spatial variation trends and significance tests (p < 0.05, dotted areas) of annual ET in t https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-g005.jpg Figure 5. Spatial variation trends and significance tests (p < 0.05, dotted areas) of growing season https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-g006.jpg Figure 6. Spatial variation trends and significance tests (p < 0.05, dotted areas) of seasonal ET in https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-t002.jpg Table 2. Correlation coefficients between temperature, precipitation and ET in different scenarios f https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-g007.jpg Figure 7. Spatial variation of correlation coefficients between ET and temperature in the ACA from 2 https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-g008.jpg Figure 8. Spatial variation of correlation coefficients between ET and precipitation in the ACA from https://www.frontiersin.org/files/Articles/1757853/fenvs-14-1757853-HTML/image_m/fenvs-14-1757853-g009.jpg Figure 9. Spatial variation of ET standard deviation in the ACA from 2021 to 2,100 under different S https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1739230/full https://www.frontiersin.org/files/Articles/1739230/fimmu-17-1739230-HTML/image_m/fimmu-17-1739230-t001.jpg Table 1. Primer sequences used for RT-qPCR. https://www.frontiersin.org/files/Articles/1739230/fimmu-17-1739230-HTML/image_m/fimmu-17-1739230-g001.jpg Figure 1. Network pharmacology analysis to identify the key targets of GSW for the treatment of PCOS https://www.frontiersin.org/files/Articles/1739230/fimmu-17-1739230-HTML/image_m/fimmu-17-1739230-g002.jpg Figure 2. Functional enrichment analysis of the common targets between GSW and PCOS. (A) GO enrichme https://www.frontiersin.org/files/Articles/1739230/fimmu-17-1739230-HTML/image_m/fimmu-17-1739230-g003.jpg Figure 3. GSW-medicated serum ameliorates apoptosis, proliferation, and hormone secretion in an in v https://www.frontiersin.org/files/Articles/1739230/fimmu-17-1739230-HTML/image_m/fimmu-17-1739230-g004.jpg Figure 4. GSW inhibits inflammatory cytokine production and modulates the PI3K/Akt/PGR signaling axi https://www.frontiersin.org/files/Articles/1739230/fimmu-17-1739230-HTML/image_m/fimmu-17-1739230-g005.jpg Figure 5. TNF-α attenuates the GSW-mediated suppression of apoptosis and activation of the PI3K/Akt https://www.frontiersin.org/files/Articles/1739230/fimmu-17-1739230-HTML/image_m/fimmu-17-1739230-g006.jpg Figure 6. Embelin and nobiletin are key bioactive components of GSW that exert anti-PCOS effects thr https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1728823/full https://www.frontiersin.org/files/Articles/1728823/fnut-13-1728823-HTML/image_m/fnut-13-1728823-g001.jpg Figure 1. The flow diagram of the study participants. https://www.frontiersin.org/files/Articles/1728823/fnut-13-1728823-HTML/image_m/fnut-13-1728823-t001.jpg Table 1. Basic characteristics of participants. https://www.frontiersin.org/files/Articles/1728823/fnut-13-1728823-HTML/image_m/fnut-13-1728823-t002.jpg Table 2. Association between serum vitamin VD and PCOS risk. https://www.frontiersin.org/files/Articles/1728823/fnut-13-1728823-HTML/image_m/fnut-13-1728823-g002.jpg Figure 2. The RCS curve showed a dose response between serum VD and PCOS risk. https://www.frontiersin.org/files/Articles/1728823/fnut-13-1728823-HTML/image_m/fnut-13-1728823-g003.jpg Figure 3. Subgroup analysis of the association between serum VD and PCOS risk. https://www.frontiersin.org/files/Articles/1728823/fnut-13-1728823-HTML/image_m/fnut-13-1728823-t003.jpg Table 3. Sensitivity analyses. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1754610/full https://www.frontiersin.org/files/Articles/1754610/fpsyg-17-1754610-HTML-r2/image_m/fpsyg-17-1754610-g001.jpg Figure 1. Research hypothesis model. https://www.frontiersin.org/files/Articles/1754610/fpsyg-17-1754610-HTML-r2/image_m/fpsyg-17-1754610-t001.jpg Table 1. Gender differences between different variables. https://www.frontiersin.org/files/Articles/1754610/fpsyg-17-1754610-HTML-r2/image_m/fpsyg-17-1754610-t002.jpg Table 2. Descriptive statistics and correlation analysis between variables. https://www.frontiersin.org/files/Articles/1754610/fpsyg-17-1754610-HTML-r2/image_m/fpsyg-17-1754610-t003.jpg Table 3. The regression analysis of perceived social support and sense of career calling between psy https://www.frontiersin.org/files/Articles/1754610/fpsyg-17-1754610-HTML-r2/image_m/fpsyg-17-1754610-t004.jpg Table 4. Analysis of the mediating effect of perceived social support and sense of career calling in https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1753230/full https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-g007.jpg Graphical Abstract https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-g001.jpg Figure 1. Illustration of the microbial electrolysis setup. (a) photograph of two separate, vertical https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-t001.jpg Table 1. Overview of the microbial electrolysis experiments of this study carried out in the flow ce https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-g002.jpg Figure 2. Mean measured parameters of current density and biofilm morphology in the duplicate cultiv https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-g003.jpg Figure 3. Correlation between averaged biovolume (BV¯ABC), accumulation rates (BV¯*ABC), substratum https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-t002.jpg Table 2. Specification of biofilm parameters determined in Des2A, Des3AB¯, Des5AB¯, and Geo1AB¯ coin https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-g004.jpg Figure 4. Representation of the VFA concentrations (cVFA) and the current density (J) of all duplica https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-t003.jpg Table 3. Representation of substrate and VFA concentrations determined by ion-exchange chromatograph https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-g005.jpg Figure 5. Taxonomic classification of the 16S amplicon sequencing of D. acetexigens cultivations Des https://www.frontiersin.org/files/Articles/1753230/fmicb-17-1753230-HTML-r2/image_m/fmicb-17-1753230-g006.jpg Figure 6. Schematic representation of the hypothesized metabolic interaction involving ethanol conve https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1674916/full https://www.frontiersin.org/files/Articles/1674916/fbioe-13-1674916-HTML/image_m/fbioe-13-1674916-g001.jpg Figure 1. BRD4 is upregulated in H2O2-treated AEC-II cells. AEC-II cells were exposed to increasing https://www.frontiersin.org/files/Articles/1674916/fbioe-13-1674916-HTML/image_m/fbioe-13-1674916-g002.jpg Figure 2. BRD4 regulates H2O2-induced inflammatory responses, oxidative stress, and apoptosis in AEC https://www.frontiersin.org/files/Articles/1674916/fbioe-13-1674916-HTML/image_m/fbioe-13-1674916-g003.jpg Figure 3. Knockdown of BRD4 alleviated H2O2-induced injury in AEC-II cells through activation of the https://www.frontiersin.org/files/Articles/1674916/fbioe-13-1674916-HTML/image_m/fbioe-13-1674916-g004.jpg Figure 4. BRD4 knockdown reverses SIRT3 inhibition in H2O2-challenged AEC-II cells via AKT activatio https://www.frontiersin.org/files/Articles/1674916/fbioe-13-1674916-HTML/image_m/fbioe-13-1674916-g005.jpg Figure 5. SIRT3 inhibition reverses the protective effects of BRD4 inhibition on H2O2-challenged AEC https://www.frontiersin.org/files/Articles/1674916/fbioe-13-1674916-HTML/image_m/fbioe-13-1674916-g006.jpg Figure 6. BRD4 inhibition mitigates HILI in mice by activating the AKT/SIRT3 signaling. SD mice were https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1682846/full https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g001.jpg Figure 1. Identification and characterization of metabolism-related genes in sepsis. (A–C) Volcano p https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g002.jpg Figure 2. Metabolism-related gene expression stratifies sepsis patients into distinct immunological https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g003.jpg Figure 3. Identification of hub genes and construction of a metabolic risk score model. (A, B) LASSO https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g004.jpg Figure 4. Hub genes demonstrate robust and accurate diagnostic performance in an independent validat https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g005.jpg Figure 5. High metabolic risk score in sepsis is associated with neutrophil and monocyte-dominant an https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g006.jpg Figure 6. Single-cell analysis of immune cell composition in septic patients. (A, B) UMAP visualizat https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g007.jpg Figure 7. Single-cell communication analysis reveals intensified monocyte–DC interactions in high-ri https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g008.jpg Figure 8. GYG1 is highly expressed in innate immune cells and associated with pro-inflammatory trans https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g009.jpg Figure 9. LNP-mediated Gyg1 silencing improves survival in an LPS-induced sepsis mouse model. (A–E) https://www.frontiersin.org/files/Articles/1682846/fimmu-16-1682846-HTML/image_m/fimmu-16-1682846-g010.jpg Figure 10. GYG1 knockdown reduces glycogen metabolism and inflammatory activation in myeloid cells i https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1598703/full https://www.frontiersin.org/files/Articles/1598703/fnut-12-1598703-HTML-r1/image_m/fnut-12-1598703-g001.jpg Figure 1. Structural diagram of the BNST. (A) The anterior BNST can be divided into dorsolateral BNS https://www.frontiersin.org/files/Articles/1598703/fnut-12-1598703-HTML-r1/image_m/fnut-12-1598703-g002.jpg Figure 2. Neural circuits involving the BNST in feeding regulation. (A) The upstream brain regions o https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1703097/full https://www.frontiersin.org/files/Articles/1703097/fimmu-17-1703097-HTML/image_m/fimmu-17-1703097-g001.jpg Figure 1. Common γc cytokine receptor usage and downstream signaling. Schematic representation of th https://www.frontiersin.org/files/Articles/1703097/fimmu-17-1703097-HTML/image_m/fimmu-17-1703097-t001.jpg Table 1. Published cases of CVID-like and IL2RG reversion immunodeficiency. https://www.frontiersin.org/files/Articles/1703097/fimmu-17-1703097-HTML/image_m/fimmu-17-1703097-g002.jpg Figure 2. IL2RG gene exons (NM_000206.3) showing pathogenic/likely pathogenic variants. The upper pa https://www.frontiersin.org/files/Articles/1703097/fimmu-17-1703097-HTML/image_m/fimmu-17-1703097-t002.jpg Table 2. Pathogenic/Likely pathogenic variants in the IL2RG gene curated by the the ClinGen SCID VCE https://www.frontiersin.org/files/Articles/1703097/fimmu-17-1703097-HTML/image_m/fimmu-17-1703097-t003.jpg Table 3. Pathogenic/Likely pathogenic variants in the IL2RG gene with more than 2 submissions in Cli https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1708920/full https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-g001.jpg Figure 1. Test and control plants of IISR-Thevam & Panniyur-I. https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-t001.jpg Table 1. Physiological and biochemical parameters of control and water stress induced plants. https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-g002.jpg Figure 2. Variations in stomatal aperture size at corresponding stress levels (A) Control (B) 14 DAS https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-t002.jpg Table 2. Quality control and read statistics of transcriptome. https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-t003.jpg Table 3. Statistics of aligned paired-end reads to the reference genome. https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-t004.jpg Table 4. Variant analysis of control and drought induced samples. https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-g003.jpg Figure 3. Heatmap of top 20 genes in various processes of drought stress. (a) Drought perception and https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-g004.jpg Figure 4. Gene Ontology and KEGG Pathway analysis of DEGs (a) Biological Process (b) Cellular Compon https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-g005.jpg Figure 5. Distribution of various parameters in the transcriptome (a) Taxonomy of DEGs (b) Transcrip https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-g006.jpg Figure 6. STRING mapping of DEGs in drought stress and their characteristic roles in various physiol https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-g007.jpg Figure 7. Cis-acting elements identified in the 2kb upstream region of the different genes. https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-g008.jpg Figure 8. qPCR validation result of top five DEGs (a) upregulated (b) downregulated. https://www.frontiersin.org/files/Articles/1708920/fpls-16-1708920-HTML/image_m/fpls-16-1708920-g009.jpg Figure 9. Schematic comparison of the responses of Accession 4226 to drought at physiological, bioch https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1659088/full https://www.frontiersin.org/files/Articles/1659088/fmars-12-1659088-HTML/image_m/fmars-12-1659088-t001.jpg Table 1. Objectives and research questions. https://www.frontiersin.org/files/Articles/1659088/fmars-12-1659088-HTML/image_m/fmars-12-1659088-g001.jpg Figure 1. The process of systematic review. https://www.frontiersin.org/files/Articles/1659088/fmars-12-1659088-HTML/image_m/fmars-12-1659088-t002.jpg Table 2. MSP enabling conditions and brief descriptions. https://www.frontiersin.org/files/Articles/1659088/fmars-12-1659088-HTML/image_m/fmars-12-1659088-g002.jpg Figure 2. MSP drivers in Asia, Oceania, and the Asia-Pacific region. https://www.frontiersin.org/files/Articles/1659088/fmars-12-1659088-HTML/image_m/fmars-12-1659088-g003.jpg Figure 3. Enabling conditions in the Asia-Pacific region. https://www.frontiersin.org/files/Articles/1659088/fmars-12-1659088-HTML/image_m/fmars-12-1659088-t003.jpg Table 3. Details of Policy and Research Implications. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1788002/full https://www.frontiersin.org/files/Articles/1788002/fonc-16-1788002-HTML/image_m/fonc-16-1788002-t001.jpg Table 1. Patient characteristics stratified by frailty category (clinical frailty scale, CFS). https://www.frontiersin.org/files/Articles/1788002/fonc-16-1788002-HTML/image_m/fonc-16-1788002-g001.jpg Figure 1. Association of frailty and clinical recommendations following CGA. (A) Stacked bars depict https://www.frontiersin.org/files/Articles/1788002/fonc-16-1788002-HTML/image_m/fonc-16-1788002-t002.jpg Table 2. Characteristics associated with treatment plan modification. https://www.frontiersin.org/files/Articles/1788002/fonc-16-1788002-HTML/image_m/fonc-16-1788002-g002.jpg Figure 2. Kaplan–Meier plots of overall and postoperative survival by frailty status and clinical re https://www.frontiersin.org/files/Articles/1788002/fonc-16-1788002-HTML/image_m/fonc-16-1788002-g003.jpg Figure 3. Alluvial diagram of patient pathways: frailty category → clinical recommendation → change https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1727363/full https://www.frontiersin.org/files/Articles/1727363/fpls-17-1727363-HTML/image_m/fpls-17-1727363-t001.jpg Table 1. The PLATZ family genes in E. grandis. https://www.frontiersin.org/files/Articles/1727363/fpls-17-1727363-HTML/image_m/fpls-17-1727363-g001.jpg Figure 1. Schematic diagram of the chromosomal distribution, conserved domain and gene structure of https://www.frontiersin.org/files/Articles/1727363/fpls-17-1727363-HTML/image_m/fpls-17-1727363-g002.jpg Figure 2. Phylogenetic analysis and multiple sequence alignment of PLATZ proteins. (A) Phylogenetic https://www.frontiersin.org/files/Articles/1727363/fpls-17-1727363-HTML/image_m/fpls-17-1727363-g003.jpg Figure 3. Circos plots of the chromosomal locations of EgPLATZs with duplication links. (A) Duplicat https://www.frontiersin.org/files/Articles/1727363/fpls-17-1727363-HTML/image_m/fpls-17-1727363-g004.jpg Figure 4. Identification of cis-acting elements in the promoter regions of EgPLATZs. (A) The distrib https://www.frontiersin.org/files/Articles/1727363/fpls-17-1727363-HTML/image_m/fpls-17-1727363-g005.jpg Figure 5. The interaction network of EgPLATZ family proteins and target proteins. Nodes represent di https://www.frontiersin.org/files/Articles/1727363/fpls-17-1727363-HTML/image_m/fpls-17-1727363-g006.jpg Figure 6. Heatmap of EgPLATZ gene expression determined by RNA-seq. Log2-fold differences in gene ex https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1749559/full https://www.frontiersin.org/files/Articles/1749559/fneur-17-1749559-HTML/image_m/fneur-17-1749559-g001.jpg Figure 1. Participant disposition and analysis populations. Of 131 participants screened, 120 were r https://www.frontiersin.org/files/Articles/1749559/fneur-17-1749559-HTML/image_m/fneur-17-1749559-t001.jpg Table 1. Baseline demographic and clinical characteristics of the ITT population. https://www.frontiersin.org/files/Articles/1749559/fneur-17-1749559-HTML/image_m/fneur-17-1749559-t002.jpg Table 2. Outcomes at baseline (0 week) and 26 weeks by group. https://www.frontiersin.org/files/Articles/1749559/fneur-17-1749559-HTML/image_m/fneur-17-1749559-t003.jpg Table 3. Adverse events during the 26-week double-blind period (ITT population, n = 106). https://www.frontiersin.org/files/Articles/1749559/fneur-17-1749559-HTML/image_m/fneur-17-1749559-t004.jpg Table 4. Reasons for dropout during the 26-week double-blind phase. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1742078/full https://www.frontiersin.org/files/Articles/1742078/fcimb-16-1742078-HTML/image_m/fcimb-16-1742078-g001.jpg Figure 1. Baseline expression and validation of genetic and pharmacologic tools in Huh7 cells. (A) B https://www.frontiersin.org/files/Articles/1742078/fcimb-16-1742078-HTML/image_m/fcimb-16-1742078-g002.jpg Figure 2. nc886 knockdown modulates HBV replication and PKR–eIF2α signaling in Huh7–HBV 1.3-mer cell https://www.frontiersin.org/files/Articles/1742078/fcimb-16-1742078-HTML/image_m/fcimb-16-1742078-g003.jpg Figure 3. Genetic epistasis between nc886 and PKR in regulating HBV replication and integrated stres https://www.frontiersin.org/files/Articles/1742078/fcimb-16-1742078-HTML/image_m/fcimb-16-1742078-g004.jpg Figure 4. Pharmacologic modulation of PKR–eIF2α signaling confirms nc886–PKR axis in HBV-replicating https://www.frontiersin.org/files/Articles/1742078/fcimb-16-1742078-HTML/image_m/fcimb-16-1742078-g005.jpg Figure 5. Proposed model of nc886–PKR–eIF2α signaling and its impact on HBV replication. Figure 5. S https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1778570/full https://www.frontiersin.org/files/Articles/1778570/fphar-17-1778570-HTML/image_m/fphar-17-1778570-g001.jpg Figure 1. The origins of delphinidin and its benefits to human health. https://www.frontiersin.org/files/Articles/1778570/fphar-17-1778570-HTML/image_m/fphar-17-1778570-g002.jpg Figure 2. The chemical structure of delphinidin. https://www.frontiersin.org/files/Articles/1778570/fphar-17-1778570-HTML/image_m/fphar-17-1778570-g003.jpg Figure 3. The biosynthesis of delphinidin. PAL, phenylalanine ammonia-lyase; C4H, cinnamate-4-hydrox https://www.frontiersin.org/files/Articles/1778570/fphar-17-1778570-HTML/image_m/fphar-17-1778570-g004.jpg Figure 4. The role of delphinidin in cancer. TME: tumor immune microenvironment. The figure was crea https://www.frontiersin.org/files/Articles/1778570/fphar-17-1778570-HTML/image_m/fphar-17-1778570-g005.jpg Figure 5. The specific molecular mechanisms of delphinidin against cancer. PD1, programmed cell deat https://www.frontiersin.org/files/Articles/1778570/fphar-17-1778570-HTML/image_m/fphar-17-1778570-g006.jpg Figure 6. Multiple signaling pathways modulated by delphinidin to exert its anticancer effects. NF-κ https://www.frontiersin.org/files/Articles/1778570/fphar-17-1778570-HTML/image_m/fphar-17-1778570-g007.jpg Figure 7. The anticancer effects of delphinidin across various tumor types and the involved signalin https://www.frontiersin.org/files/Articles/1778570/fphar-17-1778570-HTML/image_m/fphar-17-1778570-t001.jpg Table 1. The anticancer effects of delphinidin across different cancer types. https://www.frontiersin.org/files/Articles/1778570/fphar-17-1778570-HTML/image_m/fphar-17-1778570-g008.jpg Figure 8. The anti-cancer role of delphinidin in breast cancer. 6PGD, 6-phosphogluconate dehydrogena https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1781497/full https://www.frontiersin.org/files/Articles/1781497/fdgth-08-1781497-HTML/image_m/fdgth-08-1781497-g001.jpg Figure 1. Research methodology flowchart [author's elaboration]. https://www.frontiersin.org/files/Articles/1781497/fdgth-08-1781497-HTML/image_m/fdgth-08-1781497-t001.jpg Table 1. Ethical principles, themes from literature review, and corresponding e-delphi items. https://www.frontiersin.org/files/Articles/1781497/fdgth-08-1781497-HTML/image_m/fdgth-08-1781497-t002.jpg Table 2. Participant characteristics (final round, n = 27). https://www.frontiersin.org/files/Articles/1781497/fdgth-08-1781497-HTML/image_m/fdgth-08-1781497-t003.jpg Table 3. Key recommendations for integrating PROs and DBs in AI healthcare solutions. https://www.frontiersin.org/files/Articles/1781497/fdgth-08-1781497-HTML/image_m/fdgth-08-1781497-g002.jpg Figure 2. Experts’ recommendations for integration of PROs and DBs in AI-based models and their resp https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1787989/full https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g001.jpg Figure 1. UVB-induced DNA damage and viability in sea urchin coelomocytes and human PBMCs. (a) Viabi https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g002.jpg Figure 2. Bulk RNA sequencing revealed transcriptional changes in coelomocytes following UVB treatme https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g003.jpg Figure 3. The cellular landscape of control and UVB-treated coelomocytes at 6 h recovery in vitro wa https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g004.jpg Figure 4. UVB-treated coelomocytes upregulate DNA repair and stress response genes while downregulat https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g005.jpg Figure 5. Phagocytes and vibratile cells mount a robust transcriptional response to UVB challenge, i https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g006.jpg Figure 6. Proteins encoded by significantly upregulated genes in UVB-treated phagocytes and vibratil https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g007.jpg Figure 7. Immune system genes and zinc fingers are upregulated in response to UVB treatment in coelo https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g008.jpg Figure 8. DDR-immune gene differential co-expression analysis in phagocytes. Heatmap showing differe https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g009.jpg Figure 9. Functional validation of enhanced autophagy in UVB-treated coelomocytes. (a) Representativ https://www.frontiersin.org/files/Articles/1787989/fimmu-17-1787989-HTML/image_m/fimmu-17-1787989-g010.jpg Figure 10. Enhanced ubiquitination and phosphorylation in UVB-treated coelomocytes was confirmed usi https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1816360/full https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-g001.jpg Figure 1. Flowchart of patient selection from the MIMIC-IV database. https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-g002.jpg Figure 2. Identified RDW trajectories in sepsis patients. https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-t001.jpg Table 1. Clinical characteristics of patients with different RDW trajectories. https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-t002.jpg Table 2. Univariable and multivariable Cox proportional hazards models for different RDW trajectorie https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-g003.jpg Figure 3. 30-day Kaplan–Meier survival curves for patients with different trajectories. https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-g004.jpg Figure 4. 90-day Kaplan–Meier survival curves for patients with different trajectories. https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-t003.jpg Table 3. Improvement in model discrimination (C-index) by incorporating RDW trajectory groups. https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-t004.jpg Table 4. Likelihood ratio test results for incorporating RDW trajectory groups. https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-t005.jpg Table 5. Net reclassification improvement (NRI) analysis after incorporating RDW trajectory groups ( https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-g005.jpg Figure 5. Validation of trajectories using data from the First Affiliated Hospital of Kunming Medica https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-g006.jpg Figure 6. Kaplan–Meier survival curves for different trajectories derived from the First Affiliated https://www.frontiersin.org/files/Articles/1816360/fmed-13-1816360-HTML/image_m/fmed-13-1816360-g007.jpg Figure 7. The trajectory plot of the four-trajectory model. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1617429/full https://www.frontiersin.org/files/Articles/1617429/fpsyg-16-1617429-HTML/image_m/fpsyg-16-1617429-g001.jpg Figure 1. The research path. https://www.frontiersin.org/files/Articles/1617429/fpsyg-16-1617429-HTML/image_m/fpsyg-16-1617429-t001.jpg Table 1. Descriptive statistics. https://www.frontiersin.org/files/Articles/1617429/fpsyg-16-1617429-HTML/image_m/fpsyg-16-1617429-t002.jpg Table 2. Descriptive statistics of variables and their correlation coefficients (n = 354). https://www.frontiersin.org/files/Articles/1617429/fpsyg-16-1617429-HTML/image_m/fpsyg-16-1617429-t003.jpg Table 3. Mediation analysis of athletes’ DSS. https://www.frontiersin.org/files/Articles/1617429/fpsyg-16-1617429-HTML/image_m/fpsyg-16-1617429-t004.jpg Table 4. Analysis of mediating effects of DSS. https://www.frontiersin.org/files/Articles/1617429/fpsyg-16-1617429-HTML/image_m/fpsyg-16-1617429-t005.jpg Table 5. Examination of the moderating effect of athletes’ SE on PASS. https://www.frontiersin.org/files/Articles/1617429/fpsyg-16-1617429-HTML/image_m/fpsyg-16-1617429-g002.jpg Figure 2. The moderating effect of training years on PASS. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1729532/full https://www.frontiersin.org/files/Articles/1729532/fimmu-16-1729532-HTML/image_m/fimmu-16-1729532-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1729532/fimmu-16-1729532-HTML/image_m/fimmu-16-1729532-t001.jpg Table 1. Composition of liposomes presented in Figure 5. https://www.frontiersin.org/files/Articles/1729532/fimmu-16-1729532-HTML/image_m/fimmu-16-1729532-g001.jpg Figure 1. Separation and characterization of C3(H2O). (A) To characterize C3(H2O), native C3 was exp https://www.frontiersin.org/files/Articles/1729532/fimmu-16-1729532-HTML/image_m/fimmu-16-1729532-g002.jpg Figure 2. Binding of different forms of C3 to platelets. (A) To investigate if it is preferably nati https://www.frontiersin.org/files/Articles/1729532/fimmu-16-1729532-HTML/image_m/fimmu-16-1729532-g003.jpg Figure 3. C3 bound to in vitro formed clots. Clots were formed in plasma that had been centrifuged o https://www.frontiersin.org/files/Articles/1729532/fimmu-16-1729532-HTML/image_m/fimmu-16-1729532-g004.jpg Figure 4. Binding of native C3 to apoptotic PMN and HDMEC. PMN and HDMEC were treated with camptothe https://www.frontiersin.org/files/Articles/1729532/fimmu-16-1729532-HTML/image_m/fimmu-16-1729532-g005.jpg Figure 5. C3 binding, conformation and function on liposomes QCM-D analysis which allows real time a https://www.frontiersin.org/files/Articles/1729532/fimmu-16-1729532-HTML/image_m/fimmu-16-1729532-g006.jpg Figure 6. C3 conformations after contact with negatively charge liposomes with and without cholester https://www.frontiersin.org/journals/clinical-diabetes-and-healthcare/articles/10.3389/fcdhc.2026.1721649/full https://www.frontiersin.org/files/Articles/1721649/fcdhc-07-1721649-HTML/image_m/fcdhc-07-1721649-g001.jpg Figure 1. Coefficient variation trends of variable selection in LASSO regression. https://www.frontiersin.org/files/Articles/1721649/fcdhc-07-1721649-HTML/image_m/fcdhc-07-1721649-g002.jpg Figure 2. LASSO regression cross-validation curve. https://www.frontiersin.org/files/Articles/1721649/fcdhc-07-1721649-HTML/image_m/fcdhc-07-1721649-t001.jpg Table 1. Logistic regression analysis of DOL among mothers with GDM. https://www.frontiersin.org/files/Articles/1721649/fcdhc-07-1721649-HTML/image_m/fcdhc-07-1721649-g003.jpg Figure 3. Nomogram of DOL risk factors among mothers with GDM. https://www.frontiersin.org/files/Articles/1721649/fcdhc-07-1721649-HTML/image_m/fcdhc-07-1721649-g004.jpg Figure 4. ROC curve of the DOL risk prediction model for mothers with GDM. https://www.frontiersin.org/files/Articles/1721649/fcdhc-07-1721649-HTML/image_m/fcdhc-07-1721649-g005.jpg Figure 5. Internal validation of DOL risk prediction model for mothers with GDM. https://www.frontiersin.org/files/Articles/1721649/fcdhc-07-1721649-HTML/image_m/fcdhc-07-1721649-g006.jpg Figure 6. Calibration curve diagram of DOL risk prediction model for mothers with GDM. https://www.frontiersin.org/files/Articles/1721649/fcdhc-07-1721649-HTML/image_m/fcdhc-07-1721649-g007.jpg Figure 7. Clinical decision curve analysis of DOL risk prediction model for mothers with GDM. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1724230/full https://www.frontiersin.org/files/Articles/1724230/fimmu-17-1724230-HTML/image_m/fimmu-17-1724230-g001.jpg Figure 1. Workflow of transcriptomic data acquisition, preprocessing, and validation strategy. Human https://www.frontiersin.org/files/Articles/1724230/fimmu-17-1724230-HTML/image_m/fimmu-17-1724230-g002.jpg Figure 2. Results of differential expression analysis and weighted gene co-expression network analys https://www.frontiersin.org/files/Articles/1724230/fimmu-17-1724230-HTML/image_m/fimmu-17-1724230-g003.jpg Figure 3. Functional enrichment analysis and machine learning–based selection of candidate genes. (A https://www.frontiersin.org/files/Articles/1724230/fimmu-17-1724230-HTML/image_m/fimmu-17-1724230-g004.jpg Figure 4. Evaluation of diagnostic performance and immune-related transcriptional patterns associate https://www.frontiersin.org/files/Articles/1724230/fimmu-17-1724230-HTML/image_m/fimmu-17-1724230-g005.jpg Figure 5. Upregulated expression of ADRB2 and PLK2 in keloid tissues. (A) H&E and Masson’s trichrome https://www.frontiersin.org/files/Articles/1724230/fimmu-17-1724230-HTML/image_m/fimmu-17-1724230-g006.jpg Figure 6. Functional validation of ADRB2 and PLK2 in keloid fibroblasts via autophagy modulation. (A https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1762967/full https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-g001.jpg Figure 1. (a) Photoacoustic effect schematic: pulsed laser absorption by tissue chromophores generat https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-g002.jpg Figure 2. Light-RepViTSR architecture highlighting the stem convolution, RepViT block sequence, resi https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-g003.jpg Figure 3. RepViT block structure featuring identity mapping with separate token and channel mixers a https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-g004.jpg Figure 4. (a) Custom-built optical-resolution photoacoustic microscopy platform for plant vein acqui https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-g005.jpg Figure 5. Raster under-sampling procedure for scaling factors ×2, ×4, and ×8. https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-g006.jpg Figure 6. Data processing workflow for photoacoustic super-resolution reconstruction. https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-g007.jpg Figure 7. Super-resolution reconstruction of murine cerebrovascular structures across scaling factor https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-g008.jpg Figure 8. Cross-domain generalization on plant vein structures across scaling factors (×2, ×4, and × https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-t001.jpg Table 1. Quantitative evaluation with statistical significance on Duke murine cerebrovascular test s https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-t002.jpg Table 2. Quantitative evaluation with statistical significance on plant vein test set (18 images; me https://www.frontiersin.org/files/Articles/1762967/fbioe-14-1762967-HTML/image_m/fbioe-14-1762967-t003.jpg Table 3. Ablation study (×4 SR, Duke test set; mean ± SD). https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1608210/full https://www.frontiersin.org/files/Articles/1608210/fcvm-12-1608210-HTML/image_m/fcvm-12-1608210-g001.jpg Figure 1. Enrollment and exclusion flowchart. https://www.frontiersin.org/files/Articles/1608210/fcvm-12-1608210-HTML/image_m/fcvm-12-1608210-t001.jpg Table 1. Comparison of baseline data between the VExUS 0 group and the VExUS ≥1 group. https://www.frontiersin.org/files/Articles/1608210/fcvm-12-1608210-HTML/image_m/fcvm-12-1608210-t002.jpg Table 2. Correlation analysis between VExUS grading and right ventricular function indicators. https://www.frontiersin.org/files/Articles/1608210/fcvm-12-1608210-HTML/image_m/fcvm-12-1608210-g002.jpg Figure 2. Comparison of accuracy at different CART model depths. https://www.frontiersin.org/files/Articles/1608210/fcvm-12-1608210-HTML/image_m/fcvm-12-1608210-g003.jpg Figure 3. Feature importance. https://www.frontiersin.org/files/Articles/1608210/fcvm-12-1608210-HTML/image_m/fcvm-12-1608210-g004.jpg Figure 4. CART model. https://www.frontiersin.org/files/Articles/1608210/fcvm-12-1608210-HTML/image_m/fcvm-12-1608210-t003.jpg Table 3. Performance metrics of the model. https://www.frontiersin.org/files/Articles/1608210/fcvm-12-1608210-HTML/image_m/fcvm-12-1608210-g005.jpg Figure 5. Confusion matrix. https://www.frontiersin.org/files/Articles/1608210/fcvm-12-1608210-HTML/image_m/fcvm-12-1608210-g006.jpg Figure 6. ROC curve. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1653448/full https://www.frontiersin.org/files/Articles/1653448/fsufs-09-1653448-HTML/image_m/fsufs-09-1653448-g001.jpg Figure 1. The JUST GROW Framework. Urban agriculture (UA) activities determine the distribution of k https://www.frontiersin.org/files/Articles/1653448/fsufs-09-1653448-HTML/image_m/fsufs-09-1653448-g002.jpg Figure 2. Indicator-based theory of change. Depicted through a flow diagram showing how data infrast https://www.frontiersin.org/files/Articles/1653448/fsufs-09-1653448-HTML/image_m/fsufs-09-1653448-g003.jpg Figure 3. Triangle model of responsibility for action. Responsibility for action emerges when author https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1775907/full https://www.frontiersin.org/files/Articles/1775907/fpsyg-17-1775907-HTML/image_m/fpsyg-17-1775907-g001.jpg Figure 1. The Golem avatar and the virtual reality (VR) task used in the experiment. (A) The Golem a https://www.frontiersin.org/files/Articles/1775907/fpsyg-17-1775907-HTML/image_m/fpsyg-17-1775907-g002.jpg Figure 2. Group comparisons on the Trait Adjective Scale. (A) Personal familiarity, (B) social desir https://www.frontiersin.org/files/Articles/1775907/fpsyg-17-1775907-HTML/image_m/fpsyg-17-1775907-g003.jpg Figure 3. Group comparisons on sense of ownership (SoO) and sense of agency (SoA). (A) SoO scores di https://www.frontiersin.org/files/Articles/1775907/fpsyg-17-1775907-HTML/image_m/fpsyg-17-1775907-g004.jpg Figure 4. Correlation between personal familiarity and sense of agency (SoA). Scatterplots of person https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1751616/full https://www.frontiersin.org/files/Articles/1751616/fbinf-06-1751616-HTML/image_m/fbinf-06-1751616-g001.jpg Figure 1. Schematic pipeline representation. (A) Overall pipeline schema. (B) Example of supplement https://www.frontiersin.org/files/Articles/1751616/fbinf-06-1751616-HTML/image_m/fbinf-06-1751616-t001.jpg Table 1. Contingency table demonstrating HLAchecker sensitivity. FP averaged across five independent https://www.frontiersin.org/files/Articles/1751616/fbinf-06-1751616-HTML/image_m/fbinf-06-1751616-g002.jpg Figure 2. Correlation of frequencies for A-B-DRB1 haplotypes between data from www.allelefrequencies https://www.frontiersin.org/files/Articles/1751616/fbinf-06-1751616-HTML/image_m/fbinf-06-1751616-t002.jpg Table 2. Summary of analysis of 4,195 samples for discrepancies between sequences of predicted HLA a https://www.frontiersin.org/files/Articles/1751616/fbinf-06-1751616-HTML/image_m/fbinf-06-1751616-t003.jpg Table 3. Summary of pipeline prediction validations with Sanger sequencing. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1733041/full https://www.frontiersin.org/files/Articles/1733041/fimmu-17-1733041-HTML/image_m/fimmu-17-1733041-g001.jpg Figure 1. VH, DH, and JH germline sequences that contribute to CDR-H3 amino acid content. Upper pane https://www.frontiersin.org/files/Articles/1733041/fimmu-17-1733041-HTML/image_m/fimmu-17-1733041-g002.jpg Figure 2. Comparison of B cell numbers and cell turnover in WT and λ5KO B cells from bone marrow fra https://www.frontiersin.org/files/Articles/1733041/fimmu-17-1733041-HTML/image_m/fimmu-17-1733041-t001.jpg Table 1. Absolute B cell numbers per cubic millimeter and the percent of B cells undergoing apoptosi https://www.frontiersin.org/files/Articles/1733041/fimmu-17-1733041-HTML/image_m/fimmu-17-1733041-g003.jpg Figure 3. DH, DH reading frame, and JH utilization in λ5KO [top, (A–C)] versus WT [bottom, (G–I)] bo https://www.frontiersin.org/files/Articles/1733041/fimmu-17-1733041-HTML/image_m/fimmu-17-1733041-g004.jpg Figure 4. Percentage of CDR-H3 sequences with rearrangements using microhomology between either V an https://www.frontiersin.org/files/Articles/1733041/fimmu-17-1733041-HTML/image_m/fimmu-17-1733041-g005.jpg Figure 5. Percentage of CDR-H3 sequences that are devoid of N addition either between V and D (Seq w https://www.frontiersin.org/files/Articles/1733041/fimmu-17-1733041-HTML/image_m/fimmu-17-1733041-g006.jpg Figure 6. Relative amino acid utilization on a position-by-position basis at the amino and carboxy t https://www.frontiersin.org/files/Articles/1733041/fimmu-17-1733041-HTML/image_m/fimmu-17-1733041-g007.jpg Figure 7. Relative amino acid utilization on a position-by-position basis at the amino and carboxy t https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1786625/full https://www.frontiersin.org/files/Articles/1786625/fpubh-14-1786625-HTML/image_m/fpubh-14-1786625-t001.jpg Table 1. Scores of occupational exposure risk perception, occupational burnout, self-efficacy, and s https://www.frontiersin.org/files/Articles/1786625/fpubh-14-1786625-HTML/image_m/fpubh-14-1786625-t002.jpg Table 2. Univariate analysis of occupational exposure risk perception among CSSD nurses (n = 580, me https://www.frontiersin.org/files/Articles/1786625/fpubh-14-1786625-HTML/image_m/fpubh-14-1786625-t003.jpg Table 3. Stepwise multiple linear regression analysis of factors associated with occupational exposu https://www.frontiersin.org/files/Articles/1786625/fpubh-14-1786625-HTML/image_m/fpubh-14-1786625-t004.jpg Table 4. Correlation analysis between occupational exposure risk perception and self-efficacy, occup https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1753609/full https://www.frontiersin.org/files/Articles/1753609/fnins-20-1753609-HTML/image_m/fnins-20-1753609-g001.jpg Figure 1. Localization of medial, lateral reticulospinal tracts and medial longitudinal fasciculus i https://www.frontiersin.org/files/Articles/1753609/fnins-20-1753609-HTML/image_m/fnins-20-1753609-g002.jpg Figure 2. Acoustic startle reflex circuit in rats. (I) Sagittal section of the rat brain. (I-A–D) ar https://www.frontiersin.org/files/Articles/1753609/fnins-20-1753609-HTML/image_m/fnins-20-1753609-g003.jpg Figure 3. Schematic diagram of a damaged neural pathway due to stroke and spinal cord injury in rode https://www.frontiersin.org/files/Articles/1753609/fnins-20-1753609-HTML/image_m/fnins-20-1753609-g004.jpg Figure 4. Schematic diagram of spinal neural circuits related to spasticity. https://www.frontiersin.org/files/Articles/1753609/fnins-20-1753609-HTML/image_m/fnins-20-1753609-g005.jpg Figure 5. Overview of Ia afferent inputs to the medullary reticular formation via the lateral reticu https://www.frontiersin.org/files/Articles/1753609/fnins-20-1753609-HTML/image_m/fnins-20-1753609-g006.jpg Figure 6. Overview of increased Ia input in spasticity, illustrated via comparative schematic of lat https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1652671/full https://www.frontiersin.org/files/Articles/1652671/fmed-12-1652671-HTML/image_m/fmed-12-1652671-g001.jpg Figure 1. Ultrasound images of abscess. https://www.frontiersin.org/files/Articles/1652671/fmed-12-1652671-HTML/image_m/fmed-12-1652671-g002.jpg Figure 2. Brucella suis genome coverage map (coverage: 98.94%). https://www.frontiersin.org/files/Articles/1652671/fmed-12-1652671-HTML/image_m/fmed-12-1652671-t001.jpg Table 1. Laboratory test. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1729477/full https://www.frontiersin.org/files/Articles/1729477/fcell-14-1729477-HTML-r1/image_m/fcell-14-1729477-g001.jpg Figure 1. Schematic distribution of CpG sites within the mouse LEP promoter region. A schematic diag https://www.frontiersin.org/files/Articles/1729477/fcell-14-1729477-HTML-r1/image_m/fcell-14-1729477-g002.jpg Figure 2. Establishment of the gestational diabetes mellitus (GDM) mouse model. (A) Fasting blood gl https://www.frontiersin.org/files/Articles/1729477/fcell-14-1729477-HTML-r1/image_m/fcell-14-1729477-g003.jpg Figure 3. Serum leptin concentrations during pregnancy in control (WT) and GDM mice. Serum leptin le https://www.frontiersin.org/files/Articles/1729477/fcell-14-1729477-HTML-r1/image_m/fcell-14-1729477-g004.jpg Figure 4. Leptin protein expression in decidual tissues of control (WT) and GDM mice. (A) Representa https://www.frontiersin.org/files/Articles/1729477/fcell-14-1729477-HTML-r1/image_m/fcell-14-1729477-g005.jpg Figure 5. Methylation levels of the leptin (LEP) promoter in decidual tissues of WT and GDM mice. Me https://www.frontiersin.org/files/Articles/1729477/fcell-14-1729477-HTML-r1/image_m/fcell-14-1729477-g006.jpg Figure 6. Weighted regression analysis linking LEP promoter methylation to leptin levels and fasting https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1793416/full https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-g009.jpg Graphical Abstract https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-t001.jpg Table 1. Comparative overview of different pre-treatment methods of delignification of biomass for b https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-t002.jpg Table 2. Aggregated comparison of the pretreatment methods (Singh et al., 2015). https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-g001.jpg Figure 1. Classification of physical, chemical, physico-chemical, and biological pretreatment techni https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-g002.jpg Figure 2. Conversion of cellulose and hemicellulose into simple sugars and hydrgengenesis of simple https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-g003.jpg Figure 3. The molecular mechanism of photo fermentation in PNSB and complete degradation of cellulos https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-g004.jpg Figure 4. Under light exposure, photons with energy equal to or exceeding the semiconductor band gap https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-g005.jpg Figure 5. Schematic pathway for bio-hydrogen production via photo-fermentation using nano-photocatal https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-t003.jpg Table 3. Application of nanomaterials in PHFP with different lignocellulosic biomass-based H2 produc https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-t004.jpg Table 4. Mechanistic and emerging materials advances adaptation for improving the PHFP yield. https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-g006.jpg Figure 6. Technology development to implementation pathway for photocatalytic systems, depicting the https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-g007.jpg Figure 7. TRL scale for photocatalytic fundamentals to practical applications. https://www.frontiersin.org/files/Articles/1793416/fmicb-17-1793416-HTML/image_m/fmicb-17-1793416-g008.jpg Figure 8. Roadmap toward commercialization of biohydrogen from biological pathway. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1778953/full https://www.frontiersin.org/files/Articles/1778953/fpubh-14-1778953-HTML/image_m/fpubh-14-1778953-g001.jpg Figure 1. Digital Health and AI training initiatives for public health (2020–2025). https://www.frontiersin.org/files/Articles/1778953/fpubh-14-1778953-HTML/image_m/fpubh-14-1778953-t001.jpg Table 1. Integrated summary of digital health and artificial intelligence training initiatives for t https://www.frontiersin.org/files/Articles/1778953/fpubh-14-1778953-HTML/image_m/fpubh-14-1778953-g002.jpg Figure 2. Distribution of thematic content domains (2020–2025) https://www.frontiersin.org/files/Articles/1778953/fpubh-14-1778953-HTML/image_m/fpubh-14-1778953-g003.jpg Figure 3. Trend in training initiatives for safe, ethical, and sustainable AI in health (2020–2025). https://www.frontiersin.org/files/Articles/1778953/fpubh-14-1778953-HTML/image_m/fpubh-14-1778953-g004.jpg Figure 4. Distribution of thematic content domains by training format (2020–2025). https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1784474/full https://www.frontiersin.org/files/Articles/1784474/fspor-08-1784474-HTML/image_m/fspor-08-1784474-t001.jpg Table 1. Participants' characteristics. https://www.frontiersin.org/files/Articles/1784474/fspor-08-1784474-HTML/image_m/fspor-08-1784474-g001.jpg Figure 1. (A) study schematic protocol and (B) exercise modalities testing and monitoring throughout https://www.frontiersin.org/files/Articles/1784474/fspor-08-1784474-HTML/image_m/fspor-08-1784474-t002.jpg Table 2. Mean individual speeds for each locomotor zones. https://www.frontiersin.org/files/Articles/1784474/fspor-08-1784474-HTML/image_m/fspor-08-1784474-t003.jpg Table 3. Exercise modalities internal load and perceived experience. https://www.frontiersin.org/files/Articles/1784474/fspor-08-1784474-HTML/image_m/fspor-08-1784474-t004.jpg Table 4. Exercise modalities external load. https://www.frontiersin.org/files/Articles/1784474/fspor-08-1784474-HTML/image_m/fspor-08-1784474-g002.jpg Figure 2. Number of (A) accelerations and (B) decelerations (count) during the training sessions. Da https://www.frontiersin.org/files/Articles/1784474/fspor-08-1784474-HTML/image_m/fspor-08-1784474-g003.jpg Figure 3. Mean absolute (mmol.L−1) (lines) and relative (BLpeak%) (bars) blood lactate across the wa https://www.frontiersin.org/files/Articles/1784474/fspor-08-1784474-HTML/image_m/fspor-08-1784474-t005.jpg Table 5. Exercise modalities internal load during each 15 min period. https://www.frontiersin.org/files/Articles/1784474/fspor-08-1784474-HTML/image_m/fspor-08-1784474-t006.jpg Table 6. Exercise modalities external load during each 15 min period. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1771387/full https://www.frontiersin.org/files/Articles/1771387/fonc-16-1771387-HTML/image_m/fonc-16-1771387-g001.jpg Figure 1. Flow chart of the source of data of women with suspected diagnosis of ovarian cancer. https://www.frontiersin.org/files/Articles/1771387/fonc-16-1771387-HTML/image_m/fonc-16-1771387-t001.jpg Table 1. Demographic data. https://www.frontiersin.org/files/Articles/1771387/fonc-16-1771387-HTML/image_m/fonc-16-1771387-t002.jpg Table 2. Diagnostic data. https://www.frontiersin.org/files/Articles/1771387/fonc-16-1771387-HTML/image_m/fonc-16-1771387-t003.jpg Table 3. Surgical data. https://www.frontiersin.org/files/Articles/1771387/fonc-16-1771387-HTML/image_m/fonc-16-1771387-t004.jpg Table 4. Univariable and multivariable logistic regression analysis: predictors of ovarian cancer. https://www.frontiersin.org/files/Articles/1771387/fonc-16-1771387-HTML/image_m/fonc-16-1771387-t005.jpg Table 5. Pathological data. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1701624/full https://www.frontiersin.org/files/Articles/1701624/fsufs-10-1701624-HTML/image_m/fsufs-10-1701624-g001.jpg Figure 1. Impact pathways for ASP-FS interactions. https://www.frontiersin.org/files/Articles/1701624/fsufs-10-1701624-HTML/image_m/fsufs-10-1701624-g002.jpg Figure 2. Analytical framework. https://www.frontiersin.org/files/Articles/1701624/fsufs-10-1701624-HTML/image_m/fsufs-10-1701624-g003.jpg Figure 3. PRISMA flow diagram for the identification of key articles. https://www.frontiersin.org/files/Articles/1701624/fsufs-10-1701624-HTML/image_m/fsufs-10-1701624-g004.jpg Figure 4. Summary map of interventions and corresponding food system outcomes in the Sahel and Horn https://www.frontiersin.org/files/Articles/1701624/fsufs-10-1701624-HTML/image_m/fsufs-10-1701624-t001.jpg Table 1. Impact pathways for agrosilvopastoral systems transformation on food system outcomes. https://www.frontiersin.org/files/Articles/1701624/fsufs-10-1701624-HTML/image_m/fsufs-10-1701624-g005.jpg Figure 5. Driver performance for livelihoods and inclusion outcomes. https://www.frontiersin.org/files/Articles/1701624/fsufs-10-1701624-HTML/image_m/fsufs-10-1701624-g006.jpg Figure 6. Driver performance for sustainability and resilience outcomes. https://www.frontiersin.org/files/Articles/1701624/fsufs-10-1701624-HTML/image_m/fsufs-10-1701624-g007.jpg Figure 7. Driver performance for food and nutrition security outcomes. https://www.frontiersin.org/journals/radiology/articles/10.3389/fradi.2025.1660479/full https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-g001.jpg Figure 1. Challenges in viral disease diagnosis and the emergence of U-FDL-PPE. https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-g002.jpg Figure 2. U-FDL-PPE framework federated and explainable AI architecture for privacy-preserving viral https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-t002.jpg Algorithm 1. Federated Training Procedure. https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-t003.jpg Algorithm 2. Privacy-Preserving Model Updates Using Gaussian Noise. https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-g003.jpg Figure 3. U-FDL-PPE-6X: a six-component unified federated deep learning architecture with privacy-pr https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-t004.jpg Algorithm 3. Grad-CAM Heatmap Generation for Explainability. https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-g004.jpg Figure 4. Federated healthcare learning workflow. https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-g005.jpg Figure 5. Accuracy vs. roun for federated model accuracy per round. https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-g006.jpg Figure 6. Federated model loss Per round. https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-g007.jpg Figure 7. Class distribution in dataset. https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-g008.jpg Figure 8. Class distribution in dataset. https://www.frontiersin.org/files/Articles/1660479/fradi-05-1660479-HTML-r1/image_m/fradi-05-1660479-t001.jpg Table 1. Quantitative comparison of existing methods vs. proposed U-FDL-PPE framework for federated https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1750759/full https://www.frontiersin.org/files/Articles/1750759/feduc-11-1750759-HTML/image_m/feduc-11-1750759-t001.jpg Table 1. Breakdown of participant stakeholders’ institutional affiliations. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1626337/full https://www.frontiersin.org/files/Articles/1626337/fimmu-16-1626337-HTML/image_m/fimmu-16-1626337-g001.jpg Figure 1. Single-cell RNA-seq of cystic lesions in SIONFH. (A) Schematic workflow of the experimenta https://www.frontiersin.org/files/Articles/1626337/fimmu-16-1626337-HTML/image_m/fimmu-16-1626337-g002.jpg Figure 2. Identification of FBs and ECs. (A) t-SNE plot showing three subclusters of FBs. (B) t-SNE https://www.frontiersin.org/files/Articles/1626337/fimmu-16-1626337-HTML/image_m/fimmu-16-1626337-g003.jpg Figure 3. Identification of macrophage cells. (A) t-SNE plot showing three subclusters of macrophage https://www.frontiersin.org/files/Articles/1626337/fimmu-16-1626337-HTML/image_m/fimmu-16-1626337-g004.jpg Figure 4. Identification of chonrocytes cells. (A) t-SNE plot showing five subclusters of chonrocyte https://www.frontiersin.org/files/Articles/1626337/fimmu-16-1626337-HTML/image_m/fimmu-16-1626337-g005.jpg Figure 5. The pathological manifestations of cystic lesions. (A) The macroscopic appearance of cysti https://www.frontiersin.org/files/Articles/1626337/fimmu-16-1626337-HTML/image_m/fimmu-16-1626337-g006.jpg Figure 6. Identification of HTC cells. (A) t-SNE plot showing three subclusters of HTC. (B) Violin p https://www.frontiersin.org/files/Articles/1626337/fimmu-16-1626337-HTML/image_m/fimmu-16-1626337-g007.jpg Figure 7. CellChat analysis of the communications between eight cell types in cystic lesions. (A) An https://www.frontiersin.org/files/Articles/1626337/fimmu-16-1626337-HTML/image_m/fimmu-16-1626337-g008.jpg Figure 8. A graphical scheme of revealing the single-cell transcriptomic landscape of cystic lesions https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1721827/full https://www.frontiersin.org/files/Articles/1721827/fnhum-20-1721827-HTML/image_m/fnhum-20-1721827-t001.jpg Table 1. Exclusion criteria. https://www.frontiersin.org/files/Articles/1721827/fnhum-20-1721827-HTML/image_m/fnhum-20-1721827-t002.jpg Table 2. Potential confounders. https://www.frontiersin.org/files/Articles/1721827/fnhum-20-1721827-HTML/image_m/fnhum-20-1721827-t003.jpg Table 3. List of biomarkers, their role, and assay methods. https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2026.1803020/full https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g001.jpg Figure 1. Schematic diagram of the RF (Hu et al., 2025). https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g002.jpg Figure 2. Geological model of the TBM tunnel area: (a) lithology and faults; (b) rock mass classific https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g003.jpg Figure 3. The selected TBM. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-t001.jpg Table 1. The characteristics of TBM dataset. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g004.jpg Figure 4. Correlations between the input parameters. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g005.jpg Figure 5. Data distribution. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g006.jpg Figure 6. Hyperparameter optimization curves. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-t002.jpg Table 2. Hyperparameter optimization for the seven ML models. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g007.jpg Figure 7. Time consumption of different optimization algorithms. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-t003.jpg Table 3. Prediction performance of the seven ML models. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g008.jpg Figure 8. Scatter plots of PR prediction results. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g009.jpg Figure 9. Error distribution in the testing set. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g010.jpg Figure 10. Taylor diagram for prediction results of testing set. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-t004.jpg Table 4. Prediction performance of the ML models in the 10-fold experience. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g011.jpg Figure 11. SHAP analysis results: (a) mean SHAP values of features; (b) effects of features on the m https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-g012.jpg Figure 12. Dependence analysis of input features for RF model: (a) UCS; (b) TF; (c) RPM. https://www.frontiersin.org/files/Articles/1803020/feart-14-1803020-HTML/image_m/feart-14-1803020-t005.jpg Table 5. Prediction performance for PR. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1777670/full https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-t001.jpg Table 1. Demographic data of included and excluded participants. https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-g001.jpg Figure 1. Summary of absolute power spectra (APS) in pre- and post-EEG for all frequency bands. https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-t002.jpg Table 2. Relative distribution of EEG bands (in percent) before and after cognitive intervention, an https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-g002.jpg Figure 2. Absolute power spectra (APS) across different cortical regions in pre- and post-EEG for (A https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-t003.jpg Table 3. Relative distribution of EEG bands during CE1 condition (in percent) before and after cogni https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-g003.jpg Figure 3. Absolute power spectra (APS) during CE1 condition across different cortical regions in pre https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-t004.jpg Table 4. Relative distribution of EEG bands during OE condition (in percent) before and after cognit https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-g004.jpg Figure 4. Absolute power spectra (APS) during OE condition across different cortical regions in pre- https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-t005.jpg Table 5. Relative distribution of EEG bands during CE2 condition (in percent) before and after cogni https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-g005.jpg Figure 5. Absolute power spectra (APS) during CE2 condition across different cortical regions in pre https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-t006.jpg Table 6. Results of multivariate repeated measures ANOVA. https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-g006.jpg Figure 6. Paired pre–post plots for (A) overall theta, and (B) overall TBR. https://www.frontiersin.org/files/Articles/1777670/fnins-20-1777670-HTML/image_m/fnins-20-1777670-g007.jpg Figure 7. Theta-beta ratio (TBR) in pre-EEG and post-EEG across frontal, parietal and central region https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1801030/full https://www.frontiersin.org/files/Articles/1801030/fmed-13-1801030-HTML/image_m/fmed-13-1801030-t001.jpg Table 1. Illustrative routine assessment activities and example outputs that can be collated longitu https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1760555/full https://www.frontiersin.org/files/Articles/1760555/fimmu-17-1760555-HTML-r1/image_m/fimmu-17-1760555-g001.jpg Figure 1. Enrichment of colorectal cancer stem-like cells (CSLCs) by spheroid culture. (A) Morpholog https://www.frontiersin.org/files/Articles/1760555/fimmu-17-1760555-HTML-r1/image_m/fimmu-17-1760555-g002.jpg Figure 2. Expression of stemness regulators in colorectal CSLCs. This figure illustrates differentia https://www.frontiersin.org/files/Articles/1760555/fimmu-17-1760555-HTML-r1/image_m/fimmu-17-1760555-g003.jpg Figure 3. Expression of stemness markers in spheroid-enriched colorectal CSLCs. This figure illustra https://www.frontiersin.org/files/Articles/1760555/fimmu-17-1760555-HTML-r1/image_m/fimmu-17-1760555-g004.jpg Figure 4. Inhibitory immune checkpoints (ICPs) expression in spheroid-enriched CSLCs. This figure il https://www.frontiersin.org/files/Articles/1760555/fimmu-17-1760555-HTML-r1/image_m/fimmu-17-1760555-g005.jpg Figure 5. Surface expression of inhibitory immune checkpoints (ICPs) in spheroid-enriched CSLCs. (A, https://www.frontiersin.org/files/Articles/1760555/fimmu-17-1760555-HTML-r1/image_m/fimmu-17-1760555-g006.jpg Figure 6. Effect of ICP silencing on ALDH activity. ALDEFLUOR™ assay of ALDH activity in adherent SW https://www.frontiersin.org/files/Articles/1760555/fimmu-17-1760555-HTML-r1/image_m/fimmu-17-1760555-g007.jpg Figure 7. Immune checkpoint expression and association survival in colorectal cancer cohorts. (A–D) https://www.frontiersin.org/files/Articles/1760555/fimmu-17-1760555-HTML-r1/image_m/fimmu-17-1760555-g008.jpg Figure 8. The relationship between stemness index (mRNAsi) and the tumor immune microenvironment in https://www.frontiersin.org/files/Articles/1760555/fimmu-17-1760555-HTML-r1/image_m/fimmu-17-1760555-g009.jpg Figure 9. Association between cancer stemness and response to immune checkpoint inhibitors (ICIs). ( https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1764283/full https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-g001.jpg Figure 1. System workflow from raw in-field image acquisition → annotation & preprocessing → YOLOv8n https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-t001.jpg Table 1. Summary of image collection details. https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-g002.jpg Figure 2. Detailed workflow of the ZamYOLO-Maize diagnostic framework showing decision logic and par https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-g003.jpg Figure 3. Schematic diagram of the proposed YOLOv8n-based maize leaf disease detection workflow. https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-g004.jpg Figure 4. Architecture of the proposed YOLOv8n maize leaf disease detection model. https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-g005.jpg Figure 5. Detailed workflow of the ZamYOLO-Maize diagnostic framework showing decision logic and par https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-t002.jpg Table 2. Software libraries and versions used in the experimental environment. https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-g006.jpg Figure 6. Samples from the three maize diseases. https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-g007.jpg Figure 7. Visual results showing classified disease regions using the proposed Zam Yolo-Maize model. https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-t003.jpg Table 3. Hyperparameters used for the proposed framework. https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-t004.jpg Table 4. Performance metrics of the proposed YOLOv8n model. https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-t005.jpg Table 5. Comparative performance of YOLO variants. https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-g008.jpg Figure 8. Bar chart comparing the performance of YOLOv5, YOLOv8s, YOLOv10s, and YOLOv8n across key e https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-g009.jpg Figure 9. Line plot presenting the performance of four YOLO variants across standard detection metri https://www.frontiersin.org/files/Articles/1764283/frai-09-1764283-HTML/image_m/frai-09-1764283-t006.jpg Table 6. Comparative Analysis of Object Techniques. https://www.frontiersin.org/journals/high-performance-computing/articles/10.3389/fhpcp.2026.1664774/full https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-t001.jpg Table 1. COUNTDOWN_MERIC integration: MERICext API description. https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-g001.jpg Figure 1. Integration framework of COUNTDOWN and MERIC. https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-t003.jpg Algorithm 1. Energy measurement and accumulation in COUNTDOWN. https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-t002.jpg Table 2. System architecture comparison. https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-g002.jpg Figure 2. Comparison of CNTD_X86 and CNTD_MERIC_X86 across benchmarks for seven different conditions https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-g003.jpg Figure 3. Overhead analysis on NAS benchmarks results with CNTD_MERIC under Analysis mode (A–C). https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-g004.jpg Figure 4. Energy and power savings achieved under Enable mode with CNTD_MERIC (A, B). https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-g005.jpg Figure 5. Evaluation and comparison of power monitoring of CNTD_MERIC_X86 and CNTD_MERIC_A64FX (A–C) https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-g006.jpg Figure 6. Comparative analysis of MPI communication overheads using CNTD_MERIC. https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-g007.jpg Figure 7. CNTD_MERIC benchmark results on A64FX for NAS BT, ResNet50, and STREAM (A–D). https://www.frontiersin.org/files/Articles/1664774/fhpcp-04-1664774-HTML/image_m/fhpcp-04-1664774-g008.jpg Figure 8. CNTD_MERIC multinode benchmark results on A64FX with wide ResNet, BT Class D, and quantum https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1699002/full https://www.frontiersin.org/files/Articles/1699002/fsurg-12-1699002-HTML/image_m/fsurg-12-1699002-g001.jpg Figure 1. Flow diagram of patient selection. https://www.frontiersin.org/files/Articles/1699002/fsurg-12-1699002-HTML/image_m/fsurg-12-1699002-t001.jpg Table 1. Baseline characteristics of HREV and non-HREV patients. https://www.frontiersin.org/files/Articles/1699002/fsurg-12-1699002-HTML/image_m/fsurg-12-1699002-t002.jpg Table 2. Univariate logistic regression analyses to identify predictors for HREVs. https://www.frontiersin.org/files/Articles/1699002/fsurg-12-1699002-HTML/image_m/fsurg-12-1699002-t003.jpg Table 3. Independent predictive factors identified by multivariate analysis. https://www.frontiersin.org/files/Articles/1699002/fsurg-12-1699002-HTML/image_m/fsurg-12-1699002-g002.jpg Figure 2. Nomogram for predicting high-risk esophageal varices in patients with liver cirrhosis. PT, https://www.frontiersin.org/files/Articles/1699002/fsurg-12-1699002-HTML/image_m/fsurg-12-1699002-g003.jpg Figure 3. Receiver operating characteristic curves of the nomogram and other non-invasive models. AA https://www.frontiersin.org/files/Articles/1699002/fsurg-12-1699002-HTML/image_m/fsurg-12-1699002-t004.jpg Table 4. Discriminative ability of the constructed model and other models. https://www.frontiersin.org/files/Articles/1699002/fsurg-12-1699002-HTML/image_m/fsurg-12-1699002-g004.jpg Figure 4. Calibration curves of the monogram model in the developing cohort (a) and bootstrapping in https://www.frontiersin.org/files/Articles/1699002/fsurg-12-1699002-HTML/image_m/fsurg-12-1699002-g005.jpg Figure 5. Decision curve analysis of the nomogram and other non-invasive models. AAR, aspartate amin https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1743163/full https://www.frontiersin.org/files/Articles/1743163/fbioe-14-1743163-HTML-r1/image_m/fbioe-14-1743163-t001.jpg Table 1. Overview of real vehicle low-speed collision accident reproduction test. https://www.frontiersin.org/files/Articles/1743163/fbioe-14-1743163-HTML-r1/image_m/fbioe-14-1743163-g001.jpg Figure 1. Overview of bumper car crash test. (A) Commercially operated vehicle-type bumper car used https://www.frontiersin.org/files/Articles/1743163/fbioe-14-1743163-HTML-r1/image_m/fbioe-14-1743163-t002.jpg Table 2. Collision test scenarios including vehicle to vehicle and bumper car to bumper car. https://www.frontiersin.org/files/Articles/1743163/fbioe-14-1743163-HTML-r1/image_m/fbioe-14-1743163-t003.jpg Table 3. Results of low-speed collision test with occupants. https://www.frontiersin.org/files/Articles/1743163/fbioe-14-1743163-HTML-r1/image_m/fbioe-14-1743163-t004.jpg Table 4. Results of a questionnaire on whether participants felt pain after the crash test. https://www.frontiersin.org/files/Articles/1743163/fbioe-14-1743163-HTML-r1/image_m/fbioe-14-1743163-t005.jpg Table 5. Cervical MRI findings before and after the collision test in all participants. https://www.frontiersin.org/files/Articles/1743163/fbioe-14-1743163-HTML-r1/image_m/fbioe-14-1743163-t006.jpg Table 6. Electromyography (EMG) results for nerve conduction studies after collision test. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1769343/full https://www.frontiersin.org/files/Articles/1769343/fsurg-13-1769343-HTML-r1/image_m/fsurg-13-1769343-g001.jpg Figure 1. Humans frequently use aids for problem-solving. These aids become part of cognition when t https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1682584/full https://www.frontiersin.org/files/Articles/1682584/fnhum-19-1682584-HTML/image_m/fnhum-19-1682584-t001.jpg Table 1. Demographic, neuropsychological, and clinical date. https://www.frontiersin.org/files/Articles/1682584/fnhum-19-1682584-HTML/image_m/fnhum-19-1682584-t002.jpg Table 2. Brain regions showed differences in ALFF/dALFF of in the comparisons between pre-LT and HCs https://www.frontiersin.org/files/Articles/1682584/fnhum-19-1682584-HTML/image_m/fnhum-19-1682584-t003.jpg Table 3. Brain regions showed differences in ALFF/dALFF of in the comparisons between post-LT and HC https://www.frontiersin.org/files/Articles/1682584/fnhum-19-1682584-HTML/image_m/fnhum-19-1682584-t004.jpg Table 4. Brain regions showed differences in ALFF/dALFF of in the comparisons between post-LT and pr https://www.frontiersin.org/files/Articles/1682584/fnhum-19-1682584-HTML/image_m/fnhum-19-1682584-g001.jpg Figure 1. Differences in ALFF and dALFF between the pre-LT and HCs, voxel level p < 0.05, cluster le https://www.frontiersin.org/files/Articles/1682584/fnhum-19-1682584-HTML/image_m/fnhum-19-1682584-g002.jpg Figure 2. Differences in ALFF and dALFF between the post-LT and HCs, voxel level p < 0.05, cluster l https://www.frontiersin.org/files/Articles/1682584/fnhum-19-1682584-HTML/image_m/fnhum-19-1682584-g003.jpg Figure 3. Differences in ALFF and dALFF between the post-LT and pre-LT, voxel level p < 0.05, cluste https://www.frontiersin.org/files/Articles/1682584/fnhum-19-1682584-HTML/image_m/fnhum-19-1682584-t005.jpg Table 5. Correlations between the changes of ALFF/dALFF and changes of NCT-A, DST scores, prothrombi https://www.frontiersin.org/files/Articles/1682584/fnhum-19-1682584-HTML/image_m/fnhum-19-1682584-g004.jpg Figure 4. Relationship of changes in amplitude of low-frequency fluctuation (ALFF) values with chang https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1734583/full https://www.frontiersin.org/files/Articles/1734583/fpsyt-17-1734583-HTML-r1/image_m/fpsyt-17-1734583-g001.jpg Figure 1. Creative Forces site locations. Source: reproduced from National Endowment for the Arts (2 https://www.frontiersin.org/files/Articles/1734583/fpsyt-17-1734583-HTML-r1/image_m/fpsyt-17-1734583-g002.jpg Figure 2. Systems levels of military service, CATx treatment, and creative engagement. https://www.frontiersin.org/files/Articles/1734583/fpsyt-17-1734583-HTML-r1/image_m/fpsyt-17-1734583-t001.jpg Table 1. Goal- and intervention-informed creative performance integration examples. https://www.frontiersin.org/files/Articles/1734583/fpsyt-17-1734583-HTML-r1/image_m/fpsyt-17-1734583-t002.jpg Table 2. CF-CAC components and process description. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1753197/full https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g001.jpg Figure 1. Schematic diagram of container yard. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g002.jpg Figure 2. External trucks pickup. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g003.jpg Figure 3. Process of particle swarm optimization algorithm. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g004.jpg Figure 4. Improve the fitness function calculation process of particle swarm optimization algorithm. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-t001.jpg Table 1. Operation cost of container picking up from yard (2 yard cranes, unit: yuan). https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g005.jpg Figure 5. Cost of container handling operation (2 yard cranes). https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-t002.jpg Table 2. Operation cost of container picking up from yard (3 yard cranes, unit: yuan). https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g006.jpg Figure 6. Cost of container handling operation (3 yard cranes). https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g007.jpg Figure 7. Improved particle swarm optimization algorithm iteration. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g008.jpg Figure 8. Standard particle swarm optimization algorithm iteration. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-t003.jpg Table 3. Statistical summary of key performance indicators over 30 independent runs. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g009.jpg Figure 9. Total cost statistical distribution box plot. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g010.jpg Figure 10. Frequency distribution histogram of total costs from 30 independent runs. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g011.jpg Figure 11. Radar chart of coefficient of variation for different cost components. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g012.jpg Figure 12. Convergence curves with statistical bands (mean ± standard deviation). https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g013.jpg Figure 13. Algorithm convergence characteristics comparison curves. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g014.jpg Figure 14. Total cost optimization performance comparison. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g015.jpg Figure 15. Comparative analysis of cost composition decomposition. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g016.jpg Figure 16. Relocation operation efficiency comparison. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-t004.jpg Table 4. Configuration and results of multi-scale experiments. https://www.frontiersin.org/files/Articles/1753197/fmars-13-1753197-HTML/image_m/fmars-13-1753197-g017.jpg Figure 17. Multi-scale computational experiment analysis results. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1787326/full https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-g001.jpg Figure 1. The framework of this study. https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-g002.jpg Figure 2. The study areas. https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-t001.jpg Table 1. Optimized vegetation indices. https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-g003.jpg Figure 3. Changes in anthocyanin concentration in P. pruinosa leaves in different months under diffe https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-g004.jpg Figure 4. Spectral reflectance variation for different anthocyanin concentration intervals of P. pru https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-g005.jpg Figure 5. Correlation analysis between different spectral reflectance and anthocyanin concentration. https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-g006.jpg Figure 6. Heat map of the correlations between vegetation indices and anthocyanin concentration. The https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-t002.jpg Table 2. Wavelength sets selected by different feature extraction methods. https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-t003.jpg Table 3. Optimal feature combination for constructing the vegetation indices. https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-t004.jpg Table 4. Performance of models constructed using different feature extraction and modeling methods. https://www.frontiersin.org/files/Articles/1787326/fpls-17-1787326-HTML-r1/image_m/fpls-17-1787326-g007.jpg Figure 7. Verification of the optimal model for the prediction of anthocyanin concentration in P. pr https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1776098/full https://www.frontiersin.org/files/Articles/1776098/fmed-13-1776098-HTML/image_m/fmed-13-1776098-t001.jpg Table 1. Anthropometric results (n = 22). https://www.frontiersin.org/files/Articles/1776098/fmed-13-1776098-HTML/image_m/fmed-13-1776098-t002.jpg Table 2. Differences between pulmonary function and their predicted normal values according to the g https://www.frontiersin.org/files/Articles/1776098/fmed-13-1776098-HTML/image_m/fmed-13-1776098-t003.jpg Table 3. Difference in respiratory muscles strength results (mouth pressure) in between alarplasty p https://www.frontiersin.org/files/Articles/1776098/fmed-13-1776098-HTML/image_m/fmed-13-1776098-t004.jpg Table 4. Correlations between years since surgery and respiratory function in alarplasty participant https://www.frontiersin.org/files/Articles/1776098/fmed-13-1776098-HTML/image_m/fmed-13-1776098-g001.jpg Figure 1. Respiratory muscle strength compared with predicted values. Bar graph illustrating maximal https://www.frontiersin.org/files/Articles/1776098/fmed-13-1776098-HTML/image_m/fmed-13-1776098-g002.jpg Figure 2. Scatter plot showing Pearson correlation between years since surgery and FEV1. https://www.frontiersin.org/files/Articles/1776098/fmed-13-1776098-HTML/image_m/fmed-13-1776098-g003.jpg Figure 3. Scatter plot showing Pearson correlation between years since surgery and FEV1 predicted %. https://www.frontiersin.org/journals/animal-science/articles/10.3389/fanim.2026.1758227/full https://www.frontiersin.org/files/Articles/1758227/fanim-07-1758227-HTML/image_m/fanim-07-1758227-g001.jpg Figure 1. Literature search strategy and screening process. https://www.frontiersin.org/files/Articles/1758227/fanim-07-1758227-HTML/image_m/fanim-07-1758227-t001.jpg Table 1. Summary of the results of the scoping review of methodologies used to assess the effectiven https://www.frontiersin.org/files/Articles/1758227/fanim-07-1758227-HTML/image_m/fanim-07-1758227-g002.jpg Figure 2. Screenshot of the logic model in the DAFM effectiveness framework tool with an imaginary e https://www.frontiersin.org/files/Articles/1758227/fanim-07-1758227-HTML/image_m/fanim-07-1758227-g003.jpg Figure 3. Screenshot of the WFM in the DAFM effectiveness framework tool. https://www.frontiersin.org/files/Articles/1758227/fanim-07-1758227-HTML/image_m/fanim-07-1758227-g004.jpg Figure 4. Graphical representation of the weighted factor model in the DAFM effectiveness framework. https://www.frontiersin.org/files/Articles/1758227/fanim-07-1758227-HTML/image_m/fanim-07-1758227-g005.jpg Figure 5. Screenshot of the cost-effectiveness section of the DAFM effectiveness framework tool, wit https://www.frontiersin.org/files/Articles/1758227/fanim-07-1758227-HTML/image_m/fanim-07-1758227-g006.jpg Figure 6. Screenshot of the accountability section of the DAFM effectiveness framework tool. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1630569/full https://www.frontiersin.org/files/Articles/1630569/fimmu-16-1630569-HTML/image_m/fimmu-16-1630569-g001.jpg Figure 1. (A) Distribution of clinical trial phases by year. The bar chart illustrates the number of https://www.frontiersin.org/files/Articles/1630569/fimmu-16-1630569-HTML/image_m/fimmu-16-1630569-g002.jpg Figure 2. (A) Enrollment for different ARDs; (B) ARDs distribution in clinical trials; (C) timeline https://www.frontiersin.org/files/Articles/1630569/fimmu-16-1630569-HTML/image_m/fimmu-16-1630569-g003.jpg Figure 3. (A) Distribution of countries and their respective continents in chimeric antigen receptor https://www.frontiersin.org/files/Articles/1630569/fimmu-16-1630569-HTML/image_m/fimmu-16-1630569-g004.jpg Figure 4. (A) A smaller pie chart depicts the sectoral distribution of chimeric antigen receptor T-c https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1785372/full https://www.frontiersin.org/files/Articles/1785372/fcvm-13-1785372-HTML-r1/image_m/fcvm-13-1785372-g001.jpg Figure 1. Schematic of Cor triatriatum sinister. The left atrium is divided by a supramitral fibromu https://www.frontiersin.org/files/Articles/1785372/fcvm-13-1785372-HTML-r1/image_m/fcvm-13-1785372-g002.jpg Figure 2. Posteroanterior (PA) chest radiograph demonstrating bilateral pulmonary infiltrates. The i https://www.frontiersin.org/files/Articles/1785372/fcvm-13-1785372-HTML-r1/image_m/fcvm-13-1785372-g003.jpg Figure 3. Apical four-chamber transthoracic echocardiographic images demonstrating Cor triatriatum s https://www.frontiersin.org/journals/sustainable-tourism/articles/10.3389/frsut.2026.1791534/full https://www.frontiersin.org/files/Articles/1791534/frsut-05-1791534-HTML/image_m/frsut-05-1791534-g001.jpg Figure 1. Conceptual model. https://www.frontiersin.org/files/Articles/1791534/frsut-05-1791534-HTML/image_m/frsut-05-1791534-t001.jpg Table 1. Characterization of the sample (n = 233 companies). https://www.frontiersin.org/files/Articles/1791534/frsut-05-1791534-HTML/image_m/frsut-05-1791534-t002.jpg Table 2. K-means cluster analysis based on propensity to adopt sustainable practices. https://www.frontiersin.org/files/Articles/1791534/frsut-05-1791534-HTML/image_m/frsut-05-1791534-t003.jpg Table 3. Effects of sample attributes on the commitment to sustainable practices. https://www.frontiersin.org/files/Articles/1791534/frsut-05-1791534-HTML/image_m/frsut-05-1791534-t004.jpg Table 4. Correlations between the main constructs. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1737419/full https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-g001.jpg Figure 1. Flowchart of this study. https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-t001.jpg Table 1. Main characteristics of included participants before PSM. https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-t002.jpg Table 2. Basic characteristics of included participants stratified by comorbidity following PSM. https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-t003.jpg Table 3. The characteristics of included participants classified by comorbidity in training dataset. https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-t004.jpg Table 4. Baseline characteristics of independent validation dataset. https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-g002.jpg Figure 2. Forest plot displays the results of univariate and multivariate analyses assessing the ass https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-g003.jpg Figure 3. Assessment of the associations between metabolic parameter quartiles and the risk of HUA-I https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-g004.jpg Figure 4. RCS reveals the non-linear associations between metabolic parameters (A) TyG, (B) TG, (C) https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-g005.jpg Figure 5. RCS reveals the non-linear associations between metabolic parameters (A) TyG, (B) TG, (C) https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-g006.jpg Figure 6. Feature selection, Clinlabomics model development, and optimal Clinlabomics model. (A) Reg https://www.frontiersin.org/files/Articles/1737419/fendo-16-1737419-HTML/image_m/fendo-16-1737419-g007.jpg Figure 7. Optimal Clinlabomics model, its interpretation, and discriminative ability. (A) ROC curves https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1648421/full https://www.frontiersin.org/files/Articles/1648421/fmolb-12-1648421-HTML-r1/image_m/fmolb-12-1648421-t001.jpg Table 1. Relationship between gestational weeks and hCG dilution factors. https://www.frontiersin.org/files/Articles/1648421/fmolb-12-1648421-HTML-r1/image_m/fmolb-12-1648421-g001.jpg Figure 1. Flowchart of preset dilution factor process for hCG. https://www.frontiersin.org/files/Articles/1648421/fmolb-12-1648421-HTML-r1/image_m/fmolb-12-1648421-t002.jpg Table 2. Comparison of gestational week data. https://www.frontiersin.org/files/Articles/1648421/fmolb-12-1648421-HTML-r1/image_m/fmolb-12-1648421-t003.jpg Table 3. Descriptive analysis of in-laboratory TAT for hCG (min). https://www.frontiersin.org/files/Articles/1648421/fmolb-12-1648421-HTML-r1/image_m/fmolb-12-1648421-g002.jpg Figure 2. TAT distribution data graph ((A) histogram of experimental group; (B) histogram of control https://www.frontiersin.org/files/Articles/1648421/fmolb-12-1648421-HTML-r1/image_m/fmolb-12-1648421-t004.jpg Table 4. In-laboratory TAT expectation analysis for hCG (90, 80, and 60 min were taken as the expect https://www.frontiersin.org/files/Articles/1648421/fmolb-12-1648421-HTML-r1/image_m/fmolb-12-1648421-g003.jpg Figure 3. Comparison of TAT values between experimental and control groups under different expectati https://www.frontiersin.org/files/Articles/1648421/fmolb-12-1648421-HTML-r1/image_m/fmolb-12-1648421-t005.jpg Table 5. data of compliance rate and economic benefits (α is the price of the anti-hCG testing reage https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1762035/full https://www.frontiersin.org/files/Articles/1762035/fpubh-14-1762035-HTML-r1/image_m/fpubh-14-1762035-t001.jpg Table 1. Staff acceptability and feasibility survey and responses (N = 9). https://www.frontiersin.org/files/Articles/1762035/fpubh-14-1762035-HTML-r1/image_m/fpubh-14-1762035-t002.jpg Table 2. Distribution of sociodemographic features of this sample (N = 107). https://www.frontiersin.org/files/Articles/1762035/fpubh-14-1762035-HTML-r1/image_m/fpubh-14-1762035-g001.jpg Figure 1. Frequency of unmet need combinations. https://www.frontiersin.org/files/Articles/1762035/fpubh-14-1762035-HTML-r1/image_m/fpubh-14-1762035-t003.jpg Table 3. Sociodemographic factors associated with unmet need (no vs. any unmet need) using modified https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1624631/full https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-t001.jpg Table 1. Variables used in binary logistic regression analysis. https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-g001.jpg Figure 1. Exclusion process for AIS-OSA patients undergoing CPAP. https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-t002.jpg Table 2. AIS-OSA patient and hospital characteristics (2010–2019). https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-g002.jpg Figure 2. Annual incidence of CPAP in AIS-OSA patients. https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-g003.jpg Figure 3. The proportion of patients with different CPAP treatment durations. https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-t003.jpg Table 3. CPAP-associated risk factors in AIS-OSA patients. https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-t004.jpg Table 4. Relationship between CPAP treatment and disease comorbidities. https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-t005.jpg Table 5. Relationship between CPAP treatment and complications. https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-g004.jpg Figure 4. Patient demographics and hospital characteristics between the two groups. (A) Age distribu https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-g005.jpg Figure 5. Incidence of CPAP-associated comorbidities in AIS-OSA patients. https://www.frontiersin.org/files/Articles/1624631/fneur-16-1624631-HTML-r1/image_m/fneur-16-1624631-g006.jpg Figure 6. Incidence of CPAP-associated complications in AIS-OSA patients. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1817268/full https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t001.jpg Table 1. Properties of the selected Serbian input–output tables. https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t002.jpg Table 2. Comparison of data in input–output tables with GDP production structure. https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t003.jpg Table 3. Comparison of data in input–output tables with GDP expenditure structure. https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t004.jpg Table 4. Comparison of data in input–output tables with GDP income structure. https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t005.jpg Table 5. Input–output structure of the health sector in 2019, current basic prices. https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t006.jpg Table 6. Stylized 2019 industry × industry I-O table, 2019 basic current prices (mil RSD). https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t007.jpg Table 7. Intermediate inputs used by the health sector (10 industries with the highest direct effect https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-g001.jpg Figure 1. Types of multipliers. Source: Authors. https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t008.jpg Table 8. Output multipliers for Serbian health sector (current prices, 62 sectors). https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-g002.jpg Figure 2. Top 10 industries (apart from the health sector) whose output is affected by changes in th https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t009.jpg Table 9. Income multipliers for Serbian health sector (current prices, 62 sectors). https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-g003.jpg Figure 3. Top 10 industries (apart from the health sector) whose income is affected by changes in th https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t010.jpg Table 10. Employment multipliers for the Serbian health sector (current prices, 62 sectors). https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-g004.jpg Figure 4. Top 10 industries (apart from the health sector) whose employment is affected by changes i https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-t011.jpg Table 11. Value-added multipliers for the Serbian health sector (current prices, 62 sectors). https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-g005.jpg Figure 5. Top 10 industries (apart from the health sector) whose value-added is affected by changes https://www.frontiersin.org/files/Articles/1817268/fpubh-14-1817268-HTML/image_m/fpubh-14-1817268-g006.jpg Figure 6. Relationship between simple multipliers and GDP per capita. Source: Author won calculation https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1792629/full https://www.frontiersin.org/files/Articles/1792629/fpubh-14-1792629-HTML/image_m/fpubh-14-1792629-t001.jpg Table 1. PCA results. https://www.frontiersin.org/files/Articles/1792629/fpubh-14-1792629-HTML/image_m/fpubh-14-1792629-t002.jpg Table 2. Loadings matrix. https://www.frontiersin.org/files/Articles/1792629/fpubh-14-1792629-HTML/image_m/fpubh-14-1792629-t003.jpg Table 3. Variable definitions and descriptive statistics. https://www.frontiersin.org/files/Articles/1792629/fpubh-14-1792629-HTML/image_m/fpubh-14-1792629-t004.jpg Table 4. Variable coding directions. https://www.frontiersin.org/files/Articles/1792629/fpubh-14-1792629-HTML/image_m/fpubh-14-1792629-t005.jpg Table 5. Benchmark regression results. https://www.frontiersin.org/files/Articles/1792629/fpubh-14-1792629-HTML/image_m/fpubh-14-1792629-t006.jpg Table 6. Quantile regression results. https://www.frontiersin.org/files/Articles/1792629/fpubh-14-1792629-HTML/image_m/fpubh-14-1792629-t007.jpg Table 7. IV regression results. https://www.frontiersin.org/files/Articles/1792629/fpubh-14-1792629-HTML/image_m/fpubh-14-1792629-t008.jpg Table 8. Heterogeneity analysis. https://www.frontiersin.org/files/Articles/1792629/fpubh-14-1792629-HTML/image_m/fpubh-14-1792629-t009.jpg Table 9. Heterogeneity analysis. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1736902/full https://www.frontiersin.org/files/Articles/1736902/fpsyg-16-1736902-HTML/image_m/fpsyg-16-1736902-t001.jpg Table 1. Anthropometric measurements and body composition parameters of military police officers. https://www.frontiersin.org/files/Articles/1736902/fpsyg-16-1736902-HTML/image_m/fpsyg-16-1736902-t002.jpg Table 2. Physical activity times, physical fitness and mood state parameter of military police offic https://www.frontiersin.org/files/Articles/1736902/fpsyg-16-1736902-HTML/image_m/fpsyg-16-1736902-g001.jpg Figure 1. Handgrip strength of dominant hand (DH) and non-dominant hand (NDH) (A), handgrip strength https://www.frontiersin.org/files/Articles/1736902/fpsyg-16-1736902-HTML/image_m/fpsyg-16-1736902-g002.jpg Figure 2. Shooting time (A) shooting score (B) and shooting accuracy coefficient (C). Values are exp https://www.frontiersin.org/files/Articles/1736902/fpsyg-16-1736902-HTML/image_m/fpsyg-16-1736902-t003.jpg Table 3. Correlation between time, score, and performance of shots with anthropometric parameters, p https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1664621/full https://www.frontiersin.org/files/Articles/1664621/fendo-16-1664621-HTML/image_m/fendo-16-1664621-g001.jpg Figure 1. Flowchart of participant selection and inclusion in the study cohort. https://www.frontiersin.org/files/Articles/1664621/fendo-16-1664621-HTML/image_m/fendo-16-1664621-t001.jpg Table 1. Clinical, hormonal and radiological characteristics of patients at diagnosis, overall and d https://www.frontiersin.org/files/Articles/1664621/fendo-16-1664621-HTML/image_m/fendo-16-1664621-t002.jpg Table 2. Hormonal and radiological changes over time. https://www.frontiersin.org/files/Articles/1664621/fendo-16-1664621-HTML/image_m/fendo-16-1664621-t003.jpg Table 3. Multivariable logistic regression model to test the predictors for significant response in https://www.frontiersin.org/files/Articles/1664621/fendo-16-1664621-HTML/image_m/fendo-16-1664621-t004.jpg Table 4. Cohen’s kappa coefficient to test the agreement between diameter and volume response. https://www.frontiersin.org/files/Articles/1664621/fendo-16-1664621-HTML/image_m/fendo-16-1664621-t005.jpg Table 5. Area under the ROC curve (AUC) and 95% confidence intervals for volume and diameter in pred https://www.frontiersin.org/journals/human-dynamics/articles/10.3389/fhumd.2025.1538673/full https://www.frontiersin.org/files/Articles/1538673/fhumd-07-1538673-HTML/image_m/fhumd-07-1538673-g001.jpg Figure 1. Mapping the selection process: a PRISMA-inspired flowchart for bibliometric analysis. https://www.frontiersin.org/files/Articles/1538673/fhumd-07-1538673-HTML/image_m/fhumd-07-1538673-g002.jpg Figure 2. Keyword co-occurrence network map showing thematic clusters (co-occurrence of all keywords https://www.frontiersin.org/files/Articles/1538673/fhumd-07-1538673-HTML/image_m/fhumd-07-1538673-g003.jpg Figure 3. Citation network analysis of influential studies (citation analysis of documents). https://www.frontiersin.org/files/Articles/1538673/fhumd-07-1538673-HTML/image_m/fhumd-07-1538673-g004.jpg Figure 4. Global research distribution and collaboration map (country citation analysis). https://www.frontiersin.org/files/Articles/1538673/fhumd-07-1538673-HTML/image_m/fhumd-07-1538673-g005.jpg Figure 5. Cross-disciplinary knowledge integration network (co-occurrence of index keywords). https://www.frontiersin.org/files/Articles/1538673/fhumd-07-1538673-HTML/image_m/fhumd-07-1538673-t001.jpg Table 1. Annual publication trends from 2001 to 2023 (23 years of data). https://www.frontiersin.org/files/Articles/1538673/fhumd-07-1538673-HTML/image_m/fhumd-07-1538673-g006.jpg Figure 6. Contemporary theoretical framework network [co-citation (cited sources)]. https://www.frontiersin.org/files/Articles/1538673/fhumd-07-1538673-HTML/image_m/fhumd-07-1538673-t002.jpg Table 2. Top 10 most cited publications with complete bibliographic details. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1794366/full https://www.frontiersin.org/files/Articles/1794366/fpsyt-17-1794366-HTML/image_m/fpsyt-17-1794366-g001.jpg Figure 1. Conceptual framework. https://www.frontiersin.org/files/Articles/1794366/fpsyt-17-1794366-HTML/image_m/fpsyt-17-1794366-t001.jpg Table 1. Group differences in characteristics and key variables among participants. https://www.frontiersin.org/files/Articles/1794366/fpsyt-17-1794366-HTML/image_m/fpsyt-17-1794366-t002.jpg Table 2. Correlation between academic burnout, social support, anxiety and depression. https://www.frontiersin.org/files/Articles/1794366/fpsyt-17-1794366-HTML/image_m/fpsyt-17-1794366-t003.jpg Table 3. Fitness indexes of the model (full sample). https://www.frontiersin.org/files/Articles/1794366/fpsyt-17-1794366-HTML/image_m/fpsyt-17-1794366-g002.jpg Figure 2. A mediation model of the relationship between social support and academic burnout through https://www.frontiersin.org/files/Articles/1794366/fpsyt-17-1794366-HTML/image_m/fpsyt-17-1794366-t004.jpg Table 4. The standardized total, direct, and indirect effects of social support on academic burnout https://www.frontiersin.org/files/Articles/1794366/fpsyt-17-1794366-HTML/image_m/fpsyt-17-1794366-g003.jpg Figure 3. Standardized structural model (grouped by urban-rural). The two values from left to right https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1798048/full https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g001.jpg Figure 1. Spatial distribution of marine observation stations. Light yellow indicates land areas, li https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g002.jpg Figure 2. Architecture of standard SegRNN. The encoder processes segmented inputs through GRU cells, https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g003.jpg Figure 3. Architecture of Attention-Enhanced Parallel Multi-step Forecast (Attn-PMF) SegRNN. Unlike https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g004.jpg Figure 4. Root mean square error (RMSE) of SST predictions across different forecast lead times at 3 https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g005.jpg Figure 5. Correlation coefficients between predicted and observed SST across different forecast lead https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g006.jpg Figure 6. Comparison of RMSE between AI-based predictions and FIO-COM numerical forecasts across dif https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g007.jpg Figure 7. Comparison of correlation coefficients between AI-based predictions and FIO-COM numerical https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g008.jpg Figure 8. Time series comparison of observed SST (red), AI-based predictions (blue), and FIO-COM num https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g009.jpg Figure 9. Prediction errors (model - observation) for AI-based predictions (blue) and FIO-COM numeri https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g010.jpg Figure 10. Time series comparison of observed SST (red), AI-based predictions (blue), and FIO-COM nu https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-g011.jpg Figure 11. Prediction errors (model - observation) for AI-based predictions (blue) and FIO-COM numer https://www.frontiersin.org/files/Articles/1798048/fmars-13-1798048-HTML-r1/image_m/fmars-13-1798048-t001.jpg Table 1. Frequency distribution of absolute forecast errors (AFE) for AI-based predictions versus FI https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1807871/full https://www.frontiersin.org/files/Articles/1807871/fimmu-17-1807871-HTML/image_m/fimmu-17-1807871-t001.jpg Table 1. Evidence summary for cellular senescence in drug-resistant epilepsy. https://www.frontiersin.org/files/Articles/1807871/fimmu-17-1807871-HTML/image_m/fimmu-17-1807871-g001.jpg Figure 1. The iron–senescence axis: an integrative schematic linking iron dysregulation, microglial https://www.frontiersin.org/files/Articles/1807871/fimmu-17-1807871-HTML/image_m/fimmu-17-1807871-g002.jpg Figure 2. Comparison of therapeutic strategies targeting the senescent niche in drug-resistant epile https://www.frontiersin.org/files/Articles/1807871/fimmu-17-1807871-HTML/image_m/fimmu-17-1807871-t002.jpg Table 2. Comparative analysis of microglial replacement strategies. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1778951/full https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-t001.jpg Table 1. Comparison of baseline data between the two groups in training and testing set [n (%), M (P https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-g001.jpg Figure 1. Regression coefficient vs. logarithmic lambda curve. https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-g002.jpg Figure 2. Lasso regression results plot. https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-t002.jpg Table 2. Comparison of prediction performance evaluation indicators of different model training sets https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-g003.jpg Figure 3. ROC plot of the training set. https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-t003.jpg Table 3. Comparison of prediction performance evaluation indicators of different model testing sets. https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-g004.jpg Figure 4. ROC plot of the test set. https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-g005.jpg Figure 5. Calibration plots comparing the observed event rate against the predictive probabilities f https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-t004.jpg Table 4. Comparison of baseline data between the two groups in external cohort [n (%), M (P25, P75), https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-g006.jpg Figure 6. External validation ROC curve. https://www.frontiersin.org/files/Articles/1778951/fmed-13-1778951-HTML/image_m/fmed-13-1778951-g007.jpg Figure 7. Random forest model SHAP algorithm important feature ranking. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1772172/full https://www.frontiersin.org/files/Articles/1772172/fphar-17-1772172-HTML/image_m/fphar-17-1772172-g001.jpg Figure 1. Chemical structure of HA. https://www.frontiersin.org/files/Articles/1772172/fphar-17-1772172-HTML/image_m/fphar-17-1772172-g002.jpg Figure 2. Schematic of HA-based nanoparticles for AD therapy: BBB transcytosis and intracerebral the https://www.frontiersin.org/files/Articles/1772172/fphar-17-1772172-HTML/image_m/fphar-17-1772172-g003.jpg Figure 3. Schematic of HA-based hydrogels for AD therapy: properties, mechanisms, and therapeutic ou https://www.frontiersin.org/files/Articles/1772172/fphar-17-1772172-HTML/image_m/fphar-17-1772172-g004.jpg Figure 4. Schematic of HA-based hydrogels for PD therapy: platform, mechanisms, and therapeutic outc https://www.frontiersin.org/journals/disaster-and-emergency-medicine/articles/10.3389/femer.2025.1604529/full https://www.frontiersin.org/files/Articles/1604529/femer-03-1604529-HTML/image_m/femer-03-1604529-g001.jpg Figure 1. Flowchart of video search and screening process. https://www.frontiersin.org/files/Articles/1604529/femer-03-1604529-HTML/image_m/femer-03-1604529-t001.jpg Table 1. Basic video information. https://www.frontiersin.org/files/Articles/1604529/femer-03-1604529-HTML/image_m/femer-03-1604529-g002.jpg Figure 2. Panels A and B show the percentage distribution of GQS and mDISCERN scores for videos on D https://www.frontiersin.org/files/Articles/1604529/femer-03-1604529-HTML/image_m/femer-03-1604529-g003.jpg Figure 3. Panels A and B compare the distribution of GQS and mDISCERN scores for videos from differe https://www.frontiersin.org/files/Articles/1604529/femer-03-1604529-HTML/image_m/femer-03-1604529-g004.jpg Figure 4. Comparison of Quality and Reliability Scores of Videos Published by Medical Professionals https://www.frontiersin.org/files/Articles/1604529/femer-03-1604529-HTML/image_m/femer-03-1604529-g005.jpg Figure 5. Correlation and Relationships Among Evaluation Metrics of Videos on Douyin and Bilibili. https://www.frontiersin.org/files/Articles/1604529/femer-03-1604529-HTML/image_m/femer-03-1604529-g006.jpg Figure 6. Annual distribution of videos on Douyin and Bilibili. https://www.frontiersin.org/files/Articles/1604529/femer-03-1604529-HTML/image_m/femer-03-1604529-g007.jpg Figure 7. Comparison of uploader verification status distribution on Douyin and Bilibili. https://www.frontiersin.org/files/Articles/1604529/femer-03-1604529-HTML/image_m/femer-03-1604529-g008.jpg Figure 8. Comparison of video upload locations and numbers on Douyin and Bilibili. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1740709/full https://www.frontiersin.org/files/Articles/1740709/fpsyg-16-1740709-HTML/image_m/fpsyg-16-1740709-t001.jpg Table 1. Key literature and theoretical contributions to filter bubble mechanisms. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1700487/full https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-t001.jpg Table 1. Clinicopathological and treatment characteristics of patients included in the study. https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g001.jpg Figure 1. Analysis of NOS2/NO expression in control and patients with colorectal cancer. (A) NOS2 mR https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g002.jpg Figure 2. Analysis of arginase expression in control and colorectal cancer patients. (A) Arginase mR https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g003.jpg Figure 3. Histological analysis of normal mucosa and CRC was performed using hematoxylin and eosin s https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g004.jpg Figure 4. Analysis of the number of CD68+ and CD163+ cells in normal mucosa and colorectal cancer. ( https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g005.jpg Figure 5. Number of CD8-positive cells per mm² in normal mucosa and colorectal cancer. (A) Represent https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g006.jpg Figure 6. Immune cell ratios and inflammatory index levels in the control and colorectal cancer grou https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g007.jpg Figure 7. Correlation heatmap of systemic immune cell ratios, tumor cell infiltrates, and NO/ARG rat https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-t002.jpg Table 2. Comparison of baseline cell ratios and systemic inflammation indices between responders and https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-t003.jpg Table 3. Cut-Off values for prognostic stratification using ROC analysis. https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g008.jpg Figure 8. Kaplan-Meier curve of association between inflammation markers. (A) NLR, (B) PLR, (C) MLR, https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g009.jpg Figure 9. Kaplan-Meier curve of association between inflammation markers (A) NLR, (B) PLR, (C) MLR, https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g010.jpg Figure 10. Forest plot of univariate and multivariate Cox regression analyses of PFS. https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g011.jpg Figure 11. Forest plot of univariate and multivariate Cox regression analyses of OS. https://www.frontiersin.org/files/Articles/1700487/fimmu-16-1700487-HTML/image_m/fimmu-16-1700487-g012.jpg Figure 12. Combined inflammatory indices score as a prognostic biomarker for CRC. (A) PFS (B) OS. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1731687/full https://www.frontiersin.org/files/Articles/1731687/fimmu-17-1731687-HTML/image_m/fimmu-17-1731687-g001.jpg Figure 1. The NETs-macrophage axis: mechanisms in disease and targeted therapeutic strategies. NETs https://www.frontiersin.org/files/Articles/1731687/fimmu-17-1731687-HTML/image_m/fimmu-17-1731687-g002.jpg Figure 2. Mechanisms of NETs formation. NETs formation mechanisms. NETs formation can be primarily c https://www.frontiersin.org/files/Articles/1731687/fimmu-17-1731687-HTML/image_m/fimmu-17-1731687-g003.jpg Figure 3. Interactions between NETs and macrophages. This figure depicts the context-dependent, bidi https://www.frontiersin.org/files/Articles/1731687/fimmu-17-1731687-HTML/image_m/fimmu-17-1731687-g004.jpg Figure 4. NETs and macrophages in autoimmune diseases. (A) In rheumatoid arthritis (RA), local post- https://www.frontiersin.org/files/Articles/1731687/fimmu-17-1731687-HTML/image_m/fimmu-17-1731687-g005.jpg Figure 5. NETs and macrophages in atherosclerosis. Atherosclerosis (AS): Lipid infiltration and plaq https://www.frontiersin.org/files/Articles/1731687/fimmu-17-1731687-HTML/image_m/fimmu-17-1731687-g006.jpg Figure 6. Potential therapeutic targets for NET-macrophage interactions. The left side of the diagra https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1808982/full https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g001.jpg Figure 1. Distribution of partitioned significant wave height (left panels, a1–e1) and peak period ( https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g002.jpg Figure 2. Computational domains of WW3 for the (a) Pacific Ocean and (b) southeast Pacific. In subfi https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g003.jpg Figure 3. Spatial distributions of error metrics for HS in the D1 outer model. (a) BIAS, (b) RMSE, a https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g004.jpg Figure 4. Comparison of numerical results and observations at NDBC 32012. (a) HS and (b) T02. https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g005.jpg Figure 5. Typical simulated directional spectra at NDBC 32012 during (a) boreal summer and (b) borea https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-t001.jpg Table 1. Partitioned wave parameters of individual wave systems in Figure 5a. https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-t002.jpg Table 2. Partitioned wave parameters of individual wave systems in Figure 5b. https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g006.jpg Figure 6. ERA5 wind field at (a) 14:00 on July 20, 2018 and 18:00 on December 13, 2018. The main win https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g007.jpg Figure 7. Significant wave height (left panels, a1–d1) and peak period (right panels, a2–d2) of wave https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g008.jpg Figure 8. Significant wave height (left panels, a1–h1) and peak period (right panels, a2–h2) of wave https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g009.jpg Figure 9. Significant wave height (left panels, a1–b1) and peak period (right panels, a2–b2) of wave https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g010.jpg Figure 10. Significant wave height (left panels, a1–d1) and peak period (right panels, a2–d2) of wav https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g011.jpg Figure 11. Distribution of partitioned significant wave height (left panels, a1–e1) and peak period https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g012.jpg Figure 12. Segmented ERA5 wind fields at different wave system generation zones. (a) partial southea https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g013.jpg Figure 13. Wave systems from different sources at NDBC 32012 simulated using segmented ERA5 wind fie https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g014.jpg Figure 14. The wave field of significant wave height (left panels, a1–c1) and peak period (right pan https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g015.jpg Figure 15. The wave field of significant wave height (left panels, a1–c1) and peak period (right pan https://www.frontiersin.org/files/Articles/1808982/fmars-13-1808982-HTML-r1/image_m/fmars-13-1808982-g016.jpg Figure 16. Spatial and temporal evolution of wave events generated by the northern storm belt. Left https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1678631/full https://www.frontiersin.org/files/Articles/1678631/fpsyt-17-1678631-HTML/image_m/fpsyt-17-1678631-g001.jpg Figure 1. Flowchart of studies selection for inclusion in the meta. https://www.frontiersin.org/files/Articles/1678631/fpsyt-17-1678631-HTML/image_m/fpsyt-17-1678631-t001.jpg Table 1. Classification and meridian attribution of acupoints. https://www.frontiersin.org/files/Articles/1678631/fpsyt-17-1678631-HTML/image_m/fpsyt-17-1678631-g002.jpg Figure 2. Frequency ranking chart of acupoints and cluster analysis of acupoint compatibility. (A) T https://www.frontiersin.org/files/Articles/1678631/fpsyt-17-1678631-HTML/image_m/fpsyt-17-1678631-g003.jpg Figure 3. Network plots of multiple acupoint stimulation therapies, either as monotherapies or in co https://www.frontiersin.org/files/Articles/1678631/fpsyt-17-1678631-HTML/image_m/fpsyt-17-1678631-g004.jpg Figure 4. (A, B) The rankings of included acupoint stimulation therapies for different efficacy outc https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1798619/full https://www.frontiersin.org/files/Articles/1798619/fendo-17-1798619-HTML/image_m/fendo-17-1798619-g001.jpg Figure 1. Algorithm for the management of (acquired) hypothalamic dysfunction. Hypothalamic dysfunct https://www.frontiersin.org/files/Articles/1798619/fendo-17-1798619-HTML/image_m/fendo-17-1798619-g002.jpg Figure 2. Epidemiological results of German claims data analyses (collected between 2010 and 2020) f https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1816683/full https://www.frontiersin.org/files/Articles/1816683/fspor-08-1816683-HTML/image_m/fspor-08-1816683-g001.jpg Figure 1. Comparison of original RT4T and adapted MEGAFiT instructional cards highlighting changes i https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1771828/full https://www.frontiersin.org/files/Articles/1771828/fmed-13-1771828-HTML/image_m/fmed-13-1771828-t001.jpg Table 1. Demographic characteristics. https://www.frontiersin.org/files/Articles/1771828/fmed-13-1771828-HTML/image_m/fmed-13-1771828-t002.jpg Table 2. Participants’ responses to knowledge items regarding drug–drug interactions (n = 356). https://www.frontiersin.org/files/Articles/1771828/fmed-13-1771828-HTML/image_m/fmed-13-1771828-t003.jpg Table 3. Factors and predictors of knowledge scores regarding DDIs. https://www.frontiersin.org/files/Articles/1771828/fmed-13-1771828-HTML/image_m/fmed-13-1771828-g001.jpg Figure 1. Results of the multivariable linear regression for the predictors of knowledge. https://www.frontiersin.org/files/Articles/1771828/fmed-13-1771828-HTML/image_m/fmed-13-1771828-t004.jpg Table 4. Practice and daily management of drug–drug interactions (n = 356). https://www.frontiersin.org/files/Articles/1771828/fmed-13-1771828-HTML/image_m/fmed-13-1771828-t005.jpg Table 5. Factors associated with the frequency of encountering DDIs (n = 356). https://www.frontiersin.org/files/Articles/1771828/fmed-13-1771828-HTML/image_m/fmed-13-1771828-g002.jpg Figure 2. Statistical differences in the frequency of DDIs terms of cities. https://www.frontiersin.org/files/Articles/1771828/fmed-13-1771828-HTML/image_m/fmed-13-1771828-t006.jpg Table 6. Pharmacists’ responses to simulated patients (n = 134). https://www.frontiersin.org/files/Articles/1771828/fmed-13-1771828-HTML/image_m/fmed-13-1771828-t007.jpg Table 7. Statistical differences in pharmacist’s responses to simulated patients in terms of drug co https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1689958/full https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-g001.jpg Figure 1. Retrieval and screening flow chart. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-g002.jpg Figure 2. Statistics on the number of published papers. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-g003.jpg Figure 3. Top 20 keywords with the strongest citation bursts in CNKI. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-g004.jpg Figure 4. Top 16 keywords with the strongest citation bursts in WOS. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-g005.jpg Figure 5. Network diagram of the author cooperation relationship in CNKI. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-t001.jpg Table 1. Top 10 research institutions on the number of papers published in CNKI and WoS. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-g006.jpg Figure 6. Network diagram of the author cooperation relationship in WoS. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-g007.jpg Figure 7. Keywords clustering map in CNKI. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-g008.jpg Figure 8. Keywords clustering map in WOS. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-t002.jpg Table 2. Keywords cluster labels in CNKI and WOS. https://www.frontiersin.org/files/Articles/1689958/fenvs-14-1689958-HTML/image_m/fenvs-14-1689958-g009.jpg Figure 9. Co-citation of literature in WOS. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1652640/full https://www.frontiersin.org/files/Articles/1652640/fcimb-16-1652640-HTML/image_m/fcimb-16-1652640-g001.jpg Figure 1. The flow chart of the study population. https://www.frontiersin.org/files/Articles/1652640/fcimb-16-1652640-HTML/image_m/fcimb-16-1652640-g002.jpg Figure 2. The monthly distributions of M. pneumoniae pneumonia from January 2023 to December 2024. https://www.frontiersin.org/files/Articles/1652640/fcimb-16-1652640-HTML/image_m/fcimb-16-1652640-t001.jpg Table 1. Clinical and laboratory characteristics of patients with MUMPP and non-MUMPP. https://www.frontiersin.org/files/Articles/1652640/fcimb-16-1652640-HTML/image_m/fcimb-16-1652640-t002.jpg Table 2. The laboratory-clinical discordance between mutated MPP and MUMPP. https://www.frontiersin.org/files/Articles/1652640/fcimb-16-1652640-HTML/image_m/fcimb-16-1652640-t003.jpg Table 3. Multivariable logistic regressions results of association between various factors and SMPP. https://www.frontiersin.org/files/Articles/1652640/fcimb-16-1652640-HTML/image_m/fcimb-16-1652640-t004.jpg Table 4. Multivariable logistic regressions results of association between various factors and MPP a https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2025.1640022/full https://www.frontiersin.org/files/Articles/1640022/fpos-07-1640022-HTML-r1/image_m/fpos-07-1640022-g001.jpg Figure 1. The mapping of contemporary Indonesian political parties convergence. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1598440/full https://www.frontiersin.org/files/Articles/1598440/fpubh-13-1598440-HTML-r2/image_m/fpubh-13-1598440-t001.jpg Table 1. Variables and basic characteristics. https://www.frontiersin.org/files/Articles/1598440/fpubh-13-1598440-HTML-r2/image_m/fpubh-13-1598440-t002.jpg Table 2. Results of stepwise linear regression. https://www.frontiersin.org/files/Articles/1598440/fpubh-13-1598440-HTML-r2/image_m/fpubh-13-1598440-g001.jpg Figure 1. Panels (A–E) shows the before and after kernel density plots and balance test plots for th https://www.frontiersin.org/files/Articles/1598440/fpubh-13-1598440-HTML-r2/image_m/fpubh-13-1598440-t003.jpg Table 3. Robustness test based on the matching approach. https://www.frontiersin.org/files/Articles/1598440/fpubh-13-1598440-HTML-r2/image_m/fpubh-13-1598440-g002.jpg Figure 2. Mediating effect model. ***P < 0.001, **P < 0.01, *P < 0.05; BC refers to balance constitu https://www.frontiersin.org/files/Articles/1598440/fpubh-13-1598440-HTML-r2/image_m/fpubh-13-1598440-t004.jpg Table 4. Mediated effects model path coefficients. https://www.frontiersin.org/files/Articles/1598440/fpubh-13-1598440-HTML-r2/image_m/fpubh-13-1598440-t005.jpg Table 5. Mediation effect estimates. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1756842/full https://www.frontiersin.org/files/Articles/1756842/fmed-12-1756842-HTML/image_m/fmed-12-1756842-g001.jpg Figure 1. Research flowchart of this study. EGVB, esophagogastric variceal bleeding; NSBBs, non-sele https://www.frontiersin.org/files/Articles/1756842/fmed-12-1756842-HTML/image_m/fmed-12-1756842-t001.jpg Table 1. Underlying associated conditions in 92 PSVD patients. https://www.frontiersin.org/files/Articles/1756842/fmed-12-1756842-HTML/image_m/fmed-12-1756842-t002.jpg Table 2. Baseline characteristics in PSVD patients with and without sarcopenia. https://www.frontiersin.org/files/Articles/1756842/fmed-12-1756842-HTML/image_m/fmed-12-1756842-g002.jpg Figure 2. Comparison of L3-PMI among 92 patients with and without previous EGVB (A), moderate to sev https://www.frontiersin.org/files/Articles/1756842/fmed-12-1756842-HTML/image_m/fmed-12-1756842-g003.jpg Figure 3. Changes of L3-PMI among 23 PSVD patients before and 6 months after TIPS (A). The cumulativ https://www.frontiersin.org/files/Articles/1756842/fmed-12-1756842-HTML/image_m/fmed-12-1756842-g004.jpg Figure 4. The cumulative incidence of the first episode of OHE within 1 year among patients receivin https://www.frontiersin.org/files/Articles/1756842/fmed-12-1756842-HTML/image_m/fmed-12-1756842-t003.jpg Table 3. Risk factors of variceal rebleeding in multivariable Cox regression analyses. https://www.frontiersin.org/files/Articles/1756842/fmed-12-1756842-HTML/image_m/fmed-12-1756842-t004.jpg Table 4. Summary of outcomes during follow-up, stratified by treatment group and presence of sarcope https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1668140/full https://www.frontiersin.org/files/Articles/1668140/fsurg-12-1668140-HTML/image_m/fsurg-12-1668140-t001.jpg Table 1. Comparison of advantages and disadvantages of the three surgical procedures. https://www.frontiersin.org/files/Articles/1668140/fsurg-12-1668140-HTML/image_m/fsurg-12-1668140-g001.jpg Figure 1. Verumontanum processing. https://www.frontiersin.org/files/Articles/1668140/fsurg-12-1668140-HTML/image_m/fsurg-12-1668140-g002.jpg Figure 2. After enucleation of the median lobe. https://www.frontiersin.org/files/Articles/1668140/fsurg-12-1668140-HTML/image_m/fsurg-12-1668140-g003.jpg Figure 3. Enucleate the right lobe. https://www.frontiersin.org/files/Articles/1668140/fsurg-12-1668140-HTML/image_m/fsurg-12-1668140-g004.jpg Figure 4. Morcellate the gland. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1682137/full https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-t001.jpg Table 1. Serum biochemical indices of Bayinbuluke and Turpan black sheep. https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-t002.jpg Table 2. Results of slaughtering performance of Bayinbuluke sheep and Turpan black sheep. https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-t003.jpg Table 3. Physicochemical properties of muscle of Bayinbuluke and Turpan black sheep. https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-g001.jpg Figure 1. Muscle organization of Bayinbuluke and Turpan black sheep. (A) Triceps brachii muscle tiss https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-t004.jpg Table 4. Detection results of muscle tissue related data of Bayinbuluke and Turpan black sheep. https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-g002.jpg Figure 2. Volcano map of differential protein at different sites. (A) Volcano plot of differentially https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-g003.jpg Figure 3. KEGG functional annotation analysis of the three sets of differentially expressed proteins https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-g004.jpg Figure 4. Volcano plot of differential metabolites among three muscle groups. (A) Volcano plot of di https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-g005.jpg Figure 5. KEGG functional annotation analysis of differential metabolites in three muscle groups. (A https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-g006.jpg Figure 6. Volcano plots of differentially expressed genes in various conditions. (A) Volcano plot of https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-g007.jpg Figure 7. Functional annotation analysis of differentially expressed genes in KEGG for different par https://www.frontiersin.org/files/Articles/1682137/fvets-12-1682137-HTML/image_m/fvets-12-1682137-g008.jpg Figure 8. Integrated analysis of proteomics, metabolomics and transcriptomics. Circular dots represe https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1687858/full https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-t001.jpg Table 1. Serum biochemical parameters. https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-t002.jpg Table 2. Skin histological parameters. https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g001.jpg Figure 1. Comparative histological analysis of skin sections from Bayanbulak sheep and Turpan black https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g002.jpg Figure 2. Comparative analysis of epidermal layers in skin sections from Bayanbulak sheep and Turpan https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g003.jpg Figure 3. Analysis of rumen metabolite profiles in Bayanbulak sheep and Turpan black sheep. (A) Prin https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g004.jpg Figure 4. KEGG functional analysis of differential rumen metabolites in Bayanbulak sheep and Turpan https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g005.jpg Figure 5. Analysis of small intestinal metabolic profiles in Bayanbulak sheep and Turpan black sheep https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g006.jpg Figure 6. KEGG functional analysis of differential metabolites in small intestine of Bayanbulak shee https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g007.jpg Figure 7. Analysis of fecal metabolic profiles in Bayanbulak sheep and Turpan black sheep. (A) Princ https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g008.jpg Figure 8. KEGG functional analysis of differential metabolites in small intestine of Bayanbulak shee https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g009.jpg Figure 9. Analysis of shared metabolites in rumen fluid, small intestinal content, and feces of Baya https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-t003.jpg Table 3. Comparative analysis of differential digestive tract metabolites between Bayanbulak and Tur https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g010.jpg Figure 10. Multi-tissue transcriptomic profiling of differentially expressed genes in rumen and smal https://www.frontiersin.org/files/Articles/1687858/fvets-12-1687858-HTML/image_m/fvets-12-1687858-g011.jpg Figure 11. Integrated co-enrichment pathway analysis of rumen and small intestine in Bayanbulak shee https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1747679/full https://www.frontiersin.org/files/Articles/1747679/fpubh-14-1747679-HTML-r1/image_m/fpubh-14-1747679-g001.jpg Figure 1. Map of Madagascar and Androy (22). https://www.frontiersin.org/files/Articles/1747679/fpubh-14-1747679-HTML-r1/image_m/fpubh-14-1747679-t001.jpg Table 1. Focus groups description. https://www.frontiersin.org/files/Articles/1747679/fpubh-14-1747679-HTML-r1/image_m/fpubh-14-1747679-g002.jpg Figure 2. Theme prevalence chart in the focus groups. https://www.frontiersin.org/files/Articles/1747679/fpubh-14-1747679-HTML-r1/image_m/fpubh-14-1747679-g003.jpg Figure 3. Dice-Sørensen similarity heatmap. https://www.frontiersin.org/files/Articles/1747679/fpubh-14-1747679-HTML-r1/image_m/fpubh-14-1747679-g004.jpg Figure 4. Hierarchical clustering of focus groups based on Dice–Sørensen similarity. https://www.frontiersin.org/files/Articles/1747679/fpubh-14-1747679-HTML-r1/image_m/fpubh-14-1747679-t002.jpg Table 2. Themes for high-similarity clusters. https://www.frontiersin.org/files/Articles/1747679/fpubh-14-1747679-HTML-r1/image_m/fpubh-14-1747679-t003.jpg Table 3. FG15 and FG16 groups results. https://www.frontiersin.org/journals/toxicology/articles/10.3389/ftox.2026.1767925/full https://www.frontiersin.org/files/Articles/1767925/ftox-08-1767925-HTML/image_m/ftox-08-1767925-t001.jpg Table 1. Basic information on some fungicides. https://www.frontiersin.org/files/Articles/1767925/ftox-08-1767925-HTML/image_m/ftox-08-1767925-g001.jpg Figure 1. PRISMA 2020 flow diagram used for systematic literature review in this study, which includ https://www.frontiersin.org/files/Articles/1767925/ftox-08-1767925-HTML/image_m/ftox-08-1767925-g002.jpg Figure 2. Detection frequency of antifungal compounds in environmental samples. The detection freque https://www.frontiersin.org/files/Articles/1767925/ftox-08-1767925-HTML/image_m/ftox-08-1767925-g003.jpg Figure 3. Median concentration of antifungals found in different environmental matrices retrieved by https://www.frontiersin.org/files/Articles/1767925/ftox-08-1767925-HTML/image_m/ftox-08-1767925-t002.jpg Table 2. Predicted no-effect concentrations based on minimal inhibitory concentration of antifungal https://www.frontiersin.org/files/Articles/1767925/ftox-08-1767925-HTML/image_m/ftox-08-1767925-t003.jpg Table 3. Comparison of the PNECs values of antifungals obtained by the systematic literature review https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1693703/full https://www.frontiersin.org/files/Articles/1693703/fpubh-13-1693703-HTML/image_m/fpubh-13-1693703-g001.jpg Figure 1. WHO operational framework for climate resilient and low carbon health systems. https://www.frontiersin.org/files/Articles/1693703/fpubh-13-1693703-HTML/image_m/fpubh-13-1693703-t001.jpg Table 1. Summary of search terms (MeSH and free-text keywords). https://www.frontiersin.org/files/Articles/1693703/fpubh-13-1693703-HTML/image_m/fpubh-13-1693703-g002.jpg Figure 2. PRISMA flow chart of study selection. https://www.frontiersin.org/files/Articles/1693703/fpubh-13-1693703-HTML/image_m/fpubh-13-1693703-t002.jpg Table 2. Characteristics of the studies included in the scoping review. https://www.frontiersin.org/files/Articles/1693703/fpubh-13-1693703-HTML/image_m/fpubh-13-1693703-t003.jpg Table 3. Summary findings and description of main themes identified, including examples from literat https://www.frontiersin.org/files/Articles/1693703/fpubh-13-1693703-HTML/image_m/fpubh-13-1693703-g003.jpg Figure 3. Conceptual framework -study’s findings. https://www.frontiersin.org/journals/tropical-diseases/articles/10.3389/fitd.2026.1746125/full https://www.frontiersin.org/files/Articles/1746125/fitd-07-1746125-HTML/image_m/fitd-07-1746125-g001.jpg Figure 1. Mara region showing pilot districts and wards where CBC-MDV was delivered. The administrat https://www.frontiersin.org/files/Articles/1746125/fitd-07-1746125-HTML/image_m/fitd-07-1746125-t001.jpg Table 1. Determinants of owner-time-spent during vaccination campaigns. https://www.frontiersin.org/files/Articles/1746125/fitd-07-1746125-HTML/image_m/fitd-07-1746125-g002.jpg Figure 2. Number of animals vaccinated and days spent per village on the campaign in each of the thr https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1689943/full https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-t001.jpg Table 1. Basal dietary nutrient levels (air-dry basis). https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-t002.jpg Table 2. Effects of AFF on the growth performance of the finishing pigs (absolutely dry basis). https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-t003.jpg Table 3. Effects of AFF on serum biochemical indices in the finishing pigs. https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-t004.jpg Table 4. Effects of AFF on serum antioxidant indices in the finishing pigs. https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-t005.jpg Table 5. Effects of AFF on the quality of fattening pork. https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-t006.jpg Table 6. Effects of AFF on the muscle fiber characteristics of the finishing pigs. https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-g001.jpg Figure 1. Muscle fiber section HE×100 (A) control group, (B) experimental group. https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-t007.jpg Table 7. Effects of AFF on the muscle nutrient composition of the finishing pigs(%). https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-t008.jpg Table 8. Effects of AFF on amino acids in the muscle of the finishing pigs(mg/kg). https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-t009.jpg Table 9. Effects of AFF on fatty acids in the muscle of the finishing pigs(%). https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-g002.jpg Figure 2. Analysis of α-diversity of the cecal microbiota (I: Chao 1 index; II: Shannon index; III: https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-g003.jpg Figure 3. Cecal microbial PCoA analysis (A: control group, B: experimental group). https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-g004.jpg Figure 4. Effects of AFF on the relative abundance of the gut microbiota at the phylum level (I) and https://www.frontiersin.org/files/Articles/1689943/fvets-12-1689943-HTML/image_m/fvets-12-1689943-g005.jpg Figure 5. Effects of AFF on the relative abundance of the gut microbiota at the genus level (I) and https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1792261/full https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g001.jpg Figure 1. Experimental design and flowchart. N1D, N1 + no priming + post-anthesis stress; N2D, N2 + https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g002.jpg Figure 2. Effects of nitrogen fertilizer and drought priming on plant height (A), leaf area (B), abo https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g003.jpg Figure 3. Effects of nitrogen fertilizer and drought priming on maize ear (A), ear length (B), ear d https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g004.jpg Figure 4. Effects of nitrogen fertilizer and drought priming on water use efficiency for yield WUEy, https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g005.jpg Figure 5. Effects of nitrogen fertilizer and drought priming on the maximum photochemical efficiency https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g006.jpg Figure 6. Effects of nitrogen fertilizer and drought priming on superoxide anion O2-, (A) hydrogen p https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g007.jpg Figure 7. Effects of nitrogen fertilizer and drought priming on superoxide dismutase SOD, (A) peroxi https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g008.jpg Figure 8. Effects of nitrogen fertilizer and drought priming on chlorophyll index (A), flavonoid ind https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g009.jpg Figure 9. Effects of nitrogen fertilizer and drought priming on the proline (A) and soluble sugar (B https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g010.jpg Figure 10. Effects of nitrogen fertilizer and drought priming on nitrate reductase NR, (A) activity, https://www.frontiersin.org/files/Articles/1792261/fpls-17-1792261-HTML/image_m/fpls-17-1792261-g011.jpg Figure 11. Heatmap analysis of physiological indices under N fertilizer and drought priming regulati https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1677021/full https://www.frontiersin.org/files/Articles/1677021/fmed-12-1677021-HTML-r1/image_m/fmed-12-1677021-g001.jpg Figure 1. (A) MSCs come from a wide range of sources. When injecting MSCs into a rat liver fibrosis https://www.frontiersin.org/files/Articles/1677021/fmed-12-1677021-HTML-r1/image_m/fmed-12-1677021-g002.jpg Figure 2. Hepatocyte apoptosis triggers Kupffer cells (KCs) to release TGF-β and PDGF, activating HS https://www.frontiersin.org/files/Articles/1677021/fmed-12-1677021-HTML-r1/image_m/fmed-12-1677021-g003.jpg Figure 3. This diagram illustrates the cellular crosstalk during liver regeneration following injury https://www.frontiersin.org/files/Articles/1677021/fmed-12-1677021-HTML-r1/image_m/fmed-12-1677021-t001.jpg Table 1. Clinical trials of MSCs therapy for liver diseases. https://www.frontiersin.org/files/Articles/1677021/fmed-12-1677021-HTML-r1/image_m/fmed-12-1677021-g004.jpg Figure 4. (A) MSC-EVs carry miRNA, cytokines, and other bioactive molecules, regulate liver immune h https://www.frontiersin.org/journals/soil-science/articles/10.3389/fsoil.2026.1800936/full https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-g001.jpg Figure 1. Annual distribution of temperature (A) and Average rainfall (B) at the Sangalkam study sit https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-t001.jpg Table 1. Soil analyses. https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-t002.jpg Table 2. OWPs analyses. https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-g002.jpg Figure 2. Schematic layout of the experimental plots at the Institut Sénégalais de Recherches Agrico https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-t003.jpg Table 3. Composition of the treatments and average amounts of N, P, and K applied in the three carro https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-t004.jpg Table 4. Mean values of the leaf and root morphological parameters studied on carrot crops for each https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-t005.jpg Table 5. Mean values of the incidence (%) of the main pests, diseases, and parasitic weeds observed https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-g003.jpg Figure 3. Photos of visible symptoms of (A) alternaria. (B) powdery mildew. (C) root galls (Nematode https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-t006.jpg Table 6. Harvested quantity of carrots by category in each treatment (104 number.ha-1). https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-g004.jpg Figure 4. Changes in the parameters measured on carrot leaves and roots over the cycles independentl https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-g005.jpg Figure 5. Average values of (A) heights of carrot leaves, (B) lengths of carrot roots and number of https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-g006.jpg Figure 6. Pearson correlation matrix illustrating the relationships Amongst the variables studied in https://www.frontiersin.org/files/Articles/1800936/fsoil-06-1800936-HTML/image_m/fsoil-06-1800936-g007.jpg Figure 7. Principal Component Analysis (PCA) showing the projection of observations and variables on https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1786836/full https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-g009.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-g001.jpg Figure 1. Annual publications of relevant literature. https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-g002.jpg Figure 2. Country/region analysis. (A) Geographic Distribution Map of Total Publications by Country/ https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-g003.jpg Figure 3. Journal dual overlay analysis. https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-g004.jpg Figure 4. Keywords co-occurrence map. (A) Keyword Co-occurrence Map; (B) Keyword Timeline Chart. https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-t001.jpg Table 1. Keyword burst information. https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-g005.jpg Figure 5. Network plots. Crl: credible interval; DOACs: direct oral anticoagulant; LMWH: low molecul https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-g006.jpg Figure 6. Forest plots. Crl: credible interval; DOACs: direct oral anticoagulant; LMWH: low molecula https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-g007.jpg Figure 7. League tables of outcome analyses. Effect sizes are presented as OR of means with 95% Crl. https://www.frontiersin.org/files/Articles/1786836/fphar-17-1786836-HTML/image_m/fphar-17-1786836-g008.jpg Figure 8. Cumulative ranking plot. For each outcome, interventions are ranked from left (worst) to r https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1668699/full https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-g001.jpg Figure 1. The conceptual model. https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-t001.jpg Table 1. Demographic and clinical characteristics of MHD patients (N = 220). https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-t002.jpg Table 2. Multivariate linear regression analysis of factors influencing HRQoL in MHD patients (n = 2 https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-t003.jpg Table 3. Fit indices of latent profile models for spiritual well-being in MHD patients (n = 220). https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-g002.jpg Figure 2. Score probabilities of four latent profiles of spiritual well-being in MHD patients. https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-t004.jpg Table 4. Correlation analysis among spiritual well-being, family care, spiritual coping, and health- https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-t005.jpg Table 5. Mediation effects of spiritual well-being latent classes (categorical variable) on health-r https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-g003.jpg Figure 3. Chain mediation effect of family care and positive spiritual coping on health-related qual https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-g004.jpg Figure 4. Chain mediation effect of family care and negative spiritual coping on health-related qual https://www.frontiersin.org/files/Articles/1668699/fpsyg-17-1668699-HTML/image_m/fpsyg-17-1668699-t006.jpg Table 6. Chain mediation effects of family care and spiritual coping on spiritual well-being and hea https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1737480/full https://www.frontiersin.org/files/Articles/1737480/fcvm-12-1737480-HTML/image_m/fcvm-12-1737480-t001.jpg Table 1. Common indicators of blood pressure variability. https://www.frontiersin.org/files/Articles/1737480/fcvm-12-1737480-HTML/image_m/fcvm-12-1737480-t002.jpg Table 2. Summary of the relationship between blood pressure variability and postoperative delirium. https://www.frontiersin.org/files/Articles/1737480/fcvm-12-1737480-HTML/image_m/fcvm-12-1737480-t003.jpg Table 3. Summary of the relationship between blood pressure variability and postoperative acute kidn https://www.frontiersin.org/files/Articles/1737480/fcvm-12-1737480-HTML/image_m/fcvm-12-1737480-t004.jpg Table 4. Summary of the relationship between blood pressure variability and 30-day mortality after s https://www.frontiersin.org/files/Articles/1737480/fcvm-12-1737480-HTML/image_m/fcvm-12-1737480-t005.jpg Table 5. Summary of the relationship between blood pressure variability and postoperative stroke. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1745202/full https://www.frontiersin.org/files/Articles/1745202/fendo-17-1745202-HTML/image_m/fendo-17-1745202-g001.jpg Figure 1. The influence of oxidative stress on multiple organs and systems, particularly in individu https://www.frontiersin.org/files/Articles/1745202/fendo-17-1745202-HTML/image_m/fendo-17-1745202-g002.jpg Figure 2. The sequence where the release of cortisol and catecholamines induces hyperglycemia, which https://www.frontiersin.org/files/Articles/1745202/fendo-17-1745202-HTML/image_m/fendo-17-1745202-g003.jpg Figure 3. Surgery and anesthesia contribute to Postoperative Cognitive Dysfunction (POCD) and Postop https://www.frontiersin.org/files/Articles/1745202/fendo-17-1745202-HTML/image_m/fendo-17-1745202-g004.jpg Figure 4. The attention to the several complicated consequences resulting from diabetes: retinopathy https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1803288/full https://www.frontiersin.org/files/Articles/1803288/fpsyg-17-1803288-HTML/image_m/fpsyg-17-1803288-t001.jpg Table 1. Sociodemographic and clinical variables. https://www.frontiersin.org/files/Articles/1803288/fpsyg-17-1803288-HTML/image_m/fpsyg-17-1803288-t002.jpg Table 2. Description of facial action units (FACS)*. https://www.frontiersin.org/files/Articles/1803288/fpsyg-17-1803288-HTML/image_m/fpsyg-17-1803288-g001.jpg Figure 1. The figure depicts a representative trial sequence: fixation cross (1,200 ms), blank scree https://www.frontiersin.org/files/Articles/1803288/fpsyg-17-1803288-HTML/image_m/fpsyg-17-1803288-g002.jpg Figure 2. Bar plots show mean numbers of correct responses per emotion (out of 4) for the stroke gro https://www.frontiersin.org/files/Articles/1803288/fpsyg-17-1803288-HTML/image_m/fpsyg-17-1803288-g003.jpg Figure 3. Bar plots show means of ERQ scores for the stroke group and the control group, with error https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1786389/full https://www.frontiersin.org/files/Articles/1786389/fendo-17-1786389-HTML-r1/image_m/fendo-17-1786389-t001.jpg Table 1. Maternal and infant profiles. https://www.frontiersin.org/files/Articles/1786389/fendo-17-1786389-HTML-r1/image_m/fendo-17-1786389-g001.jpg Figure 1. Differential expression of adipogenic markers in infant mesenchymal stem cells from mother https://www.frontiersin.org/files/Articles/1786389/fendo-17-1786389-HTML-r1/image_m/fendo-17-1786389-g002.jpg Figure 2. Maternal obesity alters the expression of adipogenic differentiation markers in infant mes https://www.frontiersin.org/files/Articles/1786389/fendo-17-1786389-HTML-r1/image_m/fendo-17-1786389-g003.jpg Figure 3. Maternal obesity decreases mitochondrial maximal respiration and spare respiratory capacit https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1615039/full https://www.frontiersin.org/files/Articles/1615039/fimmu-16-1615039-HTML/image_m/fimmu-16-1615039-t001.jpg Table 1. PICOS framework. https://www.frontiersin.org/files/Articles/1615039/fimmu-16-1615039-HTML/image_m/fimmu-16-1615039-g001.jpg Figure 1. PRISMA-2020 flow chart illustrating the methodology of the study. https://www.frontiersin.org/files/Articles/1615039/fimmu-16-1615039-HTML/image_m/fimmu-16-1615039-g002.jpg Figure 2. Forest plot illustrating the incidence of BCGosis. https://www.frontiersin.org/files/Articles/1615039/fimmu-16-1615039-HTML/image_m/fimmu-16-1615039-g003.jpg Figure 3. DOI plot illustrating the incidence of BCGosis. https://www.frontiersin.org/files/Articles/1615039/fimmu-16-1615039-HTML/image_m/fimmu-16-1615039-g004.jpg Figure 4. Forest plot illustrating the mortality rate of infants with BCGosis. https://www.frontiersin.org/files/Articles/1615039/fimmu-16-1615039-HTML/image_m/fimmu-16-1615039-g005.jpg Figure 5. DOI plot illustrating the mortality rate of infants with BCGosis. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1704931/full https://www.frontiersin.org/files/Articles/1704931/fmicb-16-1704931-HTML-r1/image_m/fmicb-16-1704931-g001.jpg Figure 1. Etiological classification of pneumonia. CAP, community acquired pneumonia; HAP, hospital https://www.frontiersin.org/files/Articles/1704931/fmicb-16-1704931-HTML-r1/image_m/fmicb-16-1704931-g002.jpg Figure 2. Timeline showing the antibiotic history and resistance evolution through the last century. https://www.frontiersin.org/files/Articles/1704931/fmicb-16-1704931-HTML-r1/image_m/fmicb-16-1704931-t001.jpg Table 1. Plasmid-driven resistance mechanisms and strategic countermeasures. https://www.frontiersin.org/files/Articles/1704931/fmicb-16-1704931-HTML-r1/image_m/fmicb-16-1704931-t002.jpg Table 2. Therapeutic strategies against antimicrobial resistance: mechanisms, benefits, and developm https://www.frontiersin.org/files/Articles/1704931/fmicb-16-1704931-HTML-r1/image_m/fmicb-16-1704931-t003.jpg Table 3. Summary of experimental and clinical evidence supporting phage therapy for respiratory infe https://www.frontiersin.org/files/Articles/1704931/fmicb-16-1704931-HTML-r1/image_m/fmicb-16-1704931-t004.jpg Table 4. Mechanisms of antimicrobial action of metals. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1756409/full https://www.frontiersin.org/files/Articles/1756409/fpubh-14-1756409-HTML/image_m/fpubh-14-1756409-t001.jpg Table 1. Characteristics of the study participants in DIMAMO HDSS (n = 398). https://www.frontiersin.org/files/Articles/1756409/fpubh-14-1756409-HTML/image_m/fpubh-14-1756409-t002.jpg Table 2. Prevalence of chronic non-communicable disease risk factors by SES among adults aged ≥18 ye https://www.frontiersin.org/files/Articles/1756409/fpubh-14-1756409-HTML/image_m/fpubh-14-1756409-t003.jpg Table 3. Concentration indices for NCD risk factors. https://www.frontiersin.org/files/Articles/1756409/fpubh-14-1756409-HTML/image_m/fpubh-14-1756409-g001.jpg Figure 1. Decomposition of the concentration index for NCD risk factors. https://www.frontiersin.org/files/Articles/1756409/fpubh-14-1756409-HTML/image_m/fpubh-14-1756409-g002.jpg Figure 2. Concentration curve for selected NCD risk factors. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1710032/full https://www.frontiersin.org/files/Articles/1710032/fpubh-13-1710032-HTML/image_m/fpubh-13-1710032-t001.jpg Table 1. Demographic and clinical characteristics of Temirtau residents vs. controls. https://www.frontiersin.org/files/Articles/1710032/fpubh-13-1710032-HTML/image_m/fpubh-13-1710032-t002.jpg Table 2. Targeted proteins that have been chosen to undergo molecular docking analysis and their PDB https://www.frontiersin.org/files/Articles/1710032/fpubh-13-1710032-HTML/image_m/fpubh-13-1710032-g001.jpg Figure 1. PCR analysis and genotyping of the GCLM gene. (A) Electropherogram showing successful PCR https://www.frontiersin.org/files/Articles/1710032/fpubh-13-1710032-HTML/image_m/fpubh-13-1710032-t003.jpg Table 3. Restriction and genotyping of the GCLM gene region: comparison of results. https://www.frontiersin.org/files/Articles/1710032/fpubh-13-1710032-HTML/image_m/fpubh-13-1710032-g002.jpg Figure 2. Genetic analysis of GSTM1, GSTT1, and GSTP1 genes. (A) Multiplex PCR analysis on a 2% agar https://www.frontiersin.org/files/Articles/1710032/fpubh-13-1710032-HTML/image_m/fpubh-13-1710032-t004.jpg Table 4. Alleles and genotypes frequency of GSTM1, GSTT1, GSTP1 and GCLM genes in individuals from T https://www.frontiersin.org/files/Articles/1710032/fpubh-13-1710032-HTML/image_m/fpubh-13-1710032-t005.jpg Table 5. The results of the affinity of targeted proteins with MeHg. https://www.frontiersin.org/files/Articles/1710032/fpubh-13-1710032-HTML/image_m/fpubh-13-1710032-g003.jpg Figure 3. Molecular interactions between MeHg and epigenetic regulatory proteins are illustrated. (A https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1561102/full https://www.frontiersin.org/files/Articles/1561102/fcimb-15-1561102-HTML/image_m/fcimb-15-1561102-g001.jpg Figure 1. FAS-mediated apoptotic signaling pathway. https://www.frontiersin.org/files/Articles/1561102/fcimb-15-1561102-HTML/image_m/fcimb-15-1561102-g002.jpg Figure 2. FAS-mediated non-apoptotic signaling pathways and the crosstalk with apoptotic signaling p https://www.frontiersin.org/files/Articles/1561102/fcimb-15-1561102-HTML/image_m/fcimb-15-1561102-t001.jpg Table 1. Apoptosis and inflammation triggered by pathogen infection through FAS signaling. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1685096/full https://www.frontiersin.org/files/Articles/1685096/fneur-16-1685096-HTML-r1/image_m/fneur-16-1685096-t001.jpg Table 1. Multivariable logistic regression model including all candidate predictors (full model). https://www.frontiersin.org/files/Articles/1685096/fneur-16-1685096-HTML-r1/image_m/fneur-16-1685096-g001.jpg Figure 1. Calibration of the simplified NIHSS + diabetes model for in-hospital ENI-4. The x-axis sho https://www.frontiersin.org/files/Articles/1685096/fneur-16-1685096-HTML-r1/image_m/fneur-16-1685096-t002.jpg Table 2. Baseline characteristics of patients undergoing emergency mechanical thrombectomy (N = 250) https://www.frontiersin.org/files/Articles/1685096/fneur-16-1685096-HTML-r1/image_m/fneur-16-1685096-g002.jpg Figure 2. Restricted cubic spline showing the relationship between admission NIHSS and the predicted https://www.frontiersin.org/files/Articles/1685096/fneur-16-1685096-HTML-r1/image_m/fneur-16-1685096-g003.jpg Figure 3. Decision-curve analysis for in-hospital ENI-4 comparing the base model (admission NIHSS al https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1769801/full https://www.frontiersin.org/files/Articles/1769801/fonc-16-1769801-HTML/image_m/fonc-16-1769801-g001.jpg Figure 1. Study flow diagram of patient selection and cohort assembly. Patients were identified from https://www.frontiersin.org/files/Articles/1769801/fonc-16-1769801-HTML/image_m/fonc-16-1769801-t001.jpg Table 1. Baseline characteristics of patients with endometrial cancer undergoing surgery under gener https://www.frontiersin.org/files/Articles/1769801/fonc-16-1769801-HTML/image_m/fonc-16-1769801-t002.jpg Table 2. Perioperative analgesia exposure and pain control in patients with endometrial cancer (n = https://www.frontiersin.org/files/Articles/1769801/fonc-16-1769801-HTML/image_m/fonc-16-1769801-t003.jpg Table 3. Postoperative recovery outcomes and immune-inflammatory markers in patients with endometria https://www.frontiersin.org/files/Articles/1769801/fonc-16-1769801-HTML/image_m/fonc-16-1769801-t004.jpg Table 4. Multivariable logistic regression of analgesia modality and postoperative outcomes in patie https://www.frontiersin.org/files/Articles/1769801/fonc-16-1769801-HTML/image_m/fonc-16-1769801-t005.jpg Table 5. Linear mixed-effects model analysis of perioperative analgesia modality and immune-inflamma https://www.frontiersin.org/files/Articles/1769801/fonc-16-1769801-HTML/image_m/fonc-16-1769801-g002.jpg Figure 2. Perioperative immune-inflammatory trajectories by analgesia modality in patients with endo https://www.frontiersin.org/files/Articles/1769801/fonc-16-1769801-HTML/image_m/fonc-16-1769801-t006.jpg Table 6. Subgroup and sensitivity analyses of fully multimodal analgesia versus opioid-dominant IV P https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1811064/full https://www.frontiersin.org/files/Articles/1811064/fmed-13-1811064-HTML/image_m/fmed-13-1811064-t001.jpg Table 1. Baseline characteristics of preterm infants with and without admission hypothermia. https://www.frontiersin.org/files/Articles/1811064/fmed-13-1811064-HTML/image_m/fmed-13-1811064-t002.jpg Table 2. Comparison of baseline characteristics between the training and validation cohorts. https://www.frontiersin.org/files/Articles/1811064/fmed-13-1811064-HTML/image_m/fmed-13-1811064-g001.jpg Figure 1. Feature selection using least absolute shrinkage and selection operator (LASSO) regression https://www.frontiersin.org/files/Articles/1811064/fmed-13-1811064-HTML/image_m/fmed-13-1811064-t003.jpg Table 3. Comparison of evaluation metrics for six machine learning models. https://www.frontiersin.org/files/Articles/1811064/fmed-13-1811064-HTML/image_m/fmed-13-1811064-g002.jpg Figure 2. Discrimination and calibration of six machine learning models in the training cohort. (A) https://www.frontiersin.org/files/Articles/1811064/fmed-13-1811064-HTML/image_m/fmed-13-1811064-g003.jpg Figure 3. Discrimination and calibration of six machine learning models in the validation cohort. (A https://www.frontiersin.org/files/Articles/1811064/fmed-13-1811064-HTML/image_m/fmed-13-1811064-g004.jpg Figure 4. SHAP-based interpretation of the prediction model using a random forest surrogate. (A) SHA https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1755166/full https://www.frontiersin.org/files/Articles/1755166/fvets-13-1755166-HTML-r1/image_m/fvets-13-1755166-t001.jpg Table 1. MicroRNAs selected for customized qPCR plates design. https://www.frontiersin.org/files/Articles/1755166/fvets-13-1755166-HTML-r1/image_m/fvets-13-1755166-g001.jpg Figure 1. Differential expression analysis of microRNAs in visceral hemangiosarcoma via small RNA se https://www.frontiersin.org/files/Articles/1755166/fvets-13-1755166-HTML-r1/image_m/fvets-13-1755166-g002.jpg Figure 2. Volcano plot of RT-qPCR validation analysis of microRNA candidates for diagnostic markers https://www.frontiersin.org/files/Articles/1755166/fvets-13-1755166-HTML-r1/image_m/fvets-13-1755166-t002.jpg Table 2. Differentially expressed miRNAs in splenic hemangiosarcoma. https://www.frontiersin.org/files/Articles/1755166/fvets-13-1755166-HTML-r1/image_m/fvets-13-1755166-t003.jpg Table 3. Differentially expressed miRNAs in cardiac hemangiosarcoma. https://www.frontiersin.org/files/Articles/1755166/fvets-13-1755166-HTML-r1/image_m/fvets-13-1755166-g003.jpg Figure 3. KEGG pathway enrichment analysis based on target genes of differentially expressed miRNAs https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1737902/full https://www.frontiersin.org/files/Articles/1737902/fmed-13-1737902-HTML/image_m/fmed-13-1737902-t001.jpg Table 1. Baseline characteristics of the MiSight cohort at initiation. https://www.frontiersin.org/files/Articles/1737902/fmed-13-1737902-HTML/image_m/fmed-13-1737902-t002.jpg Table 2. Annualized refractive and axial outcomes with percentile-matched suppression. https://www.frontiersin.org/files/Articles/1737902/fmed-13-1737902-HTML/image_m/fmed-13-1737902-t003.jpg Table 3. Stratified annual axial outcomes in MiSight-treated children. https://www.frontiersin.org/files/Articles/1737902/fmed-13-1737902-HTML/image_m/fmed-13-1737902-g001.jpg Figure 1. Observed annual AL elongation in MiSight-treated children is plotted by age and compared w https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2026.1718055/full https://www.frontiersin.org/files/Articles/1718055/frhs-06-1718055-HTML/image_m/frhs-06-1718055-t001.jpg Table 1. Personal characteristics and single factor analysis of retention intention. https://www.frontiersin.org/files/Articles/1718055/frhs-06-1718055-HTML/image_m/frhs-06-1718055-t002.jpg Table 2. Descriptive analyses of compassion fatigue, medical narrative ability, and retention intent https://www.frontiersin.org/files/Articles/1718055/frhs-06-1718055-HTML/image_m/frhs-06-1718055-t003.jpg Table 3. Correlation coefficients of the main study variables. https://www.frontiersin.org/files/Articles/1718055/frhs-06-1718055-HTML/image_m/frhs-06-1718055-t004.jpg Table 4. The multivariate linear regression analysis of nurse’ retention intention. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1674743/full https://www.frontiersin.org/files/Articles/1674743/fimmu-16-1674743-HTML/image_m/fimmu-16-1674743-g001.jpg Figure 1. Mechanism of cancer vaccines. This illustration depicts the process of cancer vaccination https://www.frontiersin.org/files/Articles/1674743/fimmu-16-1674743-HTML/image_m/fimmu-16-1674743-t001.jpg Table 1. Selected clinical trials of cancer vaccines in pancreatic cancer. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1680053/full https://www.frontiersin.org/files/Articles/1680053/fimmu-16-1680053-HTML/image_m/fimmu-16-1680053-t001.jpg Table 1. Advantages and disadvantages of different nanocarriers in gastrointestinal cancer vaccinati https://www.frontiersin.org/files/Articles/1680053/fimmu-16-1680053-HTML/image_m/fimmu-16-1680053-g001.jpg Figure 1. Mechanisms of nanovaccines in gastrointestinal cancer therapy. Various nanoplatforms have https://www.frontiersin.org/files/Articles/1680053/fimmu-16-1680053-HTML/image_m/fimmu-16-1680053-t002.jpg Table 2. The clinical and preclinical trials of nanovaccines in gastrointestinal cancer therapy. https://www.frontiersin.org/files/Articles/1680053/fimmu-16-1680053-HTML/image_m/fimmu-16-1680053-g002.jpg Figure 2. Innovative strategies for nanovaccine-based immunotherapy in gastrointestinal cancers. The https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1750762/full https://www.frontiersin.org/files/Articles/1750762/fphar-17-1750762-HTML/image_m/fphar-17-1750762-t001.jpg Table 1. Baseline characteristics of the cohort. https://www.frontiersin.org/files/Articles/1750762/fphar-17-1750762-HTML/image_m/fphar-17-1750762-g001.jpg Figure 1. Kaplan – Meier survival analysis. (A) Overall survival (OS) curve of all patients (n = 202 https://www.frontiersin.org/files/Articles/1750762/fphar-17-1750762-HTML/image_m/fphar-17-1750762-g002.jpg Figure 2. LASSO regression for feature selection. (A) LASSO coefficient profiles of radiomics featur https://www.frontiersin.org/files/Articles/1750762/fphar-17-1750762-HTML/image_m/fphar-17-1750762-t002.jpg Table 2. Univariate and multivariate Cox regression analysis of prognostic factors. https://www.frontiersin.org/files/Articles/1750762/fphar-17-1750762-HTML/image_m/fphar-17-1750762-g003.jpg Figure 3. Nomogram for predicting overall survival (OS). The nomogram integrates sex, CA19-9 level, https://www.frontiersin.org/files/Articles/1750762/fphar-17-1750762-HTML/image_m/fphar-17-1750762-g004.jpg Figure 4. Evaluation of the nomogram performance. (A) Calibration curves for 1-, 2-, and 3-year OS s https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1675495/full https://www.frontiersin.org/files/Articles/1675495/fpsyg-17-1675495-HTML/image_m/fpsyg-17-1675495-t001.jpg Table 1. Descriptive statistics of the six primary emotions scales and the five S-I scales in relati https://www.frontiersin.org/files/Articles/1675495/fpsyg-17-1675495-HTML/image_m/fpsyg-17-1675495-t002.jpg Table 2. Summary of hierarchical multiple regression predicting the five S-I scores in relation to m https://www.frontiersin.org/files/Articles/1675495/fpsyg-17-1675495-HTML/image_m/fpsyg-17-1675495-t003.jpg Table 3. Summary of hierarchical multiple regression predicting the five S-I scores in relation to f https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1648698/full https://www.frontiersin.org/files/Articles/1648698/fimmu-16-1648698-HTML-r1/image_m/fimmu-16-1648698-t001.jpg Table 1. Next-generation vaccine platforms of bladder cancer immunotherapy. https://www.frontiersin.org/files/Articles/1648698/fimmu-16-1648698-HTML-r1/image_m/fimmu-16-1648698-g001.jpg Figure 1. Mechanism of action of Bacillus Calmette-Guérin (BCG) vaccine for bladder cancer. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1718407/full https://www.frontiersin.org/files/Articles/1718407/fpubh-14-1718407-HTML-r1/image_m/fpubh-14-1718407-t001.jpg Table 1. Socio-demographic characteristics of professionals and users (2025–2021). https://www.frontiersin.org/files/Articles/1718407/fpubh-14-1718407-HTML-r1/image_m/fpubh-14-1718407-t002.jpg Table 2. WWRR item responses: healthcare professionals 2025 (post-COVID-19 pandemic) vs. 2021 (durin https://www.frontiersin.org/files/Articles/1718407/fpubh-14-1718407-HTML-r1/image_m/fpubh-14-1718407-t003.jpg Table 3. WWRR item responses: healthcare professionals 2025 (post-COVID-19 pandemic) vs. users 2025 https://www.frontiersin.org/files/Articles/1718407/fpubh-14-1718407-HTML-r1/image_m/fpubh-14-1718407-t004.jpg Table 4. Comparison of WWRR item scores among users: 2025 (post-COVID-19 pandemic) vs. 2021 (during https://www.frontiersin.org/files/Articles/1718407/fpubh-14-1718407-HTML-r1/image_m/fpubh-14-1718407-t005.jpg Table 5. Responses to Item 7 of the WWRR questionnaire among the two samples of healthcare professio https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1744031/full https://www.frontiersin.org/files/Articles/1744031/fphar-17-1744031-HTML/image_m/fphar-17-1744031-t001.jpg Table 1. Sequences of target genes in this study. https://www.frontiersin.org/files/Articles/1744031/fphar-17-1744031-HTML/image_m/fphar-17-1744031-t002.jpg Table 2. Primary and secondary antibodies used in this study. https://www.frontiersin.org/files/Articles/1744031/fphar-17-1744031-HTML/image_m/fphar-17-1744031-g001.jpg Figure 1. Depressive-like behaviors in mice 28 days post-TBI. (A) Timeline of behavioral assessments https://www.frontiersin.org/files/Articles/1744031/fphar-17-1744031-HTML/image_m/fphar-17-1744031-g002.jpg Figure 2. TLR4 upregulation in the hippocampus 28 days post-TBI. (A) Detection methods for TLR4 chan https://www.frontiersin.org/files/Articles/1744031/fphar-17-1744031-HTML/image_m/fphar-17-1744031-g003.jpg Figure 3. TAK242 (TLR4 inhibitor) attenuates TBI-induced depressive-like behaviors. (A) Behavioral a https://www.frontiersin.org/files/Articles/1744031/fphar-17-1744031-HTML/image_m/fphar-17-1744031-g004.jpg Figure 4. Dysregulation of kynurenine pathway (KP) enzymes post-TBI and TAK242 effects. (A) Detectio https://www.frontiersin.org/files/Articles/1744031/fphar-17-1744031-HTML/image_m/fphar-17-1744031-g005.jpg Figure 5. LPS activates kynurenine pathway in BV2 cells. (A) IF staining for TLR4, IBA1, IDO, and KM https://www.frontiersin.org/files/Articles/1744031/fphar-17-1744031-HTML/image_m/fphar-17-1744031-g006.jpg Figure 6. TAK242 blocks LPS-induced kynurenine pathway activation. (A) Detection methods: ELISA, qPC https://www.frontiersin.org/files/Articles/1744031/fphar-17-1744031-HTML/image_m/fphar-17-1744031-g007.jpg Figure 7. The scheme of TLR4 medicates post-TBI depression. By elucidating that chronic TBI-induced https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1800412/full https://www.frontiersin.org/files/Articles/1800412/frai-09-1800412-HTML/image_m/frai-09-1800412-g001.jpg Figure 1. Evolution of scientific publications and citation trends on AI-driven strategic value in g https://www.frontiersin.org/files/Articles/1800412/frai-09-1800412-HTML/image_m/frai-09-1800412-t001.jpg Table 1. Most influential scientific journals in the field of AI and business strategy. https://www.frontiersin.org/files/Articles/1800412/frai-09-1800412-HTML/image_m/frai-09-1800412-g002.jpg Figure 2. Institutional co-authorship network on AI-driven strategic value in global businesses (201 https://www.frontiersin.org/files/Articles/1800412/frai-09-1800412-HTML/image_m/frai-09-1800412-g003.jpg Figure 3. International country co-authorship network on AI-driven strategic value in global busines https://www.frontiersin.org/files/Articles/1800412/frai-09-1800412-HTML/image_m/frai-09-1800412-g004.jpg Figure 4. Keyword co-occurrence network on AI-driven strategic value in global businesses (2016–2025 https://www.frontiersin.org/files/Articles/1800412/frai-09-1800412-HTML/image_m/frai-09-1800412-t002.jpg Table 2. Key thematic patterns identified in the qualitative review of the 50 most influential studi https://www.frontiersin.org/files/Articles/1800412/frai-09-1800412-HTML/image_m/frai-09-1800412-t003.jpg Table 3. Strengths and weaknesses: thematic analysis of AI’s role in strategic management and global https://www.frontiersin.org/files/Articles/1800412/frai-09-1800412-HTML/image_m/frai-09-1800412-t004.jpg Table 4. Dominant research themes in AI-driven strategic value: prevalence and descriptions from the https://www.frontiersin.org/files/Articles/1800412/frai-09-1800412-HTML/image_m/frai-09-1800412-g005.jpg Figure 5. Conceptual framework linking AI capabilities, organizational capabilities, governance mech https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1711401/full https://www.frontiersin.org/files/Articles/1711401/fimmu-17-1711401-HTML/image_m/fimmu-17-1711401-g001.jpg Figure 1. The distribution and prognostic value of TLRscore. (A) Univariate Cox regression analysis https://www.frontiersin.org/files/Articles/1711401/fimmu-17-1711401-HTML/image_m/fimmu-17-1711401-g002.jpg Figure 2. The relationship between carcinogenic driving factors and TLRscore. (A-D) The TLRscore and https://www.frontiersin.org/files/Articles/1711401/fimmu-17-1711401-HTML/image_m/fimmu-17-1711401-g003.jpg Figure 3. TLRscore-related immune infiltration analysis. (A) Immune checkpoint protein expression an https://www.frontiersin.org/files/Articles/1711401/fimmu-17-1711401-HTML/image_m/fimmu-17-1711401-g004.jpg Figure 4. TLRscore for predicting the clinical benefit of immunotherapy. (A-C) Comparison of TIDE sc https://www.frontiersin.org/files/Articles/1711401/fimmu-17-1711401-HTML/image_m/fimmu-17-1711401-g005.jpg Figure 5. TLRscore and immunotherapy. (A, B) Correlation between the TLRscore and immunotherapy resp https://www.frontiersin.org/files/Articles/1711401/fimmu-17-1711401-HTML/image_m/fimmu-17-1711401-g006.jpg Figure 6. Sensitivity correlation analyses and prediction of potential drugs. (A) Correlation of TLR https://www.frontiersin.org/files/Articles/1711401/fimmu-17-1711401-HTML/image_m/fimmu-17-1711401-g007.jpg Figure 7. Genetic regulation mechanism of TLR8 expression in LUAD. (A) Correlation analysis between https://www.frontiersin.org/files/Articles/1711401/fimmu-17-1711401-HTML/image_m/fimmu-17-1711401-g008.jpg Figure 8. Differential Expression of TLR8 in Immune Cells. (A) The relationship between TLR8 express https://www.frontiersin.org/files/Articles/1711401/fimmu-17-1711401-HTML/image_m/fimmu-17-1711401-g009.jpg Figure 9. Motolimod induces M1 polarization of macrophages and enhances their antitumor activity via https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1734384/full https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-t001.jpg Table 1. Cohort characteristics. https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-g001.jpg Figure 1. Distribution of hyper and hypo-methylated DMPs in relation to (A) CpG islands and (B) gene https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-g002.jpg Figure 2. Comparison of methylation β-values distribution of 631 DMPs in R and NR groups represented https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-g003.jpg Figure 3. Methylation box plot of (A) cg17671552 CpG site within IKZF1 gene and (B) cg19193595 CpG s https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-g004.jpg Figure 4. Clustering with complete agglomeration method using normalized β-values of 631 DMPs emerge https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-g005.jpg Figure 5. Gene ontology analysis of genes mapped to significantly differentially methylated sites lo https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-t002.jpg Table 2. Top 10 results of pathway enrichment analysis of unique genes mapped to significantly diffe https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-g006.jpg Figure 6. Methylation box plots of top genic CpG sites from female-specific analysis: (A) cg20684197 https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-g007.jpg Figure 7. Methylation box plots of top genic CpG sites from male-specific analysis: (A) cg20635102 ( https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-t003.jpg Table 3. Genes common between results obtained from joined and sex-specific DMP analysis. https://www.frontiersin.org/files/Articles/1734384/fimmu-17-1734384-HTML/image_m/fimmu-17-1734384-t004.jpg Table 4. Genes common between results obtained from sex-aggregated DMP analysis and genes emerged fr https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1785050/full https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-t001.jpg Table 1. Baseline outcome measures by intervention group. Baseline between-group differences were ex https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-g001.jpg Figure 1. Participant flow diagram. https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-t002.jpg Table 2. Four-week training protocol for the BFR-LIR, HIR, and LIR groups. https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-g002.jpg Figure 2. Representative experimental setup. (A) Shows the participant seated in the Biodex dynamome https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-t003.jpg Table 3. Isokinetic outcomes at 60°/s (Knee extension; dominant leg) Significance: *p < 0.05, **p < https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-t004.jpg Table 4. Isokinetic outcomes at 120°/s (Knee extension; dominant leg) Significance: *p < 0.05, **p < https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-t005.jpg Table 5. Between-group comparisons of Pre–Post change scores (Δ) in primary isokinetic outcomes Sign https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-t006.jpg Table 6. sEMG Outcomes at 60°/s: Mean %MVC and Dynamic RMS—Within-Group Pre–Post Comparisons Signifi https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-t007.jpg Table 7. sEMG Outcomes at 120°/s: Mean %MVC and Dynamic RMS—Within-Group Pre–Post Comparisons Signif https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-t008.jpg Table 8. Between-group comparisons of Pre–Post change scores (Δ) in sEMG and secondary outcomes Sign https://www.frontiersin.org/files/Articles/1785050/fphys-17-1785050-HTML/image_m/fphys-17-1785050-t009.jpg Table 9. Changes in functional performance and perceived exertion during isokinetic testing Signific https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1618378/full https://www.frontiersin.org/files/Articles/1618378/frai-08-1618378-HTML/image_m/frai-08-1618378-g001.jpg Figure 1. Overview of the methodological framework of this study. https://www.frontiersin.org/files/Articles/1618378/frai-08-1618378-HTML/image_m/frai-08-1618378-t001.jpg Table 1. Validated tools used to evaluate large language models’ generated information. https://www.frontiersin.org/files/Articles/1618378/frai-08-1618378-HTML/image_m/frai-08-1618378-t002.jpg Table 2. Scores obtained for information reliability and readability. https://www.frontiersin.org/files/Articles/1618378/frai-08-1618378-HTML/image_m/frai-08-1618378-t003.jpg Table 3. Scores obtained for information quality according to DISCERN. https://www.frontiersin.org/files/Articles/1618378/frai-08-1618378-HTML/image_m/frai-08-1618378-g002.jpg Figure 2. Comparison of reliability scores (JAMA benchmark criteria) between ChatGPT-4 and Gemini 1. https://www.frontiersin.org/files/Articles/1618378/frai-08-1618378-HTML/image_m/frai-08-1618378-g003.jpg Figure 3. Comparison of readability scores (Flesch–Kincaid grade level) between ChatGPT-4 and Gemini https://www.frontiersin.org/files/Articles/1618378/frai-08-1618378-HTML/image_m/frai-08-1618378-g004.jpg Figure 4. Boxplot showing a comparison of guideline concordance scores between ChatGPT-4 and Gemini https://www.frontiersin.org/files/Articles/1618378/frai-08-1618378-HTML/image_m/frai-08-1618378-t004.jpg Table 4. Comparison of concordance with guidelines scores between ChatGPT-4 and Gemini 1.5 Pro. https://www.frontiersin.org/files/Articles/1618378/frai-08-1618378-HTML/image_m/frai-08-1618378-g005.jpg Figure 5. Bland–Altman plot: agreement between models across 23 paired items. The mean difference (r https://www.frontiersin.org/journals/nanotechnology/articles/10.3389/fnano.2026.1751841/full https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-g008.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-t001.jpg Table 1. Algal-mediated synthesis of metal nanoparticles using different reagent. https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-g001.jpg Figure 1. The flow chart shows steps in silver nanoparticle synthesis: (A) Gathering Shyalina from a https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-g002.jpg Figure 2. (a) Morphological analysis of TiO2 nanoparticles biosynthesized by Spirulina with a scanni https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-g003.jpg Figure 3. Algae-mediated nanoparticles synthesis pathway. https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-t002.jpg Table 2. The lists of green algae-mediated biosynthesis of nanoparticles. https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-g004.jpg Figure 4. (a) DPPH radical inhibition activity; (b) Hydroxyl scavenging activity of UL-ZrO2NPs from https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-t003.jpg Table 3. Table showing algae mediated NPs of antioxidant properties relevant to SDGs. https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-g005.jpg Figure 5. (a) Antibacterial properties of NiO NPs on four pathogenic bacteria (b) Bar graph illustra https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-t004.jpg Table 4. Algae mediated NPs of antibacterial properties relevant to SDGs. https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-g006.jpg Figure 6. Anti-cancer effect of zirconium oxide nanoparticles synthesized byUlva lactuca extract, (a https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-g007.jpg Figure 7. Photo-catalytic degradation of pollutants like (a) Malachite Green (MG) and (b) Eosine (E) https://www.frontiersin.org/files/Articles/1751841/fnano-08-1751841-HTML/image_m/fnano-08-1751841-t005.jpg Table 5. Green algal-mediated nanoparticles for microbial and pollutant remediation aligned with SGD https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1769043/full https://www.frontiersin.org/files/Articles/1769043/fonc-16-1769043-HTML/image_m/fonc-16-1769043-g001.jpg Figure 1. Clinical timeline of the patient from diagnosis to complete metabolic response under olapa https://www.frontiersin.org/files/Articles/1769043/fonc-16-1769043-HTML/image_m/fonc-16-1769043-g002.jpg Figure 2. Serial FDG PET-CT images demonstrating disease progression after chemo-immunotherapy and s https://www.frontiersin.org/files/Articles/1769043/fonc-16-1769043-HTML/image_m/fonc-16-1769043-g003.jpg Figure 3. Electrophysiological findings consistent with chemotherapy-induced peripheral neuropathy. https://www.frontiersin.org/files/Articles/1769043/fonc-16-1769043-HTML/image_m/fonc-16-1769043-g004.jpg Figure 4. Summary of molecular profiling results showing BRCA2 mutation and associated genomic alter https://www.frontiersin.org/journals/microbiomes/articles/10.3389/frmbi.2025.1666691/full https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g001.jpg Figure 1. Microbial diversity during the 2023–2024 off-season. (A) Bacterial Shannon diversity was s https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g002.jpg Figure 2. Comparison of microbial communities between locations during the 2023–2024 off-season sepa https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g003.jpg Figure 3. Heatmaps displaying diversity within location during the 2023–2024 off-season. Bolded orde https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g004.jpg Figure 4. Microbial diversity during the 2024 cotton-growing season. Bacterial (A) and fungal (B) Sh https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g005.jpg Figure 5. Comparison of microbial communities between locations during the 2024 cotton-growing seaso https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g006.jpg Figure 6. Heatmaps displaying diversity within location during the 2024 cotton-growing season. Bolde https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g007.jpg Figure 7. Comparison of bacterial diversity at the Bottom Farm (A) and Stiles Farm (B) between the o https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g008.jpg Figure 8. Comparison of fungal diversity at the Bottom Farm (A) and Stiles Farm (B) between the off- https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g009.jpg Figure 9. Heatmaps displaying diversity within location and season. Bolded orders are significantly https://www.frontiersin.org/files/Articles/1666691/frmbi-04-1666691-HTML-r3/image_m/frmbi-04-1666691-g010.jpg Figure 10. Heatmaps displaying diversity within location and season. Bolded orders are significantly https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1638695/full https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-g001.jpg Figure 1. Hypothetical model. https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-t001.jpg Table 1. Descriptive statistics. https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-t002.jpg Table 2. Correlation analysis among major study variables. https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-t003.jpg Table 3. Multiple linear regression analysis among major study variables. https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-t004.jpg Table 4. Mediating effect analysis. https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-t005.jpg Table 5. Chain mediating effect analysis of classroom disruptive behavior and self-efficacy. https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-g002.jpg Figure 2. Path diagram for the hypothetical structural equation model. https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-t006.jpg Table 6. Chain mediating effects of the three regions. https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-t007.jpg Table 7. The chain mediating effect of the three regions under mathematical application. https://www.frontiersin.org/files/Articles/1638695/fpsyg-16-1638695-HTML/image_m/fpsyg-16-1638695-t008.jpg Table 8. The chain mediating effect of the three regions under mathematical reasoning. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1665047/full https://www.frontiersin.org/files/Articles/1665047/fpsyt-16-1665047-HTML/image_m/fpsyt-16-1665047-g001.jpg Figure 1. Flow diagram for study selection. https://www.frontiersin.org/files/Articles/1665047/fpsyt-16-1665047-HTML/image_m/fpsyt-16-1665047-t001.jpg Table 1. Summary of key studies on the impact of electronic product use in children with ADHD/TDs. https://www.frontiersin.org/files/Articles/1665047/fpsyt-16-1665047-HTML/image_m/fpsyt-16-1665047-t002.jpg Table 2. Risks and potential benefits of different types of electronic content for children with ADH https://www.frontiersin.org/files/Articles/1665047/fpsyt-16-1665047-HTML/image_m/fpsyt-16-1665047-g002.jpg Figure 2. An ecological framework for electronic product use in neurodevelopmental disorders. https://www.frontiersin.org/files/Articles/1665047/fpsyt-16-1665047-HTML/image_m/fpsyt-16-1665047-t003.jpg Table 3. Examples of digital tools/methods with potential benefits for children with ADHD/TDs, their https://www.frontiersin.org/journals/manufacturing-technology/articles/10.3389/fmtec.2025.1601903/full https://www.frontiersin.org/files/Articles/1601903/fmtec-05-1601903-HTML/image_m/fmtec-05-1601903-t001.jpg Table 1. Participants for the external reviews. https://www.frontiersin.org/files/Articles/1601903/fmtec-05-1601903-HTML/image_m/fmtec-05-1601903-g001.jpg Figure 1. A development framework for human work integrated AI systems in manufacturing. Each sectio https://www.frontiersin.org/files/Articles/1601903/fmtec-05-1601903-HTML/image_m/fmtec-05-1601903-t002.jpg Table 2. Examples of task recommendations for the workflow section in stage II. https://www.frontiersin.org/files/Articles/1601903/fmtec-05-1601903-HTML/image_m/fmtec-05-1601903-t003.jpg Table 3. Suggestions for framework improvement and its subsequent refinement. https://www.frontiersin.org/files/Articles/1601903/fmtec-05-1601903-HTML/image_m/fmtec-05-1601903-t004.jpg Table 4. Comparison of task recommendation counts between the derived framework and the SAILENT fram https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1735177/full https://www.frontiersin.org/files/Articles/1735177/fspor-08-1735177-HTML/image_m/fspor-08-1735177-g001.jpg Figure 1. (A) A discontinuity to the deep myo-aponeurotic connective tissue component of the T-junct https://www.frontiersin.org/files/Articles/1735177/fspor-08-1735177-HTML/image_m/fspor-08-1735177-t001.jpg Table 1. Structural features defining T-junction subtypes as observed on ultrasound. https://www.frontiersin.org/files/Articles/1735177/fspor-08-1735177-HTML/image_m/fspor-08-1735177-t002.jpg Table 2. Summary of rehabilitation characteristics, timelines, and other considerations following se https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2025.1733879/full https://www.frontiersin.org/files/Articles/1733879/fdmed-06-1733879-HTML/image_m/fdmed-06-1733879-t001.jpg Table 1. Search strategy used. https://www.frontiersin.org/files/Articles/1733879/fdmed-06-1733879-HTML/image_m/fdmed-06-1733879-g001.jpg Figure 1. Flowchart summarizing the selection process. https://www.frontiersin.org/files/Articles/1733879/fdmed-06-1733879-HTML/image_m/fdmed-06-1733879-t002.jpg Table 2. Characteristics of the studies in the qualitative review. https://www.frontiersin.org/files/Articles/1733879/fdmed-06-1733879-HTML/image_m/fdmed-06-1733879-g002.jpg Figure 2. Meta-analysis of the fracture resistance of teeth restored with polyethylene fibers as int https://www.frontiersin.org/files/Articles/1733879/fdmed-06-1733879-HTML/image_m/fdmed-06-1733879-g003.jpg Figure 3. Meta-analysis of the fracture resistance of teeth restored with polyethylene fibers reinfo https://www.frontiersin.org/files/Articles/1733879/fdmed-06-1733879-HTML/image_m/fdmed-06-1733879-g004.jpg Figure 4. Meta-analysis of the number of unfavorable/catastrophic failures after the fracture streng https://www.frontiersin.org/files/Articles/1733879/fdmed-06-1733879-HTML/image_m/fdmed-06-1733879-g005.jpg Figure 5. Meta-analysis of the number of unfavorable/catastrophic failures after the fracture streng https://www.frontiersin.org/files/Articles/1733879/fdmed-06-1733879-HTML/image_m/fdmed-06-1733879-t003.jpg Table 3. Risk of bias assessment. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1709872/full https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t001.jpg Table 1. Features and limitations of existing literature. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g001.jpg Figure 1. Proposed DR detection framework. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t002.jpg Table 2. Datasets attributes. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g002.jpg Figure 2. Sample DR severity images. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g003.jpg Figure 3. (a) Before enhancement (b) after enhancement. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g004.jpg Figure 4. Proposed feature extraction approach. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g005.jpg Figure 5. Comparison of classifier heads for DR severity grading. (a) Standard CNN–ViTs pipelines ty https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t003.jpg Table 3. Stage wise tensor flow via the CKANs module. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t004.jpg Table 4. Experimental settings. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g006.jpg Figure 6. Convergence behavior of IWO. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t005.jpg Table 5. Finding of five-fold cross-validation. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t006.jpg Table 6. Findings of the multi-class performance evaluation – EyePACS dataset. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g007.jpg Figure 7. Findings of the multi-class performance evaluation – MESSIDOR-2 dataset. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t007.jpg Table 7. Findings of the ablation study. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t008.jpg Table 8. Findings of the comparative analysis (proposed DR severity grading model with different hyp https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t009.jpg Table 9. The comparative analysis outcomes using the EyePACS dataset. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g008.jpg Figure 8. The comparative analysis outcomes using the MESSIDOR-2 dataset. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g009.jpg Figure 9. AUROC values for multi-class DR classification using the proposed framework. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-g010.jpg Figure 10. AUPRC values for multi-class DR classification using the proposed framework. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t010.jpg Table 10. Computing power of DR severity detection frameworks. https://www.frontiersin.org/files/Articles/1709872/fmed-13-1709872-HTML-r1/image_m/fmed-13-1709872-t011.jpg Table 11. Sample inputs and outputs with SHAP overlaps. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1771779/full https://www.frontiersin.org/files/Articles/1771779/fpubh-14-1771779-HTML/image_m/fpubh-14-1771779-g001.jpg Figure 1. Integrated determinants of scorpionism risk and vulnerability (Stage 1). This figure repre https://www.frontiersin.org/files/Articles/1771779/fpubh-14-1771779-HTML/image_m/fpubh-14-1771779-g002.jpg Figure 2. Integrated conceptual model of scorpionism: from risk and vulnerability to occurrence and https://www.frontiersin.org/files/Articles/1771779/fpubh-14-1771779-HTML/image_m/fpubh-14-1771779-t001.jpg Table 1. Six interactive components influencing scorpion ecology and envenomation risk. https://www.frontiersin.org/files/Articles/1771779/fpubh-14-1771779-HTML/image_m/fpubh-14-1771779-g003.jpg Figure 3. Snapshot of the most influential research areas in the field of scorpionism, collected fro https://www.frontiersin.org/files/Articles/1771779/fpubh-14-1771779-HTML/image_m/fpubh-14-1771779-t002.jpg Table 2. Integrated synthesis of determinants of scorpion envenomation incidence, associated mechani https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1650514/full https://www.frontiersin.org/files/Articles/1650514/fnut-12-1650514-HTML-r1/image_m/fnut-12-1650514-g001.jpg Figure 1. Study design and selection flow. HCO, healthcare organizations; CKD, chronic kidney diseas https://www.frontiersin.org/files/Articles/1650514/fnut-12-1650514-HTML-r1/image_m/fnut-12-1650514-t001.jpg Table 1. Baseline characteristics of included subjects. https://www.frontiersin.org/files/Articles/1650514/fnut-12-1650514-HTML-r1/image_m/fnut-12-1650514-t002.jpg Table 2. Primary and secondary outcomes between the vitamin D deficiency group and the control group https://www.frontiersin.org/files/Articles/1650514/fnut-12-1650514-HTML-r1/image_m/fnut-12-1650514-g002.jpg Figure 2. Kaplan–Meier time-to-event free curves of the major adverse kidney event. VDD, vitamin D d https://www.frontiersin.org/files/Articles/1650514/fnut-12-1650514-HTML-r1/image_m/fnut-12-1650514-g003.jpg Figure 3. Stratified analysis of the major adverse kidney event. y/o, years old; VDD, vitamin D defi https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1652863/full https://www.frontiersin.org/files/Articles/1652863/fcvm-13-1652863-HTML-r1/image_m/fcvm-13-1652863-g001.jpg Figure 1. Study cohort process. ESKD, end-stage kidney disease; HCOs, healthcare organizations; HF, https://www.frontiersin.org/files/Articles/1652863/fcvm-13-1652863-HTML-r1/image_m/fcvm-13-1652863-t001.jpg Table 1. Baseline characteristics of SGLT2i and control groups before and after matching. https://www.frontiersin.org/files/Articles/1652863/fcvm-13-1652863-HTML-r1/image_m/fcvm-13-1652863-t002.jpg Table 2. Hazard ratio of outcomes between SGLT2i and control groups. https://www.frontiersin.org/files/Articles/1652863/fcvm-13-1652863-HTML-r1/image_m/fcvm-13-1652863-g002.jpg Figure 2. Kaplan–Meier time-to-event free curves of the composite outcome comparison to SGLT2i and c https://www.frontiersin.org/files/Articles/1652863/fcvm-13-1652863-HTML-r1/image_m/fcvm-13-1652863-g003.jpg Figure 3. Subgroup analysis for the risk of composite outcome comparison to SGLT2i and control group https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1767438/full https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-t001.jpg Table 1. Sociodemographic and clinical characteristics of the study sample. https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-t002.jpg Table 2. Results of clinical assessment scales collected at two time points: initial hospitalization https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-t003.jpg Table 3. Baseline-to–week 12 changes in PANSS, MoCA, and STAI (Wilcoxon signed-rank test). https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-t004.jpg Table 4. Exploratory aligned rank transform (ART) analysis: p-values for the effects of individual c https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-g001.jpg Figure 1. Correlation between baseline serum C3 concentration and duration of untreated psychosis an https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-g002.jpg Figure 2. Correlation between baseline serum C3 concentration and length of hospitalization analysed https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-g003.jpg Figure 3. Correlation between baseline serum C3 concentrations and PANSS scores analysed as continuo https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-g004.jpg Figure 4. Correlation between baseline serum C4 concentrations and PANSS scores analysed as continuo https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-g005.jpg Figure 5. Correlation between baseline serum C3 concentrations and STAI scores analysed as continuou https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-g006.jpg Figure 6. Correlation Between Baseline Serum C4 Concentrations and STAI Scores analysed as continuou https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-g007.jpg Figure 7. Correlation between serum C3 concentrations and (A) CTQ-EA subscale – Emotional Abuse, and https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-g008.jpg Figure 8. Correlation between serum C4 concentrations and (A) CTQ Total, (B) the CTQ-EA - Emotional https://www.frontiersin.org/files/Articles/1767438/fpsyt-17-1767438-HTML/image_m/fpsyt-17-1767438-g009.jpg Figure 9. Correlation between baseline serum C3 Levels and MoCA scores analysed as continuous variab https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1802474/full https://www.frontiersin.org/files/Articles/1802474/fspor-08-1802474-HTML-r1/image_m/fspor-08-1802474-t001.jpg Table 1. Participant characteristics (mean ± SD). https://www.frontiersin.org/files/Articles/1802474/fspor-08-1802474-HTML-r1/image_m/fspor-08-1802474-g001.jpg Figure 1. Experimental setup and sensor placement during the standardized offensive task in para-fen https://www.frontiersin.org/files/Articles/1802474/fspor-08-1802474-HTML-r1/image_m/fspor-08-1802474-t002.jpg Table 2. IMU-derived kinematic features used for clustering (mean ± SD). https://www.frontiersin.org/files/Articles/1802474/fspor-08-1802474-HTML-r1/image_m/fspor-08-1802474-g002.jpg Figure 2. Radar plot illustrating IMU-derived kinematic profiles for the identified performance stra https://www.frontiersin.org/files/Articles/1802474/fspor-08-1802474-HTML-r1/image_m/fspor-08-1802474-t003.jpg Table 3. Task performance across identified performance strategies. https://www.frontiersin.org/files/Articles/1802474/fspor-08-1802474-HTML-r1/image_m/fspor-08-1802474-t004.jpg Table 4. Neuromuscular coordination variables derived from surface electromyography across the ident https://www.frontiersin.org/files/Articles/1802474/fspor-08-1802474-HTML-r1/image_m/fspor-08-1802474-t005.jpg Table 5. Muscle mechanical properties across strategies. https://www.frontiersin.org/files/Articles/1802474/fspor-08-1802474-HTML-r1/image_m/fspor-08-1802474-t006.jpg Table 6. Isometric strength measures across strategies. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1669177/full https://www.frontiersin.org/files/Articles/1669177/fneur-17-1669177-HTML/image_m/fneur-17-1669177-g001.jpg Figure 1. Cerebrovascular phenotype analysis of Gucy1a3−/− and Wt via MRA and perfusion techniques. https://www.frontiersin.org/files/Articles/1669177/fneur-17-1669177-HTML/image_m/fneur-17-1669177-g002.jpg Figure 2. Carbon black-gelatin solution perfusion demonstrates cerebral surface vasculature (n = 9 m https://www.frontiersin.org/files/Articles/1669177/fneur-17-1669177-HTML/image_m/fneur-17-1669177-g003.jpg Figure 3. Histopathological analysis of cerebral vasculature in Gucy1a3−/− and Wt. (a,d) Hematoxylin https://www.frontiersin.org/files/Articles/1669177/fneur-17-1669177-HTML/image_m/fneur-17-1669177-t001.jpg Table 1. ICA/BA and MCA/BA ratios of vascular diameter on MRA, blue latex perfusion, and histopathol https://www.frontiersin.org/files/Articles/1669177/fneur-17-1669177-HTML/image_m/fneur-17-1669177-g004.jpg Figure 4. Quantification of cortical microvascular density and diameter in Gucy1a3−/− and Wt. (a,e) https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1746463/full https://www.frontiersin.org/files/Articles/1746463/fpubh-14-1746463-HTML-r1/image_m/fpubh-14-1746463-g001.jpg Figure 1. Study flow diagram: Selection of HEMS dispatches for patients aged ≥65 years. https://www.frontiersin.org/files/Articles/1746463/fpubh-14-1746463-HTML-r1/image_m/fpubh-14-1746463-t001.jpg Table 1. Overall characteristics of the study population. https://www.frontiersin.org/files/Articles/1746463/fpubh-14-1746463-HTML-r1/image_m/fpubh-14-1746463-t002.jpg Table 2. Comparative analysis by diagnostic category. https://www.frontiersin.org/files/Articles/1746463/fpubh-14-1746463-HTML-r1/image_m/fpubh-14-1746463-t003.jpg Table 3. Univariable logistic regression (unadjusted OR) of clinical–operational variables by patien https://www.frontiersin.org/files/Articles/1746463/fpubh-14-1746463-HTML-r1/image_m/fpubh-14-1746463-t004.jpg Table 4. Univariable logistic regression (unadjusted OR) of clinical–operational variables versus on https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2026.1709191/full https://www.frontiersin.org/files/Articles/1709191/frsus-07-1709191-HTML/image_m/frsus-07-1709191-t001.jpg Table 1. Factors and subfactors -criterial decision. https://www.frontiersin.org/files/Articles/1709191/frsus-07-1709191-HTML/image_m/frsus-07-1709191-t002.jpg Table 2. Categories and subfactors for topic selection. https://www.frontiersin.org/files/Articles/1709191/frsus-07-1709191-HTML/image_m/frsus-07-1709191-t003.jpg Table 3. Results from a fuzzy analytic hierarchy process (fuzzy AHP) model applied to assess eco-ind https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1726106/full https://www.frontiersin.org/files/Articles/1726106/feduc-11-1726106-HTML/image_m/feduc-11-1726106-g001.jpg Figure 1. PC CARES learning circle process. https://www.frontiersin.org/files/Articles/1726106/feduc-11-1726106-HTML/image_m/feduc-11-1726106-t001.jpg Table 1. Paired participant racial/ethnic demographics, all regions and cohorts combined. https://www.frontiersin.org/files/Articles/1726106/feduc-11-1726106-HTML/image_m/feduc-11-1726106-g002.jpg Figure 2. Session attendance over time, all regions and cohorts combined. In 2020–21, one region had https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1552746/full https://www.frontiersin.org/files/Articles/1552746/fdgth-07-1552746-HTML-r1/image_m/fdgth-07-1552746-g001.jpg Figure 1. Workflow of the whisper-based model for hypernasality detection. https://www.frontiersin.org/files/Articles/1552746/fdgth-07-1552746-HTML-r1/image_m/fdgth-07-1552746-t001.jpg Table 1. Neural network architecture for hypernasality detection. https://www.frontiersin.org/files/Articles/1552746/fdgth-07-1552746-HTML-r1/image_m/fdgth-07-1552746-t002.jpg Table 2. Training optimizer and hyperparameter configuration for model training. https://www.frontiersin.org/files/Articles/1552746/fdgth-07-1552746-HTML-r1/image_m/fdgth-07-1552746-t003.jpg Table 3. Dataset distribution across training, validation, and test sets. https://www.frontiersin.org/files/Articles/1552746/fdgth-07-1552746-HTML-r1/image_m/fdgth-07-1552746-t004.jpg Table 4. Comparison of hypernasality detection models. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1604442/full https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-t001.jpg Table 1. Demographic characteristics of 1,037 patients diagnosed with melanoma. https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-t002.jpg Table 2. Clinical and pathologic features of 979 patients diagnosed with cutaneous melanoma. https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-t003.jpg Table 3. Histopathological subtype for 674 invasive cutaneous melanomas according to CAP Protocol v1 https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-t004.jpg Table 4. Pathological stage of 979 patients with cutaneous melanoma at presentation, according to th https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-t005.jpg Table 5. Sites of distant metastasis at diagnosis in 51 patients with stage IV cutaneous melanoma. https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-t006.jpg Table 6. BRAF analysis in 84 patients with stage III or IV cutaneous melanoma. https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-t007.jpg Table 7. Demographic and clinical features of 40 patients with stage III or IV cutaneous melanoma ha https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-t008.jpg Table 8. Systemic therapy in 77 patients with stage IIb or higher cutaneous melanoma. https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-g001.jpg Figure 1. Kaplan–Meier survival curves (A–E) according to pathological stage, sex, age group, histop https://www.frontiersin.org/files/Articles/1604442/fonc-15-1604442-HTML/image_m/fonc-15-1604442-t009.jpg Table 9. Adjusted hazard ratios for all-cause mortality in patients with invasive cutaneous melanoma https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1781860/full https://www.frontiersin.org/files/Articles/1781860/fphar-17-1781860-HTML/image_m/fphar-17-1781860-g001.jpg Figure 1. TMH inhibits activation of the NLRP3 inflammasome. (A) Schematic workflow of in vitro NLRP https://www.frontiersin.org/files/Articles/1781860/fphar-17-1781860-HTML/image_m/fphar-17-1781860-g002.jpg Figure 2. TMH does not affect other inflammasome or inflammatory pathways. (A,B) LPS-primed J774A.1 https://www.frontiersin.org/files/Articles/1781860/fphar-17-1781860-HTML/image_m/fphar-17-1781860-g003.jpg Figure 3. TMH inhibits NLRP3 inflammasome activation by suppressing ASC oligomerization and ATPase a https://www.frontiersin.org/files/Articles/1781860/fphar-17-1781860-HTML/image_m/fphar-17-1781860-g004.jpg Figure 4. Toxicity assessment of TMH in zebrafish embryos. (A) Survival curves of zebrafish embryos https://www.frontiersin.org/files/Articles/1781860/fphar-17-1781860-HTML/image_m/fphar-17-1781860-g005.jpg Figure 5. TMH suppresses LPS-induced inflammatory cell recruitment in zebrafish embryos. (A) Represe https://www.frontiersin.org/files/Articles/1781860/fphar-17-1781860-HTML/image_m/fphar-17-1781860-g006.jpg Figure 6. Representative chromatographic profiles of the TMH. (A) UV chromatogram monitored at 254 n https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1739210/full https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g001.jpg Figure 1. Literature screening flowchart. https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g002.jpg Figure 2. Number and type of literature related to ferroptosis and immunity. (A) Literature type dis https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g003.jpg Figure 3. Global contribution map of scientific publications. (A) Overall situation of number of pub https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g004.jpg Figure 4. Collaboration network diagram of research institutions in the literature. https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g005.jpg Figure 5. Author contributions figure. Node size represents the number of articles published by the https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-t001.jpg Table 1. Top 10 authors by publication volume. https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g006.jpg Figure 6. Journal publication contribution map. (A) Publication distribution of Ferroptosis and immu https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-t002.jpg Table 2. Top 10 most cited publications. https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g007.jpg Figure 7. Keyword analysis map. (A) Identification of Key Terms in the Field of Ferroptosis and Immu https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-t003.jpg Table 3. Top 10 research directions. https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g008.jpg Figure 8. Key mechanisms of ferroptosis. ACSL3, acyl-CoA synthetase long-chain family member 3; ACSL https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g009.jpg Figure 9. Tumor immune mechanisms of ferroptosis. ARF, p14ARF; BAP1, BRCA1-associated protein 1; CBS https://www.frontiersin.org/files/Articles/1739210/fimmu-16-1739210-HTML/image_m/fimmu-16-1739210-g010.jpg Figure 10. Bidirectional regulatory mechanisms of ferroptosis in tumor immunity. Left: Role of Ferro https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1802669/full https://www.frontiersin.org/files/Articles/1802669/fmed-13-1802669-HTML/image_m/fmed-13-1802669-g001.jpg Figure 1. Representative ultrasound images of VCI diagnosis. (A) Color Doppler (GE Voluson E10, 23 + https://www.frontiersin.org/files/Articles/1802669/fmed-13-1802669-HTML/image_m/fmed-13-1802669-g002.jpg Figure 2. Patient enrollment and screening. https://www.frontiersin.org/files/Articles/1802669/fmed-13-1802669-HTML/image_m/fmed-13-1802669-t001.jpg Table 1. Baseline maternal and pregnancy characteristics. https://www.frontiersin.org/files/Articles/1802669/fmed-13-1802669-HTML/image_m/fmed-13-1802669-t002.jpg Table 2. Ultrasonic diagnostic performance and measurements. https://www.frontiersin.org/files/Articles/1802669/fmed-13-1802669-HTML/image_m/fmed-13-1802669-t003.jpg Table 3. Comparison of maternal and neonatal outcomes. https://www.frontiersin.org/files/Articles/1802669/fmed-13-1802669-HTML/image_m/fmed-13-1802669-t004.jpg Table 4. Univariate and multivariate logistic regression analysis of VCI risk factors. https://www.frontiersin.org/files/Articles/1802669/fmed-13-1802669-HTML/image_m/fmed-13-1802669-g003.jpg Figure 3. Receiver operating characteristic (ROC) curve of the multivariate model for distinguishing https://www.frontiersin.org/files/Articles/1802669/fmed-13-1802669-HTML/image_m/fmed-13-1802669-t005.jpg Table 5. Pregnancy outcomes in VCI cases vasa previa status. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1690554/full https://www.frontiersin.org/files/Articles/1690554/fcimb-15-1690554-HTML/image_m/fcimb-15-1690554-g001.jpg Figure 1. Screening of high-impact mutant RBD antigen. (A) SimPlot analysis of nucleotide sequence s https://www.frontiersin.org/files/Articles/1690554/fcimb-15-1690554-HTML/image_m/fcimb-15-1690554-g002.jpg Figure 2. The M5-RBD antigen elicits a broad-spectrum humoral response against mutant strains. (A). https://www.frontiersin.org/files/Articles/1690554/fcimb-15-1690554-HTML/image_m/fcimb-15-1690554-g003.jpg Figure 3. HP007 plus Al(OH)3 elicits broad and durable humoral and cellular response. (A) The CpG ad https://www.frontiersin.org/files/Articles/1690554/fcimb-15-1690554-HTML/image_m/fcimb-15-1690554-g004.jpg Figure 4. M5-RBD adjuvanted with HP007 plus Al(OH)3 provides effective protection for K18-hACE2 mice https://www.frontiersin.org/files/Articles/1690554/fcimb-15-1690554-HTML/image_m/fcimb-15-1690554-g005.jpg Figure 5. M5-RBD adjuvanted with HP007 plus Al(OH)3 effectively protects K18-hACE2 mice against SARS https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1771128/full https://www.frontiersin.org/files/Articles/1771128/fpsyg-17-1771128-HTML/image_m/fpsyg-17-1771128-g001.jpg Figure 1. Hypothetical mediating effect model. https://www.frontiersin.org/files/Articles/1771128/fpsyg-17-1771128-HTML/image_m/fpsyg-17-1771128-t001.jpg Table 1. Correlations of the four main variables (N = 950). https://www.frontiersin.org/files/Articles/1771128/fpsyg-17-1771128-HTML/image_m/fpsyg-17-1771128-t002.jpg Table 2. Regression analysis of the mediating model. https://www.frontiersin.org/files/Articles/1771128/fpsyg-17-1771128-HTML/image_m/fpsyg-17-1771128-g002.jpg Figure 2. Mediating effects model. https://www.frontiersin.org/files/Articles/1771128/fpsyg-17-1771128-HTML/image_m/fpsyg-17-1771128-t003.jpg Table 3. The direct and indirect effects of the model. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1750615/full https://www.frontiersin.org/files/Articles/1750615/fendo-17-1750615-HTML/image_m/fendo-17-1750615-g001.jpg Figure 1. Flowchart. https://www.frontiersin.org/files/Articles/1750615/fendo-17-1750615-HTML/image_m/fendo-17-1750615-g002.jpg Figure 2. MACEs. (a) Network diagram; (b) Forest plot; (c) Probability line graph; (d) Funnel plot. https://www.frontiersin.org/files/Articles/1750615/fendo-17-1750615-HTML/image_m/fendo-17-1750615-g003.jpg Figure 3. Composite renal outcomes. (a) Network diagram; (b) Forest plot; (c) Probability line graph https://www.frontiersin.org/files/Articles/1750615/fendo-17-1750615-HTML/image_m/fendo-17-1750615-g004.jpg Figure 4. All-cause mortality. (a) Network diagram; (b) Forest plot; (c) Probability line graph; (d) https://www.frontiersin.org/files/Articles/1750615/fendo-17-1750615-HTML/image_m/fendo-17-1750615-g005.jpg Figure 5. Adverse events. (a) Network diagram; (b) Forest plot; (c) Probability line graph; (d) Funn https://www.frontiersin.org/files/Articles/1750615/fendo-17-1750615-HTML/image_m/fendo-17-1750615-g006.jpg Figure 6. Hypoglycemia. (a) Network diagram; (b) Forest plot; (c) Probability line graph; (d) Funnel https://www.frontiersin.org/files/Articles/1750615/fendo-17-1750615-HTML/image_m/fendo-17-1750615-g007.jpg Figure 7. Cardiovascular death. (a) Network diagram; (b) Forest plot; (c) Probability line graph; (d https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1776073/full https://www.frontiersin.org/files/Articles/1776073/fpubh-14-1776073-HTML-r2/image_m/fpubh-14-1776073-t001.jpg Table 1. Descriptive statistics for physical fitness variables in the research sample (n = 209). https://www.frontiersin.org/files/Articles/1776073/fpubh-14-1776073-HTML-r2/image_m/fpubh-14-1776073-t002.jpg Table 2. Demographic and lifestyle characteristics of the participants. https://www.frontiersin.org/files/Articles/1776073/fpubh-14-1776073-HTML-r2/image_m/fpubh-14-1776073-t003.jpg Table 3. Frequency and percentage for functional movement FMS variables under consideration by level https://www.frontiersin.org/files/Articles/1776073/fpubh-14-1776073-HTML-r2/image_m/fpubh-14-1776073-t004.jpg Table 4. Relationship between physical fitness variables and personal and academic data. https://www.frontiersin.org/files/Articles/1776073/fpubh-14-1776073-HTML-r2/image_m/fpubh-14-1776073-t005.jpg Table 5. Relationship between functional movement (FMS) variables and personal and academic data. https://www.frontiersin.org/files/Articles/1776073/fpubh-14-1776073-HTML-r2/image_m/fpubh-14-1776073-t006.jpg Table 6. General linear model (GLM) parameter estimates and significance tests for predictors of fun https://www.frontiersin.org/files/Articles/1776073/fpubh-14-1776073-HTML-r2/image_m/fpubh-14-1776073-t007.jpg Table 7. General linear model (GLM) parameter estimates and significance tests for predictors of phy https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1508751/full https://www.frontiersin.org/files/Articles/1508751/feduc-10-1508751-HTML/image_m/feduc-10-1508751-t001.jpg Table 1. Demographic characteristics of the study participants. https://www.frontiersin.org/files/Articles/1508751/feduc-10-1508751-HTML/image_m/feduc-10-1508751-t002.jpg Table 2. Distribution of stress among participants. https://www.frontiersin.org/files/Articles/1508751/feduc-10-1508751-HTML/image_m/feduc-10-1508751-t003.jpg Table 3. Association between gender and items showing ≥ 40% severe stress. https://www.frontiersin.org/files/Articles/1508751/feduc-10-1508751-HTML/image_m/feduc-10-1508751-t004.jpg Table 4. Association between year of study and items showing ≥ 40% severe stress. https://www.frontiersin.org/files/Articles/1508751/feduc-10-1508751-HTML/image_m/feduc-10-1508751-t005.jpg Table 5. Association between having previous clinical or academic experience (qualification) before https://www.frontiersin.org/files/Articles/1508751/feduc-10-1508751-HTML/image_m/feduc-10-1508751-t006.jpg Table 6. Association between period of experience (in months) and items showing ≥ 40% severe stress. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1534863/full https://www.frontiersin.org/files/Articles/1534863/fpsyt-16-1534863-HTML-r1/image_m/fpsyt-16-1534863-g001.jpg Figure 1. Flowchart of the inclusion of participants, showing the detailed data-cleaning process. https://www.frontiersin.org/files/Articles/1534863/fpsyt-16-1534863-HTML-r1/image_m/fpsyt-16-1534863-t001.jpg Table 1. Basic description and differential analysis of socio-demographic characteristics. https://www.frontiersin.org/files/Articles/1534863/fpsyt-16-1534863-HTML-r1/image_m/fpsyt-16-1534863-t002.jpg Table 2. Basic description and differential analysis of health status. https://www.frontiersin.org/files/Articles/1534863/fpsyt-16-1534863-HTML-r1/image_m/fpsyt-16-1534863-t003.jpg Table 3. Basic description and differential analysis of lifestyle. https://www.frontiersin.org/files/Articles/1534863/fpsyt-16-1534863-HTML-r1/image_m/fpsyt-16-1534863-t004.jpg Table 4. Binary logistic regression analysis results. https://www.frontiersin.org/files/Articles/1534863/fpsyt-16-1534863-HTML-r1/image_m/fpsyt-16-1534863-g002.jpg Figure 2. Forest plot based on the results of Binary logistic regression analysis. Error bars repres https://www.frontiersin.org/files/Articles/1534863/fpsyt-16-1534863-HTML-r1/image_m/fpsyt-16-1534863-g003.jpg Figure 3. Importance ranking of variables with decreasing average Gini index based on random forest https://www.frontiersin.org/files/Articles/1534863/fpsyt-16-1534863-HTML-r1/image_m/fpsyt-16-1534863-t005.jpg Table 5. Model performance evaluation metrics. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1650930/full https://www.frontiersin.org/files/Articles/1650930/fpsyg-16-1650930-HTML-r1/image_m/fpsyg-16-1650930-t001.jpg Table 1. Correlation analysis results of each variable (N = 940). https://www.frontiersin.org/files/Articles/1650930/fpsyg-16-1650930-HTML-r1/image_m/fpsyg-16-1650930-t002.jpg Table 2. Model fit indices for LCA. https://www.frontiersin.org/files/Articles/1650930/fpsyg-16-1650930-HTML-r1/image_m/fpsyg-16-1650930-g001.jpg Figure 1. Conditional probabilities of 21 items for six latent classes of adolescent family function https://www.frontiersin.org/files/Articles/1650930/fpsyg-16-1650930-HTML-r1/image_m/fpsyg-16-1650930-t003.jpg Table 3. Descriptive statistics of psychological inflexibility indicators in different latent classe https://www.frontiersin.org/files/Articles/1650930/fpsyg-16-1650930-HTML-r1/image_m/fpsyg-16-1650930-t004.jpg Table 4. Differences in psychological inflexibility indicators among different latent classes of ado https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1541273/full https://www.frontiersin.org/files/Articles/1541273/fmed-12-1541273-HTML-r1/image_m/fmed-12-1541273-t001.jpg Table 1. Demographic profile of the respondents (n = 202). https://www.frontiersin.org/files/Articles/1541273/fmed-12-1541273-HTML-r1/image_m/fmed-12-1541273-t002.jpg Table 2. Attitude of the respondents toward patient safety (n = 202). https://www.frontiersin.org/files/Articles/1541273/fmed-12-1541273-HTML-r1/image_m/fmed-12-1541273-t003.jpg Table 3. Results of Pearson’s r correlation and multiple linear regression analyses (n = 202). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1610421/full https://www.frontiersin.org/files/Articles/1610421/fpsyg-16-1610421-HTML/image_m/fpsyg-16-1610421-t001.jpg Table 1. Sample characterization. https://www.frontiersin.org/files/Articles/1610421/fpsyg-16-1610421-HTML/image_m/fpsyg-16-1610421-g001.jpg Figure 1. SEM diagram of the effect intercultural sensitivity dimensions on the relationship between https://www.frontiersin.org/files/Articles/1610421/fpsyg-16-1610421-HTML/image_m/fpsyg-16-1610421-t002.jpg Table 2. Descriptives of the studied variables. https://www.frontiersin.org/files/Articles/1610421/fpsyg-16-1610421-HTML/image_m/fpsyg-16-1610421-t003.jpg Table 3. Correlations between sociodemographic variables and study variables. https://www.frontiersin.org/files/Articles/1610421/fpsyg-16-1610421-HTML/image_m/fpsyg-16-1610421-t004.jpg Table 4. Correlations between prejudice, intercultural sensitivity, and their dimensions. https://www.frontiersin.org/files/Articles/1610421/fpsyg-16-1610421-HTML/image_m/fpsyg-16-1610421-t005.jpg Table 5. Direct and Indirect associations of SEM. https://www.frontiersin.org/journals/social-psychology/articles/10.3389/frsps.2026.1715876/full https://www.frontiersin.org/files/Articles/1715876/frsps-04-1715876-HTML/image_m/frsps-04-1715876-g001.jpg Figure 1. Theoretical model. EXT, Extremism; QFS, Quest for Significance; NFCP, Need for Cognitive C https://www.frontiersin.org/files/Articles/1715876/frsps-04-1715876-HTML/image_m/frsps-04-1715876-t001.jpg Table 1. Sociodemographic characteristics of the sample. https://www.frontiersin.org/files/Articles/1715876/frsps-04-1715876-HTML/image_m/frsps-04-1715876-t002.jpg Table 2. Descriptive data. https://www.frontiersin.org/files/Articles/1715876/frsps-04-1715876-HTML/image_m/frsps-04-1715876-t003.jpg Table 3. Correlation matrix. https://www.frontiersin.org/files/Articles/1715876/frsps-04-1715876-HTML/image_m/frsps-04-1715876-g002.jpg Figure 2. Final structural model with standardized path coefficients. Standardized coefficients are https://www.frontiersin.org/files/Articles/1715876/frsps-04-1715876-HTML/image_m/frsps-04-1715876-t004.jpg Table 4. Path analysis results. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1716603/full https://www.frontiersin.org/files/Articles/1716603/fpsyg-16-1716603-HTML/image_m/fpsyg-16-1716603-t001.jpg Table 1. Summary of characteristics of datasets included in the final merged database. https://www.frontiersin.org/files/Articles/1716603/fpsyg-16-1716603-HTML/image_m/fpsyg-16-1716603-g001.jpg Figure 1. Religious confession for each country. https://www.frontiersin.org/files/Articles/1716603/fpsyg-16-1716603-HTML/image_m/fpsyg-16-1716603-t002.jpg Table 2. Descriptive analyses. https://www.frontiersin.org/files/Articles/1716603/fpsyg-16-1716603-HTML/image_m/fpsyg-16-1716603-t003.jpg Table 3. EQ 9 items, misfit order: location and fit statistics (rating scale model). https://www.frontiersin.org/files/Articles/1716603/fpsyg-16-1716603-HTML/image_m/fpsyg-16-1716603-g002.jpg Figure 2. Person—item map. https://www.frontiersin.org/files/Articles/1716603/fpsyg-16-1716603-HTML/image_m/fpsyg-16-1716603-g003.jpg Figure 3. ICC. https://www.frontiersin.org/files/Articles/1716603/fpsyg-16-1716603-HTML/image_m/fpsyg-16-1716603-t004.jpg Table 4. DIF analysis. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1560970/full https://www.frontiersin.org/files/Articles/1560970/fsufs-09-1560970-HTML-r2/image_m/fsufs-09-1560970-g001.jpg Figure 1. Spatio-temporal ontology model of local rice varieties. https://www.frontiersin.org/files/Articles/1560970/fsufs-09-1560970-HTML-r2/image_m/fsufs-09-1560970-t001.jpg Table 1. Information on the rice extracted. https://www.frontiersin.org/files/Articles/1560970/fsufs-09-1560970-HTML-r2/image_m/fsufs-09-1560970-g002.jpg Figure 2. Rice varieties with resistance. The blue circle indicates the name of the rice variety. Ye https://www.frontiersin.org/files/Articles/1560970/fsufs-09-1560970-HTML-r2/image_m/fsufs-09-1560970-g003.jpg Figure 3. Quality characteristics and variety frequency statistics. The top 10 varieties of each qua https://www.frontiersin.org/files/Articles/1560970/fsufs-09-1560970-HTML-r2/image_m/fsufs-09-1560970-g004.jpg Figure 4. Widely used rice. Blue circles indicate rice variety names. The red circle indicates the u https://www.frontiersin.org/files/Articles/1560970/fsufs-09-1560970-HTML-r2/image_m/fsufs-09-1560970-g005.jpg Figure 5. Description of the shape, color, time to sowing, and time to maturity of “六十日” (Liu Shi Ri https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1789588/full https://www.frontiersin.org/files/Articles/1789588/fmed-13-1789588-HTML/image_m/fmed-13-1789588-t001.jpg Table 1. Baseline characteristics of trial participants. https://www.frontiersin.org/files/Articles/1789588/fmed-13-1789588-HTML/image_m/fmed-13-1789588-t002.jpg Table 2. The antibody array data of differential proteins. https://www.frontiersin.org/files/Articles/1789588/fmed-13-1789588-HTML/image_m/fmed-13-1789588-g001.jpg Figure 1. Venn diagram analysis. The differential proteins between any two groups were used to searc https://www.frontiersin.org/files/Articles/1789588/fmed-13-1789588-HTML/image_m/fmed-13-1789588-g002.jpg Figure 2. Filtering process for identification of uPAR as a SIC-specific cytokine. https://www.frontiersin.org/files/Articles/1789588/fmed-13-1789588-HTML/image_m/fmed-13-1789588-g003.jpg Figure 3. UPAR levels among three groups. The relative expression levels of uPAR in antibody array d https://www.frontiersin.org/files/Articles/1789588/fmed-13-1789588-HTML/image_m/fmed-13-1789588-g004.jpg Figure 4. The antibody array profiles. The location of uPAR is labeled with red line. The levels of https://www.frontiersin.org/files/Articles/1789588/fmed-13-1789588-HTML/image_m/fmed-13-1789588-t003.jpg Table 3. The results of ROC analysis. https://www.frontiersin.org/files/Articles/1789588/fmed-13-1789588-HTML/image_m/fmed-13-1789588-g005.jpg Figure 5. ROC curves. UPAR and some clinic parameters which were also differential between sepsis an https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1679328/full https://www.frontiersin.org/files/Articles/1679328/fimmu-16-1679328-HTML/image_m/fimmu-16-1679328-t001.jpg Table 1. Pathology, imaging examinations and screening for infection and immunity abnormalities. https://www.frontiersin.org/files/Articles/1679328/fimmu-16-1679328-HTML/image_m/fimmu-16-1679328-g001.jpg Figure 1. Clinical course of (A) Case 1, (B) Case 2, (C) Case 3, (D) Case 4 and (E) Case 5. https://www.frontiersin.org/files/Articles/1679328/fimmu-16-1679328-HTML/image_m/fimmu-16-1679328-g002.jpg Figure 2. Liver biopsy specimens from Case 3 and Case 4. (A) Case 3: Hepatocyte degeneration with in https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1656031/full https://www.frontiersin.org/files/Articles/1656031/fenvs-13-1656031-HTML/image_m/fenvs-13-1656031-g001.jpg Figure 1. Flow diagram showing the systematic procedure to select relevant articles for bibliographi https://www.frontiersin.org/files/Articles/1656031/fenvs-13-1656031-HTML/image_m/fenvs-13-1656031-g002.jpg Figure 2. Bibliometric analysis focuses on the research trends in the area of ZnO NPs for the treatm https://www.frontiersin.org/files/Articles/1656031/fenvs-13-1656031-HTML/image_m/fenvs-13-1656031-g003.jpg Figure 3. Mechanism of ZnO NP for the removal of organic pollutants. https://www.frontiersin.org/files/Articles/1656031/fenvs-13-1656031-HTML/image_m/fenvs-13-1656031-g004.jpg Figure 4. Different forms of ZnO nanomaterials. https://www.frontiersin.org/files/Articles/1656031/fenvs-13-1656031-HTML/image_m/fenvs-13-1656031-g005.jpg Figure 5. Different type of advanced ZnO nanostructures. https://www.frontiersin.org/files/Articles/1656031/fenvs-13-1656031-HTML/image_m/fenvs-13-1656031-g006.jpg Figure 6. ZnO nanomaterial doped with transition metals. https://www.frontiersin.org/files/Articles/1656031/fenvs-13-1656031-HTML/image_m/fenvs-13-1656031-g007.jpg Figure 7. Biosynthesis of ZnO NPs. https://www.frontiersin.org/files/Articles/1656031/fenvs-13-1656031-HTML/image_m/fenvs-13-1656031-g008.jpg Figure 8. Performance of different forms of ZnO NPs under varying operational conditions. https://www.frontiersin.org/files/Articles/1656031/fenvs-13-1656031-HTML/image_m/fenvs-13-1656031-g009.jpg Figure 9. Mechanism, synthesis, and factors affecting the performance of ZnO NPs. https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2026.1790416/full https://www.frontiersin.org/files/Articles/1790416/fams-12-1790416-HTML/image_m/fams-12-1790416-t001.jpg Table 1. State variables and parameters of the stocks–crypto fractional competition model with numer https://www.frontiersin.org/files/Articles/1790416/fams-12-1790416-HTML/image_m/fams-12-1790416-g001.jpg Figure 1. Observed and fitted trajectories for the stocks–crypto competition model. Markers denote n https://www.frontiersin.org/files/Articles/1790416/fams-12-1790416-HTML/image_m/fams-12-1790416-g002.jpg Figure 2. Overlay plots comparing integer-order (αf = 1) and fractional-order Under fractional-order https://www.frontiersin.org/files/Articles/1790416/fams-12-1790416-HTML/image_m/fams-12-1790416-g003.jpg Figure 3. Residual (error) time series plots of stocks–crypto fractional competition model, computed https://www.frontiersin.org/files/Articles/1790416/fams-12-1790416-HTML/image_m/fams-12-1790416-g004.jpg Figure 4. Numerical dynamics of stocks–crypto fractional competition model under different Caputo fr https://www.frontiersin.org/files/Articles/1790416/fams-12-1790416-HTML/image_m/fams-12-1790416-g005.jpg Figure 5. Numerical dynamics of stocks–crypto fractional competition model when varying the value of https://www.frontiersin.org/files/Articles/1790416/fams-12-1790416-HTML/image_m/fams-12-1790416-g006.jpg Figure 6. Numerical dynamics of stocks–crypto fractional competition model under different values of https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1740503/full https://www.frontiersin.org/files/Articles/1740503/fpls-16-1740503-HTML/image_m/fpls-16-1740503-g001.jpg Figure 1. The effects of IBA on the regulation of rooting rate and endogenous hormone levels during https://www.frontiersin.org/files/Articles/1740503/fpls-16-1740503-HTML/image_m/fpls-16-1740503-g002.jpg Figure 2. The effect of 200 mg/L IBA treatment on the transcriptome of ARs generated in peach rootst https://www.frontiersin.org/files/Articles/1740503/fpls-16-1740503-HTML/image_m/fpls-16-1740503-g003.jpg Figure 3. Preliminary analysis of proteomic data. (A) Principal component analysis among samples of https://www.frontiersin.org/files/Articles/1740503/fpls-16-1740503-HTML/image_m/fpls-16-1740503-g004.jpg Figure 4. GO and KEGG analysis of the DEPs between control and IBA. (A) Bar chart of GO enrichment a https://www.frontiersin.org/files/Articles/1740503/fpls-16-1740503-HTML/image_m/fpls-16-1740503-g005.jpg Figure 5. Analysis of differential and specific metabolites following 21 days of IBA treatment in pe https://www.frontiersin.org/files/Articles/1740503/fpls-16-1740503-HTML/image_m/fpls-16-1740503-g006.jpg Figure 6. Key pathways involved in IBA-regulated AR formation in peach rootstocks were identified th https://www.frontiersin.org/files/Articles/1740503/fpls-16-1740503-HTML/image_m/fpls-16-1740503-g007.jpg Figure 7. Analysis of DEGs in the control and IBA of plant hormone signal transduction and phenylpro https://www.frontiersin.org/files/Articles/1740503/fpls-16-1740503-HTML/image_m/fpls-16-1740503-g008.jpg Figure 8. The qRT-PCR results of the expression levels of eight DEGs. Actin was used as the referenc https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1719980/full https://www.frontiersin.org/files/Articles/1719980/fpls-16-1719980-HTML/image_m/fpls-16-1719980-t001.jpg Table 1. The exchange of robusta genetic resources that initiated breeding across the globe. https://www.frontiersin.org/files/Articles/1719980/fpls-16-1719980-HTML/image_m/fpls-16-1719980-t002.jpg Table 2. Variety development and replacement in Indonesia from 1920 to 2019. https://www.frontiersin.org/files/Articles/1719980/fpls-16-1719980-HTML/image_m/fpls-16-1719980-t003.jpg Table 3. Varieties developed and disseminated in India. https://www.frontiersin.org/files/Articles/1719980/fpls-16-1719980-HTML/image_m/fpls-16-1719980-t004.jpg Table 4. A selection of robusta varieties developed and disseminated in Brazil. https://www.frontiersin.org/files/Articles/1719980/fpls-16-1719980-HTML/image_m/fpls-16-1719980-t005.jpg Table 5. Trait packages to guide development of robusta target product profiles (TPP). https://www.frontiersin.org/files/Articles/1719980/fpls-16-1719980-HTML/image_m/fpls-16-1719980-t006.jpg Table 6. Quantitative insights into operations, knowledge generated, and progress attained by select https://www.frontiersin.org/files/Articles/1719980/fpls-16-1719980-HTML/image_m/fpls-16-1719980-g001.jpg Figure 1. Main breeding stages and processes that integrate modern breeding tools and provide for fe https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1705716/full https://www.frontiersin.org/files/Articles/1705716/fonc-15-1705716-HTML-r1/image_m/fonc-15-1705716-t001.jpg Table 1. Patient characteristics included in the study. https://www.frontiersin.org/files/Articles/1705716/fonc-15-1705716-HTML-r1/image_m/fonc-15-1705716-g001.jpg Figure 1. Comparison of TANs polarization status between NETs-high and NETs-low groups. (A) Represen https://www.frontiersin.org/files/Articles/1705716/fonc-15-1705716-HTML-r1/image_m/fonc-15-1705716-g002.jpg Figure 2. Correlation analysis between NETs expression levels and different types of TANs in the TIM https://www.frontiersin.org/files/Articles/1705716/fonc-15-1705716-HTML-r1/image_m/fonc-15-1705716-t002.jpg Table 2. Relationships between NETs, tumour-associated N1 neutrophils, N2 neutrophils, N1/N2 ratio a https://www.frontiersin.org/files/Articles/1705716/fonc-15-1705716-HTML-r1/image_m/fonc-15-1705716-g003.jpg Figure 3. Survival analysis in terms of TANs. (A, E) KaplanMeier plots for PFS and OS according to t https://www.frontiersin.org/files/Articles/1705716/fonc-15-1705716-HTML-r1/image_m/fonc-15-1705716-g004.jpg Figure 4. Survival analysis in terms of TANs expressing NETs. (A, E) KaplanMeier plots for PFS and O https://www.frontiersin.org/files/Articles/1705716/fonc-15-1705716-HTML-r1/image_m/fonc-15-1705716-g005.jpg Figure 5. Correlation between TANs and infiltration of intratumoral CD8+ T cells and Tregs in the TI https://www.frontiersin.org/files/Articles/1705716/fonc-15-1705716-HTML-r1/image_m/fonc-15-1705716-t003.jpg Table 3. Univariate and multivariate analysis of prognostic factors associated with progression-free https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2025.1657164/full https://www.frontiersin.org/files/Articles/1657164/falgy-06-1657164-HTML/image_m/falgy-06-1657164-g001.jpg Figure 1. (a,b) The condition of urticaria on the first day of admission (March 10, 2025). https://www.frontiersin.org/files/Articles/1657164/falgy-06-1657164-HTML/image_m/falgy-06-1657164-t001.jpg Table 1. List of auxiliary examination results of the patient on the day of admission. https://www.frontiersin.org/files/Articles/1657164/falgy-06-1657164-HTML/image_m/falgy-06-1657164-g002.jpg Figure 2. On the second day of hospitalization (March 11, 2025), the patient exhibited swelling in t https://www.frontiersin.org/files/Articles/1657164/falgy-06-1657164-HTML/image_m/falgy-06-1657164-g003.jpg Figure 3. (a–c) On the third day of hospitalization (March 12, 2025), the patient exhibited swelling https://www.frontiersin.org/files/Articles/1657164/falgy-06-1657164-HTML/image_m/falgy-06-1657164-g004.jpg Figure 4. (a–c) On the sixth day of hospitalization (March 16, 2025), the patient exhibited a reduct https://www.frontiersin.org/files/Articles/1657164/falgy-06-1657164-HTML/image_m/falgy-06-1657164-g005.jpg Figure 5. Illustrates the changes in the patient's vital signs during hospitalization. https://www.frontiersin.org/files/Articles/1657164/falgy-06-1657164-HTML/image_m/falgy-06-1657164-t002.jpg Table 2. Treatment process of patients before and after admission. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1796870/full https://www.frontiersin.org/files/Articles/1796870/fimmu-17-1796870-HTML-r1/image_m/fimmu-17-1796870-g001.jpg Figure 1. Schematic of PANoptosis induction mechanisms in the gut and brain. https://www.frontiersin.org/files/Articles/1796870/fimmu-17-1796870-HTML-r1/image_m/fimmu-17-1796870-g002.jpg Figure 2. Downstream effect mechanisms of PANoptosis and regulatory mechanisms of the gut-brain axis https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1776781/full https://www.frontiersin.org/files/Articles/1776781/fnut-13-1776781-HTML/image_m/fnut-13-1776781-t001.jpg Table 1. All the characteristics of the patients enrolled in the study presented based on 30-days mo https://www.frontiersin.org/files/Articles/1776781/fnut-13-1776781-HTML/image_m/fnut-13-1776781-g001.jpg Figure 1. Association of nutritional biomarkers at baseline with aneurysm status, postoperative acut https://www.frontiersin.org/files/Articles/1776781/fnut-13-1776781-HTML/image_m/fnut-13-1776781-g002.jpg Figure 2. Receiver operating characteristic (ROC) curve analysis of preoperative nutritional biomark https://www.frontiersin.org/files/Articles/1776781/fnut-13-1776781-HTML/image_m/fnut-13-1776781-t002.jpg Table 2. Univariate analysis: the association of demographic data, comorbidities, laboratory data, a https://www.frontiersin.org/files/Articles/1776781/fnut-13-1776781-HTML/image_m/fnut-13-1776781-t003.jpg Table 3. Multivariate analysis: the association between baseline nutritional biomarkers and poor out https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1799793/full https://www.frontiersin.org/files/Articles/1799793/fimmu-17-1799793-HTML/image_m/fimmu-17-1799793-t001.jpg Table 1. Primer sequences for M1 and M2 macrophage-associated genes. https://www.frontiersin.org/files/Articles/1799793/fimmu-17-1799793-HTML/image_m/fimmu-17-1799793-g001.jpg Figure 1. Macrophage depletion abrogates Ts-Hsp70-mediated protection against T. spiralis infection https://www.frontiersin.org/files/Articles/1799793/fimmu-17-1799793-HTML/image_m/fimmu-17-1799793-g002.jpg Figure 2. Serum cytokine levels in T. spiralis-infected mice with or without Ts-Hsp70 immunization a https://www.frontiersin.org/files/Articles/1799793/fimmu-17-1799793-HTML/image_m/fimmu-17-1799793-g003.jpg Figure 3. Macrophage polarization in the spleen and mesenteric lymph nodes (MLN) of T. spiralis-infe https://www.frontiersin.org/files/Articles/1799793/fimmu-17-1799793-HTML/image_m/fimmu-17-1799793-g004.jpg Figure 4. Macrophage polarization in the intestine and diaphragm of T. spiralis-infected mice with o https://www.frontiersin.org/files/Articles/1799793/fimmu-17-1799793-HTML/image_m/fimmu-17-1799793-g005.jpg Figure 5. Expression of M1 and M2 polarization markers in peritoneal macrophages stimulated with Ts- https://www.frontiersin.org/files/Articles/1799793/fimmu-17-1799793-HTML/image_m/fimmu-17-1799793-g006.jpg Figure 6. Effects of Ts-Hsp70 on macrophage-induced T lymphocyte proliferation and phagocytosis (A, https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1661379/full https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-t001.jpg Table 1. Distribution of items. https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-g001.jpg Figure 1. Confirmatory factor model (work concern). https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-g002.jpg Figure 2. Confirmatory factor model (teacher effectiveness). https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-t002.jpg Table 2. Model fitting results. https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-t003.jpg Table 3. Descriptive statistics result for the level of work concerns. https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-t004.jpg Table 4. Descriptive statistics result in the level of teaching effectiveness. https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-t005.jpg Table 5. Results of multicollinearity test. https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-t006.jpg Table 6. Correlation between work concerns and teaching effectiveness. https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-g003.jpg Figure 3. Structural equation model (SEM). https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-t007.jpg Table 7. Structural equation model (SEM) fit. https://www.frontiersin.org/files/Articles/1661379/fpsyg-16-1661379-HTML/image_m/fpsyg-16-1661379-t008.jpg Table 8. Multiple linear regression analysis for work concerns on teaching effectiveness. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1663272/full https://www.frontiersin.org/files/Articles/1663272/fcimb-15-1663272-HTML-r1/image_m/fcimb-15-1663272-g001.jpg Figure 1. Selection flow chart. EMR, electronic medical record; MP IgM, M. pneumoniae immunoglobulin https://www.frontiersin.org/files/Articles/1663272/fcimb-15-1663272-HTML-r1/image_m/fcimb-15-1663272-t001.jpg Table 1. Baseline characteristics of participants before and after propensity score weighting. https://www.frontiersin.org/files/Articles/1663272/fcimb-15-1663272-HTML-r1/image_m/fcimb-15-1663272-t002.jpg Table 2. Clinical manifestations and laboratory test results of infection with M. pneumoniae. https://www.frontiersin.org/files/Articles/1663272/fcimb-15-1663272-HTML-r1/image_m/fcimb-15-1663272-t003.jpg Table 3. Effects of M. pneumoniae infection on pregnant women before and after propensity score weig https://www.frontiersin.org/files/Articles/1663272/fcimb-15-1663272-HTML-r1/image_m/fcimb-15-1663272-g002.jpg Figure 2. (A) Subgroup analysis of adverse maternal events. (B) Subgroup analysis of cesarean sectio https://www.frontiersin.org/files/Articles/1663272/fcimb-15-1663272-HTML-r1/image_m/fcimb-15-1663272-g003.jpg Figure 3. (A) Subgroup analysis of preterm infant. (B) Subgroup analysis of fetal distress. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1760511/full https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-g001.jpg Figure 1. Flowchart of the selected study population. https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t001.jpg Table 1. General characteristics of the participants. https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t002.jpg Table 2. Distribution of high CMRI in children and adolescents. https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t003.jpg Table 3. Multivariate quantile regression analysis between pubertal stage and weight status and BRI https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t004.jpg Table 4. Multivariate quantile regression analysis between pubertal stage and weight status and BRI https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t005.jpg Table 5. Multivariate quantile regression analysis between pubertal stage and weight status and ABSI https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t006.jpg Table 6. Multivariate quantile regression analysis between pubertal stage and weight status and ABSI https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t007.jpg Table 7. Percentile reference values for BRI and ABSI in girls. https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t008.jpg Table 8. Percentile reference values for BRI and ABSI in boys. https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-g002.jpg Figure 2. Binary logistic regression analysis of high BRI/ABSI levels and the presence of high cardi https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t009.jpg Table 9. The sensitivity, specificity, and AUC of different obesity indicators in detecting high CMR https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-g003.jpg Figure 3. The AUC and YI of different obesity indicators in detecting high CMRI in girls. High CMRI https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-t010.jpg Table 10. The sensitivity, specificity, and AUC of different obesity indicators in detecting high CM https://www.frontiersin.org/files/Articles/1760511/fnut-13-1760511-HTML-r1/image_m/fnut-13-1760511-g004.jpg Figure 4. The AUC and YI of different obesity indicators in detecting high CMRI in boys. High CMRI d https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1762000/full https://www.frontiersin.org/files/Articles/1762000/fpubh-14-1762000-HTML-r1/image_m/fpubh-14-1762000-t001.jpg Table 1. Socio-demographic characteristics of the study participated blood donors. https://www.frontiersin.org/files/Articles/1762000/fpubh-14-1762000-HTML-r1/image_m/fpubh-14-1762000-t002.jpg Table 2. Blood donor’s satisfaction levels in a mobile blood donation campaign. https://www.frontiersin.org/files/Articles/1762000/fpubh-14-1762000-HTML-r1/image_m/fpubh-14-1762000-t003.jpg Table 3. Donor satisfaction levels by subgroup in the mobile blood donation campaign. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1661465/full https://www.frontiersin.org/files/Articles/1661465/fimmu-16-1661465-HTML/image_m/fimmu-16-1661465-g001.jpg Figure 1. Early immune responses in the skin following primary Schistosoma mansoni cercarial exposur https://www.frontiersin.org/files/Articles/1661465/fimmu-16-1661465-HTML/image_m/fimmu-16-1661465-t001.jpg Table 1. Immune cells involved in the response to Schistosoma mansoni cercarial infection. https://www.frontiersin.org/files/Articles/1661465/fimmu-16-1661465-HTML/image_m/fimmu-16-1661465-g002.jpg Figure 2. Cytokine production and functional interactions of immune cells during first contact and r https://www.frontiersin.org/files/Articles/1661465/fimmu-16-1661465-HTML/image_m/fimmu-16-1661465-g003.jpg Figure 3. Early immune responses in the skin following reinfection with Schistosoma mansoni. Re-expo https://www.frontiersin.org/files/Articles/1661465/fimmu-16-1661465-HTML/image_m/fimmu-16-1661465-t002.jpg Table 2. Experimental models used to study early cutaneous immune responses to Schistosoma mansoni c https://www.frontiersin.org/files/Articles/1661465/fimmu-16-1661465-HTML/image_m/fimmu-16-1661465-g004.jpg Figure 4. Immunomodulatory effects of Schistosoma mansoni cercaria. During skin penetration, cercari https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1781414/full https://www.frontiersin.org/files/Articles/1781414/fpsyg-17-1781414-HTML/image_m/fpsyg-17-1781414-t001.jpg Table 1. Mean, standard deviation, asymmetry, and kurtosis. https://www.frontiersin.org/files/Articles/1781414/fpsyg-17-1781414-HTML/image_m/fpsyg-17-1781414-t002.jpg Table 2. Goodness-of-fit indices of the PTSD-COVID-19 scale. https://www.frontiersin.org/files/Articles/1781414/fpsyg-17-1781414-HTML/image_m/fpsyg-17-1781414-g001.jpg Figure 1. One-dimensional model of the PTSD-COVID-19 scale. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1758928/full https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-t001.jpg Table 1. The number of participants in the CHUK program in the years 2022–2024. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-t002.jpg Table 2. Trends in overweight and obesity by year and sex. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-g001.jpg Figure 1. Age-stratified overweight and obesity rates by sex. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-t003.jpg Table 3. Trends in hypertension prevalence. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-g002.jpg Figure 2. Age-stratified hypertension rates by sex. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-t004.jpg Table 4. Trends in elevated total cholesterol by year and sex. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-g003.jpg Figure 3. Age-stratified cholesterol rates by sex. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-t005.jpg Table 5. Trends in the prevalence of newly diagnosed diabetes by year and age group in women. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-g004.jpg Figure 4. Prevalence of screen-detected diabetes by age group, sex, and year. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-t006.jpg Table 6. Trends in smoking prevalence by year and sex. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-g005.jpg Figure 5. Age-stratified smoking rates by sex. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-t007.jpg Table 7. Trends in low physical activity by year and sex. https://www.frontiersin.org/files/Articles/1758928/fpubh-14-1758928-HTML-r1/image_m/fpubh-14-1758928-g006.jpg Figure 6. Age-stratified physical activity rates by sex. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1707554/full https://www.frontiersin.org/files/Articles/1707554/fnins-19-1707554-HTML/image_m/fnins-19-1707554-g001.jpg Figure 1. Schematic diagram of the deep cervical lymphovenous bypass procedure. dCLV, deep cervical https://www.frontiersin.org/files/Articles/1707554/fnins-19-1707554-HTML/image_m/fnins-19-1707554-t001.jpg Table 1. Timeline of clinical course and intervention. https://www.frontiersin.org/files/Articles/1707554/fnins-19-1707554-HTML/image_m/fnins-19-1707554-g002.jpg Figure 2. Surgical procedure for the deep cervical lymphovenous bypass. Step 1: exposure and fluores https://www.frontiersin.org/files/Articles/1707554/fnins-19-1707554-HTML/image_m/fnins-19-1707554-g003.jpg Figure 3. Perioperative workflow and postoperative monitoring protocol for deep cervical lymphovenou https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2026.1738324/full https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-g001.jpg Figure 1. Variation characteristics of daily temperature, rainfall and soil temperature during sampl https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-g002.jpg Figure 2. Seasonal variations of greenhouse gas fluxes in paddy fields under five rice varieties. (A https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-t001.jpg Table 1. CH4 emissions from different rice varieties during various growth stages (unit kg·ha-1). https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-t002.jpg Table 2. N2O emissions from different rice varieties during various growth stages (unit kg·ha-1). https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-t003.jpg Table 3. Comparison of yield and component factors of different rice varieties. https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-g003.jpg Figure 3. GWP and GHGI of different rice varieties. https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-g004.jpg Figure 4. Characteristics of the aboveground parts of different varieties at different growth stages https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-g005.jpg Figure 5. Characteristics of the underground parts of five rice varieties across different growth st https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-g006.jpg Figure 6. Correlation analysis of CH4, N2O, yield and physiological characters of rice plants. The s https://www.frontiersin.org/files/Articles/1738324/fagro-08-1738324-HTML/image_m/fagro-08-1738324-g007.jpg Figure 7. PCA analysis of methane and nitrous oxide on rice plant physiological characteristics. (A) https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1716584/full https://www.frontiersin.org/files/Articles/1716584/fspor-08-1716584-HTML/image_m/fspor-08-1716584-g001.jpg Figure 1. Process of study selection from initial identification to inclusion as per the preferred r https://www.frontiersin.org/files/Articles/1716584/fspor-08-1716584-HTML/image_m/fspor-08-1716584-g002.jpg Figure 2. The list and frequency of each test in the general- (light brown), athletic (green), and c https://www.frontiersin.org/files/Articles/1716584/fspor-08-1716584-HTML/image_m/fspor-08-1716584-t001.jpg Table 1. Modified joanna briggs institute (JBI) critical appraisal checklist. https://www.frontiersin.org/files/Articles/1716584/fspor-08-1716584-HTML/image_m/fspor-08-1716584-g003.jpg Figure 3. The frequency of articles in the general- (light brown), athletic (green), and clinical (b https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1720646/full https://www.frontiersin.org/files/Articles/1720646/fmed-12-1720646-HTML/image_m/fmed-12-1720646-g001.jpg Figure 1. Participant selection flowchart for the NHANES. https://www.frontiersin.org/files/Articles/1720646/fmed-12-1720646-HTML/image_m/fmed-12-1720646-t001.jpg Table 1. Baseline characteristics of the NHANES population stratified by stroke. https://www.frontiersin.org/files/Articles/1720646/fmed-12-1720646-HTML/image_m/fmed-12-1720646-t002.jpg Table 2. Participants demographics and baseline characteristics in Shaoyang area. https://www.frontiersin.org/files/Articles/1720646/fmed-12-1720646-HTML/image_m/fmed-12-1720646-t003.jpg Table 3. The correlation between UHR and stroke. (NHANES) https://www.frontiersin.org/files/Articles/1720646/fmed-12-1720646-HTML/image_m/fmed-12-1720646-t004.jpg Table 4. Correlation between UHR and stroke in Shaoyang area. https://www.frontiersin.org/files/Articles/1720646/fmed-12-1720646-HTML/image_m/fmed-12-1720646-g002.jpg Figure 2. Dose-response relationship between the Uric acid to high-density lipoprotein cholesterol r https://www.frontiersin.org/files/Articles/1720646/fmed-12-1720646-HTML/image_m/fmed-12-1720646-g003.jpg Figure 3. Subgroup analysis of the correlation between UHR and stroke risk. https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2025.1628691/full https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g001.jpg Figure 1. Schematic of groundwater flow in the Souss-Massa aquifer system: groundwater flow from the https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-t001.jpg Table 1. Selected meteorological stations within the Souss-Massa basin. https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-t002.jpg Table 2. Selected piezometers within the Souss-Massa plain. https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g002.jpg Figure 2. Location map of the studied area and ground measurement stations. https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-t003.jpg Table 3. Drought-designated bands based on SPI (Mckee et al., 1993). https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-t004.jpg Table 4. Drought classification according to SWI (Bhuiyan, 2004). https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-t005.jpg Table 5. EHI values interpretation. https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g003.jpg Figure 3. Flowchart of the used methodology (Adopted from Sadeghfam et al., 2018). https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g004.jpg Figure 4. Standardized precipitation index (SPI) time series for the selected meteorological station https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g005.jpg Figure 5. The mean of SPI-3 for meteorological stations in the Souss-Massa basin from January 1982 t https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g006.jpg Figure 6. Groundwater depth variation (GWL, in meters) across different regions of the Souss-Massa B https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g007.jpg Figure 7. Standardized water-level index (SWI) anomalies and correlation with the standardized preci https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g008.jpg Figure 8. Standardized water-level index (SWI) anomalies and correlation with the standardized preci https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g009.jpg Figure 9. Spatial distribution of resistance under different drought bands in the Souss-Massa aquife https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g010.jpg Figure 10. Spatial distribution of GDR under different drought bands in the Souss-Massa aquifer. https://www.frontiersin.org/files/Articles/1628691/frwa-07-1628691-HTML-r1/image_m/frwa-07-1628691-g011.jpg Figure 11. Spatial distribution of EHI under different drought bands in the Souss-Massa aquifer. https://www.frontiersin.org/journals/soil-science/articles/10.3389/fsoil.2025.1653400/full https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g001.jpg Figure 1. Location of the Tadla Plain, Morocco (a), extent of the study area shown on a Landsat 8 RG https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g002.jpg Figure 2. Flowchart detailing the workflow for soil salinity mapping, from data acquisition to map g https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-t001.jpg Table 1. Search space and the best hyperparameters. https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g003.jpg Figure 3. Boxplots of descriptive statistics for ECe and predictor variables, including spectral ban https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g004.jpg Figure 4. Correlation matrix among ECe and predictor variables, including spectral bands and indices https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g005.jpg Figure 5. Comparison between measured and predicted EC values for the training dataset using for mac https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-t002.jpg Table 2. Evaluation of metrics for the train set. https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g006.jpg Figure 6. Comparison of observed EC with model predictions acoss 80 training observations. https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-t003.jpg Table 3. Evaluation of metrics for the test set. https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g007.jpg Figure 7. Comparison between measured and predicted EC values for the testing dataset using for mach https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g008.jpg Figure 8. Comparison of observed EC with model predictions acoss 11 testing observations. https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-t004.jpg Table 4. Nemenyi post-hoc test p-values for pairwise model comparisons. https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g009.jpg Figure 9. R2 performance across 20-fold cross-validation on the test splits for four regression mode https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g010.jpg Figure 10. Soil salinity maps generated using KNN (a), SVR (b), RF (c), and MLP (d). https://www.frontiersin.org/files/Articles/1653400/fsoil-05-1653400-HTML/image_m/fsoil-05-1653400-g011.jpg Figure 11. Percentage of soil salinity classes predicted by each model. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1680970/full https://www.frontiersin.org/files/Articles/1680970/fmed-12-1680970-HTML/image_m/fmed-12-1680970-t001.jpg Table 1. Basic characteristics. https://www.frontiersin.org/files/Articles/1680970/fmed-12-1680970-HTML/image_m/fmed-12-1680970-t002.jpg Table 2. Inflammatory markers between PVT and non-PVT groups. https://www.frontiersin.org/files/Articles/1680970/fmed-12-1680970-HTML/image_m/fmed-12-1680970-g001.jpg Figure 1. Inflammatory markers in portal blood between PVT and non-PVT groups. (a) LPS, (b) IL-6, (c https://www.frontiersin.org/files/Articles/1680970/fmed-12-1680970-HTML/image_m/fmed-12-1680970-g002.jpg Figure 2. Inflammatory markers in peripheral venous blood between PVT and non-PVT groups. (a) LPS, ( https://www.frontiersin.org/files/Articles/1680970/fmed-12-1680970-HTML/image_m/fmed-12-1680970-t003.jpg Table 3. Inflammatory markers between portal and peripheral venous blood. https://www.frontiersin.org/files/Articles/1680970/fmed-12-1680970-HTML/image_m/fmed-12-1680970-g003.jpg Figure 3. Correlation analysis between LPS and IL-6, IL-8, TNF-α, sNox2-dp in portal vein blood of c https://www.frontiersin.org/files/Articles/1680970/fmed-12-1680970-HTML/image_m/fmed-12-1680970-g004.jpg Figure 4. Microflora composition analysis of portal vein in PVT and non-PVT groups. (A) species comm https://www.frontiersin.org/files/Articles/1680970/fmed-12-1680970-HTML/image_m/fmed-12-1680970-g005.jpg Figure 5. Analysis of portal and peripheral venous blood microflora in PVT group. (A) species commun https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1741923/full https://www.frontiersin.org/files/Articles/1741923/fnins-20-1741923-HTML/image_m/fnins-20-1741923-g001.jpg Figure 1. (A) Experimental task including epochs of a 2-s visual-motor task following by a 24.25-s c https://www.frontiersin.org/files/Articles/1741923/fnins-20-1741923-HTML/image_m/fnins-20-1741923-g002.jpg Figure 2. Examples of spatial variations in the dynamics of HRF in the SC for two representative sub https://www.frontiersin.org/files/Articles/1741923/fnins-20-1741923-HTML/image_m/fnins-20-1741923-g003.jpg Figure 3. An example of voxel-wise variations in the HRF dynamics along the rostro-caudal axis in th https://www.frontiersin.org/files/Articles/1741923/fnins-20-1741923-HTML/image_m/fnins-20-1741923-g004.jpg Figure 4. (A) 1mm-thick band-shaped ROIs expanded along the rostro-caudal axis in one example subjec https://www.frontiersin.org/files/Articles/1741923/fnins-20-1741923-HTML/image_m/fnins-20-1741923-g005.jpg Figure 5. Dilation-based variations in the temporal dynamics of HRFs across subjects for both (A) th https://www.frontiersin.org/files/Articles/1741923/fnins-20-1741923-HTML/image_m/fnins-20-1741923-g006.jpg Figure 6. The distributions of peak amplitudes of mean pHRFs across 1mm-dilated ROIs within the rost https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1767846/full https://www.frontiersin.org/files/Articles/1767846/fpsyg-17-1767846-HTML/image_m/fpsyg-17-1767846-g001.jpg Figure 1. Conceptual framework illustrating the hypothesized direct and indirect effects of digital https://www.frontiersin.org/files/Articles/1767846/fpsyg-17-1767846-HTML/image_m/fpsyg-17-1767846-t001.jpg Table 1. Overview of indicators. https://www.frontiersin.org/files/Articles/1767846/fpsyg-17-1767846-HTML/image_m/fpsyg-17-1767846-t002.jpg Table 2. Descriptive statistics. https://www.frontiersin.org/files/Articles/1767846/fpsyg-17-1767846-HTML/image_m/fpsyg-17-1767846-t003.jpg Table 3. Least squares regression results. https://www.frontiersin.org/files/Articles/1767846/fpsyg-17-1767846-HTML/image_m/fpsyg-17-1767846-t004.jpg Table 4. Results of endogeneity tests. https://www.frontiersin.org/files/Articles/1767846/fpsyg-17-1767846-HTML/image_m/fpsyg-17-1767846-t005.jpg Table 5. Results of robustness test. https://www.frontiersin.org/files/Articles/1767846/fpsyg-17-1767846-HTML/image_m/fpsyg-17-1767846-t006.jpg Table 6. Table of mediational effect test results. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1695311/full https://www.frontiersin.org/files/Articles/1695311/fgene-17-1695311-HTML/image_m/fgene-17-1695311-g001.jpg Figure 1. Endoscopic findings in the patient: numerous polyps of similar size located in the fundus https://www.frontiersin.org/files/Articles/1695311/fgene-17-1695311-HTML/image_m/fgene-17-1695311-g002.jpg Figure 2. (A) Sanger sequencing results for APC promoter 1B showing heterozygous c.-181dupC variant. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1764422/full https://www.frontiersin.org/files/Articles/1764422/fpsyt-17-1764422-HTML/image_m/fpsyt-17-1764422-g001.jpg Figure 1. Analyzing the regularity of the EEG signal using q-statistics for quantifying neural compl https://www.frontiersin.org/files/Articles/1764422/fpsyt-17-1764422-HTML/image_m/fpsyt-17-1764422-g002.jpg Figure 2. Speculative (non empirical) model regarding q-value dynamics across various mental context https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1724293/full https://www.frontiersin.org/files/Articles/1724293/fpubh-14-1724293-HTML/image_m/fpubh-14-1724293-g001.jpg Figure 1. Decision-making pathways influencing whether older people adopt digital environment soluti https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1639438/full https://www.frontiersin.org/files/Articles/1639438/fmicb-16-1639438-HTML-r1/image_m/fmicb-16-1639438-g001.jpg Figure 1. An overview of the experimental design. Stage 1 corresponds to the preparation of the micr https://www.frontiersin.org/files/Articles/1639438/fmicb-16-1639438-HTML-r1/image_m/fmicb-16-1639438-t001.jpg Table 1. The predicted ionic concentrations were derived from the model by Melwani Daswani et al. (2 https://www.frontiersin.org/files/Articles/1639438/fmicb-16-1639438-HTML-r1/image_m/fmicb-16-1639438-g002.jpg Figure 2. Barplot of relative abundances of identified taxa at the genus level using 16S rRNA gene s https://www.frontiersin.org/files/Articles/1639438/fmicb-16-1639438-HTML-r1/image_m/fmicb-16-1639438-g003.jpg Figure 3. Micrographs of acridine-orange-stained cells under a 40× magnification (scale bar applies https://www.frontiersin.org/files/Articles/1639438/fmicb-16-1639438-HTML-r1/image_m/fmicb-16-1639438-g004.jpg Figure 4. Transmission electron microscope images at low and high magnifications of cells grown unde https://www.frontiersin.org/files/Articles/1639438/fmicb-16-1639438-HTML-r1/image_m/fmicb-16-1639438-g005.jpg Figure 5. Neighbour-joining tree of 16S rRNA gene sequence similarity from 1,000 bootstrap replicate https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1790571/full https://www.frontiersin.org/files/Articles/1790571/fonc-16-1790571-HTML/image_m/fonc-16-1790571-t001.jpg Table 1. Patient characteristics (n = 333). https://www.frontiersin.org/files/Articles/1790571/fonc-16-1790571-HTML/image_m/fonc-16-1790571-g001.jpg Figure 1. Immunohistochemical patterns of PD-L1 expression stained by the SP263. (a) Negative PD-L1 https://www.frontiersin.org/files/Articles/1790571/fonc-16-1790571-HTML/image_m/fonc-16-1790571-g002.jpg Figure 2. Digital image analysis of PD-L1 expression (clone SP263) in non-small cell lung cancer (NS https://www.frontiersin.org/files/Articles/1790571/fonc-16-1790571-HTML/image_m/fonc-16-1790571-t002.jpg Table 2. Performance of uPath relative to expert assessment at clinically relevant TPS thresholds. https://www.frontiersin.org/files/Articles/1790571/fonc-16-1790571-HTML/image_m/fonc-16-1790571-t003.jpg Table 3. Three-tier agreement between uPath and an expert pathologist in PD-L1 TPS categorization. V https://www.frontiersin.org/files/Articles/1790571/fonc-16-1790571-HTML/image_m/fonc-16-1790571-g003.jpg Figure 3. Scatter plot demonstrating high concordance in PD-L1 assessment between uPath and the path https://www.frontiersin.org/files/Articles/1790571/fonc-16-1790571-HTML/image_m/fonc-16-1790571-g004.jpg Figure 4. Changes in immunotherapy eligibility based on PD-L1 assessments by uPath versus the pathol https://www.frontiersin.org/files/Articles/1790571/fonc-16-1790571-HTML/image_m/fonc-16-1790571-g005.jpg Figure 5. Median TPS differences between uPath and the pathologist, stratified by classification cat https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1779377/full https://www.frontiersin.org/files/Articles/1779377/feduc-11-1779377-HTML/image_m/feduc-11-1779377-g001.jpg Figure 1. Universal design for learning guidelines (Center for Applied Special Technology, CAST, 201 https://www.frontiersin.org/files/Articles/1779377/feduc-11-1779377-HTML/image_m/feduc-11-1779377-g002.jpg Figure 2. Structure of the UDL-OMT and relationship with the reference framework. https://www.frontiersin.org/files/Articles/1779377/feduc-11-1779377-HTML/image_m/feduc-11-1779377-t001.jpg Table 1. Examples of items translated and links to guidelines and checkpoints. https://www.frontiersin.org/files/Articles/1779377/feduc-11-1779377-HTML/image_m/feduc-11-1779377-t002.jpg Table 2. Composition of the sample. https://www.frontiersin.org/files/Articles/1779377/feduc-11-1779377-HTML/image_m/feduc-11-1779377-t003.jpg Table 3. Scale reliability: Cronbach’s alpha (evaluator 1). https://www.frontiersin.org/files/Articles/1779377/feduc-11-1779377-HTML/image_m/feduc-11-1779377-t004.jpg Table 4. ICC results. https://www.frontiersin.org/files/Articles/1779377/feduc-11-1779377-HTML/image_m/feduc-11-1779377-t005.jpg Table 5. Items that do not reach a significant agreement. https://www.frontiersin.org/files/Articles/1779377/feduc-11-1779377-HTML/image_m/feduc-11-1779377-t006.jpg Table 6. Problematic item statements. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1769706/full https://www.frontiersin.org/files/Articles/1769706/fendo-17-1769706-HTML/image_m/fendo-17-1769706-t001.jpg Table 1. Baseline characteristics of children with overweight/obesity and were newly diagnosed with https://www.frontiersin.org/files/Articles/1769706/fendo-17-1769706-HTML/image_m/fendo-17-1769706-g001.jpg Figure 1. Descriptive annual prevalence of overweight/obesity among children at type 1 diabetes diag https://www.frontiersin.org/files/Articles/1769706/fendo-17-1769706-HTML/image_m/fendo-17-1769706-t002.jpg Table 2. Outcomes associated with body mass index (BMI) z-scores in children newly diagnosed with ty https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1764305/full https://www.frontiersin.org/files/Articles/1764305/fimmu-17-1764305-HTML/image_m/fimmu-17-1764305-g001.jpg Figure 1. (a) Endoscopic image from the descending colon demonstrating colitis with continuous infla https://www.frontiersin.org/files/Articles/1764305/fimmu-17-1764305-HTML/image_m/fimmu-17-1764305-g002.jpg Figure 2. (a) Endoscopic image from the region surrounding the anastomosis showing residual divertic https://www.frontiersin.org/files/Articles/1764305/fimmu-17-1764305-HTML/image_m/fimmu-17-1764305-g003.jpg Figure 3. (a) Follow-up endoscopic images after 7 months of adalimumab weekly monotherapy showing re https://www.frontiersin.org/files/Articles/1764305/fimmu-17-1764305-HTML/image_m/fimmu-17-1764305-g004.jpg Figure 4. Timeline of the patient’s clinical course showing symptom onset, diagnostic evaluation, tr https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1704668/full https://www.frontiersin.org/files/Articles/1704668/fsurg-13-1704668-HTML/image_m/fsurg-13-1704668-t001.jpg Table 1. Evidence map of temporalis muscle variants (study-level data). https://www.frontiersin.org/files/Articles/1704668/fsurg-13-1704668-HTML/image_m/fsurg-13-1704668-g001.jpg Figure 1. Temporalis muscle in lateral cranial view showing fan-like fiber arrangement and tendinous https://www.frontiersin.org/files/Articles/1704668/fsurg-13-1704668-HTML/image_m/fsurg-13-1704668-g002.jpg Figure 2. Cadaveric lateral view of the temporalis muscle showing the superficial portion (STM) and https://www.frontiersin.org/files/Articles/1704668/fsurg-13-1704668-HTML/image_m/fsurg-13-1704668-t002.jpg Table 2. Classification quick reference for the temporalis muscle (types I to IV). https://www.frontiersin.org/files/Articles/1704668/fsurg-13-1704668-HTML/image_m/fsurg-13-1704668-i001.jpg Algorithm 1. MRI/US-guided assignment for temporalis muscle classification (side-level). It is inten https://www.frontiersin.org/files/Articles/1704668/fsurg-13-1704668-HTML/image_m/fsurg-13-1704668-t003.jpg Table 3. Advanced imaging of the temporalis muscle: incremental value, limitations and clinical indi https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1681527/full https://www.frontiersin.org/files/Articles/1681527/fneur-16-1681527-HTML/image_m/fneur-16-1681527-g001.jpg Figure 1. Flow of patient recruitment CHIMES (characterization of ocrelizumab in minorities with mul https://www.frontiersin.org/files/Articles/1681527/fneur-16-1681527-HTML/image_m/fneur-16-1681527-t001.jpg Table 1. Socio-demographic characteristics of the study population. https://www.frontiersin.org/files/Articles/1681527/fneur-16-1681527-HTML/image_m/fneur-16-1681527-g002.jpg Figure 2. Reasons for initiating rituximab or ocrelizumab. https://www.frontiersin.org/files/Articles/1681527/fneur-16-1681527-HTML/image_m/fneur-16-1681527-g003.jpg Figure 3. Flowchart showing drug swaps before B-cell-targeted therapy. https://www.frontiersin.org/files/Articles/1681527/fneur-16-1681527-HTML/image_m/fneur-16-1681527-g004.jpg Figure 4. Changes in outcomes before and after B-cell therapy: disability was defined by ambulation https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1759279/full https://www.frontiersin.org/files/Articles/1759279/fnagi-18-1759279-HTML/image_m/fnagi-18-1759279-g001.jpg Figure 1. Transcriptomic identification of Alzheimer’s disease–associated genes. Transcriptomic anal https://www.frontiersin.org/files/Articles/1759279/fnagi-18-1759279-HTML/image_m/fnagi-18-1759279-t001.jpg Table 1. Ferroptosis-related differentially expressed genes implicated in AD. https://www.frontiersin.org/files/Articles/1759279/fnagi-18-1759279-HTML/image_m/fnagi-18-1759279-g002.jpg Figure 2. Identification and network characterization of ferroptosis-associated genes in Alzheimer’s https://www.frontiersin.org/files/Articles/1759279/fnagi-18-1759279-HTML/image_m/fnagi-18-1759279-g003.jpg Figure 3. Immune landscape and ferroptosis–immune interactions in Alzheimer’s disease. Immune cell i https://www.frontiersin.org/files/Articles/1759279/fnagi-18-1759279-HTML/image_m/fnagi-18-1759279-g004.jpg Figure 4. Hub gene identification and structural evaluation of PPARG as a therapeutic target. Networ https://www.frontiersin.org/files/Articles/1759279/fnagi-18-1759279-HTML/image_m/fnagi-18-1759279-g005.jpg Figure 5. scRNA-seq reveals neuronal enrichment of PPARG expression in Alzheimer’s disease. Cell-typ https://www.frontiersin.org/files/Articles/1759279/fnagi-18-1759279-HTML/image_m/fnagi-18-1759279-t002.jpg Table 2. Cell-type–specific expression patterns of PPARG in Alzheimer’s disease revealed by scRNA-se https://www.frontiersin.org/files/Articles/1759279/fnagi-18-1759279-HTML/image_m/fnagi-18-1759279-g006.jpg Figure 6. Functional inhibition of PPARG attenuates neuronal ferroptosis in vitro. PPARG inhibition https://www.frontiersin.org/files/Articles/1759279/fnagi-18-1759279-HTML/image_m/fnagi-18-1759279-g007.jpg Figure 7. Integrative analytical framework identifying PPARG-mediated ferroptosis in Alzheimer’s dis https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1694156/full https://www.frontiersin.org/files/Articles/1694156/feduc-11-1694156-HTML/image_m/feduc-11-1694156-t001.jpg Table 1. Key characteristics of Panama’s public universities. https://www.frontiersin.org/files/Articles/1694156/feduc-11-1694156-HTML/image_m/feduc-11-1694156-t002.jpg Table 2. R&D expenditure, funding structure and personnel in Panama (2014–2023). https://www.frontiersin.org/files/Articles/1694156/feduc-11-1694156-HTML/image_m/feduc-11-1694156-t003.jpg Table 3. Summary of the results of interviews with university authorities. https://www.frontiersin.org/files/Articles/1694156/feduc-11-1694156-HTML/image_m/feduc-11-1694156-g001.jpg Figure 1. Perception of the protagonist role of research in the sustainability of higher education. https://www.frontiersin.org/files/Articles/1694156/feduc-11-1694156-HTML/image_m/feduc-11-1694156-g002.jpg Figure 2. Actions needed to make higher education sustainable. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1710405/full https://www.frontiersin.org/files/Articles/1710405/fonc-15-1710405-HTML/image_m/fonc-15-1710405-g001.jpg Figure 1. An overview of clinical application scenarios for ultrasound-based radiomics in breast can https://www.frontiersin.org/files/Articles/1710405/fonc-15-1710405-HTML/image_m/fonc-15-1710405-g002.jpg Figure 2. The typical workflow of ultrasound-based radiomics. BMUS, B-mode ultrasound; CDFI, color d https://www.frontiersin.org/files/Articles/1710405/fonc-15-1710405-HTML/image_m/fonc-15-1710405-t001.jpg Table 1. Summary of ultrasound radiomics in the breast lesion diagnosis. https://www.frontiersin.org/files/Articles/1710405/fonc-15-1710405-HTML/image_m/fonc-15-1710405-t002.jpg Table 2. Summary of ultrasound radiomics in predicting molecular subtypes of breast cancer. https://www.frontiersin.org/files/Articles/1710405/fonc-15-1710405-HTML/image_m/fonc-15-1710405-t003.jpg Table 3. Summary of ultrasound radiomics in predicting lymph node status of breast cancer. https://www.frontiersin.org/files/Articles/1710405/fonc-15-1710405-HTML/image_m/fonc-15-1710405-t004.jpg Table 4. Summary of ultrasound radiomics in predicting the response of NAC. https://www.frontiersin.org/files/Articles/1710405/fonc-15-1710405-HTML/image_m/fonc-15-1710405-t005.jpg Table 5. Summary of ultrasound radiomics in predicting the prognosis of breast cancer. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1763068/full https://www.frontiersin.org/files/Articles/1763068/fimmu-17-1763068-HTML/image_m/fimmu-17-1763068-g001.jpg Figure 1. mIF profiling of immune cells in CRC. Representative mIF images of serial tissue sections https://www.frontiersin.org/files/Articles/1763068/fimmu-17-1763068-HTML/image_m/fimmu-17-1763068-g002.jpg Figure 2. Spatial enrichment of PD-L1+ and CD163+ myeloid cells in CRC. (A) Representative mIF image https://www.frontiersin.org/files/Articles/1763068/fimmu-17-1763068-HTML/image_m/fimmu-17-1763068-g003.jpg Figure 3. Spatial distribution and correlation of CD8+ and PD-L1+ cells in CRC. (A) Four representat https://www.frontiersin.org/files/Articles/1763068/fimmu-17-1763068-HTML/image_m/fimmu-17-1763068-t001.jpg Table 1. Associations of immune cell subsets with CD8+ and PD-L1+ cells in CRC. https://www.frontiersin.org/files/Articles/1763068/fimmu-17-1763068-HTML/image_m/fimmu-17-1763068-g004.jpg Figure 4. Association of PD-1+ and CD8+PD-1+ subsets in the immune microenvironment. (A) Correlation https://www.frontiersin.org/files/Articles/1763068/fimmu-17-1763068-HTML/image_m/fimmu-17-1763068-g005.jpg Figure 5. Region-specific infiltration of CD56+ NK cells with CD8+ T cells. (A) CD56+ NK cell densit https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1719995/full https://www.frontiersin.org/files/Articles/1719995/fmed-13-1719995-HTML/image_m/fmed-13-1719995-g001.jpg Figure 1. Schematic diagram and quality control of transcriptomic data. (A) Schematic diagram summar https://www.frontiersin.org/files/Articles/1719995/fmed-13-1719995-HTML/image_m/fmed-13-1719995-g002.jpg Figure 2. Differentially expressed genes and ligand-receptor interaction analysis. (A,B) Volcano plo https://www.frontiersin.org/files/Articles/1719995/fmed-13-1719995-HTML/image_m/fmed-13-1719995-g003.jpg Figure 3. Gene ontology (GO) analysis of differentially expressed ligands and construction of PPI (p https://www.frontiersin.org/files/Articles/1719995/fmed-13-1719995-HTML/image_m/fmed-13-1719995-g004.jpg Figure 4. Immunofluorescence of synovium tissue. Immunofluorescence of IL6 and IL1B in normal and OA https://www.frontiersin.org/files/Articles/1719995/fmed-13-1719995-HTML/image_m/fmed-13-1719995-g005.jpg Figure 5. Prediction of molecular communication between blood and synovium in osteoarthritis. Based https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1729325/full https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t001.jpg Table 1. Basic information of essential anticancer medicines. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t002.jpg Table 2. Basic information of innovative anticancer medicines. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-g001.jpg Figure 1. Availability distribution of EAMs and IAMs in hospitals and pharmacies (%). https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t003.jpg Table 3. Availability of 41 anticancer medicines in nine cities. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t004.jpg Table 4. Availability of EAMs in hospitals and community pharmacies. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t005.jpg Table 5. Availability of IAMs in hospitals and community pharmacies. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t006.jpg Table 6. Analysis of MPR of EAMs in 45 hospitals in Jiangsu Province. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t007.jpg Table 7. Analysis of MPR of IAMs in 45 hospitals in Jiangsu Province. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t008.jpg Table 8. Analysis of MPR of IAMs in 45 community pharmacies in Jiangsu Province. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t009.jpg Table 9. Median affordability of EAMs in Jiangsu Province. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t010.jpg Table 10. Median affordability of IAMs in hospitals. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-t011.jpg Table 11. Affordability of IAMs in community pharmacies. https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-g002.jpg Figure 2. Comparison of the availability and affordability of EAMs (include LPGs and OBs) in hospita https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-g003.jpg Figure 3. Comprehensive analysis of the availability and affordability of LPGs in community pharmaci https://www.frontiersin.org/files/Articles/1729325/fpubh-13-1729325-HTML/image_m/fpubh-13-1729325-g004.jpg Figure 4. Comparison of the availability and affordability of IAMs. (A) Objective to analyze the ava https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1814103/full https://www.frontiersin.org/files/Articles/1814103/fimmu-17-1814103-HTML/image_m/fimmu-17-1814103-g001.jpg Figure 1. Description of the selection process recruiting the publications dedicated to microbiota a https://www.frontiersin.org/files/Articles/1814103/fimmu-17-1814103-HTML/image_m/fimmu-17-1814103-t001.jpg Table 1. Descriptive characteristics of the studies related to intestinal microbiota in patients wit https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1585562/full https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-t001.jpg Table 1. The Innovation Forum process and administrative team. https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-g001.jpg Figure 1. Innovation Forum evaluation survey. Example NPS calculation: The Net Promoter Score (NPS) https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-g002.jpg Figure 2. Agile Nudge University Innovation Forum dashboard. ANU, Agile Nudge University. https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-t002.jpg Table 2. Example of the generated solutions. https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-t003.jpg Table 3. The counts of contacted, respondents, registered individuals and attendees; number of atten https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-t004.jpg Table 4. Numbers, ranges, means, and standard deviation of contacted, respondent, registered individ https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-g003.jpg Figure 3. Trends in the numbers of contacted, respondents, registered individuals, and Innovation Fo https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-t005.jpg Table 5. Summary of statistical testing and significance of the difference between the number of con https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-t006.jpg Table 6. Level of correlation between the counts of contacted, respondent, registered and attendees. https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-g004.jpg Figure 4. Innovation Forum attendees’ NPS (Net Promoter Scores). https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-t007.jpg Table 7. Net Promoter Scores (NPS) averages and standard deviations (SD) for the three attendee coho https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-t008.jpg Table 8. Statistical testing of Net Promoter Scores (NPS) comparison between attendee cohorts. https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-g005.jpg Figure 5. Boxplot representation of the NPS (Net Promoter Scores) by cohort of attendees. https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-t009.jpg Table 9. Numbers of main solutions, sub-solutions, detailed solutions, total number solutions, exist https://www.frontiersin.org/files/Articles/1585562/fpubh-13-1585562-HTML-r1/image_m/fpubh-13-1585562-g006.jpg Figure 6. Control charts with the trends in the numbers of Contacts, Respondents, Registrants, and A https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1766377/full https://www.frontiersin.org/files/Articles/1766377/fmed-13-1766377-HTML/image_m/fmed-13-1766377-t001.jpg Table 1. Hematological and genetic test results for Family 1. https://www.frontiersin.org/files/Articles/1766377/fmed-13-1766377-HTML/image_m/fmed-13-1766377-g001.jpg Figure 1. (A) Pedigree chart of the case study. (B) Next-generation high-throughput sequencing varia https://www.frontiersin.org/files/Articles/1766377/fmed-13-1766377-HTML/image_m/fmed-13-1766377-g002.jpg Figure 2. Electrophoretic diagram of (A) grandmother (I-2), (B) father (II-1) and (C) proband (III-1 https://www.frontiersin.org/files/Articles/1766377/fmed-13-1766377-HTML/image_m/fmed-13-1766377-g003.jpg Figure 3. (A) Electrophoretic diagram of the proband from Family 2. (B) Next-generation high-through https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1650085/full https://www.frontiersin.org/files/Articles/1650085/fdgth-07-1650085-HTML-r1/image_m/fdgth-07-1650085-g001.jpg Figure 1. Schematic overview of the study design and data analysis pipeline. Participants enrolled i https://www.frontiersin.org/files/Articles/1650085/fdgth-07-1650085-HTML-r1/image_m/fdgth-07-1650085-g002.jpg Figure 2. Boxplot comparison of selected features for low (Lw) and high (Hg) cognitive load across p https://www.frontiersin.org/files/Articles/1650085/fdgth-07-1650085-HTML-r1/image_m/fdgth-07-1650085-t001.jpg Table 1. For the extracted features, the “x” marks if the feature was included in the reduced featur https://www.frontiersin.org/files/Articles/1650085/fdgth-07-1650085-HTML-r1/image_m/fdgth-07-1650085-g003.jpg Figure 3. Subject-Independent (Minimum, Mean, Maximum) LOO-CV Results (F1) of generalised models acr https://www.frontiersin.org/files/Articles/1650085/fdgth-07-1650085-HTML-r1/image_m/fdgth-07-1650085-g004.jpg Figure 4. Averaged (Minimum, Mean, Maximum) Five-Fold CV Results (F1) across Participant-Specific Pe https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1682681/full https://www.frontiersin.org/files/Articles/1682681/fpsyt-16-1682681-HTML/image_m/fpsyt-16-1682681-t001.jpg Table 1. Comparison of baseline characteristics between the two groups. https://www.frontiersin.org/files/Articles/1682681/fpsyt-16-1682681-HTML/image_m/fpsyt-16-1682681-t002.jpg Table 2. Comparison of self-care indices between the two groups. https://www.frontiersin.org/files/Articles/1682681/fpsyt-16-1682681-HTML/image_m/fpsyt-16-1682681-t003.jpg Table 3. Correlations between baseline characteristics and SDS scores in the depression group. https://www.frontiersin.org/files/Articles/1682681/fpsyt-16-1682681-HTML/image_m/fpsyt-16-1682681-t004.jpg Table 4. Correlation between self-care ability and SDS scores in the depression group. https://www.frontiersin.org/files/Articles/1682681/fpsyt-16-1682681-HTML/image_m/fpsyt-16-1682681-g001.jpg Figure 1. Correlation between self-care ability and SDS scores in the depression group. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1744877/full https://www.frontiersin.org/files/Articles/1744877/fpsyt-16-1744877-HTML/image_m/fpsyt-16-1744877-t001.jpg Table 1. Comparison of socio-demographics and family burden scale scores between caregivers with car https://www.frontiersin.org/files/Articles/1744877/fpsyt-16-1744877-HTML/image_m/fpsyt-16-1744877-t002.jpg Table 2. Factors associated with caregiving burden. https://www.frontiersin.org/files/Articles/1744877/fpsyt-16-1744877-HTML/image_m/fpsyt-16-1744877-t003.jpg Table 3. Predictive capacity of associated factors. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1759879/full https://www.frontiersin.org/files/Articles/1759879/fnagi-18-1759879-HTML-r1/image_m/fnagi-18-1759879-g001.jpg Figure 1. Graphical representation of the experimental design. https://www.frontiersin.org/files/Articles/1759879/fnagi-18-1759879-HTML-r1/image_m/fnagi-18-1759879-g002.jpg Figure 2. Results of the static balance test. Data are presented as mean ± standard error (SE). *p < https://www.frontiersin.org/files/Articles/1759879/fnagi-18-1759879-HTML-r1/image_m/fnagi-18-1759879-g003.jpg Figure 3. Results of the dynamic balance test. Data are presented as mean ± standard error (SE). *p  https://www.frontiersin.org/files/Articles/1759879/fnagi-18-1759879-HTML-r1/image_m/fnagi-18-1759879-g004.jpg Figure 4. Results of the sample entropy analysis. Data are presented as mean ± standard error (SE). https://www.frontiersin.org/files/Articles/1759879/fnagi-18-1759879-HTML-r1/image_m/fnagi-18-1759879-g005.jpg Figure 5. Results of the correlation between static and dynamic balance. ρ, Spearman’s rank correlat https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1777412/full https://www.frontiersin.org/files/Articles/1777412/fped-14-1777412-HTML/image_m/fped-14-1777412-g001.jpg Figure 1. The von-Mises stress distribution and model configuration. https://www.frontiersin.org/files/Articles/1777412/fped-14-1777412-HTML/image_m/fped-14-1777412-g002.jpg Figure 2. The effective strain vs. angle correction progress. The maximum strain was less than the c https://www.frontiersin.org/files/Articles/1777412/fped-14-1777412-HTML/image_m/fped-14-1777412-g003.jpg Figure 3. The pseudo strain rate vs. angle correction. Three-stage strain rate variation was observe https://www.frontiersin.org/files/Articles/1777412/fped-14-1777412-HTML/image_m/fped-14-1777412-g004.jpg Figure 4. The rotation angles vs. effective strain. α, β and γ are the rotation angles around three https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2026.1795130/full https://www.frontiersin.org/files/Articles/1795130/fncel-20-1795130-HTML/image_m/fncel-20-1795130-g001.jpg Figure 1. Failure of postnatal pre- and post-synaptic scaling at neuromuscular junctions. Schematic https://www.frontiersin.org/files/Articles/1795130/fncel-20-1795130-HTML/image_m/fncel-20-1795130-g002.jpg Figure 2. Schematic representation of impaired presynaptic maturation at the NMJ in SMA summarizing https://www.frontiersin.org/files/Articles/1795130/fncel-20-1795130-HTML/image_m/fncel-20-1795130-t001.jpg Table 1. Quantal content at the neuromuscular junction in spinal muscular atrophy models during post https://www.frontiersin.org/files/Articles/1795130/fncel-20-1795130-HTML/image_m/fncel-20-1795130-g003.jpg Figure 3. Delayed and incomplete maturation of presynaptic neurotransmitter release at vulnerable NM https://www.frontiersin.org/files/Articles/1795130/fncel-20-1795130-HTML/image_m/fncel-20-1795130-g004.jpg Figure 4. Cellular mechanisms contributing to motor nerve terminal destabilisation in SMA. Multiple, https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1526754/full https://www.frontiersin.org/files/Articles/1526754/fonc-15-1526754-HTML-r1/image_m/fonc-15-1526754-g001.jpg Figure 1. Typical pathological manifestations of GLMIP. Staining: H&E staining: 40×. (Credit: Depart https://www.frontiersin.org/files/Articles/1526754/fonc-15-1526754-HTML-r1/image_m/fonc-15-1526754-t001.jpg Table 1. Demographic and clinical characteristics of 29 patients with GLMIP. https://www.frontiersin.org/files/Articles/1526754/fonc-15-1526754-HTML-r1/image_m/fonc-15-1526754-g002.jpg Figure 2. Typical clinical symptoms of GLMIP. (A) Extensive redness and swelling of the left breast https://www.frontiersin.org/files/Articles/1526754/fonc-15-1526754-HTML-r1/image_m/fonc-15-1526754-g003.jpg Figure 3. Correlation analysis of demographic characteristics. Rectangle with red border means P<0.0 https://www.frontiersin.org/files/Articles/1526754/fonc-15-1526754-HTML-r1/image_m/fonc-15-1526754-t002.jpg Table 2. Speculation on the related pathogenic factors of 29 cases of GLMIP. https://www.frontiersin.org/files/Articles/1526754/fonc-15-1526754-HTML-r1/image_m/fonc-15-1526754-t003.jpg Table 3. Comparison of patients with GLMIP treated by TCM alone and integrated TCM and western medic https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1774223/full https://www.frontiersin.org/files/Articles/1774223/fpls-17-1774223-HTML-r1/image_m/fpls-17-1774223-t001.jpg Table 1. Overview of the climate and soil properties of the three sampling sites. https://www.frontiersin.org/files/Articles/1774223/fpls-17-1774223-HTML-r1/image_m/fpls-17-1774223-g001.jpg Figure 1. Photosynthetic pigment content in plants harvested in site 1, site 2 and site 3. (A) chlor https://www.frontiersin.org/files/Articles/1774223/fpls-17-1774223-HTML-r1/image_m/fpls-17-1774223-g002.jpg Figure 2. Numbers of C. quitensis (A) and Arabidopsis putative TAIR orthologous (C) DEGs in differen https://www.frontiersin.org/files/Articles/1774223/fpls-17-1774223-HTML-r1/image_m/fpls-17-1774223-g003.jpg Figure 3. Enrichment analysis of orthologous DEGs in different comparison groups. KEGG pathways and https://www.frontiersin.org/files/Articles/1774223/fpls-17-1774223-HTML-r1/image_m/fpls-17-1774223-g004.jpg Figure 4. Diagram of the phenylpropanoid pathway leading to lignin, flavonoid and coumarin biosynthe https://www.frontiersin.org/files/Articles/1774223/fpls-17-1774223-HTML-r1/image_m/fpls-17-1774223-g005.jpg Figure 5. Relative abundance (%) of the most represented fungal and bacterial phyla (A) and bacteria https://www.frontiersin.org/files/Articles/1774223/fpls-17-1774223-HTML-r1/image_m/fpls-17-1774223-g006.jpg Figure 6. Heatmaps of the top 30 putative differentially abundant taxa in the pairwise comparison be https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1688153/full https://www.frontiersin.org/files/Articles/1688153/fphys-17-1688153-HTML/image_m/fphys-17-1688153-t001.jpg Table 1. Baseline characteristics of patients in the development and validation cohorts. https://www.frontiersin.org/files/Articles/1688153/fphys-17-1688153-HTML/image_m/fphys-17-1688153-g001.jpg Figure 1. Prediction of long-term prognosis using 41 variables via the coefficient trend of the LASS https://www.frontiersin.org/files/Articles/1688153/fphys-17-1688153-HTML/image_m/fphys-17-1688153-g002.jpg Figure 2. The Boruta algorithm was applied to identify potential predictors of patients. (A) The scr https://www.frontiersin.org/files/Articles/1688153/fphys-17-1688153-HTML/image_m/fphys-17-1688153-g003.jpg Figure 3. Multivariate Cox regression analysis of the development cohort to identify independent pro https://www.frontiersin.org/files/Articles/1688153/fphys-17-1688153-HTML/image_m/fphys-17-1688153-g004.jpg Figure 4. Development of a predictive nomogram via multivariate Cox regression analysis. https://www.frontiersin.org/files/Articles/1688153/fphys-17-1688153-HTML/image_m/fphys-17-1688153-g005.jpg Figure 5. Receiver operating characteristic (ROC) curve analysis of the MACEs prediction accuracy of https://www.frontiersin.org/files/Articles/1688153/fphys-17-1688153-HTML/image_m/fphys-17-1688153-g006.jpg Figure 6. Calibration curves of the MACE-free survival predictors in the development (A) and validat https://www.frontiersin.org/files/Articles/1688153/fphys-17-1688153-HTML/image_m/fphys-17-1688153-g007.jpg Figure 7. Decision curve analysis (DCA) for predicting MACE-free survival at 2 years (A,D) 3 years ( https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1739374/full https://www.frontiersin.org/files/Articles/1739374/fphys-17-1739374-HTML/image_m/fphys-17-1739374-g001.jpg Figure 1. Flowchart of the inclusion of participants in the study. https://www.frontiersin.org/files/Articles/1739374/fphys-17-1739374-HTML/image_m/fphys-17-1739374-t001.jpg Table 1. Comparison of patient characteristics between the training and validation cohorts. https://www.frontiersin.org/files/Articles/1739374/fphys-17-1739374-HTML/image_m/fphys-17-1739374-g002.jpg Figure 2. The Boruta plot illustrates the significance of diverse clinical and metabolic indicators https://www.frontiersin.org/files/Articles/1739374/fphys-17-1739374-HTML/image_m/fphys-17-1739374-g003.jpg Figure 3. The Receiver Operating Characteristic (ROC) curves for both the training (A) and independe https://www.frontiersin.org/files/Articles/1739374/fphys-17-1739374-HTML/image_m/fphys-17-1739374-g004.jpg Figure 4. (A) The calibration curves for various machine learning, assessed on the independent valid https://www.frontiersin.org/files/Articles/1739374/fphys-17-1739374-HTML/image_m/fphys-17-1739374-g005.jpg Figure 5. (A) SHAP interpretation of the XGBoost model. The SHAP (SHapley Additive exPlanations) sum https://www.frontiersin.org/journals/food-science-and-technology/articles/10.3389/frfst.2025.1552638/full https://www.frontiersin.org/files/Articles/1552638/frfst-05-1552638-HTML/image_m/frfst-05-1552638-g001.jpg Figure 1. Infrared spectra of unheated and heated corn oil. https://www.frontiersin.org/files/Articles/1552638/frfst-05-1552638-HTML/image_m/frfst-05-1552638-g002.jpg Figure 2. (a-d) Gas chromatogram of unheated corn oil heated at 180, 220, and 240°C for 12 h in corn https://www.frontiersin.org/files/Articles/1552638/frfst-05-1552638-HTML/image_m/frfst-05-1552638-g003.jpg Figure 3. Plots showing the correlation between ln (Ct/C0) and time for the C18:2-9c,12t in the pres https://www.frontiersin.org/files/Articles/1552638/frfst-05-1552638-HTML/image_m/frfst-05-1552638-t001.jpg Table 1. Kinetic parameters for (1) formation of C18:2-9c,12t; (2) formation of C18:2-9t,12c; (3) fo https://www.frontiersin.org/files/Articles/1552638/frfst-05-1552638-HTML/image_m/frfst-05-1552638-t002.jpg Table 2. Dynamic model of heat-induced isomers for concentration, heating temperature, and heating t https://www.frontiersin.org/files/Articles/1552638/frfst-05-1552638-HTML/image_m/frfst-05-1552638-g004.jpg Figure 4. Plots showing the correlation between Ct and time for the C18:2-9t,12t in the presence of https://www.frontiersin.org/files/Articles/1552638/frfst-05-1552638-HTML/image_m/frfst-05-1552638-g005.jpg Figure 5. Plots showing the correlation between ln (Ct/C0) and time for the t,t-CLAs in the presence https://www.frontiersin.org/files/Articles/1552638/frfst-05-1552638-HTML/image_m/frfst-05-1552638-g006.jpg Figure 6. Plots showing the correlation between ln (Ct/C0) and time for C18:2-9c,11t in the presence https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1733013/full https://www.frontiersin.org/files/Articles/1733013/fpubh-14-1733013-HTML/image_m/fpubh-14-1733013-t001.jpg Table 1. Modules of public health in action curriculum. https://www.frontiersin.org/files/Articles/1733013/fpubh-14-1733013-HTML/image_m/fpubh-14-1733013-g001.jpg Figure 1. Model of public health in action in high schools. https://www.frontiersin.org/files/Articles/1733013/fpubh-14-1733013-HTML/image_m/fpubh-14-1733013-g002.jpg Figure 2. Number of activities by their focus areas. https://www.frontiersin.org/files/Articles/1733013/fpubh-14-1733013-HTML/image_m/fpubh-14-1733013-g003.jpg Figure 3. Public health club activities by SDG goals. https://www.frontiersin.org/files/Articles/1733013/fpubh-14-1733013-HTML/image_m/fpubh-14-1733013-t002.jpg Table 2. Summary table of PH club activities by school. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1755914/full https://www.frontiersin.org/files/Articles/1755914/fcvm-13-1755914-HTML/image_m/fcvm-13-1755914-g001.jpg Figure 1. TEE-guided localisation and exclusion of an intercostal artery inflow at the T5 level: pre https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1813434/full https://www.frontiersin.org/files/Articles/1813434/fpls-17-1813434-HTML/image_m/fpls-17-1813434-g001.jpg Figure 1. Morphometric parameters of Olea europaea plants under different concentrations of PEG and https://www.frontiersin.org/files/Articles/1813434/fpls-17-1813434-HTML/image_m/fpls-17-1813434-g002.jpg Figure 2. SEM images of olive leaves exposed to PEG (0, 1, 2, and 4%) (A–D) and to NaCl treatment (0 https://www.frontiersin.org/files/Articles/1813434/fpls-17-1813434-HTML/image_m/fpls-17-1813434-g003.jpg Figure 3. Transcript levels of marker genes relative to the reference gene β-actin after 1%, 2%, 4% https://www.frontiersin.org/files/Articles/1813434/fpls-17-1813434-HTML/image_m/fpls-17-1813434-g004.jpg Figure 4. Chlorophylls (A–C), carotenoids (D) and anthocyanins (E) content in control plants and in https://www.frontiersin.org/files/Articles/1813434/fpls-17-1813434-HTML/image_m/fpls-17-1813434-g005.jpg Figure 5. Detection of ROS in PEG and NaCl-treated samples. The detection of ROS was carried out by https://www.frontiersin.org/files/Articles/1813434/fpls-17-1813434-HTML/image_m/fpls-17-1813434-g006.jpg Figure 6. Quantification of green fluorescence detected using DCFH2-DA in PEG and NaCl-treated plant https://www.frontiersin.org/files/Articles/1813434/fpls-17-1813434-HTML/image_m/fpls-17-1813434-g007.jpg Figure 7. The level of TBARS in olive leaves under 1%, 2%, 4% PEG and 50 mM, 100 mM, 200 mM NaCl is https://www.frontiersin.org/files/Articles/1813434/fpls-17-1813434-HTML/image_m/fpls-17-1813434-g008.jpg Figure 8. Antioxidant enzymes activities in olive leaves under 1%, 2%, 4% PEG and 50 mM, 100 mM, 200 https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1681724/full https://www.frontiersin.org/files/Articles/1681724/fimmu-16-1681724-HTML/image_m/fimmu-16-1681724-g001.jpg Figure 1. Dasatinib and quercetin (D+Q) treatment ameliorates EAE and reduces senescent microglial p https://www.frontiersin.org/files/Articles/1681724/fimmu-16-1681724-HTML/image_m/fimmu-16-1681724-g002.jpg Figure 2. Navitoclax treatment ameliorates EAE and reduces senescent microglial phenotypes in middle https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1801497/full https://www.frontiersin.org/files/Articles/1801497/fimmu-17-1801497-HTML/image_m/fimmu-17-1801497-g001.jpg Figure 1. Schematic diagram of developmental origins and colonization pathways of retinal microglia https://www.frontiersin.org/files/Articles/1801497/fimmu-17-1801497-HTML/image_m/fimmu-17-1801497-g002.jpg Figure 2. Functions of microglia and MDMs. Microglia perform essential functions including immune su https://www.frontiersin.org/files/Articles/1801497/fimmu-17-1801497-HTML/image_m/fimmu-17-1801497-g003.jpg Figure 3. Cellular markers of microglia and MDMs. https://www.frontiersin.org/files/Articles/1801497/fimmu-17-1801497-HTML/image_m/fimmu-17-1801497-g004.jpg Figure 4. Polarization lineages and regulatory mechanisms of retinal microglia and MDMs in glaucoma. https://www.frontiersin.org/files/Articles/1801497/fimmu-17-1801497-HTML/image_m/fimmu-17-1801497-g005.jpg Figure 5. Activation and polarization of microglia and MDMs in glaucoma. Microglia rapidly shift fro https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2026.1755090/full https://www.frontiersin.org/files/Articles/1755090/froh-07-1755090-HTML/image_m/froh-07-1755090-g001.jpg Figure 1. Preparation of the L-PRF dentin block. Representative images of the process for L-PRF and https://www.frontiersin.org/files/Articles/1755090/froh-07-1755090-HTML/image_m/froh-07-1755090-g002.jpg Figure 2. Surgical technique for treating infrabony periodontal defects with L-PRF and autologous de https://www.frontiersin.org/files/Articles/1755090/froh-07-1755090-HTML/image_m/froh-07-1755090-t001.jpg Table 1. Clinical and imaging defect analyses. https://www.frontiersin.org/files/Articles/1755090/froh-07-1755090-HTML/image_m/froh-07-1755090-g003.jpg Figure 3. L-PRF dentin block treatment improves clinical and CBCT imaging parameters following treat https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1680225/full https://www.frontiersin.org/files/Articles/1680225/fnut-12-1680225-HTML/image_m/fnut-12-1680225-t001.jpg Table 1. Baseline characteristics of study participants according to E-DII score categories. https://www.frontiersin.org/files/Articles/1680225/fnut-12-1680225-HTML/image_m/fnut-12-1680225-t002.jpg Table 2. Association between the E-DII score and AAA incidence. https://www.frontiersin.org/files/Articles/1680225/fnut-12-1680225-HTML/image_m/fnut-12-1680225-g001.jpg Figure 1. Association of the E-DII score and its components with AAA risk. (A) Restricted cubic spli https://www.frontiersin.org/files/Articles/1680225/fnut-12-1680225-HTML/image_m/fnut-12-1680225-g002.jpg Figure 2. Joint association of the E-DII score and polygenic risk score with AAA risk. Analyzes were https://www.frontiersin.org/files/Articles/1680225/fnut-12-1680225-HTML/image_m/fnut-12-1680225-g003.jpg Figure 3. Mediation analysis of inflammatory index SIRI in the association between E-DII score and A https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1755868/full https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-t001.jpg Table 1. Physiochemical properties of compost, compost tea and microalgal strains before the experim https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-t002.jpg Table 2. Experimental treatment combinations. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-t003.jpg Table 3. Physicochemical analysis of soil before plantation selected for experiment. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g001.jpg Figure 1. (a) PBR setup for microalgal strains T. nygaardii and C. acicularis (b) Cultivation and bi https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-t004.jpg Table 4. OD measurements of T. nygaardii and C. acicularis microalgal strains. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g002.jpg Figure 2. FTIR of microalgal strains (a) T. nygaardii, and (b) C. acicularis. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g003.jpg Figure 3. XRD spectrum of (a) T. nygaardii and (b) C. acicularis. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g004.jpg Figure 4. SEM images of (a) T. nygaardii, and (b) C. acicularis. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g005.jpg Figure 5. Field experimental setup showing wheat; (a) after 1 month tillering stage, (b) after 3-mon https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-t005.jpg Table 5. Post-harvest soil analysis under different treatments. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g006.jpg Figure 6. Chlorophyll content in wheat and spinach plants. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g007.jpg Figure 7. Plant antioxidant enzyme activities (a) SOD (b) Peroxidase (c) Catalase. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g008.jpg Figure 8. Plant morphological analysis with different combinations of microalgal strains; (a) Spinac https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g009.jpg Figure 9. Shoot length of wheat and spinach crops under various treatments. https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g010.jpg Figure 10. Fresh and Dry weight of edible crops at the maturity stage; (a) wheat crop, (b) spinach c https://www.frontiersin.org/files/Articles/1755868/fmicb-17-1755868-HTML-r1/image_m/fmicb-17-1755868-g011.jpg Figure 11. Root length of wheat and spinach plants under different treatments. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1655969/full https://www.frontiersin.org/files/Articles/1655969/fimmu-16-1655969-HTML/image_m/fimmu-16-1655969-g001.jpg Figure 1. Historical and epidemiological context of BCG vaccination and tuberculosis in Brazil. (A) https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1733691/full https://www.frontiersin.org/files/Articles/1733691/fphar-17-1733691-HTML/image_m/fphar-17-1733691-g001.jpg Figure 1. Latent sensitization phenomena in the reserpine-model. In (A) the experimental design is i https://www.frontiersin.org/files/Articles/1733691/fphar-17-1733691-HTML/image_m/fphar-17-1733691-g002.jpg Figure 2. Subcutaneous naloxone had a similar effect to intrathecal naltrexone (NTX), in restoring h https://www.frontiersin.org/files/Articles/1733691/fphar-17-1733691-HTML/image_m/fphar-17-1733691-g003.jpg Figure 3. Effect of μ, δ and κ opioid receptor specific antagonists, on latent sensitization, the ex https://www.frontiersin.org/files/Articles/1733691/fphar-17-1733691-HTML/image_m/fphar-17-1733691-g004.jpg Figure 4. The ovariectomy (OVX) did not modify the recall of hyperalgesia and allodynia produced by https://www.frontiersin.org/files/Articles/1733691/fphar-17-1733691-HTML/image_m/fphar-17-1733691-g005.jpg Figure 5. Quantification of β-endorphin in female rats in the reserpine-model. Serum β-endorphin lev https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1758972/full https://www.frontiersin.org/files/Articles/1758972/fped-14-1758972-HTML/image_m/fped-14-1758972-t001.jpg Table 1. Regularly monitored clinical parameters used for statistical analyses. https://www.frontiersin.org/files/Articles/1758972/fped-14-1758972-HTML/image_m/fped-14-1758972-t002.jpg Table 2. Descriptive statistics of prenatal and birth parameters in the entire cohort. https://www.frontiersin.org/files/Articles/1758972/fped-14-1758972-HTML/image_m/fped-14-1758972-t003.jpg Table 3. Number and percentage of medical diagnoses in the entire cohort. https://www.frontiersin.org/files/Articles/1758972/fped-14-1758972-HTML/image_m/fped-14-1758972-t004.jpg Table 4. Comparison of gestational age and birth weight for affected and control groups. https://www.frontiersin.org/files/Articles/1758972/fped-14-1758972-HTML/image_m/fped-14-1758972-t005.jpg Table 5. Statistical comparison of prenatal parameters (G1), birth related parameters (G2), and infa https://www.frontiersin.org/files/Articles/1758972/fped-14-1758972-HTML/image_m/fped-14-1758972-t006.jpg Table 6. Timing of cPVL and IVH diagnoses for patients with. https://www.frontiersin.org/files/Articles/1758972/fped-14-1758972-HTML/image_m/fped-14-1758972-t007.jpg Table 7. Statistical comparison of regularly measured (G4) and calculated parameters (G5). https://www.frontiersin.org/journals/organizational-psychology/articles/10.3389/forgp.2026.1720321/full https://www.frontiersin.org/files/Articles/1720321/forgp-04-1720321-HTML/image_m/forgp-04-1720321-g001.jpg Figure 1. Study model. Temporal headers derived from Alipour and Mohammed (2023). https://www.frontiersin.org/files/Articles/1720321/forgp-04-1720321-HTML/image_m/forgp-04-1720321-t001.jpg Table 1. Means, standard deviations, Cronbach's alpha, and correlations of variables. https://www.frontiersin.org/files/Articles/1720321/forgp-04-1720321-HTML/image_m/forgp-04-1720321-t002.jpg Table 2. Polynomial regression results of person-supervisor synchrony preference and temporal self-e https://www.frontiersin.org/files/Articles/1720321/forgp-04-1720321-HTML/image_m/forgp-04-1720321-t003.jpg Table 3. Results of the response surface analysis. https://www.frontiersin.org/files/Articles/1720321/forgp-04-1720321-HTML/image_m/forgp-04-1720321-g002.jpg Figure 2. Response surface relating temporal self-efficacy to supervisor-supervisee synchrony prefer https://www.frontiersin.org/files/Articles/1720321/forgp-04-1720321-HTML/image_m/forgp-04-1720321-t004.jpg Table 4. Results of the hierarchical regression testing. https://www.frontiersin.org/files/Articles/1720321/forgp-04-1720321-HTML/image_m/forgp-04-1720321-g003.jpg Figure 3. The moderating effect of coordinative complexity on temporal self-efficacy and time pressu https://www.frontiersin.org/files/Articles/1720321/forgp-04-1720321-HTML/image_m/forgp-04-1720321-g004.jpg Figure 4. The moderating effect of temporal ambiguity on temporal self-efficacy and time pressure. + https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1771970/full https://www.frontiersin.org/files/Articles/1771970/fonc-16-1771970-HTML/image_m/fonc-16-1771970-t001.jpg Table 1. Distribution of sample characteristics by cancer screening status among Puerto Ricans aged https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1680352/full https://www.frontiersin.org/files/Articles/1680352/fpsyt-17-1680352-HTML-r1/image_m/fpsyt-17-1680352-t001.jpg Table 1. Descriptive statistics by item of the DSM-XC (Total sample N = 3,101). https://www.frontiersin.org/files/Articles/1680352/fpsyt-17-1680352-HTML-r1/image_m/fpsyt-17-1680352-t002.jpg Table 2. Model fit indices for the exploratory and confirmatory factor analyses. https://www.frontiersin.org/files/Articles/1680352/fpsyt-17-1680352-HTML-r1/image_m/fpsyt-17-1680352-g001.jpg Figure 1. The factor loadings in the 6-factor general solution (CFA estimates reported). Color inten https://www.frontiersin.org/files/Articles/1680352/fpsyt-17-1680352-HTML-r1/image_m/fpsyt-17-1680352-g002.jpg Figure 2. The factor loadings in the 5-factor bifactor solution (CFA estimates reported). Color inte https://www.frontiersin.org/files/Articles/1680352/fpsyt-17-1680352-HTML-r1/image_m/fpsyt-17-1680352-g003.jpg Figure 3. The factor loadings in the Caspi-inspired 4-factor bifactor solution (CFA estimates report https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1658950/full https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-g001.jpg Figure 1. Flow diagram for identification of studies in the systematic review. https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-t001.jpg Table 1. Characteristics of the studies included in this review. https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-t002.jpg Table 2. Demographic characteristics of participants. https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-g002.jpg Figure 2. Risk of bias (ROB) analysis highlighting results in all domains examined within the nine i https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-g003.jpg Figure 3. Network meta-analysis of various interventions on cardiopulmonary fitness. (A) Network of https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-t003.jpg Table 3. SUCRA ranking of dietary interventions for cardiopulmonary outcomes at high altitude. https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-g004.jpg Figure 4. League table displaying pairwise comparisons among various interventions on VO2max (A), RP https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-g005.jpg Figure 5. Surface Under the Cumulative Ranking Curve (SUCRA) illustrating the cumulative probability https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-g006.jpg Figure 6. Funnel plot detailing publication bias in the studies reporting the impact of dietary nitr https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-t004.jpg Table 4. Egger's test for publication bias in dietary interventions on cardiopulmonary outcomes at h https://www.frontiersin.org/files/Articles/1658950/fnut-12-1658950-HTML/image_m/fnut-12-1658950-t005.jpg Table 5. Deviance information criterion (DIC) comparison between consistency and inconsistency model https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1740527/full https://www.frontiersin.org/files/Articles/1740527/fendo-16-1740527-HTML/image_m/fendo-16-1740527-t001.jpg Table 1. Clinical characteristics of study subjects according to BMI (mean ± SD). https://www.frontiersin.org/files/Articles/1740527/fendo-16-1740527-HTML/image_m/fendo-16-1740527-t002.jpg Table 2. Hormonal values stratified by weight status [median (25th percentile, 75th percentile)]. https://www.frontiersin.org/files/Articles/1740527/fendo-16-1740527-HTML/image_m/fendo-16-1740527-g001.jpg Figure 1. Bar charts of sex hormone levels in ICPP girls with different weight statuses [median (25t https://www.frontiersin.org/files/Articles/1740527/fendo-16-1740527-HTML/image_m/fendo-16-1740527-t003.jpg Table 3. Pearson’s correlation table on BMI and sexual development-related indicators stratified by https://www.frontiersin.org/files/Articles/1740527/fendo-16-1740527-HTML/image_m/fendo-16-1740527-t004.jpg Table 4. Partial correlation analysis of BMI and sexual development-related indicators after adjusti https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1774350/full https://www.frontiersin.org/files/Articles/1774350/fped-14-1774350-HTML/image_m/fped-14-1774350-g001.jpg Figure 1. Patient disposition. https://www.frontiersin.org/files/Articles/1774350/fped-14-1774350-HTML/image_m/fped-14-1774350-t001.jpg Table 1. Baseline characteristics of monthly and quarterly triptorelin groups. https://www.frontiersin.org/files/Articles/1774350/fped-14-1774350-HTML/image_m/fped-14-1774350-t002.jpg Table 2. Data in CPP girls during and after triptorelin treatment. https://www.frontiersin.org/files/Articles/1774350/fped-14-1774350-HTML/image_m/fped-14-1774350-t003.jpg Table 3. PAH before and after triptorelin treatment. https://www.frontiersin.org/files/Articles/1774350/fped-14-1774350-HTML/image_m/fped-14-1774350-g002.jpg Figure 2. Uterine volume before and after triptorelin treatment. https://www.frontiersin.org/files/Articles/1774350/fped-14-1774350-HTML/image_m/fped-14-1774350-t004.jpg Table 4. ADRs in CPP girls during triptorelin treatment. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1751344/full https://www.frontiersin.org/files/Articles/1751344/fmed-13-1751344-HTML-r1/image_m/fmed-13-1751344-t001.jpg Table 1. Characteristics of the patients at baseline. https://www.frontiersin.org/files/Articles/1751344/fmed-13-1751344-HTML-r1/image_m/fmed-13-1751344-t002.jpg Table 2. Immune characteristics of different risk stratifications of MIPI-C. https://www.frontiersin.org/files/Articles/1751344/fmed-13-1751344-HTML-r1/image_m/fmed-13-1751344-g001.jpg Figure 1. Kaplan–Meier survival curves of peripheral blood immune cell subsets in patients with MCL. https://www.frontiersin.org/files/Articles/1751344/fmed-13-1751344-HTML-r1/image_m/fmed-13-1751344-g002.jpg Figure 2. Construction of prognostic models and comparison of prognostic capabilities. (A) Ranking o https://www.frontiersin.org/files/Articles/1751344/fmed-13-1751344-HTML-r1/image_m/fmed-13-1751344-g003.jpg Figure 3. Kaplan–Meier survival curves of different prognostic model. (A,B) PFS and OS curves strati https://www.frontiersin.org/files/Articles/1751344/fmed-13-1751344-HTML-r1/image_m/fmed-13-1751344-g004.jpg Figure 4. Calibration curves of prognostic models for PFS prediction. (A,B) Calibration curves for 3 https://www.frontiersin.org/files/Articles/1751344/fmed-13-1751344-HTML-r1/image_m/fmed-13-1751344-t003.jpg Table 3. Previously reported immune cells for the prognosis of lymphoma. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1772010/full https://www.frontiersin.org/files/Articles/1772010/fcimb-16-1772010-HTML/image_m/fcimb-16-1772010-g001.jpg Figure 1. Probiotic-mediated restoration of intestinal barrier function and modulation of mast cells https://www.frontiersin.org/files/Articles/1772010/fcimb-16-1772010-HTML/image_m/fcimb-16-1772010-t001.jpg Table 1. Evidence of interactions between mast cells (MCs) and probiotics: probiotic strains, experi https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1800667/full https://www.frontiersin.org/files/Articles/1800667/fphar-17-1800667-HTML-r1/image_m/fphar-17-1800667-g001.jpg Figure 1. Upregulation of NAMPT and pro-inflammatory cytokines in rat brain after cerebral ischemia- https://www.frontiersin.org/files/Articles/1800667/fphar-17-1800667-HTML-r1/image_m/fphar-17-1800667-g002.jpg Figure 2. Comparison of cerebral infarct volume and neurological deficit scores among the four rat g https://www.frontiersin.org/files/Articles/1800667/fphar-17-1800667-HTML-r1/image_m/fphar-17-1800667-g003.jpg Figure 3. Comparison of inflammatory cytokine levels in brain tissue among the four rat groups. (A) https://www.frontiersin.org/files/Articles/1800667/fphar-17-1800667-HTML-r1/image_m/fphar-17-1800667-g004.jpg Figure 4. Comparison of NAMPT Content and NAD+ levels in brain tissue among the four rat groups. (A) https://www.frontiersin.org/files/Articles/1800667/fphar-17-1800667-HTML-r1/image_m/fphar-17-1800667-g005.jpg Figure 5. Comparison of cytotoxicity and viability among the four microglial cell groups. (A) Statis https://www.frontiersin.org/files/Articles/1800667/fphar-17-1800667-HTML-r1/image_m/fphar-17-1800667-g006.jpg Figure 6. Comparison of inflammatory cytokine levels in microglial cell culture supernatants among t https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1755745/full https://www.frontiersin.org/files/Articles/1755745/fmicb-17-1755745-HTML/image_m/fmicb-17-1755745-t001.jpg Table 1. Phenotypic and biochemical characteristics of the isolated strains. https://www.frontiersin.org/files/Articles/1755745/fmicb-17-1755745-HTML/image_m/fmicb-17-1755745-g001.jpg Figure 1. Phylogenetic trees of the LAB isolates. (A) JF1; (B) JF2; (C) R3; (D) R10; (E) AR1. https://www.frontiersin.org/files/Articles/1755745/fmicb-17-1755745-HTML/image_m/fmicb-17-1755745-g002.jpg Figure 2. pH curves and growth curves of the LAB isolates. (A) pH curves of the LAB isolates. (B) Gr https://www.frontiersin.org/files/Articles/1755745/fmicb-17-1755745-HTML/image_m/fmicb-17-1755745-t002.jpg Table 2. Effects of different inoculants and inoculation levels on fermentation quality of corn stov https://www.frontiersin.org/files/Articles/1755745/fmicb-17-1755745-HTML/image_m/fmicb-17-1755745-t003.jpg Table 3. Effects of different inoculants and inoculation levels on chemical composition of corn stov https://www.frontiersin.org/files/Articles/1755745/fmicb-17-1755745-HTML/image_m/fmicb-17-1755745-t004.jpg Table 4. Effects of different inoculants and inoculation levels on aerobic stability of corn stover https://www.frontiersin.org/files/Articles/1755745/fmicb-17-1755745-HTML/image_m/fmicb-17-1755745-t005.jpg Table 5. Comprehensive evaluation of corn stover silage quality under different inoculants and inocu https://www.frontiersin.org/files/Articles/1755745/fmicb-17-1755745-HTML/image_m/fmicb-17-1755745-g003.jpg Figure 3. Pearson correlation analysis of fermentation quality, nutritional composition, and aerobic https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1677433/full https://www.frontiersin.org/files/Articles/1677433/fimmu-16-1677433-HTML/image_m/fimmu-16-1677433-g001.jpg Figure 1. Neuroinflammation-associated hallmarks of viral infections in the CNS. Overview of key mec https://www.frontiersin.org/files/Articles/1677433/fimmu-16-1677433-HTML/image_m/fimmu-16-1677433-g002.jpg Figure 2. Barriers and emerging strategies for the clinical translation and enhancing therapeutic ef https://www.frontiersin.org/files/Articles/1677433/fimmu-16-1677433-HTML/image_m/fimmu-16-1677433-g003.jpg Figure 3. Summary of clinical trials on MSC-based therapies targeting viral infections. Overview of https://www.frontiersin.org/files/Articles/1677433/fimmu-16-1677433-HTML/image_m/fimmu-16-1677433-t001.jpg Table 1. Completed clinical studies on clinicaltrials.gov evaluating the use of MSC or their derivat https://www.frontiersin.org/files/Articles/1677433/fimmu-16-1677433-HTML/image_m/fimmu-16-1677433-t002.jpg Table 2. Clinical studies registered on clinicaltrials.gov that evaluate the safety and efficacy of https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1612509/full https://www.frontiersin.org/files/Articles/1612509/fpubh-13-1612509-HTML-r1/image_m/fpubh-13-1612509-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/files/Articles/1612509/fpubh-13-1612509-HTML-r1/image_m/fpubh-13-1612509-g001.jpg Figure 1. Interactions between systems using Bronfenbrenner’s ecological model to organize and inter https://www.frontiersin.org/files/Articles/1612509/fpubh-13-1612509-HTML-r1/image_m/fpubh-13-1612509-g002.jpg Figure 2. Themes and subthemes. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1682149/full https://www.frontiersin.org/files/Articles/1682149/fpubh-13-1682149-HTML/image_m/fpubh-13-1682149-g001.jpg Figure 1. Balancing the Proximity-Security Continuum: An integrated model leverages hospital resourc https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1672118/full https://www.frontiersin.org/files/Articles/1672118/fpubh-13-1672118-HTML/image_m/fpubh-13-1672118-t001.jpg Table 1. Elements of the model that guided the literature review and document analysis. https://www.frontiersin.org/files/Articles/1672118/fpubh-13-1672118-HTML/image_m/fpubh-13-1672118-t002.jpg Table 2. Chronology of key events in the implementation of iCCM in Burkina Faso (2008–2024). https://www.frontiersin.org/files/Articles/1672118/fpubh-13-1672118-HTML/image_m/fpubh-13-1672118-g001.jpg Figure 1. Diagram illustrating the selection process, grounds for exclusion and final number of docu https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1659703/full https://www.frontiersin.org/files/Articles/1659703/fpubh-13-1659703-HTML-r1/image_m/fpubh-13-1659703-g001.jpg Figure 1. Conceptual framework for analysis. https://www.frontiersin.org/files/Articles/1659703/fpubh-13-1659703-HTML-r1/image_m/fpubh-13-1659703-g002.jpg Figure 2. Timeline of key events concerning the period between January 2018 to December 2021. https://www.frontiersin.org/files/Articles/1659703/fpubh-13-1659703-HTML-r1/image_m/fpubh-13-1659703-g003.jpg Figure 3. Source: Zimbabwe District Health Information Software Version 2 maternal indicators. https://www.frontiersin.org/files/Articles/1659703/fpubh-13-1659703-HTML-r1/image_m/fpubh-13-1659703-g004.jpg Figure 4. Source: Zimbabwe District Health Information Software Version 2 child indicators data. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1707846/full https://www.frontiersin.org/files/Articles/1707846/fpubh-13-1707846-HTML/image_m/fpubh-13-1707846-t001.jpg Table 1. Open coding (partial examples). https://www.frontiersin.org/files/Articles/1707846/fpubh-13-1707846-HTML/image_m/fpubh-13-1707846-t002.jpg Table 2. Axial coding. https://www.frontiersin.org/files/Articles/1707846/fpubh-13-1707846-HTML/image_m/fpubh-13-1707846-g001.jpg Figure 1. Mechanism model of unintended consequences in the implementation of physical and health in https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1706653/full https://www.frontiersin.org/files/Articles/1706653/fpubh-13-1706653-HTML/image_m/fpubh-13-1706653-t001.jpg Table 1. A summary of research needs and recommendations. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1708209/full https://www.frontiersin.org/files/Articles/1708209/fpubh-13-1708209-HTML/image_m/fpubh-13-1708209-t001.jpg Table 1. Characteristics of Dutch policymakers who participated in the study, 2023. https://www.frontiersin.org/files/Articles/1708209/fpubh-13-1708209-HTML/image_m/fpubh-13-1708209-t002.jpg Table 2. Reasons for citizen involvement in local policymaking on health promotion as reported by po https://www.frontiersin.org/files/Articles/1708209/fpubh-13-1708209-HTML/image_m/fpubh-13-1708209-g001.jpg Figure 1. Methods for citizen involvement based on closed-ended questions, as reported by policymake https://www.frontiersin.org/files/Articles/1708209/fpubh-13-1708209-HTML/image_m/fpubh-13-1708209-t003.jpg Table 3. Challenges for citizen involvement as reported by policymakers from the municipalities invo https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1718345/full https://www.frontiersin.org/files/Articles/1718345/fpubh-13-1718345-HTML/image_m/fpubh-13-1718345-g001.jpg Figure 1. Causal loop diagram of flood-related community resilience strategies, their drivers and ef https://www.frontiersin.org/files/Articles/1718345/fpubh-13-1718345-HTML/image_m/fpubh-13-1718345-g002.jpg Figure 2. Balancing loop B1. https://www.frontiersin.org/files/Articles/1718345/fpubh-13-1718345-HTML/image_m/fpubh-13-1718345-g003.jpg Figure 3. Reinforcing loops R1. https://www.frontiersin.org/files/Articles/1718345/fpubh-13-1718345-HTML/image_m/fpubh-13-1718345-g004.jpg Figure 4. Reinforcing loops R2. https://www.frontiersin.org/files/Articles/1718345/fpubh-13-1718345-HTML/image_m/fpubh-13-1718345-g005.jpg Figure 5. Reinforcing loops R3. https://www.frontiersin.org/files/Articles/1718345/fpubh-13-1718345-HTML/image_m/fpubh-13-1718345-g006.jpg Figure 6. Reinforcing loops R4. https://www.frontiersin.org/files/Articles/1718345/fpubh-13-1718345-HTML/image_m/fpubh-13-1718345-g007.jpg Figure A1. Combined metal model of community actors’ flood resilience strategies, their drivers and https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1739655/full https://www.frontiersin.org/files/Articles/1739655/fpubh-14-1739655-HTML-r1/image_m/fpubh-14-1739655-t001.jpg Table 1. Operationalization of theoretical framework of acceptability (TFA) for the WHO- labor care https://www.frontiersin.org/files/Articles/1739655/fpubh-14-1739655-HTML-r1/image_m/fpubh-14-1739655-t002.jpg Table 2. Description of participant information (midwives) involved in in depth interview in Tanzani https://www.frontiersin.org/files/Articles/1739655/fpubh-14-1739655-HTML-r1/image_m/fpubh-14-1739655-g001.jpg Figure 1. Theoretical framework of labor care guide (LCG) acceptability among midwives in Tanzania. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1752521/full https://www.frontiersin.org/files/Articles/1752521/fpubh-14-1752521-HTML/image_m/fpubh-14-1752521-t001.jpg Table 1. Typological Framework of Rural Public Policy Integration in China. https://www.frontiersin.org/files/Articles/1752521/fpubh-14-1752521-HTML/image_m/fpubh-14-1752521-g001.jpg Figure 1. Schematic diagram of the evolution of rural public health governance in China. Solid dots https://www.frontiersin.org/files/Articles/1752521/fpubh-14-1752521-HTML/image_m/fpubh-14-1752521-t002.jpg Table 2. A comparative analysis of policy integration types. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1721943/full https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g001.jpg Figure 1. ARHGAP44 gene expression patterns in human cancers. (A) UALCAN prediction of ARHGAP44 gene https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g002.jpg Figure 2. ARHGAP44 gene expression in sarcoma and association with clinical pathological parameters. https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g003.jpg Figure 3. Association between ARHGAP44 and prognostic risk across pan-cancer patients. (A) The assoc https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g004.jpg Figure 4. The phosphorylation modulation of ARHGAP44 gene expression and the gene variation types in https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g005.jpg Figure 5. Association between the ARHGAP44 gene and HRR-related gene signature, as well as MRR prote https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g006.jpg Figure 6. ARHGAP44 interaction and gene enrichment analysis and association with cytoskeleton-relate https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g007.jpg Figure 7. Association between ARHGAP44 and three main Rho GTPases in cancers. (A) ARHGAP44 associati https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g008.jpg Figure 8. Cancers mRNAsi score distribution in different ARHGAP44 gene expressed samples. (A) ARHGAP https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g009.jpg Figure 9. ARHGAP44 association with TIC distribution and CD8+ T-cell infiltration in cancers. (A) As https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-g010.jpg Figure 10. ARHGAP44 association with cancers MSI and TMB as well as certain drug sensitivity. ARHGAP https://www.frontiersin.org/files/Articles/1721943/fonc-16-1721943-HTML/image_m/fonc-16-1721943-t001.jpg Table 1. The 10 compounds correlated with ARHGAP44 using the RNAactDrug platform. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1694344/full https://www.frontiersin.org/files/Articles/1694344/fpsyg-16-1694344-HTML/image_m/fpsyg-16-1694344-t001.jpg Table 1. Variations by gender and academic year in university students. https://www.frontiersin.org/files/Articles/1694344/fpsyg-16-1694344-HTML/image_m/fpsyg-16-1694344-t002.jpg Table 2. Correlation analysis between variables (N = 1,436). https://www.frontiersin.org/files/Articles/1694344/fpsyg-16-1694344-HTML/image_m/fpsyg-16-1694344-t003.jpg Table 3. Regression analysis between variables. https://www.frontiersin.org/files/Articles/1694344/fpsyg-16-1694344-HTML/image_m/fpsyg-16-1694344-t004.jpg Table 4. Mediation analysis results. https://www.frontiersin.org/files/Articles/1694344/fpsyg-16-1694344-HTML/image_m/fpsyg-16-1694344-g001.jpg Figure 1. Mediation effect diagram. https://www.frontiersin.org/files/Articles/1694344/fpsyg-16-1694344-HTML/image_m/fpsyg-16-1694344-t005.jpg Table 5. Model fit indices for the LPA of anxiety and psychological resilience among university stud https://www.frontiersin.org/files/Articles/1694344/fpsyg-16-1694344-HTML/image_m/fpsyg-16-1694344-g002.jpg Figure 2. Latent classes of anxiety and psychological resilience. A1–A7 represent the 7 items for an https://www.frontiersin.org/files/Articles/1694344/fpsyg-16-1694344-HTML/image_m/fpsyg-16-1694344-t006.jpg Table 6. Differences in physical activity across anxiety and psychological resilience profiles. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1701168/full https://www.frontiersin.org/files/Articles/1701168/fpsyg-16-1701168-HTML-r1/image_m/fpsyg-16-1701168-t001.jpg Table 1. Demographic characteristics and group differences among older adults. https://www.frontiersin.org/files/Articles/1701168/fpsyg-16-1701168-HTML-r1/image_m/fpsyg-16-1701168-t002.jpg Table 2. Variable statistics and relationships in alexithymia, physical activity, and eating behavio https://www.frontiersin.org/files/Articles/1701168/fpsyg-16-1701168-HTML-r1/image_m/fpsyg-16-1701168-t003.jpg Table 3. Mediation effect regression results. https://www.frontiersin.org/files/Articles/1701168/fpsyg-16-1701168-HTML-r1/image_m/fpsyg-16-1701168-g001.jpg Figure 1. Mediation effect diagram. https://www.frontiersin.org/files/Articles/1701168/fpsyg-16-1701168-HTML-r1/image_m/fpsyg-16-1701168-t004.jpg Table 4. Mediating effects of physical activity between alexithymia and eating behavior. https://www.frontiersin.org/files/Articles/1701168/fpsyg-16-1701168-HTML-r1/image_m/fpsyg-16-1701168-t005.jpg Table 5. Latent Profile Analysis models. https://www.frontiersin.org/files/Articles/1701168/fpsyg-16-1701168-HTML-r1/image_m/fpsyg-16-1701168-g002.jpg Figure 2. Latent profiles of alexithymia. https://www.frontiersin.org/files/Articles/1701168/fpsyg-16-1701168-HTML-r1/image_m/fpsyg-16-1701168-t006.jpg Table 6. Group differences across latent profiles. https://www.frontiersin.org/files/Articles/1701168/fpsyg-16-1701168-HTML-r1/image_m/fpsyg-16-1701168-t007.jpg Table 7. Multiple comparisons. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1779638/full https://www.frontiersin.org/files/Articles/1779638/fimmu-17-1779638-HTML-r2/image_m/fimmu-17-1779638-t001.jpg Table 1. Overview of genes represented in the model grouped by genetic effects. https://www.frontiersin.org/files/Articles/1779638/fimmu-17-1779638-HTML-r2/image_m/fimmu-17-1779638-g001.jpg Figure 1. Model of the real-time learning component, conceived as the tumor genetic mutation process https://www.frontiersin.org/files/Articles/1779638/fimmu-17-1779638-HTML-r2/image_m/fimmu-17-1779638-g002.jpg Figure 2. The architecture of the agent-based learning model designed for investigating the influenc https://www.frontiersin.org/files/Articles/1779638/fimmu-17-1779638-HTML-r2/image_m/fimmu-17-1779638-g003.jpg Figure 3. Cell counts from simulation runs using untrained models initialized with default patient p https://www.frontiersin.org/files/Articles/1779638/fimmu-17-1779638-HTML-r2/image_m/fimmu-17-1779638-g004.jpg Figure 4. Female models derived from the analysis of four female patients, the two line-graphs corre https://www.frontiersin.org/files/Articles/1779638/fimmu-17-1779638-HTML-r2/image_m/fimmu-17-1779638-g005.jpg Figure 5. Male models derived from the analysis of three male patients, the two line-graphs correspo https://www.frontiersin.org/files/Articles/1779638/fimmu-17-1779638-HTML-r2/image_m/fimmu-17-1779638-g006.jpg Figure 6. Panels (A, B) illustrate the final average mutation mask for the seven patients in the unt https://www.frontiersin.org/files/Articles/1779638/fimmu-17-1779638-HTML-r2/image_m/fimmu-17-1779638-t002.jpg Table 2. Numerical results on simulation outcomes from the untrained and trained models of the seven https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1761646/full https://www.frontiersin.org/files/Articles/1761646/fspor-08-1761646-HTML/image_m/fspor-08-1761646-g001.jpg Figure 1. Circuit training data collection. https://www.frontiersin.org/files/Articles/1761646/fspor-08-1761646-HTML/image_m/fspor-08-1761646-t001.jpg Table 1. ANOVA interaction effect variables. https://www.frontiersin.org/files/Articles/1761646/fspor-08-1761646-HTML/image_m/fspor-08-1761646-g002.jpg Figure 2. Mean propulsive velocity in squat and bench press. Absolute participant values (plots) and https://www.frontiersin.org/files/Articles/1761646/fspor-08-1761646-HTML/image_m/fspor-08-1761646-g003.jpg Figure 3. Velocity loss in squat and bench press. Absolute participant values (plots) and Cohen's ef https://www.frontiersin.org/files/Articles/1761646/fspor-08-1761646-HTML/image_m/fspor-08-1761646-g004.jpg Figure 4. Repetitions per second per protocol (left y-axis) are presented in bars and total repetiti https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1710221/full https://www.frontiersin.org/files/Articles/1710221/fnins-19-1710221-HTML/image_m/fnins-19-1710221-g001.jpg Figure 1. Effects of the insertion of an interphase delay in biphasic current pulses with different https://www.frontiersin.org/files/Articles/1710221/fnins-19-1710221-HTML/image_m/fnins-19-1710221-g002.jpg Figure 2. Comparison of neural excitation elicited by biphasic current pulses with varying interphas https://www.frontiersin.org/files/Articles/1710221/fnins-19-1710221-HTML/image_m/fnins-19-1710221-g003.jpg Figure 3. Comparison of neural excitation elicited by pulse-train stimulation. (A,B) Space-time plot https://www.frontiersin.org/files/Articles/1710221/fnins-19-1710221-HTML/image_m/fnins-19-1710221-g004.jpg Figure 4. Comparison of neural excitation elicited by pulse-train stimulation under synaptic blockad https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1722453/full https://www.frontiersin.org/files/Articles/1722453/fgene-16-1722453-HTML/image_m/fgene-16-1722453-g001.jpg Figure 1. This figure summarizes a multilayered framework linking histone modifications to the patho https://www.frontiersin.org/files/Articles/1722453/fgene-16-1722453-HTML/image_m/fgene-16-1722453-t001.jpg Table 1. Classification and major characteristics of mammalian HDAC isoforms. https://www.frontiersin.org/files/Articles/1722453/fgene-16-1722453-HTML/image_m/fgene-16-1722453-g002.jpg Figure 2. HDAC-mediated mechanisms in depression and therapeutic intervention. Chronic stress increa https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1654933/full https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-g001.jpg Figure 1. Weekly meteorological data of winter season during 2019–20 and 2020–21. https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-t001.jpg Table 1. Treatment details. https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-t002.jpg Table 2. Details of irrigation schedules in wheat. https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-t003.jpg Table 3. Effect of crop establishment methods, irrigation regimes and precision N management options https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-t004.jpg Table 4. Effect of crop establishment methods, irrigation regimes and precision N management options https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-t005.jpg Table 5. Effect of crop establishment methods, irrigation regimes and precision N management options https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-t006.jpg Table 6. Effect of crop establishment methods, irrigation regimes and precision N management options https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-t007.jpg Table 7. Effect of crop establishment methods, irrigation regimes and precision N management options https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-t008.jpg Table 8. Effect of crop establishment methods, irrigation regimes and precision N management options https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-g002.jpg Figure 2. Effect of irrigation regimes and precision N management options on N2O emission in zero-ti https://www.frontiersin.org/files/Articles/1654933/fpls-16-1654933-HTML-r1/image_m/fpls-16-1654933-g003.jpg Figure 3. Pearson correlation matrix analysis of different growth and yield parameters. SPAD, Soil P https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1796860/full https://www.frontiersin.org/files/Articles/1796860/fendo-17-1796860-HTML/image_m/fendo-17-1796860-t001.jpg Table 1. Key endocrine and immunohistochemistry results. https://www.frontiersin.org/files/Articles/1796860/fendo-17-1796860-HTML/image_m/fendo-17-1796860-g001.jpg Figure 1. (A) preoperative sagittal, coronal, and vertical-axis MRI of the brain showing a tumor in https://www.frontiersin.org/files/Articles/1796860/fendo-17-1796860-HTML/image_m/fendo-17-1796860-g002.jpg Figure 2. Hematoxylin and eosin stain of the tumor tissue in this case. Tumor cells are spindle-shap https://www.frontiersin.org/files/Articles/1796860/fendo-17-1796860-HTML/image_m/fendo-17-1796860-t002.jpg Table 2. Timeline of the episode of care. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1674109/full https://www.frontiersin.org/files/Articles/1674109/fendo-16-1674109-HTML/image_m/fendo-16-1674109-t001.jpg Table 1. Demographic characteristics of the study population. https://www.frontiersin.org/files/Articles/1674109/fendo-16-1674109-HTML/image_m/fendo-16-1674109-t002.jpg Table 2. Comparison of the sonographic features of the nodules having different histopathologic diag https://www.frontiersin.org/files/Articles/1674109/fendo-16-1674109-HTML/image_m/fendo-16-1674109-g001.jpg Figure 1. Comparisons of ultrasonographic features in classic PTC and aggressive subtypes: Compositi https://www.frontiersin.org/files/Articles/1674109/fendo-16-1674109-HTML/image_m/fendo-16-1674109-g002.jpg Figure 2. The representative ultrasonographic appearance of classic PTC and aggressive PTC. C-PTC (A https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1773895/full https://www.frontiersin.org/files/Articles/1773895/fmed-13-1773895-HTML/image_m/fmed-13-1773895-g001.jpg Figure 1. Photobleaching behavior of autofluorescence of sporadic BCC, NBCCS-associated BCC, SCC, an https://www.frontiersin.org/files/Articles/1773895/fmed-13-1773895-HTML/image_m/fmed-13-1773895-g002.jpg Figure 2. Comparison of the means of BCC, NBCCS-associated-BCC, SCC lesion groups inner, junction re https://www.frontiersin.org/files/Articles/1773895/fmed-13-1773895-HTML/image_m/fmed-13-1773895-g003.jpg Figure 3. Representative examples of sporadic BCC on a 75-year-old male patient (A), 53-year-old mal https://www.frontiersin.org/files/Articles/1773895/fmed-13-1773895-HTML/image_m/fmed-13-1773895-g004.jpg Figure 4. Representative examples of NBCCS-associated BCC on a 45-year-old female patient (A,B), and https://www.frontiersin.org/files/Articles/1773895/fmed-13-1773895-HTML/image_m/fmed-13-1773895-g005.jpg Figure 5. Representative examples of SCC on a 71-year-old female patient (A), 74-year-old female pat https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1748173/full https://www.frontiersin.org/files/Articles/1748173/fpubh-14-1748173-HTML-r1/image_m/fpubh-14-1748173-t001.jpg Table 1. Therapeutic ranges (Cmin) and detection limits for antimicrobial agents. https://www.frontiersin.org/files/Articles/1748173/fpubh-14-1748173-HTML-r1/image_m/fpubh-14-1748173-t002.jpg Table 2. Demographic and TDM monitoring characteristics of patients. https://www.frontiersin.org/files/Articles/1748173/fpubh-14-1748173-HTML-r1/image_m/fpubh-14-1748173-t003.jpg Table 3. Therapeutic drug monitoring outcomes for 14 antimicrobial agents. https://www.frontiersin.org/files/Articles/1748173/fpubh-14-1748173-HTML-r1/image_m/fpubh-14-1748173-g001.jpg Figure 1. Distribution of plasma antimicrobial concentrations relative to recommended therapeutic wi https://www.frontiersin.org/files/Articles/1748173/fpubh-14-1748173-HTML-r1/image_m/fpubh-14-1748173-g002.jpg Figure 2. Departmental variations in TDM utilization rates for antimicrobial agents. PIP, Piperacill https://www.frontiersin.org/files/Articles/1748173/fpubh-14-1748173-HTML-r1/image_m/fpubh-14-1748173-g003.jpg Figure 3. Distribution of subtherapeutic antimicrobial plasma concentrations across clinical departm https://www.frontiersin.org/files/Articles/1748173/fpubh-14-1748173-HTML-r1/image_m/fpubh-14-1748173-g004.jpg Figure 4. Distribution of supratherapeutic plasma drug concentrations across clinical departments. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1764817/full https://www.frontiersin.org/files/Articles/1764817/fvets-13-1764817-HTML-r1/image_m/fvets-13-1764817-g001.jpg Figure 1. Representative gross and histopathological hematoxylin & eosin-stained images of the epile https://www.frontiersin.org/files/Articles/1764817/fvets-13-1764817-HTML-r1/image_m/fvets-13-1764817-g002.jpg Figure 2. Special staining of the extracellular matrix and immunofluorescence of VEGF and related ch https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1749606/full https://www.frontiersin.org/files/Articles/1749606/feduc-11-1749606-HTML/image_m/feduc-11-1749606-t001.jpg Table 1. Comparison of the two LLM’s accuracy on the health law questions in CNMLE. https://www.frontiersin.org/files/Articles/1749606/feduc-11-1749606-HTML/image_m/feduc-11-1749606-g001.jpg Figure 1. Comparison of the two LLM’s accuracy by question category on the health law questions in C https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1792850/full https://www.frontiersin.org/files/Articles/1792850/fpsyg-17-1792850-HTML/image_m/fpsyg-17-1792850-g001.jpg Figure 1. Allostasis-based predictive model linking fatigue and sense of agency. Top to bottom: Inte https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1766460/full https://www.frontiersin.org/files/Articles/1766460/fimmu-17-1766460-HTML/image_m/fimmu-17-1766460-g001.jpg Figure 1. Schematic diagram of PMOP and bone immunology. Upper part: Postmenopausal estrogen deficie https://www.frontiersin.org/files/Articles/1766460/fimmu-17-1766460-HTML/image_m/fimmu-17-1766460-g002.jpg Figure 2. Schematic diagram of changes in bone remodeling in the bone marrow microenvironment and PM https://www.frontiersin.org/files/Articles/1766460/fimmu-17-1766460-HTML/image_m/fimmu-17-1766460-g003.jpg Figure 3. Schematic diagram of the role of the adipose–immune–bone axis and the vascular–nerve–matri https://www.frontiersin.org/files/Articles/1766460/fimmu-17-1766460-HTML/image_m/fimmu-17-1766460-g004.jpg Figure 4. Schematic diagram of immune reprogramming and bone marrow microenvironment imbalance in PM https://www.frontiersin.org/files/Articles/1766460/fimmu-17-1766460-HTML/image_m/fimmu-17-1766460-g005.jpg Figure 5. Schematic diagram of key signaling pathways and molecular networks in the postmenopausal b https://www.frontiersin.org/files/Articles/1766460/fimmu-17-1766460-HTML/image_m/fimmu-17-1766460-g006.jpg Figure 6. Schematic diagram of the role of the gut-bone-immune axis in the mechanism of osteoporosis https://www.frontiersin.org/files/Articles/1766460/fimmu-17-1766460-HTML/image_m/fimmu-17-1766460-t001.jpg Table 1. Overview of stratified intervention strategies targeting the bone immune microenvironment i https://www.frontiersin.org/files/Articles/1766460/fimmu-17-1766460-HTML/image_m/fimmu-17-1766460-g007.jpg Figure 7. Schematic diagram of the application of multi-omics technology in the study of the bone im https://www.frontiersin.org/files/Articles/1766460/fimmu-17-1766460-HTML/image_m/fimmu-17-1766460-g008.jpg Figure 8. Research on the bone immune microenvironment of PMOP – stratification – integrated interve https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1796414/full https://www.frontiersin.org/files/Articles/1796414/fneur-17-1796414-HTML/image_m/fneur-17-1796414-g001.jpg Figure 1. PRISMA flow diagram. PRISMA, preferred reporting items for systematic reviews and meta-ana https://www.frontiersin.org/files/Articles/1796414/fneur-17-1796414-HTML/image_m/fneur-17-1796414-g002.jpg Figure 2. Forest plot diagram of auditory recovery and vestibular function. https://www.frontiersin.org/files/Articles/1796414/fneur-17-1796414-HTML/image_m/fneur-17-1796414-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1796414/fneur-17-1796414-HTML/image_m/fneur-17-1796414-t002.jpg Table 2. Meta-analysis of vestibular function and hearing improvement. https://www.frontiersin.org/files/Articles/1796414/fneur-17-1796414-HTML/image_m/fneur-17-1796414-t003.jpg Table 3. Sensitivity analysis of hearing recovery based on caloric test. https://www.frontiersin.org/files/Articles/1796414/fneur-17-1796414-HTML/image_m/fneur-17-1796414-t004.jpg Table 4. Sensitivity analysis of hearing recovery based on vHIT for the PC. https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2026.1741762/full https://www.frontiersin.org/files/Articles/1741762/fncir-20-1741762-HTML/image_m/fncir-20-1741762-g001.jpg Figure 1. Schematic representation of the experimental procedure and experimental trials under the s https://www.frontiersin.org/files/Articles/1741762/fncir-20-1741762-HTML/image_m/fncir-20-1741762-g002.jpg Figure 2. Clusters of increased (in orange) and decreased (in teal) BOLD signals associated with per https://www.frontiersin.org/files/Articles/1741762/fncir-20-1741762-HTML/image_m/fncir-20-1741762-t001.jpg Table 1. Clusters of increased and decreased BOLD signals associated with performance in the sociall https://www.frontiersin.org/files/Articles/1741762/fncir-20-1741762-HTML/image_m/fncir-20-1741762-g003.jpg Figure 3. Clusters of overlap in RPS-induced increases in TMFC for the ROIs with RPS-related BOLD si https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1763514/full https://www.frontiersin.org/files/Articles/1763514/fimmu-17-1763514-HTML/image_m/fimmu-17-1763514-g001.jpg Figure 1. Schematic representation of the LISP-2 protein structure and analysis of the production an https://www.frontiersin.org/files/Articles/1763514/fimmu-17-1763514-HTML/image_m/fimmu-17-1763514-g002.jpg Figure 2. Prevalence of anti-Pv_LISP-2 antibodies and association between IgG and IgM response in in https://www.frontiersin.org/files/Articles/1763514/fimmu-17-1763514-HTML/image_m/fimmu-17-1763514-g003.jpg Figure 3. Profile of the IgG and IgM antibody response against Pv_LISP-2 during the acute (D0) and c https://www.frontiersin.org/files/Articles/1763514/fimmu-17-1763514-HTML/image_m/fimmu-17-1763514-g004.jpg Figure 4. Dynamics of IgG and IgM response during acute (D0) and convalescent phase (Conv 50 dpi and https://www.frontiersin.org/files/Articles/1763514/fimmu-17-1763514-HTML/image_m/fimmu-17-1763514-g005.jpg Figure 5. Associations between antibody responses to the recombinant antigen and days of symptoms. P https://www.frontiersin.org/files/Articles/1763514/fimmu-17-1763514-HTML/image_m/fimmu-17-1763514-t001.jpg Table 1. Correlation between the Reactivity Index (RI) of IgM antibody in patients in the acute phas https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1714793/full https://www.frontiersin.org/files/Articles/1714793/fped-13-1714793-HTML-r1/image_m/fped-13-1714793-t001.jpg Table 1. Comparison of general data between group A and group B (mean ± SD). https://www.frontiersin.org/files/Articles/1714793/fped-13-1714793-HTML-r1/image_m/fped-13-1714793-g001.jpg Figure 1. Surgical approaches for laparoscopic pyeloplasty. (A) For left-sided hydronephrosis, the t https://www.frontiersin.org/files/Articles/1714793/fped-13-1714793-HTML-r1/image_m/fped-13-1714793-g002.jpg Figure 2. Step-by-step illustrations of laparoscopic dismembered pyeloplasty. (A) The ureteropelvic https://www.frontiersin.org/files/Articles/1714793/fped-13-1714793-HTML-r1/image_m/fped-13-1714793-t002.jpg Table 2. Comparison of intraoperative and postoperative conditions between group A and group B (mean https://www.frontiersin.org/files/Articles/1714793/fped-13-1714793-HTML-r1/image_m/fped-13-1714793-t003.jpg Table 3. Comparison of preoperative vs. postoperative APD and Min.RCT within. Group A and Group B (m https://www.frontiersin.org/files/Articles/1714793/fped-13-1714793-HTML-r1/image_m/fped-13-1714793-t004.jpg Table 4. Comparison of APD and Min.RCT between group A and group B (mean ± SD). https://www.frontiersin.org/files/Articles/1714793/fped-13-1714793-HTML-r1/image_m/fped-13-1714793-g003.jpg Figure 3. Superior postoperative cosmetic outcome. The use of a transumbilical trocar configuration https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1763583/full https://www.frontiersin.org/files/Articles/1763583/fpubh-14-1763583-HTML-r1/image_m/fpubh-14-1763583-g001.jpg Figure 1. Mechanism diagram of frailty in the older adults. https://www.frontiersin.org/files/Articles/1763583/fpubh-14-1763583-HTML-r1/image_m/fpubh-14-1763583-t001.jpg Table 1. Prescription form for decline exercise. https://www.frontiersin.org/files/Articles/1763583/fpubh-14-1763583-HTML-r1/image_m/fpubh-14-1763583-g002.jpg Figure 2. Theory-driven approach to managing frail older adults. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1706057/full https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-g001.jpg Figure 1. Theoretical analytical framework for PHC. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-g002.jpg Figure 2. Flowchart of the fsQCA procedure. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-t001.jpg Table 1. Calibration of outcome and condition variables. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-t002.jpg Table 2. Necessary condition analysis. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-t003.jpg Table 3. Configurational analysis of strong PHC capacity. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-g003.jpg Figure 3. Cases explained by Configuration 1. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-g004.jpg Figure 4. Cases explained by Configuration 2. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-g005.jpg Figure 5. Cases explained by Configuration 3. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-g006.jpg Figure 6. Cases explained by Configuration 4. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-t004.jpg Table 4. Configurational Analysis of Weak PHC capacity. https://www.frontiersin.org/files/Articles/1706057/fpubh-13-1706057-HTML/image_m/fpubh-13-1706057-t005.jpg https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1800061/full https://www.frontiersin.org/files/Articles/1800061/feduc-11-1800061-HTML/image_m/feduc-11-1800061-t001.jpg Table 1. Coding dictionary mapping psychological risks to regulatory blind spots. https://www.frontiersin.org/files/Articles/1800061/feduc-11-1800061-HTML/image_m/feduc-11-1800061-t002.jpg Table 2. Comparative analysis of regulatory frameworks and psychological implications. https://www.frontiersin.org/files/Articles/1800061/feduc-11-1800061-HTML/image_m/feduc-11-1800061-t003.jpg Table 3. Comparative analysis of explicit psychosocial protection mechanisms. https://www.frontiersin.org/files/Articles/1800061/feduc-11-1800061-HTML/image_m/feduc-11-1800061-g001.jpg Figure 1. The regulatory blind spot mechanism. https://www.frontiersin.org/files/Articles/1800061/feduc-11-1800061-HTML/image_m/feduc-11-1800061-t004.jpg Table 4. Paradigm evolution of AI regulatory mechanisms and psychological protection capabilities. https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1746714/full https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g001.jpg Figure 1. (A) Current spatial omics cost upwards of $15,000 per slide, creating a high cost bottlene https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g002.jpg Figure 2. Two-dimensional UMAP projection of patch-level embeddings extracted from our colon whole-s https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g003.jpg Figure 3. Distribution of annotation timestamps for the four Visium HD tissue types used to train th https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g004.jpg Figure 4. Top row: regions of interest ranked highest by predicted cellular diversity. Bottom row: r https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-t001.jpg Table 1. Performance of SpatialFinder against other baseline VLMs across four datasets on the cell d https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-t002.jpg Table 2. Performance of SpatialFinder against other baseline VLMs across three datasets on the tumor https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g005.jpg Figure 5. Top-K selection accuracy for each tissue type, evaluated across multiple K values. Spatial https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g006.jpg Figure 6. Comparison of predicted and ground-truth heatmaps for cellular diversity (left) and tumor https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g007.jpg Figure 7. Integrated model heatmap showing the top 50% of candidate regions gated by predicted tumor https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g008.jpg Figure 8. Spearman’s ρ as a function of the weighting parameter used to combine the human-in-the-loo https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g009.jpg Figure 9. Intersection-over-Union (IoU) scores across multiple K values as a function of the weighti https://www.frontiersin.org/files/Articles/1746714/fbinf-06-1746714-HTML/image_m/fbinf-06-1746714-g010.jpg Figure 10. Top-K performance of SpatialFinder as a function of pathologist annotation time. Top row: https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1778266/full https://www.frontiersin.org/files/Articles/1778266/fbioe-14-1778266-HTML/image_m/fbioe-14-1778266-g001.jpg Figure 1. PRISMA flowchart of study selection process. PRISMA flowchart illustrating the process of https://www.frontiersin.org/files/Articles/1778266/fbioe-14-1778266-HTML/image_m/fbioe-14-1778266-t001.jpg Table 1. Summary of the included studies. https://www.frontiersin.org/files/Articles/1778266/fbioe-14-1778266-HTML/image_m/fbioe-14-1778266-g002.jpg Figure 2. Overview of the included studies and patient populations. (A) Pie chart showing the distri https://www.frontiersin.org/files/Articles/1778266/fbioe-14-1778266-HTML/image_m/fbioe-14-1778266-t002.jpg Table 2. Study characteristics of implant and injection-based approaches. https://www.frontiersin.org/files/Articles/1778266/fbioe-14-1778266-HTML/image_m/fbioe-14-1778266-g003.jpg Figure 3. Summary of materials used and characteristics of implants and injections treatments. (A) P https://www.frontiersin.org/files/Articles/1778266/fbioe-14-1778266-HTML/image_m/fbioe-14-1778266-g004.jpg Figure 4. Post-treatment outcomes and adverse events analysis. (A) Usage frequency of the various po https://www.frontiersin.org/files/Articles/1778266/fbioe-14-1778266-HTML/image_m/fbioe-14-1778266-g005.jpg Figure 5. Risk-of-bias assessment across study designs. Results of the risk-of-bias evaluation condu https://www.frontiersin.org/files/Articles/1778266/fbioe-14-1778266-HTML/image_m/fbioe-14-1778266-g006.jpg Figure 6. Summary plot of risk-of-bias assessment across study designs. Summary of the risk-of-bias https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1681624/full https://www.frontiersin.org/files/Articles/1681624/fvets-12-1681624-HTML-r1/image_m/fvets-12-1681624-g001.jpg Figure 1. Regional seroprevalence of IBR and BVD in Kazakhstan 2021–2024 (Map generated using mapcha https://www.frontiersin.org/files/Articles/1681624/fvets-12-1681624-HTML-r1/image_m/fvets-12-1681624-g002.jpg Figure 2. Determination of immune background (presence of Abs in serum samples prior to the experime https://www.frontiersin.org/files/Articles/1681624/fvets-12-1681624-HTML-r1/image_m/fvets-12-1681624-g003.jpg Figure 3. Dynamics of Abs titers to BoHV-1 and BVDV during 9 months post-vaccination period. * – p < https://www.frontiersin.org/files/Articles/1681624/fvets-12-1681624-HTML-r1/image_m/fvets-12-1681624-g004.jpg Figure 4. Dynamics of the temperature changes in calves after experimental challenge. Group A – vacc https://www.frontiersin.org/files/Articles/1681624/fvets-12-1681624-HTML-r1/image_m/fvets-12-1681624-g005.jpg Figure 5. Necropsy of the calf from Group C. Arrows indicate nodules in the lung tissue. https://www.frontiersin.org/files/Articles/1681624/fvets-12-1681624-HTML-r1/image_m/fvets-12-1681624-g006.jpg Figure 6. Complete blood count. Group A – vaccinated, challenged with BoHV-1; Group C – unvaccinated https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1652086/full https://www.frontiersin.org/files/Articles/1652086/fdgth-07-1652086-HTML-r1/image_m/fdgth-07-1652086-t001.jpg Table 1. Participant demographics and experience with digital tools. https://www.frontiersin.org/files/Articles/1652086/fdgth-07-1652086-HTML-r1/image_m/fdgth-07-1652086-t002.jpg Table 2. Framework matrix of digitization of clinical data for antibiotic stewardship from the study https://www.frontiersin.org/files/Articles/1652086/fdgth-07-1652086-HTML-r1/image_m/fdgth-07-1652086-g001.jpg Figure 1. Conceptual dynamics for effective antibiotic stewardship through clinical data digitizatio https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1792598/full https://www.frontiersin.org/files/Articles/1792598/fneur-17-1792598-HTML/image_m/fneur-17-1792598-g001.jpg Figure 1. Antemortem clinical MRI demonstrates thinning of the corpus callosum and white matter hype https://www.frontiersin.org/files/Articles/1792598/fneur-17-1792598-HTML/image_m/fneur-17-1792598-g002.jpg Figure 2. Histopathology-MRI correlation of choroid plexus (ChP) cysts. Gross examination (A) and LF https://www.frontiersin.org/files/Articles/1792598/fneur-17-1792598-HTML/image_m/fneur-17-1792598-g003.jpg Figure 3. Immunohistochemical analyses of choroid plexus (ChP) cyst (A–D) include aquaporin-1 (AQP1) https://www.frontiersin.org/files/Articles/1792598/fneur-17-1792598-HTML/image_m/fneur-17-1792598-g004.jpg Figure 4. Postmortem high-resolution T2-weighted images (A–C) demonstrate prominent perivascular enl https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1792322/full https://www.frontiersin.org/files/Articles/1792322/feduc-11-1792322-HTML-r1/image_m/feduc-11-1792322-g001.jpg Figure 1. Deckard entering gym hall, under operator control, at Donaghpatrick National School, Galwa https://www.frontiersin.org/files/Articles/1792322/feduc-11-1792322-HTML-r1/image_m/feduc-11-1792322-g002.jpg Figure 2. Supervised handshake concluding the session. Students extend their hands while maintaining https://www.frontiersin.org/journals/audiology-and-otology/articles/10.3389/fauot.2026.1767522/full https://www.frontiersin.org/files/Articles/1767522/fauot-04-1767522-HTML/image_m/fauot-04-1767522-g001.jpg Figure 1. Audiological and middle-ear functional assessment. (A) Pure-tone audiogram showing normal https://www.frontiersin.org/files/Articles/1767522/fauot-04-1767522-HTML/image_m/fauot-04-1767522-g002.jpg Figure 2. Right temporal HRCT: petrosal bone hyper pneumatization. https://www.frontiersin.org/files/Articles/1767522/fauot-04-1767522-HTML/image_m/fauot-04-1767522-g003.jpg Figure 3. HRCT of the right temporal bone—coronal plane: (A) conventional bone window, and (B) inver https://www.frontiersin.org/files/Articles/1767522/fauot-04-1767522-HTML/image_m/fauot-04-1767522-g004.jpg Figure 4. HRCT of the right temporal bone: (A) axial conventional bone window, and (B) coronal inver https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1755878/full https://www.frontiersin.org/files/Articles/1755878/fdgth-08-1755878-HTML/image_m/fdgth-08-1755878-t001.jpg Table 1. Machine learning model performance metrics. https://www.frontiersin.org/files/Articles/1755878/fdgth-08-1755878-HTML/image_m/fdgth-08-1755878-t002.jpg Table 2. Final model training parameters. https://www.frontiersin.org/files/Articles/1755878/fdgth-08-1755878-HTML/image_m/fdgth-08-1755878-g001.jpg Figure 1. Confusion matrix SVM. https://www.frontiersin.org/files/Articles/1755878/fdgth-08-1755878-HTML/image_m/fdgth-08-1755878-g002.jpg Figure 2. Confusion matrix linear regression. https://www.frontiersin.org/files/Articles/1755878/fdgth-08-1755878-HTML/image_m/fdgth-08-1755878-g003.jpg Figure 3. Confusion matrix XGBoost. https://www.frontiersin.org/files/Articles/1755878/fdgth-08-1755878-HTML/image_m/fdgth-08-1755878-g004.jpg Figure 4. SHAP summary plot showing the top ten most influential features contributing to oxygen the https://www.frontiersin.org/files/Articles/1755878/fdgth-08-1755878-HTML/image_m/fdgth-08-1755878-t003.jpg Table 3. Key predictive features and clinical significance. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1762268/full https://www.frontiersin.org/files/Articles/1762268/fpsyt-17-1762268-HTML/image_m/fpsyt-17-1762268-t001.jpg Table 1. Descriptive statistics and bivariate correlations for study variables. https://www.frontiersin.org/files/Articles/1762268/fpsyt-17-1762268-HTML/image_m/fpsyt-17-1762268-g001.jpg Figure 1. Direct effects model of childhood maltreatment on NSSI and cyber aggression. Standardized https://www.frontiersin.org/files/Articles/1762268/fpsyt-17-1762268-HTML/image_m/fpsyt-17-1762268-g002.jpg Figure 2. Structural equation model of self-compassion mediating the relationship between childhood https://www.frontiersin.org/files/Articles/1762268/fpsyt-17-1762268-HTML/image_m/fpsyt-17-1762268-t002.jpg Table 2. Bootstrap analysis of indirect effects in the mediation model. https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2025.1677490/full https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-t001.jpg Table 1. Structure of respondents in the quantitative survey. https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-g001.jpg Figure 1. Operationalization for quantitative survey. Source: own production. https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-g002.jpg Figure 2. Operationalization for focus groups. Source: own production. https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-g003.jpg Figure 3. Generation Z values. Source: own data. https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-t002.jpg Table 2. Brand ambassadors of Adidas - Athletes. https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-t003.jpg Table 3. Brand ambassadors of Adidas - celebrities/influencers. https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-t004.jpg Table 4. Awareness of campaigns. https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-t005.jpg Table 5. Emotional response to campaigns – questionnaire survey (n = 199). https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-t006.jpg Table 6. Perception to campaigns and communication – questionnaire survey (n = 199). https://www.frontiersin.org/files/Articles/1677490/fcomm-10-1677490-HTML-r2/image_m/fcomm-10-1677490-g004.jpg Figure 4. Thematic map. Source: own production. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1736169/full https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-g001.jpg Figure 1. Theoretical framework of the modified schema of the theory of planned behaviour. https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-g002.jpg Figure 2. Operationalization of the construct studied. https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-t001.jpg Table 1. Structure of the research sample. https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-t002.jpg Table 2. Respondent awareness of sustainability attributes within the product life cycle context. https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-t003.jpg Table 3. The importance of sustainability attributes when purchasing. https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-t004.jpg Table 4. Attitudes vs. subjective norms. https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-t005.jpg Table 5. The importance of aspects influencing the discrepancy between attitude and behavior. https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-t006.jpg Table 6. Purchase probability vs. willingness to pay—descriptive statistics. https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-t007.jpg Table 7. ANOVA results. https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-g003.jpg Figure 3. Purchase probability vs. willingness to pay (means with 95% confidence intervals). 1 = 0%– https://www.frontiersin.org/files/Articles/1736169/fspor-08-1736169-HTML/image_m/fspor-08-1736169-g004.jpg Figure 4. Purchase probability vs. willingness to pay (Means with standard deviations). 1 = 0%–9%, 2 https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1785163/full https://www.frontiersin.org/files/Articles/1785163/fmicb-17-1785163-HTML/image_m/fmicb-17-1785163-g001.jpg Figure 1. Survival of mice and Galleria mellonella larvae infected with Achromobacter xylosoxidans G https://www.frontiersin.org/files/Articles/1785163/fmicb-17-1785163-HTML/image_m/fmicb-17-1785163-g002.jpg Figure 2. Survival of mice and Galleria mellonella larvae infected with Achromobacter xylosoxidans N https://www.frontiersin.org/files/Articles/1785163/fmicb-17-1785163-HTML/image_m/fmicb-17-1785163-g003.jpg Figure 3. Survival of infected Galleria mellonella larvae under imipenem (IMI) treatment. Galleria l https://www.frontiersin.org/files/Articles/1785163/fmicb-17-1785163-HTML/image_m/fmicb-17-1785163-g004.jpg Figure 4. Growth and association of Achromobacter xylosoxidans in Galleria. Galleria larvae were inf https://www.frontiersin.org/files/Articles/1785163/fmicb-17-1785163-HTML/image_m/fmicb-17-1785163-g005.jpg Figure 5. Uptake of Ax by hemocytes. Galleria larvae were infected with GN050-GFP at 102 CFU. At 24 https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1708314/full https://www.frontiersin.org/files/Articles/1708314/fonc-16-1708314-HTML/image_m/fonc-16-1708314-g001.jpg Figure 1. Clinical timeline and staining results from two small cell ovarian cancer patients. https://www.frontiersin.org/files/Articles/1708314/fonc-16-1708314-HTML/image_m/fonc-16-1708314-t001.jpg Table 1. Summary of somatic SMARCA4 variants in two small cell ovarian cancer samples. https://www.frontiersin.org/files/Articles/1708314/fonc-16-1708314-HTML/image_m/fonc-16-1708314-g002.jpg Figure 2. Genomic characterisation of two small cell ovarian tumours. (a) Lollipop plot of somatic S https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1685424/full https://www.frontiersin.org/files/Articles/1685424/fendo-16-1685424-HTML/image_m/fendo-16-1685424-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1685424/fendo-16-1685424-HTML/image_m/fendo-16-1685424-g001.jpg Figure 1. Study selection flow diagram. Flow of information through the different phases of the syst https://www.frontiersin.org/files/Articles/1685424/fendo-16-1685424-HTML/image_m/fendo-16-1685424-t001.jpg Table 1. Effect of RAS inhibitors on gut composition, function, and pathologies in human and animal https://www.frontiersin.org/files/Articles/1685424/fendo-16-1685424-HTML/image_m/fendo-16-1685424-g002.jpg Figure 2. Schematic representation of the bidirectional relationship between Renin-Angiotensin Syste https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1816661/full https://www.frontiersin.org/files/Articles/1816661/fendo-17-1816661-HTML/image_m/fendo-17-1816661-g001.jpg Figure 1. Study search and selection flowchart. https://www.frontiersin.org/files/Articles/1816661/fendo-17-1816661-HTML/image_m/fendo-17-1816661-t001.jpg Table 1. The basic characteristics of the included studies. https://www.frontiersin.org/files/Articles/1816661/fendo-17-1816661-HTML/image_m/fendo-17-1816661-t002.jpg Table 2. The establishment of the prediction model. https://www.frontiersin.org/files/Articles/1816661/fendo-17-1816661-HTML/image_m/fendo-17-1816661-t003.jpg Table 3. The performance of the prediction model and the prediction factors. https://www.frontiersin.org/files/Articles/1816661/fendo-17-1816661-HTML/image_m/fendo-17-1816661-g002.jpg Figure 2. Random effects forest plot of the AUC of the best ML-based prediction model for depression https://www.frontiersin.org/files/Articles/1816661/fendo-17-1816661-HTML/image_m/fendo-17-1816661-g003.jpg Figure 3. Random effects forest plot of AUC in model type subgroup for predicting depression risk in https://www.frontiersin.org/files/Articles/1816661/fendo-17-1816661-HTML/image_m/fendo-17-1816661-g004.jpg Figure 4. Random effects forest plot of AUC in sample size subgroup for predicting depression risk i https://www.frontiersin.org/files/Articles/1816661/fendo-17-1816661-HTML/image_m/fendo-17-1816661-g005.jpg Figure 5. Random effects forest plot of AUC in fitting processing subgroup for predicting depression https://www.frontiersin.org/files/Articles/1816661/fendo-17-1816661-HTML/image_m/fendo-17-1816661-t004.jpg Table 4. The results of the subgroup analysis. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1732361/full https://www.frontiersin.org/files/Articles/1732361/fendo-17-1732361-HTML-r1/image_m/fendo-17-1732361-t001.jpg Table 1. Baseline characteristics of different serum uric acid tertiles. https://www.frontiersin.org/files/Articles/1732361/fendo-17-1732361-HTML-r1/image_m/fendo-17-1732361-t002.jpg Table 2. Association between serum urine acid and Wagner or infection degree. https://www.frontiersin.org/files/Articles/1732361/fendo-17-1732361-HTML-r1/image_m/fendo-17-1732361-t003.jpg Table 3. HRs of the association between UA and DFU Outcomes in Univariate and Multivariate Cox Propo https://www.frontiersin.org/journals/nanotechnology/articles/10.3389/fnano.2026.1794336/full https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-g009.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-g001.jpg Figure 1. Schematic representation of the importance of Aspergillus penicillioides VDRVYF biomolecul https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-g002.jpg Figure 2. (a) Results of the batch adsorption studies of TiO2 nanoparticles on Cd elimination: effec https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-g003.jpg Figure 3. Adsorption isotherms and kinetics models for the adsorption of (a) Cd and (b) Cr by TiO2 n https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-t001.jpg Table 1. Adsorption isotherm and kinetics studies. https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-t002.jpg Table 2. Atomic absorption spectroscopy study of the adsorption of Cd and Cr on TiO2 nanoparticles. https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-g004.jpg Figure 4. (a) Color comparison of the industrial wastewater samples before (S1–S5) and after (T1–T5) https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-g005.jpg Figure 5. Physicochemical parameters of the (a) untreated and (b) TiO2-nanoparticle-treated wastewat https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-g006.jpg Figure 6. Comparison of toxic metal concentrations in industrial wastewater samples (S1–S5) before a https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-g007.jpg Figure 7. (a) Percentage adsorption of toxic metal ions achieved using the TiO2 nanoparticles; (b) r https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-g008.jpg Figure 8. (a) Phytotoxicity study of the TiO2 nanoparticles: (i) control as well as (ii) 25 µL, (iii https://www.frontiersin.org/files/Articles/1794336/fnano-08-1794336-HTML/image_m/fnano-08-1794336-t003.jpg Table 3. Root and shoot lengths of Pisum sativum. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1761702/full https://www.frontiersin.org/files/Articles/1761702/fnagi-18-1761702-HTML/image_m/fnagi-18-1761702-t001.jpg Table 1. Mitochondrial dysfunction in microglia in AD. https://www.frontiersin.org/files/Articles/1761702/fnagi-18-1761702-HTML/image_m/fnagi-18-1761702-g001.jpg Figure 1. Sex differences in mitochondrial dysfunction in AD. Males exhibit higher levels of oxidati https://www.frontiersin.org/files/Articles/1761702/fnagi-18-1761702-HTML/image_m/fnagi-18-1761702-t002.jpg Table 2. Mitochondrial pathways in AD: molecular regulators, pathological alterations, functional co https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1736577/full https://www.frontiersin.org/files/Articles/1736577/fneur-17-1736577-HTML/image_m/fneur-17-1736577-t001.jpg Table 1. Complete list of the neuropsychological tests administered, divided into cognitive domains. https://www.frontiersin.org/files/Articles/1736577/fneur-17-1736577-HTML/image_m/fneur-17-1736577-t002.jpg Table 2. Clinical and sociodemographic data of the patient cohort at the time of the cognitive evalu https://www.frontiersin.org/files/Articles/1736577/fneur-17-1736577-HTML/image_m/fneur-17-1736577-g001.jpg Figure 1. Results of STAIX1 (A) and 2 (B) in acromegalic patients divided according to disease statu https://www.frontiersin.org/files/Articles/1736577/fneur-17-1736577-HTML/image_m/fneur-17-1736577-t003.jpg Table 3. Results of the neuropsychologic tests in the two cohort (patients and controls). https://www.frontiersin.org/files/Articles/1736577/fneur-17-1736577-HTML/image_m/fneur-17-1736577-g002.jpg Figure 2. Neuropsychological evaluation in the total perspective group. Y-axis: Patients’ test score https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1748677/full https://www.frontiersin.org/files/Articles/1748677/fcimb-16-1748677-HTML/image_m/fcimb-16-1748677-g001.jpg Figure 1. Three major types of autophagy. Autophagy can be divided into three main forms: macroautop https://www.frontiersin.org/files/Articles/1748677/fcimb-16-1748677-HTML/image_m/fcimb-16-1748677-g002.jpg Figure 2. Autophagy-related diseases affecting major organ systems. Autophagy plays a role in the de https://www.frontiersin.org/files/Articles/1748677/fcimb-16-1748677-HTML/image_m/fcimb-16-1748677-g003.jpg Figure 3. Molecular mechanisms and processes of autophagosome formation. Autophagy initiation is reg https://www.frontiersin.org/files/Articles/1748677/fcimb-16-1748677-HTML/image_m/fcimb-16-1748677-g004.jpg Figure 4. Xenophagy and LC3-associated phagocytosis (LAP) in host defense against M. tb. (1) During https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2026.1784533/full https://www.frontiersin.org/files/Articles/1784533/fspas-13-1784533-HTML-r1/image_m/fspas-13-1784533-t001.jpg Table 1. Chemical composition of lunar LHS-1 and Martian MGS-1 regolith, according to Long-Fox and B https://www.frontiersin.org/files/Articles/1784533/fspas-13-1784533-HTML-r1/image_m/fspas-13-1784533-t002.jpg Table 2. Bibliographic survey of the main impacts on plants of excess oxide components, considering https://www.frontiersin.org/files/Articles/1784533/fspas-13-1784533-HTML-r1/image_m/fspas-13-1784533-t003.jpg Table 3. Examples of fungal species with potential applications and their relationships with iron. https://www.frontiersin.org/files/Articles/1784533/fspas-13-1784533-HTML-r1/image_m/fspas-13-1784533-t004.jpg Table 4. Fungi isolated on the ISS or identified by metagenomics, with potential use as plant growth https://www.frontiersin.org/files/Articles/1784533/fspas-13-1784533-HTML-r1/image_m/fspas-13-1784533-g001.jpg Figure 1. Overview of possible pathways of iron uptake by plants considering their interaction with https://www.frontiersin.org/journals/horticulture/articles/10.3389/fhort.2026.1762580/full https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t001.jpg Table 1. Summary of recent studies on the application of AI in tomato leaf classification. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g001.jpg Figure 1. System framework for the proposed method. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g002.jpg Figure 2. Sample of tomato leaf dataset (Haidarh et al., 2025; Kiruthika et al., 2025). https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g003.jpg Figure 3. Enhanced image; (a-c) input (bacterial spot, early blight, and healthy leaf) tomato leaf, https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t002.jpg Table 2. Architecture for DenseNet121 and EfficientNetB0 for feature extraction. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g004.jpg Figure 4. Structure of a random forest. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g005.jpg Figure 5. Sample of the tomato plant leaf dataset partitioning. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t003.jpg Table 3. Performance comparison of all models on the independent test set. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t004.jpg Table 4. Hyperparameter values used in the implementation. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g006.jpg Figure 6. Confusion matrix for test data for baseline DenseNet121. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g007.jpg Figure 7. Confusion matrix for test data analysis for EfficientNetB0. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g008.jpg Figure 8. Confusion matrix for test data analysis for DenseNet121 and EfficientNetB0. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g009.jpg Figure 9. Confusion matrix of the proposed hybrid framework on the independent test set. Strong diag https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t005.jpg Table 5. Performance over 10 runs (Mean ± Std). https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t006.jpg Table 6. Paired t-test between ensemble and SVM (10 seeds). https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t007.jpg Table 7. Wilcoxon signed-rank test (non-parametric). https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t008.jpg Table 8. Ablation analysis of each component of the tomato leaf disease classification framework. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t009.jpg Table 9. Ablation analysis of the fusion component of the tomato leaf disease classification framewo https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t010.jpg Table 10. Per-class metrics of the proposed model on the in-domain test set. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-g010.jpg Figure 10. Cross dataset with diverse illumination and background conditions (A) Bacterial leaf spot https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t011.jpg Table 11. Cross-dataset test accuracy. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t012.jpg Table 12. Quantitative comparison of computational complexity and deployment cost. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t013.jpg Table 13. Experimental comparison with mainstream agricultural AI-specific models. https://www.frontiersin.org/files/Articles/1762580/fhort-05-1762580-HTML-r1/image_m/fhort-05-1762580-t014.jpg Table 14. Comparison of the proposed model with existing models. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1772033/full https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g001.jpg Figure 1. DXR alleviates hepatic steatosis and injury in MCD diet-induced MASH mice. (A) Representat https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g002.jpg Figure 2. DXR improves liver pathology in HFHCD-induced MASH mice. (A) Experimental scheme of HFHCD- https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g003.jpg Figure 3. DXR reduces hepatic lipid accumulation and serum biochemical markers. (A–D) NAS, steatosis https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g004.jpg Figure 4. Bioinformatic analysis of DXR targets in MASH. (A) Core gene hub (M&D) identified by inter https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g005.jpg Figure 5. DXR modulates lipid synthesis, inflammation, and oxidative stress in liver. (A–E) DXR down https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g006.jpg Figure 6. DXR protects hepatocytes from apoptosis. (A, B) TUNEL staining showed reduced apoptotic ce https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g007.jpg Figure 7. DXR activates PI3K/AKT and Keap1/Nrf2/HO-1 signaling. (A) Immunofluorescence showed increa https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-t001.jpg Table 1. Active components derived from DXP docking into PI3K and Keap1. https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g008.jpg Figure 8. Molecular dynamics of DXR flavonoids with PI3K. (A–I) RMSD, RMSF, hydrogen bonds, Rg, SASA https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g009.jpg Figure 9. Molecular dynamics of DXR flavonoids with Keap1. (A–I) RMSD, RMSF, hydrogen bonds, Rg, SAS https://www.frontiersin.org/files/Articles/1772033/fendo-17-1772033-HTML/image_m/fendo-17-1772033-g010.jpg Figure 10. Four flavonoids regulate p-AKT1 and Nrf2 in vitro. (A, B) Quercetin and luteolin increase https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1752298/full https://www.frontiersin.org/files/Articles/1752298/fimmu-17-1752298-HTML/image_m/fimmu-17-1752298-g001.jpg Figure 1. Mechanisms and theoretical advantages of neoadjuvant immunotherapy. This schematic elucida https://www.frontiersin.org/files/Articles/1752298/fimmu-17-1752298-HTML/image_m/fimmu-17-1752298-t001.jpg Table 1. Critical comparison of emerging biomarkers in neoadjuvant NSCLC treatment. https://www.frontiersin.org/files/Articles/1752298/fimmu-17-1752298-HTML/image_m/fimmu-17-1752298-g002.jpg Figure 2. Integrative framework for future personalized therapy. This schematic illustrates a biolog https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1782399/full https://www.frontiersin.org/files/Articles/1782399/fimmu-17-1782399-HTML/image_m/fimmu-17-1782399-g001.jpg Figure 1. The immuno-radiological axis in NsCLC brain metastases: mechanisms and therapeutic implica https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1710840/full https://www.frontiersin.org/files/Articles/1710840/fnhum-20-1710840-HTML/image_m/fnhum-20-1710840-g001.jpg Figure 1. Illustration of step-to-step transition (STST) characteristics, beginning with the minimum https://www.frontiersin.org/files/Articles/1710840/fnhum-20-1710840-HTML/image_m/fnhum-20-1710840-g002.jpg Figure 2. Illustration of group average step length and step width (left side panel), and step rate https://www.frontiersin.org/files/Articles/1710840/fnhum-20-1710840-HTML/image_m/fnhum-20-1710840-g003.jpg Figure 3. Mean ± SD for the S index values. Data for young (black) and older adults (green) walking https://www.frontiersin.org/files/Articles/1710840/fnhum-20-1710840-HTML/image_m/fnhum-20-1710840-g004.jpg Figure 4. Black and green color represent the group average for young adults (YA) and older adults ( https://www.frontiersin.org/files/Articles/1710840/fnhum-20-1710840-HTML/image_m/fnhum-20-1710840-g005.jpg Figure 5. Summary of age- and speed-related changes in STST timing and impulse variables contributin https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1756454/full https://www.frontiersin.org/files/Articles/1756454/fpsyg-17-1756454-HTML/image_m/fpsyg-17-1756454-t001.jpg Table 1. Participant flow across data collection waves. https://www.frontiersin.org/files/Articles/1756454/fpsyg-17-1756454-HTML/image_m/fpsyg-17-1756454-t002.jpg Table 2. Games used in the training. https://www.frontiersin.org/files/Articles/1756454/fpsyg-17-1756454-HTML/image_m/fpsyg-17-1756454-t003.jpg Table 3. Descriptives of the sample. https://www.frontiersin.org/files/Articles/1756454/fpsyg-17-1756454-HTML/image_m/fpsyg-17-1756454-t004.jpg Table 4. Descriptive statistics for gambling-related measures by group and time. https://www.frontiersin.org/files/Articles/1756454/fpsyg-17-1756454-HTML/image_m/fpsyg-17-1756454-t005.jpg Table 5. ANCOVA results with class random intercept. https://www.frontiersin.org/files/Articles/1756454/fpsyg-17-1756454-HTML/image_m/fpsyg-17-1756454-t006.jpg Table 6. Themes and subthemes emerging from the student focus groups with illustrative quotes. https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1810835/full https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g001.jpg Figure 1. Overview of the proposed procedure, showing the reconstruction process of a corrupted indi https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g002.jpg Figure 2. In a K-nodes SOM, K-many completion rules R1,R2,…,RK are defined, using the partitions of https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g003.jpg Figure 3. Quality, similarity and accuracy. Clustering quality, clustering similarity and allele pre https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g004.jpg Figure 4. Clustering quality and similarity on corrupted versions of the grapevine population, as th https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g005.jpg Figure 5. Clustering quality and similarity on corrupted versions of the grapevine population, using https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g006.jpg Figure 6. Clustering quality and similarity on corrupted versions of the extended (real plus synthet https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g007.jpg Figure 7. Clustering quality, similarity and allele accuracy on corrupted versions of the extended ( https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-t001.jpg Table 1. Average read-count reconstruction error on the grapevine datasets. https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-t002.jpg Table 2. Mapping between the 22 genes having a statistical significant local error contribution and https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g008.jpg Figure 8. Gene feature distributions for the input gene list compared with the genomic background. K https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g009.jpg Figure 9. Gene Ontology (GO) enrichment analysis of the gene having a significant Local Error Contri https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-t003.jpg Table 3. Mapping between the genes having a statistical significant discriminant index and STRING da https://www.frontiersin.org/files/Articles/1810835/fbinf-06-1810835-HTML/image_m/fbinf-06-1810835-g010.jpg Figure 10. Gene Ontology (GO) enrichment analysis of genes having significant discriminative index. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1741802/full https://www.frontiersin.org/files/Articles/1741802/fmolb-13-1741802-HTML/image_m/fmolb-13-1741802-g001.jpg Figure 1. A PCA depicting the data variance in a 2D score plot based on 197 serum metabolites and li https://www.frontiersin.org/files/Articles/1741802/fmolb-13-1741802-HTML/image_m/fmolb-13-1741802-g002.jpg Figure 2. (A) A 2D score plot when using OPLS-DA was used to characterize temporal changes in the se https://www.frontiersin.org/files/Articles/1741802/fmolb-13-1741802-HTML/image_m/fmolb-13-1741802-t001.jpg Table 1. Summary of 12 leading serum metabolites/lipids that increased after an eight-week PD medita https://www.frontiersin.org/files/Articles/1741802/fmolb-13-1741802-HTML/image_m/fmolb-13-1741802-g003.jpg Figure 3. (A) A Pearson correlation matrix for the 12 top-ranked serum metabolites and lipids that u https://www.frontiersin.org/files/Articles/1741802/fmolb-13-1741802-HTML/image_m/fmolb-13-1741802-g004.jpg Figure 4. Box plots and ANOVA for nine (of the 12) top-ranked serum metabolites/lipid species that i https://www.frontiersin.org/files/Articles/1741802/fmolb-13-1741802-HTML/image_m/fmolb-13-1741802-g005.jpg Figure 5. DIABLO integrative multi-omic analyses of metabolites, DNA methylation sites, and cognitiv https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2026.1779375/full https://www.frontiersin.org/files/Articles/1779375/fpos-08-1779375-HTML/image_m/fpos-08-1779375-g001.jpg Figure 1. New dispute settlement cases opened per year, 1995–2025. This graph was created based on d https://www.frontiersin.org/files/Articles/1779375/fpos-08-1779375-HTML/image_m/fpos-08-1779375-g002.jpg Figure 2. Appeals filed per year, 1995–2025. This graph was created based on data retrieved from the https://www.frontiersin.org/files/Articles/1779375/fpos-08-1779375-HTML/image_m/fpos-08-1779375-g003.jpg Figure 3. The U.S. as appellant and appellee, 1995–2025. This graph was created based on data retrie https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1819744/full https://www.frontiersin.org/files/Articles/1819744/fimmu-17-1819744-HTML/image_m/fimmu-17-1819744-g001.jpg Figure 1. Induction and consequences of trained immunity in the lung. https://www.frontiersin.org/files/Articles/1819744/fimmu-17-1819744-HTML/image_m/fimmu-17-1819744-t001.jpg Table 1. Summary of molecular mechanisms driving pulmonary trained immunity. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1727415/full https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-g001.jpg Figure 1. Heatmap representation of the molecular docking scores of phytochemicals identified in sel https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-t001.jpg Table 1. The binding affinities (docking scores) and the inhibition constants of the selected compou https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-g002.jpg Figure 2. Superimposed structures of the co-crystallised ligands in their co-crystallised (magenta) https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-t002.jpg Table 2. Ligand interaction types and amino acid residues at 1NAX AND 7X1T. https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-g003.jpg Figure 3. (A) The interaction networks between phytochemicals (in magenta) and receptor 1NAX. (B) Th https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-t003.jpg Table 3. The in silico ADMET properties of top leads. https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-g004.jpg Figure 4. The radar chart of selected physicochemical properties of hits from M. oleifera, N. sativa https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-t004.jpg Table 4. The simulation results of the complexes of the hit compounds on the receptors. https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-g005.jpg Figure 5. (a) The MD simulation results for hit compounds and co-crystallised ligands on the recepto https://www.frontiersin.org/files/Articles/1727415/fendo-17-1727415-HTML/image_m/fendo-17-1727415-t005.jpg Table 5. The MM/GBSA binding free energy estimation of the protein–ligand complex using the MD traje https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1814522/full https://www.frontiersin.org/files/Articles/1814522/fpsyg-17-1814522-HTML/image_m/fpsyg-17-1814522-g001.jpg Figure 1. Research model. https://www.frontiersin.org/files/Articles/1814522/fpsyg-17-1814522-HTML/image_m/fpsyg-17-1814522-t001.jpg Table 1. Means, standard deviations, and Pearson correlations between study variables (N = 1,032). https://www.frontiersin.org/files/Articles/1814522/fpsyg-17-1814522-HTML/image_m/fpsyg-17-1814522-t002.jpg Table 2. Hierarchical regression models testing predictors of employee work engagement and perceived https://www.frontiersin.org/files/Articles/1814522/fpsyg-17-1814522-HTML/image_m/fpsyg-17-1814522-t003.jpg Table 3. Direct, indirect, and total effects of adaptability, job crafting, strengths use, and work https://www.frontiersin.org/files/Articles/1814522/fpsyg-17-1814522-HTML/image_m/fpsyg-17-1814522-g002.jpg Figure 2. Empirical structural model presenting direct and indirect standardized effects of adaptabi https://www.frontiersin.org/journals/space-technologies/articles/10.3389/frspt.2025.1690460/full https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g001.jpg Figure 1. Scenario-based simulation of total objects launched annually through 2050, with variable v https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-t001.jpg Table 1. 10-year averages for the outflow categories in the material flow analysis. https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g002.jpg Figure 2. Sankey diagram displaying the percentage of launched objects that become outflows or space https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g003.jpg Figure 3. Classification of the number of objects that make up the flow of material each year based https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g004.jpg Figure 4. Scenario planning framework flowchart illustrating the relationship of variables used in t https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-t002.jpg Table 2. Space technology material composition percentages and market value. https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-t003.jpg Table 3. Space technology material degradation rates. https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g005.jpg Figure 5. Material market value framework flowchart illustrating the relationship of variables used https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-t004.jpg Table 4. Proportion of space missions categorized into each launch vehicle class within the specifie https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g006.jpg Figure 6. Transportation cost reduction framework flowchart illustrating the relationship of variabl https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g007.jpg Figure 7. Predicted number of cumulative catastrophic collisions each year in LEO based on PMD rates https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g008.jpg Figure 8. Object removal risk reduction value framework flowchart illustrating the relationship of v https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g009.jpg Figure 9. Total value framework flowchart illustrating the relationship among components that compri https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g010.jpg Figure 10. Cumulative material market value of space debris through 2050 across Low Growth, Baseline https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g011.jpg Figure 11. Cumulative transportation cost reduction value of space debris through 2050 across Low Gr https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g012.jpg Figure 12. Cumulative object removal risk reduction value of space debris through 2050 across Low Gr https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-t005.jpg Table 5. Total and itemized values for each component of the framework based on the launch rate scen https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g013.jpg Figure 13. Total value of space debris by category through 2050 across Low Growth, Baseline, and Hig https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-g014.jpg Figure 14. Relative composition of the total value of space debris based on the three analyzed sourc https://www.frontiersin.org/files/Articles/1690460/frspt-06-1690460-HTML/image_m/frspt-06-1690460-t006.jpg Table 6. Total value sensitivity to variables of interest. https://www.frontiersin.org/journals/environmental-archaeology/articles/10.3389/fearc.2025.1704457/full https://www.frontiersin.org/files/Articles/1704457/fearc-04-1704457-HTML/image_m/fearc-04-1704457-g001.jpg Figure 1. Archaeological Context of the San José galleon Shipwreck (ARC-DIMAR, 2022, p. 145). https://www.frontiersin.org/files/Articles/1704457/fearc-04-1704457-HTML/image_m/fearc-04-1704457-g002.jpg Figure 2. Archaeological evidence lying on the seabed in the archaeological context of the San José https://www.frontiersin.org/files/Articles/1704457/fearc-04-1704457-HTML/image_m/fearc-04-1704457-g003.jpg Figure 3. Gathering with the “Mamos” in the Sierra Nevada de Santa Marta before visiting the coastal https://www.frontiersin.org/files/Articles/1704457/fearc-04-1704457-HTML/image_m/fearc-04-1704457-g004.jpg Figure 4. Visits to the coast with the “Mamos” during the exploration of sacred sites. https://www.frontiersin.org/files/Articles/1704457/fearc-04-1704457-HTML/image_m/fearc-04-1704457-g005.jpg Figure 5. Mapping exercise with the Arhuaco indigenous representing the dimensions of the sea. https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2026.1649622/full https://www.frontiersin.org/files/Articles/1649622/fragi-07-1649622-HTML-r1/image_m/fragi-07-1649622-g001.jpg Figure 1. Study design. MPV, Measurement physiological variables; PV, physiological variables; 5-STS https://www.frontiersin.org/files/Articles/1649622/fragi-07-1649622-HTML-r1/image_m/fragi-07-1649622-t001.jpg Table 1. Descriptive statistic of the included patients. https://www.frontiersin.org/files/Articles/1649622/fragi-07-1649622-HTML-r1/image_m/fragi-07-1649622-t002.jpg Table 2. Construct validity. Associations between SARC-T mean propulsive velocity and EWGSOP2-recomm https://www.frontiersin.org/files/Articles/1649622/fragi-07-1649622-HTML-r1/image_m/fragi-07-1649622-t003.jpg Table 3. Known-groups validity: Differences in SARC-T and functional test performance between contro https://www.frontiersin.org/files/Articles/1649622/fragi-07-1649622-HTML-r1/image_m/fragi-07-1649622-t004.jpg Table 4. Physiological response to SARC-T. https://www.frontiersin.org/files/Articles/1649622/fragi-07-1649622-HTML-r1/image_m/fragi-07-1649622-t005.jpg Table 5. Physiological response across functional tests in the sarcopenia group. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1751407/full https://www.frontiersin.org/files/Articles/1751407/fpsyt-17-1751407-HTML/image_m/fpsyt-17-1751407-g001.jpg Figure 1. The hypothesized four-type model of entrapment of suicidality including the degree of resp https://www.frontiersin.org/files/Articles/1751407/fpsyt-17-1751407-HTML/image_m/fpsyt-17-1751407-t001.jpg Table 1. Demographic, clinical and treatment characteristics of cases (n=75). https://www.frontiersin.org/files/Articles/1751407/fpsyt-17-1751407-HTML/image_m/fpsyt-17-1751407-t002.jpg Table 2. Intraclass Correlation Coefficient values examining the reliability of subtype agreement am https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1747281/full https://www.frontiersin.org/files/Articles/1747281/fmed-13-1747281-HTML/image_m/fmed-13-1747281-g001.jpg Figure 1. The flow chart in our study. The dataset was partitioned into training set and validation https://www.frontiersin.org/files/Articles/1747281/fmed-13-1747281-HTML/image_m/fmed-13-1747281-t001.jpg Table 1. Baseline clinical characteristics in training group and validation group. https://www.frontiersin.org/files/Articles/1747281/fmed-13-1747281-HTML/image_m/fmed-13-1747281-g002.jpg Figure 2. Feature selection using LASSO, Boruta, and logistic regression methods. (A) LASSO regressi https://www.frontiersin.org/files/Articles/1747281/fmed-13-1747281-HTML/image_m/fmed-13-1747281-g003.jpg Figure 3. Construction and visualization of the predictive model for NOAF in elderly hypertensive AM https://www.frontiersin.org/files/Articles/1747281/fmed-13-1747281-HTML/image_m/fmed-13-1747281-g004.jpg Figure 4. Multicollinearity assessment among selected predictors. (A) Variance inflation factor anal https://www.frontiersin.org/files/Articles/1747281/fmed-13-1747281-HTML/image_m/fmed-13-1747281-g005.jpg Figure 5. Calibration curves for the nomogram-predicted risk of NOAF. (A) Calibration plot in the tr https://www.frontiersin.org/files/Articles/1747281/fmed-13-1747281-HTML/image_m/fmed-13-1747281-g006.jpg Figure 6. Discrimination performance of the NOAF prediction model. (A) ROC curves for each individua https://www.frontiersin.org/files/Articles/1747281/fmed-13-1747281-HTML/image_m/fmed-13-1747281-g007.jpg Figure 7. Clinical utility evaluation of the NOAF prediction model. (A,B) Decision curve analysis fo https://www.frontiersin.org/files/Articles/1747281/fmed-13-1747281-HTML/image_m/fmed-13-1747281-g008.jpg Figure 8. SHAP analysis of feature importance and contribution in the NOAF prediction model. (A) SHA https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1712221/full https://www.frontiersin.org/files/Articles/1712221/fmed-13-1712221-HTML/image_m/fmed-13-1712221-g001.jpg Figure 1. Flowchart of patient selection in the MIMIC-IV database. A total of 85,242 patients with f https://www.frontiersin.org/files/Articles/1712221/fmed-13-1712221-HTML/image_m/fmed-13-1712221-t001.jpg Table 1. Baseline characteristics of the study population. https://www.frontiersin.org/files/Articles/1712221/fmed-13-1712221-HTML/image_m/fmed-13-1712221-g002.jpg Figure 2. Feature selection and importance ranking for risk prediction. (A) LASSO coefficient profil https://www.frontiersin.org/files/Articles/1712221/fmed-13-1712221-HTML/image_m/fmed-13-1712221-g003.jpg Figure 3. Model performance evaluation in training and validation cohorts. (A) Receiver operating ch https://www.frontiersin.org/files/Articles/1712221/fmed-13-1712221-HTML/image_m/fmed-13-1712221-t002.jpg Table 2. Performance of machine learning models in the validation set (30% of the cohort). https://www.frontiersin.org/files/Articles/1712221/fmed-13-1712221-HTML/image_m/fmed-13-1712221-t003.jpg Table 3. Performance of machine learning models in the external validation (EICU database). https://www.frontiersin.org/files/Articles/1712221/fmed-13-1712221-HTML/image_m/fmed-13-1712221-g004.jpg Figure 4. SHAP analysis of the predictive model. (A) SHAP summary plot showing the distribution of f https://www.frontiersin.org/files/Articles/1712221/fmed-13-1712221-HTML/image_m/fmed-13-1712221-g005.jpg Figure 5. SHAP-based individualized prediction interpretation. (A,B) SHAP force plots for representa https://www.frontiersin.org/journals/nephrology/articles/10.3389/fneph.2025.1526182/full https://www.frontiersin.org/files/Articles/1526182/fneph-05-1526182-HTML/image_m/fneph-05-1526182-g005.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1526182/fneph-05-1526182-HTML/image_m/fneph-05-1526182-g001.jpg Figure 1. Flow chart of randomized controlled trial on the efficacy of febuxostat. After patients as https://www.frontiersin.org/files/Articles/1526182/fneph-05-1526182-HTML/image_m/fneph-05-1526182-t001.jpg Table 1. Baseline analytical and characteristics in the study population. https://www.frontiersin.org/files/Articles/1526182/fneph-05-1526182-HTML/image_m/fneph-05-1526182-t002.jpg Table 2. Evolution of kidney and cardiac biomarker levels by treatment duration. https://www.frontiersin.org/files/Articles/1526182/fneph-05-1526182-HTML/image_m/fneph-05-1526182-g002.jpg Figure 2. Mean changes of kidney markers and inflammation marker by treatment (febuxostat group n=15 https://www.frontiersin.org/files/Articles/1526182/fneph-05-1526182-HTML/image_m/fneph-05-1526182-t003.jpg Table 3. Linear mixed-effects model for eGFR over time in both groups. https://www.frontiersin.org/files/Articles/1526182/fneph-05-1526182-HTML/image_m/fneph-05-1526182-g003.jpg Figure 3. Mean changes of cardiac biomarkers over time by treatment (febuxostat group n=156 vs. cont https://www.frontiersin.org/files/Articles/1526182/fneph-05-1526182-HTML/image_m/fneph-05-1526182-g004.jpg Figure 4. Mean changes in the echocardiograph findings by treatment (febuxostat group n=156 vs. cont https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1764989/full https://www.frontiersin.org/files/Articles/1764989/fonc-16-1764989-HTML/image_m/fonc-16-1764989-g001.jpg Figure 1. CT images of the male patient before surgical treatment. An irregular mass shadow (yellow https://www.frontiersin.org/files/Articles/1764989/fonc-16-1764989-HTML/image_m/fonc-16-1764989-g002.jpg Figure 2. The appearance of the mass under cystoscopy. A blue follicular-like lesion measuring appro https://www.frontiersin.org/files/Articles/1764989/fonc-16-1764989-HTML/image_m/fonc-16-1764989-g003.jpg Figure 3. Pathological manifestations of the tumor with IHC staining. (A) HE staining (× 4); (B) HE https://www.frontiersin.org/files/Articles/1764989/fonc-16-1764989-HTML/image_m/fonc-16-1764989-g004.jpg Figure 4. Enhanced CT examination at the 6-month postoperative follow-up. The images show normal pos https://www.frontiersin.org/files/Articles/1764989/fonc-16-1764989-HTML/image_m/fonc-16-1764989-g005.jpg Figure 5. CT images before and after the covered stent implantation. (A, B) Enhanced CT reveals left https://www.frontiersin.org/files/Articles/1764989/fonc-16-1764989-HTML/image_m/fonc-16-1764989-t001.jpg Table 1. Summary of all reported cases from our literature review. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1636744/full https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-g001.jpg Figure 1. Location of Xuzhou City. (a) Location of Xuzhou in Yangtze River Delta; (b) administrative https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-t001.jpg Table 1. Description of research data sources. https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-g002.jpg Figure 2. Model diagram of economic-social-ecological low-carbon development efficiency. Source: ada https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-g003.jpg Figure 3. “Carbon emission-territorial space” correspondence framework. Source: authors. https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-t002.jpg Table 2. Methods for carbon emission spatialization method based on geospatial big data. https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-t003.jpg Table 3. Carbon emission calculation results of urban sectors. https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-g004.jpg Figure 4. Proportion of carbon emissions by departments and land use. Source: authors. https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-g005.jpg Figure 5. Carbon emissions and sink of district and county units in Xuzhou City. (a) Carbon emission https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-g006.jpg Figure 6. Low-carbon development efficiency of district and county units in Xuzhou City. (a) Economi https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-g007.jpg Figure 7. Low-carbon development efficiency of district and county units in Xuzhou City. (a) Ecologi https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-t004.jpg Table 4. Classification basis for low-carbon zoning. https://www.frontiersin.org/files/Articles/1636744/fenvs-13-1636744-HTML/image_m/fenvs-13-1636744-g008.jpg Figure 8. Spatial pattern of low carbon zoning in Xuzhou city. Source: authors. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1676981/full https://www.frontiersin.org/files/Articles/1676981/fpubh-13-1676981-HTML-r1/image_m/fpubh-13-1676981-g001.jpg Figure 1. Characteristics of participants by arms. https://www.frontiersin.org/files/Articles/1676981/fpubh-13-1676981-HTML-r1/image_m/fpubh-13-1676981-t001.jpg Table 1. Characteristics of participants by arms. https://www.frontiersin.org/files/Articles/1676981/fpubh-13-1676981-HTML-r1/image_m/fpubh-13-1676981-t002.jpg Table 2. Effect of intervention on WaSH practices over time among the participantsa (post-interventi https://www.frontiersin.org/files/Articles/1676981/fpubh-13-1676981-HTML-r1/image_m/fpubh-13-1676981-t003.jpg Table 3. Characteristics of Anganwadi schools by study arm over time (post-intervention)a. https://www.frontiersin.org/files/Articles/1676981/fpubh-13-1676981-HTML-r1/image_m/fpubh-13-1676981-g002.jpg Figure 2. Effect of intervention on WaSH practices and health outcomes by study. https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2025.1649356/full https://www.frontiersin.org/files/Articles/1649356/ffgc-08-1649356-HTML/image_m/ffgc-08-1649356-g001.jpg Figure 1. Schematic diagram of database retrieval and screening process. https://www.frontiersin.org/files/Articles/1649356/ffgc-08-1649356-HTML/image_m/ffgc-08-1649356-g002.jpg Figure 2. Analysis of publications related to forest carbon stock estimation techniques. (a) Global https://www.frontiersin.org/files/Articles/1649356/ffgc-08-1649356-HTML/image_m/ffgc-08-1649356-g003.jpg Figure 3. Analysis of disciplinary categories and journals related to publications on forest carbon https://www.frontiersin.org/files/Articles/1649356/ffgc-08-1649356-HTML/image_m/ffgc-08-1649356-g004.jpg Figure 4. Analysis of contributions by different countries and institutions to forest carbon storage https://www.frontiersin.org/files/Articles/1649356/ffgc-08-1649356-HTML/image_m/ffgc-08-1649356-t001.jpg Table 1. Representative biomass or carbon estimation methods and their characteristics. https://www.frontiersin.org/files/Articles/1649356/ffgc-08-1649356-HTML/image_m/ffgc-08-1649356-g005.jpg Figure 5. Keyword analysis of forest carbon stock estimation techniques from 2008 to 2025. (a) Analy https://www.frontiersin.org/files/Articles/1649356/ffgc-08-1649356-HTML/image_m/ffgc-08-1649356-g006.jpg Figure 6. Trends in accuracy of models for forest carbon stock estimation techniques. (a) Accuracy o https://www.frontiersin.org/files/Articles/1649356/ffgc-08-1649356-HTML/image_m/ffgc-08-1649356-g007.jpg Figure 7. Trends in research on global forest carbon stock estimation at different scales (small, me https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1781435/full https://www.frontiersin.org/files/Articles/1781435/fmed-13-1781435-HTML-r1/image_m/fmed-13-1781435-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1781435/fmed-13-1781435-HTML-r1/image_m/fmed-13-1781435-t002.jpg Table 2. Tumor characteristics. https://www.frontiersin.org/files/Articles/1781435/fmed-13-1781435-HTML-r1/image_m/fmed-13-1781435-g001.jpg Figure 1. Kaplan–Meier survival curve. https://www.frontiersin.org/files/Articles/1781435/fmed-13-1781435-HTML-r1/image_m/fmed-13-1781435-t003.jpg Table 3. Treatment modalities. https://www.frontiersin.org/files/Articles/1781435/fmed-13-1781435-HTML-r1/image_m/fmed-13-1781435-t004.jpg Table 4. Factors associated with mortality in colorectal cancer patients. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1732674/full https://www.frontiersin.org/files/Articles/1732674/fmed-12-1732674-HTML/image_m/fmed-12-1732674-g001.jpg Figure 1. Bedside chest radiograph (DR) of the patient. The image demonstrates increased lung markin https://www.frontiersin.org/files/Articles/1732674/fmed-12-1732674-HTML/image_m/fmed-12-1732674-g002.jpg Figure 2. Multidetector computed tomography (MDCT) of the patient’s chest. Blue arrows indicate redu https://www.frontiersin.org/files/Articles/1732674/fmed-12-1732674-HTML/image_m/fmed-12-1732674-g003.jpg Figure 3. Timeline of clinical course, antimicrobial therapy, and trends of key indicators. https://www.frontiersin.org/files/Articles/1732674/fmed-12-1732674-HTML/image_m/fmed-12-1732674-g004.jpg Figure 4. Chest CT image of the patient obtained 3 days after initiation of targeted treatment. Blue https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1591073/full https://www.frontiersin.org/files/Articles/1591073/feduc-10-1591073-HTML/image_m/feduc-10-1591073-t001.jpg Table 1. The dimension of engineering thinking. https://www.frontiersin.org/files/Articles/1591073/feduc-10-1591073-HTML/image_m/feduc-10-1591073-t002.jpg Table 2. One item from ETAS. https://www.frontiersin.org/files/Articles/1591073/feduc-10-1591073-HTML/image_m/feduc-10-1591073-t003.jpg Table 3. Reliability statistics. https://www.frontiersin.org/files/Articles/1591073/feduc-10-1591073-HTML/image_m/feduc-10-1591073-t004.jpg Table 4. Pearson correlation coefficients. https://www.frontiersin.org/files/Articles/1591073/feduc-10-1591073-HTML/image_m/feduc-10-1591073-t005.jpg Table 5. Rotated factor loadings of the 28 items across six factors. https://www.frontiersin.org/files/Articles/1591073/feduc-10-1591073-HTML/image_m/feduc-10-1591073-g001.jpg Figure 1. Bar and line chart of total engineering thinking scores. https://www.frontiersin.org/files/Articles/1591073/feduc-10-1591073-HTML/image_m/feduc-10-1591073-t006.jpg Table 6. Descriptive statistics for second-level dimensions of engineering thinking. https://www.frontiersin.org/files/Articles/1591073/feduc-10-1591073-HTML/image_m/feduc-10-1591073-g002.jpg Figure 2. Pearson correlation between self-efficacy and practical performance. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1749066/full https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-t001.jpg Table 1. Conceptualization of epistemic practices (EPs). https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-t002.jpg Table 2. Conceptualization of non-epistemic practices (N-EPs). https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-g001.jpg Figure 1. Preliminarily proposed conceptual framework for SPs proficiency. IV, investigating; AQ, as https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-t003.jpg Table 3. Descriptive information concerning the video-recorded lessons. https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-t004.jpg Table 4. Proposed performance levels of the epistemic practices (EPs). https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-t005.jpg Table 5. Mapping relationship between N-EPs and the core constructs of FRA in SPOP. https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-g002.jpg Figure 2. Graphical representation of the three models. https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-t006.jpg Table 6. Model comparison of scientific practices (SPs). https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-t007.jpg Table 7. Correlation coefficients between the five dimensions. https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-g003.jpg Figure 3. Probability curve of PCOI items (before revision). https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-g004.jpg Figure 4. Probability curve of PCOI items (revised). https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-t008.jpg Table 8. Fit statistics for 12 items in the revised Scientific Practices Observation Protocol (SPOP) https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-g005.jpg Figure 5. Item-person map with the five-dimensional model. https://www.frontiersin.org/files/Articles/1749066/feduc-11-1749066-HTML/image_m/feduc-11-1749066-t009.jpg Table 9. Summary table of the DIF analysis based on the Mantel-Haenszel method. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1829774/full https://www.frontiersin.org/files/Articles/1829774/fpubh-14-1829774-HTML-r1/image_m/fpubh-14-1829774-t001.jpg Table 1. Demographics of participating nursing assistants. https://www.frontiersin.org/files/Articles/1829774/fpubh-14-1829774-HTML-r1/image_m/fpubh-14-1829774-t002.jpg Table 2. Nursing assistants’ descriptive and item response distributions (%). https://www.frontiersin.org/files/Articles/1829774/fpubh-14-1829774-HTML-r1/image_m/fpubh-14-1829774-t003.jpg Table 3. Pattern matrix with factor loadings; Basel Extent of Rationing of Nursing Care for Nursing https://www.frontiersin.org/files/Articles/1829774/fpubh-14-1829774-HTML-r1/image_m/fpubh-14-1829774-t004.jpg Table 4. Overview of subscales, number of items, Cronbach’s alpha. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1755626/full https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-g001.jpg Figure 1. Study inclusion flowchart. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-t001.jpg Table 1. Baseline characteristics of studies included in the meta-analysis. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-g002.jpg Figure 2. Quality evaluation of incorporated literature. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-g003.jpg Figure 3. Forest plot of 28-day mortality rate. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-g004.jpg Figure 4. Forest plot of in-hospital mortality rate. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-g005.jpg Figure 5. Forest plot of 90-day mortality rate. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-g006.jpg Figure 6. Forest plot of re-infection rate. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-t002.jpg Table 2. The results of the meta-analysis of the infection sites. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-g007.jpg Figure 7. Forest plot of the incidence rate of gastric and duodenal bleeding. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-g008.jpg Figure 8. Forest plot of length of stay in ICU. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-g009.jpg Figure 9. Forest plot of hospital stay time. https://www.frontiersin.org/files/Articles/1755626/fmed-13-1755626-HTML-r1/image_m/fmed-13-1755626-t003.jpg Table 3. Subgroup analysis of clinical outcomes stratified by comparator type and study design. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1580370/full https://www.frontiersin.org/files/Articles/1580370/fonc-15-1580370-HTML/image_m/fonc-15-1580370-t001.jpg Table 1. AITL clinicodemographic characteristics(n=140). https://www.frontiersin.org/files/Articles/1580370/fonc-15-1580370-HTML/image_m/fonc-15-1580370-g001.jpg Figure 1. First-line treatment efficacy and DOR of 140 AITL patients. (A) Treatment efficacy for pat https://www.frontiersin.org/files/Articles/1580370/fonc-15-1580370-HTML/image_m/fonc-15-1580370-g002.jpg Figure 2. Kaplan-Meier curves of PFS and OS. (A) PFS of all 140 AITL patients. (B) OS of all 140 AIT https://www.frontiersin.org/files/Articles/1580370/fonc-15-1580370-HTML/image_m/fonc-15-1580370-t002.jpg Table 2. Univariate analysis for OS and PFS and multivariate analysis for OS. https://www.frontiersin.org/files/Articles/1580370/fonc-15-1580370-HTML/image_m/fonc-15-1580370-g003.jpg Figure 3. Kaplan-Meier curves for PFS in AITL patients stratified by different prognostic models: (A https://www.frontiersin.org/files/Articles/1580370/fonc-15-1580370-HTML/image_m/fonc-15-1580370-g004.jpg Figure 4. Kaplan-Meier curves for OS in AITL patients stratified by different prognostic models: (A) https://www.frontiersin.org/files/Articles/1580370/fonc-15-1580370-HTML/image_m/fonc-15-1580370-t003.jpg Table 3. Risk stratification in lymphoma patients using seven prognostic models. https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2026.1751002/full https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g001.jpg Figure 1. Incidents such as search and rescue require operators to fuse multiple sources of informat https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g002.jpg Figure 2. Scenario text page, reflecting a realistic backcountry search and rescue scenario. https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g003.jpg Figure 3. Graphical plate model of the system’s supporting algorithm. Grey random variables are infe https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g004.jpg Figure 4. Developed user interface, which allows dynamic reprogramming of autonomous behavior throug https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g005.jpg Figure 5. Results from RINAO showing various modalities of input leading to greater or lesser concen https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g006.jpg Figure 6. Demographics of evaluated expert users show a broad diversity of age and experience. https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g007.jpg Figure 7. Results of the user study across Sanneman evaluative criteria, where higher values indicat https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g008.jpg Figure 8. Results of the user study using the location error metric, where a lower value indicates a https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g009.jpg Figure 9. Inference efficiency for the evaluated algorithm compared to the IRL baseline. https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g010.jpg Figure 10. Selection of trajectories provided by subjects, generated by RINAO and IRL. Each subject’ https://www.frontiersin.org/files/Articles/1751002/frobt-13-1751002-HTML/image_m/frobt-13-1751002-g011.jpg Figure 11. Results from the system usability scale. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1683856/full https://www.frontiersin.org/files/Articles/1683856/fnut-12-1683856-HTML/image_m/fnut-12-1683856-t001.jpg Table 1. Sociodemographic, anthropometric and clinic characteristics of participants. https://www.frontiersin.org/files/Articles/1683856/fnut-12-1683856-HTML/image_m/fnut-12-1683856-t002.jpg Table 2. Body composition analysis of participants. https://www.frontiersin.org/files/Articles/1683856/fnut-12-1683856-HTML/image_m/fnut-12-1683856-t003.jpg Table 3. Clinical blood parameters of participants. https://www.frontiersin.org/files/Articles/1683856/fnut-12-1683856-HTML/image_m/fnut-12-1683856-t004.jpg Table 4. ECM/BCM groups characterization accordingly to sex and age groups. https://www.frontiersin.org/files/Articles/1683856/fnut-12-1683856-HTML/image_m/fnut-12-1683856-t005.jpg Table 5. Differential analysis for clinical, nutritional and functional indexes accordingly to ECM/B https://www.frontiersin.org/files/Articles/1683856/fnut-12-1683856-HTML/image_m/fnut-12-1683856-t006.jpg Table 6. Linear regression analysis for the prediction of dependent variables CCI, MNA, PNI, GNRI an https://www.frontiersin.org/journals/nephrology/articles/10.3389/fneph.2025.1618775/full https://www.frontiersin.org/files/Articles/1618775/fneph-05-1618775-HTML-r1/image_m/fneph-05-1618775-g001.jpg Figure 1. Complete metabolic panel, complete blood count, and other serological markers. https://www.frontiersin.org/files/Articles/1618775/fneph-05-1618775-HTML-r1/image_m/fneph-05-1618775-g002.jpg Figure 2. MRI of the right lower extremity. (A) T1-weighted sagittal view of the right ankle and foo https://www.frontiersin.org/files/Articles/1618775/fneph-05-1618775-HTML-r1/image_m/fneph-05-1618775-g003.jpg Figure 3. Key features between common pathologies. CK, creatine kinase; ESR, erythrocyte sedimentati https://www.frontiersin.org/files/Articles/1618775/fneph-05-1618775-HTML-r1/image_m/fneph-05-1618775-g004.jpg Figure 4. Relevant timeline of events. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1682181/full https://www.frontiersin.org/files/Articles/1682181/fpubh-13-1682181-HTML/image_m/fpubh-13-1682181-t001.jpg Table 1. Frequency distribution of sociodemographic, behavioral, and anthropometric variables among https://www.frontiersin.org/files/Articles/1682181/fpubh-13-1682181-HTML/image_m/fpubh-13-1682181-t002.jpg Table 2. Associations between sociodemographic, behavioral factors and risk factors with BMI categor https://www.frontiersin.org/files/Articles/1682181/fpubh-13-1682181-HTML/image_m/fpubh-13-1682181-t003.jpg Table 3. Strength of association and its odds ratio of overweight/obese and underweight with gender, https://www.frontiersin.org/files/Articles/1682181/fpubh-13-1682181-HTML/image_m/fpubh-13-1682181-t004.jpg Table 4. Adjusted odds ratios for overweight/obese and underweight status with screen time duration, https://www.frontiersin.org/files/Articles/1682181/fpubh-13-1682181-HTML/image_m/fpubh-13-1682181-g001.jpg Figure 1. Structural equation model (SEM) of relationships between diet, screen time, gender, physic https://www.frontiersin.org/files/Articles/1682181/fpubh-13-1682181-HTML/image_m/fpubh-13-1682181-g002.jpg Figure 2. Structural equation model (SEM) comparing relationships between diet, screen time, gender, https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1810159/full https://www.frontiersin.org/files/Articles/1810159/fendo-17-1810159-HTML/image_m/fendo-17-1810159-t001.jpg Table 1. Clinical parameters of patients with early-stage (Group 1) and advanced-stage (Group 2) of https://www.frontiersin.org/files/Articles/1810159/fendo-17-1810159-HTML/image_m/fendo-17-1810159-t002.jpg Table 2. Laboratory parameters of patients with early-stage (group 1) and advanced-stage (group 2) o https://www.frontiersin.org/files/Articles/1810159/fendo-17-1810159-HTML/image_m/fendo-17-1810159-t003.jpg Table 3. Laboratory parameters of group 2 patients with and without CAN. https://www.frontiersin.org/files/Articles/1810159/fendo-17-1810159-HTML/image_m/fendo-17-1810159-t004.jpg Table 4. CAN parameters in patients with early stage (group 1) and advanced stage (group 2) of DKD. https://www.frontiersin.org/files/Articles/1810159/fendo-17-1810159-HTML/image_m/fendo-17-1810159-t005.jpg Table 5. Association between CAN parameters and vitamin D concentration according to the Institute o https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1633456/full https://www.frontiersin.org/files/Articles/1633456/fonc-15-1633456-HTML/image_m/fonc-15-1633456-g001.jpg Figure 1. Anatomical images (a-c) and model preference maps overlaid on b = 800 s/mm2 images (d-f) f https://www.frontiersin.org/files/Articles/1633456/fonc-15-1633456-HTML/image_m/fonc-15-1633456-g002.jpg Figure 2. Healthy volunteer repeatability. Median D, median f, and pIVIM values are plotted for repe https://www.frontiersin.org/files/Articles/1633456/fonc-15-1633456-HTML/image_m/fonc-15-1633456-g003.jpg Figure 3. Median D, median f, pIVIM, and tumour volume as a function of time. Each colour represents https://www.frontiersin.org/files/Articles/1633456/fonc-15-1633456-HTML/image_m/fonc-15-1633456-g004.jpg Figure 4. Impact of b-values on ADC. Median ADC calculated using minimum b-values of (a) 0 and (b) 1 https://www.frontiersin.org/journals/ophthalmology/articles/10.3389/fopht.2026.1776913/full https://www.frontiersin.org/files/Articles/1776913/fopht-06-1776913-HTML/image_m/fopht-06-1776913-g001.jpg Figure 1. (A) Examination at presentation demonstrates limitation in extraocular movements in all di https://www.frontiersin.org/files/Articles/1776913/fopht-06-1776913-HTML/image_m/fopht-06-1776913-g002.jpg Figure 2. (A) Brain T2-weighted fluid-attenuated inversion recovery axial magnetic resonance image ( https://www.frontiersin.org/files/Articles/1776913/fopht-06-1776913-HTML/image_m/fopht-06-1776913-g003.jpg Figure 3. The histopathologic appearance of the biopsy specimen shows that the infiltrate is compose https://www.frontiersin.org/files/Articles/1776913/fopht-06-1776913-HTML/image_m/fopht-06-1776913-g004.jpg Figure 4. (A, B) Fundus photographs demonstrate improvement of SRD with persistent patchy areas of h https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1779772/full https://www.frontiersin.org/files/Articles/1779772/fphar-17-1779772-HTML/image_m/fphar-17-1779772-g001.jpg Figure 1. Schematic representation of the helping behaviour box, based on Sato et al. (2015), with m https://www.frontiersin.org/files/Articles/1779772/fphar-17-1779772-HTML/image_m/fphar-17-1779772-g002.jpg Figure 2. Latencies to door-opening and percentages of door-opening during the naïve cycle of helpin https://www.frontiersin.org/files/Articles/1779772/fphar-17-1779772-HTML/image_m/fphar-17-1779772-g003.jpg Figure 3. Effects of MDMA (10, 5, 1, 0.5 or 0.25 mg/kg i.p.) in the latency to door-opening and the https://www.frontiersin.org/files/Articles/1779772/fphar-17-1779772-HTML/image_m/fphar-17-1779772-g004.jpg Figure 4. Effects of MDMA (10, 5, 1, 0.5 or 0.25 mg/kg i.p.) in the latency to door-opening and the https://www.frontiersin.org/files/Articles/1779772/fphar-17-1779772-HTML/image_m/fphar-17-1779772-g005.jpg Figure 5. Effect of MDMA (10 mg/kg i.p.) on LTD evoked in the NAc core as assessed by in vivo electr https://www.frontiersin.org/files/Articles/1779772/fphar-17-1779772-HTML/image_m/fphar-17-1779772-g006.jpg Figure 6. Effects of MDMA (10 mg/kg i.p.), OXT antagonist Ato and 5-HT2A antagonist Ketan on LTP evo https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1739457/full https://www.frontiersin.org/files/Articles/1739457/fphar-17-1739457-HTML/image_m/fphar-17-1739457-t001.jpg Table 1. The qPCR primers. https://www.frontiersin.org/files/Articles/1739457/fphar-17-1739457-HTML/image_m/fphar-17-1739457-g001.jpg Figure 1. Chronic ureteral obstruction led to renal fibrosis and CD206+ macrophage accumulation in U https://www.frontiersin.org/files/Articles/1739457/fphar-17-1739457-HTML/image_m/fphar-17-1739457-g002.jpg Figure 2. Characterization of UUO-induced renal fibrosis in mice. (A) Representative kidney images o https://www.frontiersin.org/files/Articles/1739457/fphar-17-1739457-HTML/image_m/fphar-17-1739457-g003.jpg Figure 3. CD206+ macrophages were accumulated in UUO kidneys. (A) qPCR analysis for Il1b, Il6, Tnf, https://www.frontiersin.org/files/Articles/1739457/fphar-17-1739457-HTML/image_m/fphar-17-1739457-g004.jpg Figure 4. RP-182 peptide inhibited UUO-induced renal fibrosis. (A) The schematic of the experimental https://www.frontiersin.org/files/Articles/1739457/fphar-17-1739457-HTML/image_m/fphar-17-1739457-g005.jpg Figure 5. RP-182 peptide inhibited UUO-induced kidney inflammation. (A) qPCR analysis for renal expr https://www.frontiersin.org/files/Articles/1739457/fphar-17-1739457-HTML/image_m/fphar-17-1739457-g006.jpg Figure 6. RP-182 peptide inhibited M2 macrophage polarization. (A) The schematic of the experimental https://www.frontiersin.org/files/Articles/1739457/fphar-17-1739457-HTML/image_m/fphar-17-1739457-g007.jpg Figure 7. RP-182 peptide inhibited the MMT process. (A) The schematic of the experimental design. (B https://www.frontiersin.org/files/Articles/1739457/fphar-17-1739457-HTML/image_m/fphar-17-1739457-g008.jpg Figure 8. RP-182 peptide inhibited Wnt/β-catenin signaling pathway in M2 macrophages. (A) Volcano pl https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1763884/full https://www.frontiersin.org/files/Articles/1763884/fdgth-08-1763884-HTML/image_m/fdgth-08-1763884-g001.jpg Figure 1. PRISMA chart. https://www.frontiersin.org/files/Articles/1763884/fdgth-08-1763884-HTML/image_m/fdgth-08-1763884-t001.jpg Table 1. Ethical challenges in AI-driven healthcare systems in LMICs. https://www.frontiersin.org/files/Articles/1763884/fdgth-08-1763884-HTML/image_m/fdgth-08-1763884-t002.jpg Table 2. Regulatory and policy barriers to AI implementation. https://www.frontiersin.org/files/Articles/1763884/fdgth-08-1763884-HTML/image_m/fdgth-08-1763884-t003.jpg Table 3. Implementation challenges in LMICs. https://www.frontiersin.org/files/Articles/1763884/fdgth-08-1763884-HTML/image_m/fdgth-08-1763884-t004.jpg Table 4. Inclusive AI development: guiding principles and actions. https://www.frontiersin.org/files/Articles/1763884/fdgth-08-1763884-HTML/image_m/fdgth-08-1763884-t005.jpg Table 5. Case examples of AI in LMICs. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1611984/full https://www.frontiersin.org/files/Articles/1611984/fimmu-16-1611984-HTML/image_m/fimmu-16-1611984-g001.jpg Figure 1. PRISMA flow diagram. This flow diagram depicts the systematic process of selecting studies https://www.frontiersin.org/files/Articles/1611984/fimmu-16-1611984-HTML/image_m/fimmu-16-1611984-t001.jpg Table 1. Baseline characteristics of included studies. https://www.frontiersin.org/files/Articles/1611984/fimmu-16-1611984-HTML/image_m/fimmu-16-1611984-t002.jpg Table 2. Efficacy and safety of included studies. https://www.frontiersin.org/files/Articles/1611984/fimmu-16-1611984-HTML/image_m/fimmu-16-1611984-t003.jpg Table 3. Quality evaluation (D1: Clear research purpose; D2: Continuity of inclusion of patients; D3 https://www.frontiersin.org/files/Articles/1611984/fimmu-16-1611984-HTML/image_m/fimmu-16-1611984-g002.jpg Figure 2. Forest plots of efficacy outcomes for BsAb vs. CAR T therapy. (A) Complete remission (CR): https://www.frontiersin.org/files/Articles/1611984/fimmu-16-1611984-HTML/image_m/fimmu-16-1611984-g003.jpg Figure 3. Comparison of efficacy and adverse events for CAR T-cells vs. BsAb. The figure compares th https://www.frontiersin.org/files/Articles/1611984/fimmu-16-1611984-HTML/image_m/fimmu-16-1611984-g004.jpg Figure 4. Forest plots of severe adverse events for BsAb vs. CAR T therapy, pooled grade ≥3 adverse https://www.frontiersin.org/files/Articles/1611984/fimmu-16-1611984-HTML/image_m/fimmu-16-1611984-t004.jpg Table 4. Meta‐regression analysis using study‐level characteristics in relation to CR. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1749241/full https://www.frontiersin.org/files/Articles/1749241/fpubh-13-1749241-HTML/image_m/fpubh-13-1749241-t001.jpg Table 1. Distribution of overall IPCAF scores and scores by core component among 128 general hospita https://www.frontiersin.org/files/Articles/1749241/fpubh-13-1749241-HTML/image_m/fpubh-13-1749241-g001.jpg Figure 1. Distribution of scores for the eight core components (CC). Each CC has a maximum score of https://www.frontiersin.org/files/Articles/1749241/fpubh-13-1749241-HTML/image_m/fpubh-13-1749241-t002.jpg Table 2. Classification of IPC implementation levels in secondary and tertiary general hospitals in https://www.frontiersin.org/files/Articles/1749241/fpubh-13-1749241-HTML/image_m/fpubh-13-1749241-g002.jpg Figure 2. Distribution of IPCAF scores among general hospitals in the Inner Mongolia Autonomous Regi https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1719952/full https://www.frontiersin.org/files/Articles/1719952/fonc-15-1719952-HTML/image_m/fonc-15-1719952-g001.jpg Figure 1. Comparison of body composition parameters and the combinations with GNRI according to sex. https://www.frontiersin.org/files/Articles/1719952/fonc-15-1719952-HTML/image_m/fonc-15-1719952-t001.jpg Table 1. Patient characteristics according to sex-specific quartiles of SMI×GNRI levels. https://www.frontiersin.org/files/Articles/1719952/fonc-15-1719952-HTML/image_m/fonc-15-1719952-g002.jpg Figure 2. The Kaplan-Meier curves for overall survival and disease-free survival are presented for g https://www.frontiersin.org/files/Articles/1719952/fonc-15-1719952-HTML/image_m/fonc-15-1719952-t002.jpg Table 2. Univariate analysis of baseline characteristics and overall survival in GC patients in the https://www.frontiersin.org/files/Articles/1719952/fonc-15-1719952-HTML/image_m/fonc-15-1719952-t003.jpg Table 3. Multivariate analysis of body composition parameters and overall survival in GC patients. https://www.frontiersin.org/files/Articles/1719952/fonc-15-1719952-HTML/image_m/fonc-15-1719952-t004.jpg Table 4. Multivariate analysis of the combination of body composition parameters and GNRI and overal https://www.frontiersin.org/files/Articles/1719952/fonc-15-1719952-HTML/image_m/fonc-15-1719952-g003.jpg Figure 3. Kaplan-Meier analysis of overall survival, with patient stratification based on sex-specif https://www.frontiersin.org/files/Articles/1719952/fonc-15-1719952-HTML/image_m/fonc-15-1719952-g004.jpg Figure 4. Disease-free survival Kaplan-Meier curves for patients stratified by sex-specific quartile https://www.frontiersin.org/files/Articles/1719952/fonc-15-1719952-HTML/image_m/fonc-15-1719952-t005.jpg Table 5. The predictive and discriminatory power of different body composition parameters and the co https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1759292/full https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-g001.jpg Figure 1. Flowchart of patient selection and analytical process. https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-g002.jpg Figure 2. Body composition schematic diagram. The cross-sectional CT image of the third lumbar verte https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-t001.jpg Table 1. Baseline characteristics of patients in the training and validation sets. https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-g003.jpg Figure 3. Feature selection graph. (A) The LASSO-path graph illustrates the variable selection proce https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-g004.jpg Figure 4. Kaplan-Meier survival analysis was performed based on nine characteristics. https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-g005.jpg Figure 5. Performance evaluation of the OS prognosis model. (A) Nomogram for OS prognosis model. The https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-g006.jpg Figure 6. Performance evaluation of the PFS prognosis model. (A) Nomogram for PFS prognosis model. T https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-g007.jpg Figure 7. Performance evaluation of the DFS prognosis model. (A) Nomogram for DFS prognosis model. T https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-g008.jpg Figure 8. Construction and evaluation of the PRSM. (A, B) K-M survival curves of the low, intermedia https://www.frontiersin.org/files/Articles/1759292/fimmu-17-1759292-HTML/image_m/fimmu-17-1759292-t002.jpg Table 2. Predictive performance of all models in the training and validation sets. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1769867/full https://www.frontiersin.org/files/Articles/1769867/fcvm-13-1769867-HTML/image_m/fcvm-13-1769867-g001.jpg Figure 1. Echocardiographic and coronary CTA findings in a 36-year-old man with localized aortic roo https://www.frontiersin.org/files/Articles/1769867/fcvm-13-1769867-HTML/image_m/fcvm-13-1769867-g002.jpg Figure 2. Intraoperative photograph of Case 1 showing an intimal flap confined to the aortic root (w https://www.frontiersin.org/files/Articles/1769867/fcvm-13-1769867-HTML/image_m/fcvm-13-1769867-g003.jpg Figure 3. Echocardiographic and coronary computed tomography angiography (CTA) findings in a 68-year https://www.frontiersin.org/files/Articles/1769867/fcvm-13-1769867-HTML/image_m/fcvm-13-1769867-g004.jpg Figure 4. Postoperative histopathological findings of the two cases. (A) Hematoxylin and eosin (H&E) https://www.frontiersin.org/files/Articles/1769867/fcvm-13-1769867-HTML/image_m/fcvm-13-1769867-t001.jpg Table 1. Summary of the clinical and imaging characteristics of reported cases (n = 33). https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1790302/full https://www.frontiersin.org/files/Articles/1790302/fnins-20-1790302-HTML/image_m/fnins-20-1790302-g001.jpg Figure 1. Stratified analyses of the association between anisometropia and impaired near stereoacuit https://www.frontiersin.org/journals/nephrology/articles/10.3389/fneph.2026.1776371/full https://www.frontiersin.org/files/Articles/1776371/fneph-06-1776371-HTML-r1/image_m/fneph-06-1776371-t001.jpg Table 1. Sociodemographic characteristics of the three studied populations. https://www.frontiersin.org/files/Articles/1776371/fneph-06-1776371-HTML-r1/image_m/fneph-06-1776371-g001.jpg Figure 1. Average daily time spent by an APN managing the telemonitoring application for 90 patients https://www.frontiersin.org/files/Articles/1776371/fneph-06-1776371-HTML-r1/image_m/fneph-06-1776371-t002.jpg Table 2. Application activity and communication metrics during the study period. https://www.frontiersin.org/files/Articles/1776371/fneph-06-1776371-HTML-r1/image_m/fneph-06-1776371-t003.jpg Table 3. Self-reported perceptions and satisfaction by group. https://www.frontiersin.org/files/Articles/1776371/fneph-06-1776371-HTML-r1/image_m/fneph-06-1776371-t004.jpg Table 4. Answers to open-ended questions grouped by subgroups. https://www.frontiersin.org/files/Articles/1776371/fneph-06-1776371-HTML-r1/image_m/fneph-06-1776371-t005.jpg Table 5. Collected data from STOPCO and JAMCO users after questionnaire reminders. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1783630/full https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g010.jpg Graphical Abstract. The effect of potassium sorbate (PS) on development of early life stage of D. me https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g001.jpg Figure 1. The development time of flies exposed to PS. (A) Pupation time of flies exposed to PS at c https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g002.jpg Figure 2. Effects of PS on D. melanogaster offspring larval development. (A) Pupation time of offspr https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g003.jpg Figure 3. Survival curves of PS-exposed D. melanogaster. (A) Lifespan of female flies exposed to PS https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-t001.jpg Table 1. Statistics for survival curves. https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g004.jpg Figure 4. Gut microbial composition and species enrichment analysis. (A) Gut microbial composition a https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g005.jpg Figure 5. α diversity of gut microbiota in PS-exposed flies and their offspring. (A) Chao1. (B) Fait https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g006.jpg Figure 6. β diversity of gut microbiota in PS-exposed flies and their offspring. (A) Principal coord https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-t002.jpg Table 2. Permanova of microbiota based on bray-curtis distance. https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g007.jpg Figure 7. Function prediction of gut microbiota in PS-exposed flies and their offspring. (A) KEGG pa https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g008.jpg Figure 8. Significant pathway (ANOVA analysis) of gut microbial function in PS-exposed flies and the https://www.frontiersin.org/files/Articles/1783630/fmicb-17-1783630-HTML/image_m/fmicb-17-1783630-g009.jpg Figure 9. Gene expressions of flies exposed to PS. (A) Hormone-related genes. (B) IIS pathway-relate https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1732134/full https://www.frontiersin.org/files/Articles/1732134/fphar-16-1732134-HTML/image_m/fphar-16-1732134-g001.jpg Figure 1. Systems-level framework linking TCM interventions to the gut–liver–brain–immune axis in T2 https://www.frontiersin.org/files/Articles/1732134/fphar-16-1732134-HTML/image_m/fphar-16-1732134-g002.jpg Figure 2. Systems pharmacology map linking TCM formulas/metabolites to functional modules and T2DM o https://www.frontiersin.org/files/Articles/1732134/fphar-16-1732134-HTML/image_m/fphar-16-1732134-t001.jpg Table 1. Systems pharmacology functional-module mapping of TCM interventions in T2DM (ConPhyMP-align https://www.frontiersin.org/files/Articles/1732134/fphar-16-1732134-HTML/image_m/fphar-16-1732134-t002.jpg Table 2. Major anti-T2DM classical formula lineages and representative modern derivatives (ConPhyMP https://www.frontiersin.org/files/Articles/1732134/fphar-16-1732134-HTML/image_m/fphar-16-1732134-g003.jpg Figure 3. Node-based model of TCM regulation of the gut–liver–brain–immune axis in T2DM. HLJDT and L https://www.frontiersin.org/files/Articles/1732134/fphar-16-1732134-HTML/image_m/fphar-16-1732134-g004.jpg Figure 4. Convergence-module framework linking multi-component TCM to shared drivers and organ-speci https://www.frontiersin.org/files/Articles/1732134/fphar-16-1732134-HTML/image_m/fphar-16-1732134-g005.jpg Figure 5. Roadmap for modernizing evidence-based integrated TCM in T2DM care. The schematic summariz https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2026.1746383/full https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-g001.jpg Figure 1. Conceptual framework based on general system theory of Ludwig Von Bertalanffy (25). https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-g002.jpg Figure 2. Visual analog scale (VAS) (6). https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t001.jpg Table 1. Distribution of the study groups (control and experimental group) according to their demogr https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t002.jpg Table 2. Distribution of the study groups (control and experimental group) according to their previo https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t003.jpg Table 3. Descriptive item-level distribution of pelvic floor disability Index (PFDI-20) symptoms At https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t004.jpg Table 4. Univariate analysis of variance (ANCOVA) and independent samples t-test for PFDI-20 subscal https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t005.jpg Table 5. Descriptive item-level distribution of female sexual function Index (FSFI) desire and arous https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t006.jpg Table 6. Univariate analysis of variance (ANCOVA) and independent samples t-test for FSFI domains in https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t007.jpg Table 7. Univariate analysis of variance (ANCOVA) and independent samples t-test for pain scales in https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t008.jpg Table 8. Repeated measures MANOVA for all measured variables in both groups (control and experimenta https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t009.jpg Table 9. Independent samples effect sizes of the Kegel exercise intervention among study groups (con https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t010.jpg Table 10. Correlation between sociodemographic data and pre- and post-tests for the four domains. https://www.frontiersin.org/files/Articles/1746383/frph-08-1746383-HTML/image_m/frph-08-1746383-t011.jpg Table 11. Correlation between participants’ previous pregnancy history and pre and post-tests for th https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1740067/full https://www.frontiersin.org/files/Articles/1740067/fimmu-17-1740067-HTML/image_m/fimmu-17-1740067-g001.jpg Figure 1. Timeline of comprehensive HSCT management. Line plots show dynamic risk levels for six com https://www.frontiersin.org/files/Articles/1740067/fimmu-17-1740067-HTML/image_m/fimmu-17-1740067-g002.jpg Figure 2. This schematic presents a systems-level overview of major complications following hematopo https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1801008/full https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-t001.jpg Table 1. Operationalization of research objectives into GST constructs and predictive analytics meth https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-g001.jpg Figure 1. A conceptual framework, an adaptation of the General Systems Theory. Adapted from Von Bert https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-t002.jpg Table 2. Mapping of Section 12(3) Sub-clauses to policy dimensions using NLP classification. https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-t003.jpg Table 3. Pilot performance scores for expropriation act sub-clauses based on DALRRD, programmes 4 an https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-g002.jpg Figure 2. Correlation heatmap analysis of structural, environmental, and socio-economic predictive f https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-g003.jpg Figure 3. Projected outcomes of water and climate indices on agricultural productivity trends (2014– https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-g004.jpg Figure 4. Comparative analysis of structural capacity and equity gap divergence. Author’s calculatio https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-g005.jpg Figure 5. Feature importance analysis for productivity and equity predictive factors. Author’s compu https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-g006.jpg Figure 6. Longitudinal trends in potential, utilization, and equity scores with expropriation act cl https://www.frontiersin.org/files/Articles/1801008/fsufs-10-1801008-HTML-r1/image_m/fsufs-10-1801008-g007.jpg Figure 7. Policy classification tree model output and recommended interventions over time. Author’s https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1718613/full https://www.frontiersin.org/files/Articles/1718613/frai-08-1718613-HTML/image_m/frai-08-1718613-t001.jpg Table 1. Italian universities with active AI policies (as of August 2025). https://www.frontiersin.org/files/Articles/1718613/frai-08-1718613-HTML/image_m/frai-08-1718613-t002.jpg Table 2. Interpretation guidelines for reliability coefficients. https://www.frontiersin.org/files/Articles/1718613/frai-08-1718613-HTML/image_m/frai-08-1718613-t003.jpg Table 3. Descriptive statistics for XAI-ED CAF governance dimensions (N = 14). https://www.frontiersin.org/files/Articles/1718613/frai-08-1718613-HTML/image_m/frai-08-1718613-g001.jpg Figure 1. Coverage of XAI-ED CAF governance dimensions across Italian universities (N = 14). Horizon https://www.frontiersin.org/files/Articles/1718613/frai-08-1718613-HTML/image_m/frai-08-1718613-g002.jpg Figure 2. Institutional-level scores across XAI-ED CAF governance dimensions. Scores normalized to 0 https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2026.1733685/full https://www.frontiersin.org/files/Articles/1733685/frhs-06-1733685-HTML/image_m/frhs-06-1733685-g001.jpg Figure 1. Stages of the research feeding into development of FrEEIA readiness assessment tool. https://www.frontiersin.org/files/Articles/1733685/frhs-06-1733685-HTML/image_m/frhs-06-1733685-t001.jpg Table 1. Potential domains for inclusion in the FrEEIA readiness assessment tool. https://www.frontiersin.org/files/Articles/1733685/frhs-06-1733685-HTML/image_m/frhs-06-1733685-t002.jpg Table 2. Comparison of statements/domains in the adaptation of the readiness thinking tool® (wanders https://www.frontiersin.org/files/Articles/1733685/frhs-06-1733685-HTML/image_m/frhs-06-1733685-t003.jpg Table 3. FrEEIA readiness assessment tool statements. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1722316/full https://www.frontiersin.org/files/Articles/1722316/fcvm-13-1722316-HTML-r1/image_m/fcvm-13-1722316-g001.jpg Figure 1. Chest CT scan revealed a mediastinal space-occupying lesion. (A) The first chest CT scan; https://www.frontiersin.org/files/Articles/1722316/fcvm-13-1722316-HTML-r1/image_m/fcvm-13-1722316-g002.jpg Figure 2. Enhanced chest CT revealed a mediastinal space-occupying lesion. (A) Mediastinal window an https://www.frontiersin.org/files/Articles/1722316/fcvm-13-1722316-HTML-r1/image_m/fcvm-13-1722316-g003.jpg Figure 3. Chest magnetic resonance imaging shows a space-occupying lesion in the anterior mediastinu https://www.frontiersin.org/files/Articles/1722316/fcvm-13-1722316-HTML-r1/image_m/fcvm-13-1722316-g004.jpg Figure 4. Intraoperative conditions and postoperative pathological results. (A,B) The location of th https://www.frontiersin.org/files/Articles/1722316/fcvm-13-1722316-HTML-r1/image_m/fcvm-13-1722316-g005.jpg Figure 5. The chest CT scan after the operation indicated that the postoperative condition was good. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1681221/full https://www.frontiersin.org/files/Articles/1681221/fdgth-07-1681221-HTML-r1/image_m/fdgth-07-1681221-t001.jpg Table 1. Participant demographics. https://www.frontiersin.org/files/Articles/1681221/fdgth-07-1681221-HTML-r1/image_m/fdgth-07-1681221-g001.jpg Figure 1. Assessment journey illustrating the typical stages of neuropsychological assessment. Icons https://www.frontiersin.org/files/Articles/1681221/fdgth-07-1681221-HTML-r1/image_m/fdgth-07-1681221-t002.jpg Table 2. Opportunities, benefits and barriers to technology integration. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1727725/full https://www.frontiersin.org/files/Articles/1727725/fpubh-13-1727725-HTML/image_m/fpubh-13-1727725-g001.jpg Figure 1. Conceptual logic model for tele-follow-up in pediatric epilepsy. Inputs (education, device https://www.frontiersin.org/files/Articles/1727725/fpubh-13-1727725-HTML/image_m/fpubh-13-1727725-t001.jpg Table 1. KPI and inequity panel (operational specification). https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1699082/full https://www.frontiersin.org/files/Articles/1699082/fped-13-1699082-HTML/image_m/fped-13-1699082-g001.jpg Figure 1. (A–D) Show the child's right hand, left hand, left foot, and right foot in sequence, all o https://www.frontiersin.org/files/Articles/1699082/fped-13-1699082-HTML/image_m/fped-13-1699082-t001.jpg Table 1. Laboratory test results of the child. https://www.frontiersin.org/files/Articles/1699082/fped-13-1699082-HTML/image_m/fped-13-1699082-g002.jpg Figure 2. The 24-hour ambulatory electroencephalogram (EEG) for the child exhibiting abnormal findin https://www.frontiersin.org/files/Articles/1699082/fped-13-1699082-HTML/image_m/fped-13-1699082-g003.jpg Figure 3. (A–C) T1-weighted axial images. (A) Symmetric slightly hyperintense signals on T1WI are no https://www.frontiersin.org/files/Articles/1699082/fped-13-1699082-HTML/image_m/fped-13-1699082-g004.jpg Figure 4. A table summarizing the GLI2 mutation types and mutation sites of the child and his parent https://www.frontiersin.org/files/Articles/1699082/fped-13-1699082-HTML/image_m/fped-13-1699082-g005.jpg Figure 5. (A) Shows the structural diagram of the GLI2 gene: the blue parts represent exons (abbrevi https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1631477/full https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-t001.jpg Table 1. The basic features of studies. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g002.jpg Figure 2. ROB assessment of the studies included in the meta-analyses. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g003.jpg Figure 3. Meta-analysis of the effects of TRE on SBP. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g004.jpg Figure 4. Meta-analysis of the effects of TRE on DBP. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g005.jpg Figure 5. Meta-analysis of the effects of TRE on HR. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g006.jpg Figure 6. Meta-analysis of the effects of TRE on FBG. Panel (A) shows the main analysis, and Panel ( https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g007.jpg Figure 7 Meta-analysis of the effects of TRE on FINS. Panel (A) shows the main analysis, and Panel ( https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g008.jpg Figure 8. Meta-analysis of the effects of TRE on HOMA-IR. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g009.jpg Figure 9. Meta-analysis of the effects of TRE on BMI. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g010.jpg Figure 10. Meta-analysis of the effects of TRE on TC. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g011.jpg Figure 11 Meta-analysis of the effects of TRE on TG. Panel (A) shows the main analysis, and Panel (B https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g012.jpg Figure 12. Meta-analysis of the effects of TRE on HDL-C. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g013.jpg Figure 13. Meta-analysis of the effects of TRE on LDL-C. https://www.frontiersin.org/files/Articles/1631477/fnut-12-1631477-HTML-r1/image_m/fnut-12-1631477-g014.jpg Figure 14. Funnel plots. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1745358/full https://www.frontiersin.org/files/Articles/1745358/fnut-13-1745358-HTML/image_m/fnut-13-1745358-g001.jpg Figure 1. Synthetic pathway of n-3 PUFAs (molecular structure created by https://app.molview.com). T https://www.frontiersin.org/files/Articles/1745358/fnut-13-1745358-HTML/image_m/fnut-13-1745358-g002.jpg Figure 2. DHA and EPA regulate lipid metabolism through multiple mechanisms, including modulation of https://www.frontiersin.org/files/Articles/1745358/fnut-13-1745358-HTML/image_m/fnut-13-1745358-g003.jpg Figure 3. The roles and critical windows of maternal DHA and EPA supplementation in transgenerationa https://www.frontiersin.org/files/Articles/1745358/fnut-13-1745358-HTML/image_m/fnut-13-1745358-t001.jpg Table 1. Clinical studies and meta-analysis of maternal DHA/EPA supplementation affecting the offspr https://www.frontiersin.org/files/Articles/1745358/fnut-13-1745358-HTML/image_m/fnut-13-1745358-t002.jpg Table 2. Animal studies of maternal DHA/EPA supplementation affecting the offspring. https://www.frontiersin.org/files/Articles/1745358/fnut-13-1745358-HTML/image_m/fnut-13-1745358-g004.jpg Figure 4. Mechanism of maternal DHA/EPA supplementation transgenerational effects on lipid metabolis https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1782361/full https://www.frontiersin.org/files/Articles/1782361/fmicb-17-1782361-HTML-r1/image_m/fmicb-17-1782361-t001.jpg Table 1. Clinical studies of maternal prebiotic/probiotic supplementation and its primary maternal o https://www.frontiersin.org/files/Articles/1782361/fmicb-17-1782361-HTML-r1/image_m/fmicb-17-1782361-t002.jpg Table 2. Animal studies of maternal prebiotic/probiotic supplementation and its primary maternal out https://www.frontiersin.org/files/Articles/1782361/fmicb-17-1782361-HTML-r1/image_m/fmicb-17-1782361-t003.jpg Table 3. Clinical studies of maternal prebiotic/probiotic supplementation and its effects on offspri https://www.frontiersin.org/files/Articles/1782361/fmicb-17-1782361-HTML-r1/image_m/fmicb-17-1782361-t004.jpg Table 4. Animal studies of maternal prebiotic/probiotic supplementation and its effects on offspring https://www.frontiersin.org/files/Articles/1782361/fmicb-17-1782361-HTML-r1/image_m/fmicb-17-1782361-g001.jpg Figure 1. Main effects of maternal probiotic/prebiotic supplementation on offspring. Maternal probio https://www.frontiersin.org/files/Articles/1782361/fmicb-17-1782361-HTML-r1/image_m/fmicb-17-1782361-g002.jpg Figure 2. Main effects of maternal prebiotic/probiotic supplementation on pregnant women and offspri https://www.frontiersin.org/files/Articles/1782361/fmicb-17-1782361-HTML-r1/image_m/fmicb-17-1782361-g003.jpg Figure 3. Possible mechanisms of intergenerational effects of maternal prebiotic and probiotic inter https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1770037/full https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-g001.jpg Figure 1. Flow diagram of patient cohort selection. https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-t001.jpg Table 1. Clinical characteristics of patients. https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-g002.jpg Figure 2. Workflow of study. The clinical information of 162 HR+/HER2- breast cancer patients and HE https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-g003.jpg Figure 3. Selection process for the feature and machine learning algorithm for the nac efficacy pred https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-t002.jpg Table 2. Summary of the AUC values and predictive efficacy metrics obtained for the multiple machine https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-g004.jpg Figure 4. Evaluation of the predictive performance of the NAC efficacy prediction model. (A, B) The https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-g005.jpg Figure 5. Survival curves stratified by NAC efficacy groups. (A) Disease-free survival curves strati https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-t003.jpg Table 3. Multivariate Cox regression analysis for disease-free survival. https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-g006.jpg Figure 6. Evaluation of the predictive performance of the recurrence prediction model. (A, B) The RO https://www.frontiersin.org/files/Articles/1770037/fonc-16-1770037-HTML-r1/image_m/fonc-16-1770037-t004.jpg Table 4. Multivariate Cox regression analysis for overall survival of patients. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1694093/full https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-t001.jpg Table 1. Characteristics of the participants. https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-g001.jpg Figure 1. Concentrations of HMOs from colostrum to 4 months postpartum in breast milk. (A) 2′FL; (B) https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-g002.jpg Figure 2. Relative abundances of HMOs from colostrum to 4 months postpartum in breast milk. The orde https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-g003.jpg Figure 3. Characteristics of extracellular vesicles in breast milk. (A) Transmission electron microg https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-g004.jpg Figure 4. RNA concentrations in human breast MEVs. RNA concentration in breast MEVs during lactation https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-t002.jpg Table 2. Top 20 most highly expressed miRNAs in MEVs. https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-g005.jpg Figure 5. A heatmap showing Spearman correlations among HMO concentrations and the top 20 highly exp https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-g006.jpg Figure 6. Relationships between immunity- and development-related miRNAs detected in breast MEVs and https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-g007.jpg Figure 7. HMOs are detected in breast milk or extracellular vesicles. 2′FL, 3FL, 3′SL, 6′SL, LDFT, L https://www.frontiersin.org/files/Articles/1694093/fnut-12-1694093-HTML-r3/image_m/fnut-12-1694093-g008.jpg Figure 8. The overview of this study. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1787039/full https://www.frontiersin.org/files/Articles/1787039/fphar-17-1787039-HTML/image_m/fphar-17-1787039-g001.jpg Figure 1. The flow-process diagram of erectile dysfunction (ED) from FAERS database. https://www.frontiersin.org/files/Articles/1787039/fphar-17-1787039-HTML/image_m/fphar-17-1787039-t001.jpg Table 1. The sequence of primers. https://www.frontiersin.org/files/Articles/1787039/fphar-17-1787039-HTML/image_m/fphar-17-1787039-t002.jpg Table 2. Top 5 countries of patients with ADEs of ED. https://www.frontiersin.org/files/Articles/1787039/fphar-17-1787039-HTML/image_m/fphar-17-1787039-g002.jpg Figure 2. The drugs associated ED from FAERS database. (A) The forest plot for top 30 drugs associat https://www.frontiersin.org/files/Articles/1787039/fphar-17-1787039-HTML/image_m/fphar-17-1787039-g003.jpg Figure 3. Identification and function prediction of shared targets between drugs and ED. (A,B) The v https://www.frontiersin.org/files/Articles/1787039/fphar-17-1787039-HTML/image_m/fphar-17-1787039-g004.jpg Figure 4. FGFR1, SERPINE1, TGFB2, and TGFBR2 were identified as key genes in ED. (A) The venn diagra https://www.frontiersin.org/files/Articles/1787039/fphar-17-1787039-HTML/image_m/fphar-17-1787039-g005.jpg Figure 5. Fibroblasts were identified as key cell in ED. (A) Identification of cell clusters in ED. https://www.frontiersin.org/files/Articles/1787039/fphar-17-1787039-HTML/image_m/fphar-17-1787039-g006.jpg Figure 6. The correlation analysis between cell types with ED. (A) Correlation analysis of cell type https://www.frontiersin.org/files/Articles/1787039/fphar-17-1787039-HTML/image_m/fphar-17-1787039-g007.jpg Figure 7. Pseudotime analysis and cell communication. (A) The differentiation station of fibroblasts https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1771427/full https://www.frontiersin.org/files/Articles/1771427/fonc-16-1771427-HTML-r1/image_m/fonc-16-1771427-g001.jpg Figure 1. Conventional imaging findings of the patient. (A) Ultrasound: right breast shows an irregu https://www.frontiersin.org/files/Articles/1771427/fonc-16-1771427-HTML-r1/image_m/fonc-16-1771427-g002.jpg Figure 2. Multi-parametric DLCT analysis of the patient. (A) Conventional CT image, (B) Effective at https://www.frontiersin.org/files/Articles/1771427/fonc-16-1771427-HTML-r1/image_m/fonc-16-1771427-t001.jpg Table 1. Clinical timeline of the case. This timeline summarizes key events from initial presentatio https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1695764/full https://www.frontiersin.org/files/Articles/1695764/fphys-16-1695764-HTML-r2/image_m/fphys-16-1695764-t001.jpg Table 1. Anatomical-functional-molecular integrated imaging evaluation system. https://www.frontiersin.org/files/Articles/1695764/fphys-16-1695764-HTML-r2/image_m/fphys-16-1695764-t002.jpg Table 2. Imaging modalities for the diagnosis of CAD: A comparison between ICA and ECA dissections. https://www.frontiersin.org/files/Articles/1695764/fphys-16-1695764-HTML-r2/image_m/fphys-16-1695764-g001.jpg Figure 1. Borgess classification of carotid artery dissection. https://www.frontiersin.org/files/Articles/1695764/fphys-16-1695764-HTML-r2/image_m/fphys-16-1695764-g002.jpg Figure 2. Classification of carotid artery dissection in 2024. https://www.frontiersin.org/files/Articles/1695764/fphys-16-1695764-HTML-r2/image_m/fphys-16-1695764-g003.jpg Figure 3. (A–D) Evolution of CAD animal models. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1622275/full https://www.frontiersin.org/files/Articles/1622275/fcvm-12-1622275-HTML-r1/image_m/fcvm-12-1622275-g001.jpg Figure 1. Study flow chart. HFpEF, heart failure with preserved ejection fraction; LAVI, left atrial https://www.frontiersin.org/files/Articles/1622275/fcvm-12-1622275-HTML-r1/image_m/fcvm-12-1622275-t001.jpg Table 1. Baseline clinical and laboratory characteristics. https://www.frontiersin.org/files/Articles/1622275/fcvm-12-1622275-HTML-r1/image_m/fcvm-12-1622275-t002.jpg Table 2. Procedural characteristics and medications prescribed after percutaneous coronary intervent https://www.frontiersin.org/files/Articles/1622275/fcvm-12-1622275-HTML-r1/image_m/fcvm-12-1622275-t003.jpg Table 3. Baseline echocardiographic parameters. https://www.frontiersin.org/files/Articles/1622275/fcvm-12-1622275-HTML-r1/image_m/fcvm-12-1622275-g002.jpg Figure 2. Kaplan–Meier event curve of the cumulative incidence of all-cause mortality according to u https://www.frontiersin.org/files/Articles/1622275/fcvm-12-1622275-HTML-r1/image_m/fcvm-12-1622275-t004.jpg Table 4. Cumulative outcomes according to uric acid level in patients with HFpEF and AMI. https://www.frontiersin.org/files/Articles/1622275/fcvm-12-1622275-HTML-r1/image_m/fcvm-12-1622275-g003.jpg Figure 3. Prognostic impact of serum uric acid level in patients with an acute myocardial infarction https://www.frontiersin.org/files/Articles/1622275/fcvm-12-1622275-HTML-r1/image_m/fcvm-12-1622275-g004.jpg Figure 4. Forest plot for all-cause death. HR, hazard ratio; CI, confidence interval; eGFR, estimate https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1726282/full https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-t001.jpg Table 1. Basic information of the three enzyme replacement therapies for Gaucher disease. https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-t002.jpg Table 2. Fourfold table of measures of disproportionality. https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-t003.jpg Table 3. Four major algorithms used for signal detection. https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-t004.jpg Table 4. Cases characteristics of in the FAERS database. https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-g001.jpg Figure 1. System organ class distribution of positively associated preferred terms for imiglucerase. https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-g002.jpg Figure 2. System organ class distribution of positively associated preferred terms for velaglucerase https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-g003.jpg Figure 3. System organ class distribution of positively associated preferred terms for taliglucerase https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-t005.jpg Table 5. ADR signals of the top 15 reported cases: the “Cases” column indicates the number of unique https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-g004.jpg Figure 4. Time cumulative distribution curve for AEs. https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-t006.jpg Table 6. Incidence of adverse events. https://www.frontiersin.org/files/Articles/1726282/fmed-13-1726282-HTML/image_m/fmed-13-1726282-t007.jpg Table 7. Proposed drug-specific risk-monitoring recommendations based on pharmacovigilance signals. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1777023/full https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-t001.jpg Table 1. Nutrient composition and ingredients of the experimental diets. https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-t002.jpg Table 2. Primers for quantitative real-time PCR. https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-t003.jpg Table 3. Growth performance of Hu sheep fed diets containing differently processed cotton stalks. https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-g001.jpg Figure 1. Fermented cotton stalks preserve colonic epithelial morphology and ultrastructure in Hu sh https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-g002.jpg Figure 2. Effects of differently processed cotton stalk diets on colonic fermentation characteristic https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-g003.jpg Figure 3. Effects of differently processed cotton stalk diets on colonic microbial community structu https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-g004.jpg Figure 4. Metabolomic profiling of colonic digesta in Hu sheep fed differently processed cotton stal https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-g005.jpg Figure 5. Fermented cotton stalks attenuate colonic inflammatory responses in Hu sheep. (A–D) Relati https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-g006.jpg Figure 6. Fermented cotton stalks inhibit NF-κB/MLCK signaling and enhance tight-junction protein ab https://www.frontiersin.org/files/Articles/1777023/fnut-13-1777023-HTML/image_m/fnut-13-1777023-g007.jpg Figure 7. Proposed mechanism by which fermented cotton stalk diets improve hindgut microecology and https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2026.1750882/full https://www.frontiersin.org/files/Articles/1750882/fnmol-19-1750882-HTML/image_m/fnmol-19-1750882-g001.jpg Figure 1. Experimental procedures and network pharmacology-based strategy for predicting the possibl https://www.frontiersin.org/files/Articles/1750882/fnmol-19-1750882-HTML/image_m/fnmol-19-1750882-g002.jpg Figure 2. Network pharmacology-driven identification of molecular mechanisms for DEX in mitigating C https://www.frontiersin.org/files/Articles/1750882/fnmol-19-1750882-HTML/image_m/fnmol-19-1750882-g003.jpg Figure 3. Effects of DEX on nerve and brain injury in rats of each group. (A) Neurological function https://www.frontiersin.org/files/Articles/1750882/fnmol-19-1750882-HTML/image_m/fnmol-19-1750882-g004.jpg Figure 4. Ultrastructural observation of brain tissue in rats of each group by TEM. (A) Structures o https://www.frontiersin.org/files/Articles/1750882/fnmol-19-1750882-HTML/image_m/fnmol-19-1750882-g005.jpg Figure 5. The effect of DEX on the motor ability of rats in each group. (A) Open field trajectory ma https://www.frontiersin.org/files/Articles/1750882/fnmol-19-1750882-HTML/image_m/fnmol-19-1750882-g006.jpg Figure 6. The effect of DEX on the expression level of TJs in rats of each group. (A,B) Immunofluore https://www.frontiersin.org/files/Articles/1750882/fnmol-19-1750882-HTML/image_m/fnmol-19-1750882-g007.jpg Figure 7. Effects of DEX on the expression levels of related proteins in rats of each group. (A–D) W https://www.frontiersin.org/files/Articles/1750882/fnmol-19-1750882-HTML/image_m/fnmol-19-1750882-g008.jpg Figure 8. Specific mechanism by DEX improves the BBB and alleviates CIRI induced brain injury via th https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1664775/full https://www.frontiersin.org/files/Articles/1664775/fmicb-16-1664775-HTML/image_m/fmicb-16-1664775-t001.jpg Table 1. Sample labels (Sample ID), species, sources of isolates, and previous publications (if any) https://www.frontiersin.org/files/Articles/1664775/fmicb-16-1664775-HTML/image_m/fmicb-16-1664775-t002.jpg Table 2. Primers and probes used in the analysis. https://www.frontiersin.org/files/Articles/1664775/fmicb-16-1664775-HTML/image_m/fmicb-16-1664775-g001.jpg Figure 1. Taqman PCR identification of E. marmotae by species-specific primers and probes for genes https://www.frontiersin.org/files/Articles/1664775/fmicb-16-1664775-HTML/image_m/fmicb-16-1664775-g002.jpg Figure 2. MALDI-TOF-MS spectra for (A). E. marmotae C4B (clinical), (B). E. marmotae TW14264 (enviro https://www.frontiersin.org/files/Articles/1664775/fmicb-16-1664775-HTML/image_m/fmicb-16-1664775-g003.jpg Figure 3. Comparative analysis of MALDI-TOF-MS peaks for E. marmotae and E. coli in the m/z range of https://www.frontiersin.org/files/Articles/1664775/fmicb-16-1664775-HTML/image_m/fmicb-16-1664775-g004.jpg Figure 4. MALDI-TOF-MS spectra for E. marmotae HFH1, highlighting the E. marmotae-specific peak whic https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1729604/full https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-t001.jpg Table 1. Oligonucleotide primers used for RT-qPCR. https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g001.jpg Figure 1. Pan-genome map showing the core and accessory genes, compared among 18 genomes of E. marmo https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g002.jpg Figure 2. Snippy v4.6 was used to find single nucleotide polymorphisms in E. marmotae, using E. coli https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g003.jpg Figure 3. Virulence genes found in 18 E. marmotae genomes from Ram lab and GenBank using Virulence F https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g004.jpg Figure 4. The presence of antibiotic resistance genes in 18 strains of E. marmotae and 3 strains of https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g005.jpg Figure 5. Antimicrobial susceptibility of E. marmotae strains to various antibiotics, tested on the https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g006.jpg Figure 6. Representative examples of swim zone (spread) diameters of E. marmotae and E. coli after i https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g007.jpg Figure 7. Motility determined by swim zone (spread) diameters of E. marmotae and E. coli after 24 h https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g008.jpg Figure 8. Interspecies comparison of gene expression in E. marmotae (n = 6 strains) and E. coli (n = https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g009.jpg Figure 9. Effect of temperature on gene expression, as determined by the ratio of expression (37°C:2 https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g010.jpg Figure 10. Biofilm formation of environmental E. marmotae strains in LB Broth for 48 h at 37°C and 2 https://www.frontiersin.org/files/Articles/1729604/fmicb-16-1729604-HTML/image_m/fmicb-16-1729604-g011.jpg Figure 11. Flagellar gene classes and structural components. (A) The genetically defined hierarchy o https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1758313/full https://www.frontiersin.org/files/Articles/1758313/fmed-13-1758313-HTML/image_m/fmed-13-1758313-g001.jpg Figure 1. Workflow of the whole research. Image acquisition, processing, radiomic analysis, and mode https://www.frontiersin.org/files/Articles/1758313/fmed-13-1758313-HTML/image_m/fmed-13-1758313-t001.jpg Table 1. Baseline patient characteristics. https://www.frontiersin.org/files/Articles/1758313/fmed-13-1758313-HTML/image_m/fmed-13-1758313-t002.jpg Table 2. Demographic and clinical characteristics in the training and test cohorts. https://www.frontiersin.org/files/Articles/1758313/fmed-13-1758313-HTML/image_m/fmed-13-1758313-g002.jpg Figure 2. Radiomic feature selection by using LASSO logistic regression. (a) Optimal regularization https://www.frontiersin.org/files/Articles/1758313/fmed-13-1758313-HTML/image_m/fmed-13-1758313-t003.jpg Table 3. Radiomic feature final selected by LASSO regression and the coefficient to develop the radi https://www.frontiersin.org/files/Articles/1758313/fmed-13-1758313-HTML/image_m/fmed-13-1758313-g003.jpg Figure 3. The predictive performance of machine learning models based on the radiomic signature for https://www.frontiersin.org/files/Articles/1758313/fmed-13-1758313-HTML/image_m/fmed-13-1758313-g004.jpg Figure 4. The calibration curves of models for predicting osteoporosis. LR (a), SVM (b), XGBoost (c) https://www.frontiersin.org/files/Articles/1758313/fmed-13-1758313-HTML/image_m/fmed-13-1758313-g005.jpg Figure 5. ROC analysis showing that the performance of the radiomics signature model was better than https://www.frontiersin.org/files/Articles/1758313/fmed-13-1758313-HTML/image_m/fmed-13-1758313-g006.jpg Figure 6. Decision curve analysis for each model. The y-axis displays the net benefit, a metric that https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1799600/full https://www.frontiersin.org/files/Articles/1799600/fvets-13-1799600-HTML-r2/image_m/fvets-13-1799600-g001.jpg Figure 1. Example of THERABAND® exercise in canine patient. https://www.frontiersin.org/files/Articles/1799600/fvets-13-1799600-HTML-r2/image_m/fvets-13-1799600-g002.jpg Figure 2. THERABAND® example colors. https://www.frontiersin.org/files/Articles/1799600/fvets-13-1799600-HTML-r2/image_m/fvets-13-1799600-g003.jpg Figure 3. THERABAND® loaded onto Instron biomechanical testing device. https://www.frontiersin.org/files/Articles/1799600/fvets-13-1799600-HTML-r2/image_m/fvets-13-1799600-g004.jpg Figure 4. Theraband stretched using the Instron biomechanical testing device. https://www.frontiersin.org/files/Articles/1799600/fvets-13-1799600-HTML-r2/image_m/fvets-13-1799600-g005.jpg Figure 5. Force generated at various ERB elongation using 10 cm and 40 cm length ERB. https://www.frontiersin.org/files/Articles/1799600/fvets-13-1799600-HTML-r2/image_m/fvets-13-1799600-g006.jpg Figure 6. Mean tensile forces (± SD) produced by ERBs at elongation ratios of 1.25, 1.50, and 1.75 r https://www.frontiersin.org/files/Articles/1799600/fvets-13-1799600-HTML-r2/image_m/fvets-13-1799600-t001.jpg Table 1. Calculated linear equations of elongation for each ERB color and resulting Pearson’s correl https://www.frontiersin.org/files/Articles/1799600/fvets-13-1799600-HTML-r2/image_m/fvets-13-1799600-t002.jpg Table 2. Mean forces (N ± standard deviation) produced using different ERB lengths, various elongati https://www.frontiersin.org/files/Articles/1799600/fvets-13-1799600-HTML-r2/image_m/fvets-13-1799600-t003.jpg Table 3. Chart for veterinary ERB dosing. https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2026.1732749/full https://www.frontiersin.org/files/Articles/1732749/fdmed-07-1732749-HTML-r1/image_m/fdmed-07-1732749-g001.jpg Figure 1. Boundary conditions and load application scheme. The inferior surface of the mandible was https://www.frontiersin.org/files/Articles/1732749/fdmed-07-1732749-HTML-r1/image_m/fdmed-07-1732749-t001.jpg Table 1. Mechanical properties of materials used in the finite element model. https://www.frontiersin.org/files/Articles/1732749/fdmed-07-1732749-HTML-r1/image_m/fdmed-07-1732749-t002.jpg Table 2. Adaptive solution passes required for convergence of each implant configuration. https://www.frontiersin.org/files/Articles/1732749/fdmed-07-1732749-HTML-r1/image_m/fdmed-07-1732749-t003.jpg Table 3. Peak von Mises stress (MPa) in the PEEK framework under vertical (0°) and 30° oblique loadi https://www.frontiersin.org/files/Articles/1732749/fdmed-07-1732749-HTML-r1/image_m/fdmed-07-1732749-g002.jpg Figure 2. Three-dimensional von Mises stress distribution (MPa) within the PEEK framework under 300 https://www.frontiersin.org/files/Articles/1732749/fdmed-07-1732749-HTML-r1/image_m/fdmed-07-1732749-t004.jpg Table 4. Peak von Mises stress (MPa) in the crestal cortical bone under vertical (0°) and 30° obliqu https://www.frontiersin.org/files/Articles/1732749/fdmed-07-1732749-HTML-r1/image_m/fdmed-07-1732749-g003.jpg Figure 3. Three-dimensional von Mises stress distribution (MPa) within the crestal cortical bone und https://www.frontiersin.org/files/Articles/1732749/fdmed-07-1732749-HTML-r1/image_m/fdmed-07-1732749-t005.jpg Table 5. Maximum principal strain in crestal cortical bone under vertical (0°) and 30° oblique loadi https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1680300/full https://www.frontiersin.org/files/Articles/1680300/fcvm-13-1680300-HTML-r1/image_m/fcvm-13-1680300-t001.jpg Table 1. Clinical features, genetic findings, and ACMG/AMP classification criteria for subject A, B https://www.frontiersin.org/files/Articles/1680300/fcvm-13-1680300-HTML-r1/image_m/fcvm-13-1680300-g001.jpg Figure 1. Schematic representation of the clinical history, diagnostic, and treatment in three Ecuad https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1703885/full https://www.frontiersin.org/files/Articles/1703885/fmed-13-1703885-HTML/image_m/fmed-13-1703885-g001.jpg Figure 1. Pedigree of the proband with CYLD cutaneous syndrome. The proband (arrow) presented with m https://www.frontiersin.org/files/Articles/1703885/fmed-13-1703885-HTML/image_m/fmed-13-1703885-g002.jpg Figure 2. Histopathology and immunohistochemistry of scalp adnexal tumors. Hematoxylin and eosin (H& https://www.frontiersin.org/files/Articles/1703885/fmed-13-1703885-HTML/image_m/fmed-13-1703885-g003.jpg Figure 3. Structural impact of CYLD and MSH2 variants. (A) Schematic representation of the CYLD prot https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1682390/full https://www.frontiersin.org/files/Articles/1682390/fimmu-17-1682390-HTML/image_m/fimmu-17-1682390-g001.jpg Figure 1. Possible pathways for pancreatic microbial translocation include the Duodenopancreatic ret https://www.frontiersin.org/files/Articles/1682390/fimmu-17-1682390-HTML/image_m/fimmu-17-1682390-t001.jpg Table 1. Primary pathways of microbial translocation (ductal, vascular, lymphatic, phagocytosis-medi https://www.frontiersin.org/files/Articles/1682390/fimmu-17-1682390-HTML/image_m/fimmu-17-1682390-t002.jpg Table 2. Microbial characteristics associated with survival in pancreatic cancer. https://www.frontiersin.org/files/Articles/1682390/fimmu-17-1682390-HTML/image_m/fimmu-17-1682390-g002.jpg Figure 2. Core Intervention Methods for Microbial Regulation. Diet: fasting 24h, ketogenic diets, hi https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1700711/full https://www.frontiersin.org/files/Articles/1700711/fendo-16-1700711-HTML/image_m/fendo-16-1700711-g001.jpg Figure 1. Preoperative MRI images of non-invasive and invasive pituitary adenomas. Panels (A–D) show https://www.frontiersin.org/files/Articles/1700711/fendo-16-1700711-HTML/image_m/fendo-16-1700711-g002.jpg Figure 2. Intraoperative monitoring of intrasellar pressure (ISP). Panels (A, C) show the method of https://www.frontiersin.org/files/Articles/1700711/fendo-16-1700711-HTML/image_m/fendo-16-1700711-t001.jpg Table 1. General clinical characteristics of 84 patients (omitted here, values consistent with origi https://www.frontiersin.org/files/Articles/1700711/fendo-16-1700711-HTML/image_m/fendo-16-1700711-t002.jpg Table 2. Comparison of general characteristics between invasive and non-invasive pituitary adenomas. https://www.frontiersin.org/files/Articles/1700711/fendo-16-1700711-HTML/image_m/fendo-16-1700711-t003.jpg Table 3. Relationship between ISP, tumor invasiveness, and tumor size. https://www.frontiersin.org/files/Articles/1700711/fendo-16-1700711-HTML/image_m/fendo-16-1700711-g003.jpg Figure 3. Correlation between ISP and pituitary adenoma size/volume (Knosp classification). Panel (A https://www.frontiersin.org/files/Articles/1700711/fendo-16-1700711-HTML/image_m/fendo-16-1700711-t004.jpg Table 4. Relationship between ISP and endocrine dysfunction preoperatively and 12 weeks postoperativ https://www.frontiersin.org/files/Articles/1700711/fendo-16-1700711-HTML/image_m/fendo-16-1700711-t005.jpg Table 5. Relationship between ISP and pituitary apoplexy. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1703480/full https://www.frontiersin.org/files/Articles/1703480/fphar-16-1703480-HTML/image_m/fphar-16-1703480-g001.jpg Figure 1. Initially explored Ψ derivatives in comparison to their 2,4,5- and 3,4,5-trisubstituted co https://www.frontiersin.org/files/Articles/1703480/fphar-16-1703480-HTML/image_m/fphar-16-1703480-g002.jpg Figure 2. Chemical structures of the studied Ψ derivatives. Overall, increasing fluorination and inc https://www.frontiersin.org/files/Articles/1703480/fphar-16-1703480-HTML/image_m/fphar-16-1703480-g003.jpg Figure 3. Comparison of phenethylamine-type Ψ derivatives and their 2,4,5- and 3,4,5-trisubstituted https://www.frontiersin.org/files/Articles/1703480/fphar-16-1703480-HTML/image_m/fphar-16-1703480-g004.jpg Figure 4. Comparison of amphetamine-type Ψ derivatives and their 2,4,5- and 3,4,5-trisubstituted cou https://www.frontiersin.org/files/Articles/1703480/fphar-16-1703480-HTML/image_m/fphar-16-1703480-t001.jpg Table 1. Serotonin receptor binding affinities and activation potencies of 4-alkoxy-substituted 2,6- https://www.frontiersin.org/files/Articles/1703480/fphar-16-1703480-HTML/image_m/fphar-16-1703480-t002.jpg Table 2. Monoamine receptor and transporter binding affinities of 4-alkoxy-substituted 2,6-dimethoxy https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1668957/full https://www.frontiersin.org/files/Articles/1668957/fphar-16-1668957-HTML/image_m/fphar-16-1668957-g001.jpg Figure 1. Experimental timeline. (A) Cocaine reward in 26 adult Sprague-Dawley rats as assessed by a https://www.frontiersin.org/files/Articles/1668957/fphar-16-1668957-HTML/image_m/fphar-16-1668957-g002.jpg Figure 2. A single exposure place preference paradigm induced cocaine reward in adult rats. (A) Time https://www.frontiersin.org/files/Articles/1668957/fphar-16-1668957-HTML/image_m/fphar-16-1668957-g003.jpg Figure 3. Adolescent polydrug exposure increased cocaine reward in adulthood as assessed by a single https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1597511/full https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g001.jpg Figure 1. Workflow of the integrated bibliometric and bioinformatics analysis for identifying N-glyc https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g002.jpg Figure 2. Thematic evolution and research trends. (A) The temporal progression of research themes in https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g003.jpg Figure 3. Keyword analysis and research focus. (A) Presents a three-field plot linking key studies, https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g004.jpg Figure 4. Key differentially expressed and N-glycosylation-related genes. (A–F) Focus on the GSE5281 https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g005.jpg Figure 5. Validation of gene biomarkers and diagnostic models. (A–D) Analyze the GSE48350 dataset: m https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g006.jpg Figure 6. Molecular interactions of TMEM59. (A) (GSE5281) shows a heatmap revealing a strong negativ https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g007.jpg Figure 7. Transcription factors regulating N-glycosylation-related genes. (A) Identifies 44 shared t https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g008.jpg Figure 8. Diagnostic potential of transcription factors. (A–D) Analyze the GSE5281 dataset: ROC curv https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g009.jpg Figure 9. Optimization of diagnostic models. (A–C) Analyze the GSE5281 dataset: a radar plot compare https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g010.jpg Figure 10. Key molecular features and interactions. (A–K) Focus on the GSE5281 dataset: ROC curves d https://www.frontiersin.org/files/Articles/1597511/fnagi-17-1597511-HTML/image_m/fnagi-17-1597511-g011.jpg Figure 11. N-glycosylation-related molecular features. (A–K) Analyze the GSE48350 dataset: ROC curve https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1617106/full https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-g001.jpg Figure 1. Molecular pathogenesis of Parkinson’s disease. https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-g002.jpg Figure 2. Distribution of Parkinson’s disease by age group and sex. This bar graph illustrates the p https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-g003.jpg Figure 3. Global prevalence of Parkinson’s disease (1990–2019). This bar chart compares the regional https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-g004.jpg Figure 4. Cost burden across regions and care sectors in Parkinson’s disease. This grouped bar chart https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-g005.jpg Figure 5. Economic cost breakdown between direct and indirect components by PD stage. This side-by-s https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-t001.jpg Table 1. Efficacy comparison of Levodopa and DBS across clinical parameters including symptoms, side https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-g006.jpg Figure 6. Emerging therapeutic modalities in Parkinson’s disease. This infographic highlights four c https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-t002.jpg Table 2. Emerging research priorities and recommended actions in Parkinson’s disease (PD) management https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-t003.jpg Table 3. Actionable research priorities and proposed interventions for advancing Parkinson’s disease https://www.frontiersin.org/files/Articles/1617106/fnagi-17-1617106-HTML/image_m/fnagi-17-1617106-t004.jpg Table 4. Common comorbidities in Parkinson’s disease, their prevalence, underlying mechanisms, and c https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2025.1619869/full https://www.frontiersin.org/files/Articles/1619869/frhs-05-1619869-HTML/image_m/frhs-05-1619869-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1803757/full https://www.frontiersin.org/files/Articles/1803757/fmicb-17-1803757-HTML/image_m/fmicb-17-1803757-g001.jpg Figure 1. Schematic of the production process and characteristics of the Aspergillus cristatus ferme https://www.frontiersin.org/files/Articles/1803757/fmicb-17-1803757-HTML/image_m/fmicb-17-1803757-g002.jpg Figure 2. Aspergillus cristatus fermentation changed Panax ginseng saponins. (A) Differentially accu https://www.frontiersin.org/files/Articles/1803757/fmicb-17-1803757-HTML/image_m/fmicb-17-1803757-g003.jpg Figure 3. Differentially expressed genes (DEGs) in Aspergillus cristatus during fermenting Panax gin https://www.frontiersin.org/files/Articles/1803757/fmicb-17-1803757-HTML/image_m/fmicb-17-1803757-g004.jpg Figure 4. Gene enrichment analysis of the down-regulated SSTF genes in Aspergillus cristatus. (A) GO https://www.frontiersin.org/files/Articles/1803757/fmicb-17-1803757-HTML/image_m/fmicb-17-1803757-g005.jpg Figure 5. Gene enrichment analysis of the up-regulated SSTF genes in Aspergillus cristatus. (A) GO a https://www.frontiersin.org/files/Articles/1803757/fmicb-17-1803757-HTML/image_m/fmicb-17-1803757-g006.jpg Figure 6. Differentially accumulated metabolites (DAMs) in Panax ginseng during interaction with Asp https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1695623/full https://www.frontiersin.org/files/Articles/1695623/fmed-12-1695623-HTML-r1/image_m/fmed-12-1695623-g001.jpg Figure 1. pAMTLER study design. CR, cervical radiculopathy; R, randomized allocation; CG, control gr https://www.frontiersin.org/files/Articles/1695623/fmed-12-1695623-HTML-r1/image_m/fmed-12-1695623-g002.jpg Figure 2. pAMTLER SPIRIT schedule. AR, adherence rate; DR, drop-out rate; RR, retention rate; CRE, c https://www.frontiersin.org/files/Articles/1695623/fmed-12-1695623-HTML-r1/image_m/fmed-12-1695623-g003.jpg Figure 3. Illustration of wearing semi-hard cervical collars. Wearing a cervical collar immobilizes https://www.frontiersin.org/files/Articles/1695623/fmed-12-1695623-HTML-r1/image_m/fmed-12-1695623-g004.jpg Figure 4. Illustration of pressing-kneading manipulation. white dot, mastoid process; red dot, the 7 https://www.frontiersin.org/files/Articles/1695623/fmed-12-1695623-HTML-r1/image_m/fmed-12-1695623-g005.jpg Figure 5. Illustration of traction manipulation. With the occiput and chin as contact points, apply https://www.frontiersin.org/files/Articles/1695623/fmed-12-1695623-HTML-r1/image_m/fmed-12-1695623-t001.jpg Table 1. Pulling angles during manual intermittent traction. https://www.frontiersin.org/files/Articles/1695623/fmed-12-1695623-HTML-r1/image_m/fmed-12-1695623-g006.jpg Figure 6. Illustration of mechanical traction. Black dot, acromion; red dot, ear apex; white dot, ve https://www.frontiersin.org/files/Articles/1695623/fmed-12-1695623-HTML-r1/image_m/fmed-12-1695623-t002.jpg Table 2. Composition and determination of direct costs. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1724529/full https://www.frontiersin.org/files/Articles/1724529/fmed-13-1724529-HTML/image_m/fmed-13-1724529-g001.jpg Figure 1. Study flow chart. PD, primary dysmenorrhea; XFZY, Xuefu Zhuyu oral liquid; FAS, full analy https://www.frontiersin.org/files/Articles/1724529/fmed-13-1724529-HTML/image_m/fmed-13-1724529-t001.jpg Table 1. Participants' demographics and baseline characteristics. https://www.frontiersin.org/files/Articles/1724529/fmed-13-1724529-HTML/image_m/fmed-13-1724529-t002.jpg Table 2. Primary outcome assessment. https://www.frontiersin.org/files/Articles/1724529/fmed-13-1724529-HTML/image_m/fmed-13-1724529-t003.jpg Table 3. Analysis of painkiller consumption. https://www.frontiersin.org/files/Articles/1724529/fmed-13-1724529-HTML/image_m/fmed-13-1724529-g002.jpg Figure 2. Subgroup analysis. XFZY, Xuefu Zhuyu oral liquid; VAS, visual analog scale; CI, confidence https://www.frontiersin.org/files/Articles/1724529/fmed-13-1724529-HTML/image_m/fmed-13-1724529-t004.jpg Table 4. Secondary outcomes assessments. https://www.frontiersin.org/files/Articles/1724529/fmed-13-1724529-HTML/image_m/fmed-13-1724529-t005.jpg Table 5. Safety analysis. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1620785/full https://www.frontiersin.org/files/Articles/1620785/fphys-16-1620785-HTML/image_m/fphys-16-1620785-g001.jpg Figure 1. Study procedures Legends: NYHA, New York Heart Association; BMI, body mass index; HFrEF, h https://www.frontiersin.org/files/Articles/1620785/fphys-16-1620785-HTML/image_m/fphys-16-1620785-t001.jpg Table 1. Cardiopulmonary and hemodynamic parameters collected in this study. https://www.frontiersin.org/files/Articles/1620785/fphys-16-1620785-HTML/image_m/fphys-16-1620785-t002.jpg Table 2. Participants’ characteristics (n = 30). https://www.frontiersin.org/files/Articles/1620785/fphys-16-1620785-HTML/image_m/fphys-16-1620785-t003.jpg Table 3. Ventilation and gas exchange data during maximal exercise and resting hemodynamic data. https://www.frontiersin.org/files/Articles/1620785/fphys-16-1620785-HTML/image_m/fphys-16-1620785-t004.jpg Table 4. Comparison of cardiopulmonary and hemodynamic responses to Baduanjin exercise or cycle exer https://www.frontiersin.org/files/Articles/1620785/fphys-16-1620785-HTML/image_m/fphys-16-1620785-g002.jpg Figure 2. Comparison of the real-time cardiopulmonary responses of (a) VO2max, (b) %VO2max, (c) HR, https://www.frontiersin.org/files/Articles/1620785/fphys-16-1620785-HTML/image_m/fphys-16-1620785-g003.jpg Figure 3. Comparison of the real-time pulmonary responses of (a) respiratory rate, (b) minute ventil https://www.frontiersin.org/files/Articles/1620785/fphys-16-1620785-HTML/image_m/fphys-16-1620785-g004.jpg Figure 4. Comparison of the real-time hemodynamic responses of (a) stroke volume, (b) cardiac output https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1719899/full https://www.frontiersin.org/files/Articles/1719899/fmed-13-1719899-HTML/image_m/fmed-13-1719899-t001.jpg Table 1. Baseline characteristics of patients with cirrhosis by sarcopenic obesity (n = 769). https://www.frontiersin.org/files/Articles/1719899/fmed-13-1719899-HTML/image_m/fmed-13-1719899-t002.jpg Table 2. Univariable and multivariable analysis of risk factors for sarcopenic obesity in patients w https://www.frontiersin.org/files/Articles/1719899/fmed-13-1719899-HTML/image_m/fmed-13-1719899-g001.jpg Figure 1. ABAH nomograph based on predictors of sarcopenic obesity. BMI, body mass index; ABAH, age– https://www.frontiersin.org/files/Articles/1719899/fmed-13-1719899-HTML/image_m/fmed-13-1719899-g002.jpg Figure 2. Average risk of sarcopenic obesity by ABAH score. The illustrated group shows an increasin https://www.frontiersin.org/files/Articles/1719899/fmed-13-1719899-HTML/image_m/fmed-13-1719899-g003.jpg Figure 3. Hazard ratio of death by ABAH score in subgroups of cirrhosis patients. ABAH, age–BMI–alco https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1815558/full https://www.frontiersin.org/files/Articles/1815558/fnut-13-1815558-HTML/image_m/fnut-13-1815558-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1815558/fnut-13-1815558-HTML/image_m/fnut-13-1815558-g001.jpg Figure 1. Mechanisms of probiotic action in gut health, including microbial competition, short-chain https://www.frontiersin.org/files/Articles/1815558/fnut-13-1815558-HTML/image_m/fnut-13-1815558-t001.jpg Table 1. Comparative analysis of probiotic delivery systems with performance metrics. https://www.frontiersin.org/files/Articles/1815558/fnut-13-1815558-HTML/image_m/fnut-13-1815558-g002.jpg Figure 2. Overview of technological innovations in probiotic fortification, including encapsulation https://www.frontiersin.org/files/Articles/1815558/fnut-13-1815558-HTML/image_m/fnut-13-1815558-g003.jpg Figure 3. Fermentation-based fortification of dairy and plant-based functional foods, illustrating m https://www.frontiersin.org/files/Articles/1815558/fnut-13-1815558-HTML/image_m/fnut-13-1815558-t002.jpg Table 2. Global regulatory standards for probiotics in functional foods. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1553947/full https://www.frontiersin.org/files/Articles/1553947/fneur-16-1553947-HTML-r1/image_m/fneur-16-1553947-g001.jpg Figure 1. Flow diagram of the trial. https://www.frontiersin.org/files/Articles/1553947/fneur-16-1553947-HTML-r1/image_m/fneur-16-1553947-t001.jpg Table 1. Demographic characteristics of participants with post-stroke dysphagia. https://www.frontiersin.org/files/Articles/1553947/fneur-16-1553947-HTML-r1/image_m/fneur-16-1553947-t002.jpg Table 2. Univariable and multivariable analyses of recovery of swallowing disorder—at discharge. https://www.frontiersin.org/files/Articles/1553947/fneur-16-1553947-HTML-r1/image_m/fneur-16-1553947-t003.jpg Table 3. Univariable and multivariable analyses of recovery of swallowing disorder—at 90 ± 7 days af https://www.frontiersin.org/files/Articles/1553947/fneur-16-1553947-HTML-r1/image_m/fneur-16-1553947-t004.jpg Table 4. Univariable and multivariable analyses of recovery of swallowing disorder—at 180 ± 7 days a https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1759535/full https://www.frontiersin.org/files/Articles/1759535/fimmu-17-1759535-HTML/image_m/fimmu-17-1759535-g001.jpg Figure 1. The method of H2 intraperitoneal injection and experimental process. (A) The sterile salin https://www.frontiersin.org/files/Articles/1759535/fimmu-17-1759535-HTML/image_m/fimmu-17-1759535-t001.jpg Table 1. The information of antibodies. https://www.frontiersin.org/files/Articles/1759535/fimmu-17-1759535-HTML/image_m/fimmu-17-1759535-g002.jpg Figure 2. H2 pretreatment alleviates LPS-induced acute liver injury in mice.(A). The liver H&E stain https://www.frontiersin.org/files/Articles/1759535/fimmu-17-1759535-HTML/image_m/fimmu-17-1759535-g003.jpg Figure 3. H2 pretreatment improves LPS-induced hepatic oxidative stress in mice. (A) The immunofluor https://www.frontiersin.org/files/Articles/1759535/fimmu-17-1759535-HTML/image_m/fimmu-17-1759535-g004.jpg Figure 4. H2 pretreatment inhibits TLR4-IKK-NFκB innate immune signaling in the liver of LPS-challen https://www.frontiersin.org/files/Articles/1759535/fimmu-17-1759535-HTML/image_m/fimmu-17-1759535-g005.jpg Figure 5. H2 pretreatment inhibits hepatic MAPK signaling in LPS-challenged mice. (A) The immunoblot https://www.frontiersin.org/files/Articles/1759535/fimmu-17-1759535-HTML/image_m/fimmu-17-1759535-g006.jpg Figure 6. H2 pretreatment suppresses pro-inflammatory cytokines expression and cleavage in the liver https://www.frontiersin.org/files/Articles/1759535/fimmu-17-1759535-HTML/image_m/fimmu-17-1759535-g007.jpg Figure 7. H2 pretreatment inhibits NLRP3 inflammasome activation both in LPS-challenged mice and in https://www.frontiersin.org/files/Articles/1759535/fimmu-17-1759535-HTML/image_m/fimmu-17-1759535-g008.jpg Figure 8. H2 pretreatment inhibites pyroptosis signaling in the liver of LPS-challenged mice. (A) Th https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1809643/full https://www.frontiersin.org/files/Articles/1809643/fendo-17-1809643-HTML/image_m/fendo-17-1809643-t001.jpg Table 1. Baseline data. https://www.frontiersin.org/files/Articles/1809643/fendo-17-1809643-HTML/image_m/fendo-17-1809643-t002.jpg Table 2. Association between quartiles of the TG/HDL-C ratio and odds of low BMD based on multivaria https://www.frontiersin.org/files/Articles/1809643/fendo-17-1809643-HTML/image_m/fendo-17-1809643-g001.jpg Figure 1. RCS explores the nonlinear dose-response relationship between TG/HDL-C ratio and BMD. The https://www.frontiersin.org/files/Articles/1809643/fendo-17-1809643-HTML/image_m/fendo-17-1809643-t003.jpg Table 3. Subgroup analysis of TG/HDL-C and low BMD risk in lumbar L1–4 and femoral neck. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1782306/full https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g001.jpg Figure 1. Overview of the proposed hierarchical CNN–Swin fusion framework. A multi-scale ResNet-base https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g002.jpg Figure 2. Representative samples across the four classes. https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g003.jpg Figure 3. End-to-end pipeline for cross-dataset MRI classification, including preprocessing, multi-m https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-t001.jpg Table 1. Class-wise distribution of the BRISC2025 dataset. https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g004.jpg Figure 4. Class-wise distribution of the BRISC2025 dataset across training and testing splits. https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-t002.jpg Table 2. Class-wise distribution of the BT-MRI dataset. https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g005.jpg Figure 5. Class-wise distribution of the BT-MRI dataset across training and testing splits. https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-t003.jpg Table 3. Training hyperparameters used in all experiments. https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-t004.jpg Table 4. Cross-Dataset classification performance (train on source dataset, test on target dataset). https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g006.jpg Figure 6. Training and validation dynamics of the proposed CNN-Swin fusion model across three random https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g007.jpg Figure 7. Raw (left) and normalized (right) confusion matrices for the proposed CNN-Swin fusion mode https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g008.jpg Figure 8. ROC (left) and Precision–Recall (right) curves for the proposed CNN–Swin fusion model unde https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-t005.jpg Table 5. Calibration performance under Cross-Dataset evaluation. https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g009.jpg Figure 9. Reliability diagram comparing predicted confidence and empirical accuracy for the proposed https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g010.jpg Figure 10. Histogram of predicted confidence values for correct and incorrect predictions under Cros https://www.frontiersin.org/files/Articles/1782306/fnins-20-1782306-HTML/image_m/fnins-20-1782306-g011.jpg Figure 11. Multi-scale attention visualization of the CNN–Swin fusion model, illustrating progressiv https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2026.1762152/full https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-t001.jpg Table 1. Structural parameters of orthotropic steel bridge deck. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g001.jpg Figure 1. Cross-section of orthotropic steel bridge deck. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g002.jpg Figure 2. Flowchart of the experimental scheme. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g003.jpg Figure 3. Layout of strain detection position. (a) The lower surface of the bridge deck. (b) The sur https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g004.jpg Figure 4. Layout of Strain gauge pasting on. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g005.jpg Figure 5. Test loading diagram bridge deck pavement surface. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g006.jpg Figure 6. Strain detection diagram of the lower surface of the bridge deck. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g007.jpg Figure 7. Load positions. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-t002.jpg Table 2. Driving speed and driving times corresponding to each working condition. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g008.jpg Figure 8. Dynamic strain peak data diagram of the lower surface of the bridge deck. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g009.jpg Figure 9. Dynamic strain time history curve of the lower surface of the bridge deck. (a) Set Speed t https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-t003.jpg Table 3. Comparison of pulse action time and actual driving speed. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g010.jpg Figure 10. The relationship between the dynamic strain peak value of the lower surface of the bridge https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-t004.jpg Table 4. Comparison of dynamic and static load strain data on the lower surface of the bridge deck. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g011.jpg Figure 11. Data graph of peak dynamic strain of strain of the deck pavement surface under an axle lo https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g012.jpg Figure 12. Data graph of peak-to-peak dynamic the deck pavement surface under an axle load of 100 kN https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g013.jpg Figure 13. Data graph of peak dynamic strain of the deck pavement surface under an axle load of 150 https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g014.jpg Figure 14. Data graph of peak-to-peak dynamic strain of the deck pavement surface under an axle load https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g015.jpg Figure 15. Time-history curve of dynamic strain on the deck pavement surface under axle load of 100 https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g016.jpg Figure 16. Time-history curve of dynamic strain on the deck pavement surface under axle load of 150 https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-t005.jpg Table 5. Comparison of pulse action time and actual driving speed under each planned speed of 100kN https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g017.jpg Figure 17. Relationship curve between strain pulse action time and vehicle speed. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g018.jpg Figure 18. Curve of relationship between peak value of dynamic strain and vehicle speed on the deck https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-t006.jpg Table 6. Comparison of dynamic and static load and strain data of the deck pavement surface. https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-t007.jpg Table 7. Comparison data of peak dynamic strain of the deck pavement surface under axle load of 100k https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g019.jpg Figure 19. Schematic diagram of transverse tensile strain path on the surface of bridge deck pavemen https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-t008.jpg Table 8. Comparison between finite element calculation and detection values of transverse strain on https://www.frontiersin.org/files/Articles/1762152/fmats-13-1762152-HTML-r1/image_m/fmats-13-1762152-g020.jpg Figure 20. Comparison between finite element calculation and detection values of transverse strain o https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1785881/full https://www.frontiersin.org/files/Articles/1785881/fpubh-14-1785881-HTML-r3/image_m/fpubh-14-1785881-g001.jpg Figure 1. Theoretical framework. https://www.frontiersin.org/files/Articles/1785881/fpubh-14-1785881-HTML-r3/image_m/fpubh-14-1785881-t001.jpg Table 1. Detailed description and calculation methods of relevant variables. https://www.frontiersin.org/files/Articles/1785881/fpubh-14-1785881-HTML-r3/image_m/fpubh-14-1785881-t002.jpg Table 2. Benchmark regression results. https://www.frontiersin.org/files/Articles/1785881/fpubh-14-1785881-HTML-r3/image_m/fpubh-14-1785881-t003.jpg Table 3. Mediation regression results. https://www.frontiersin.org/files/Articles/1785881/fpubh-14-1785881-HTML-r3/image_m/fpubh-14-1785881-t004.jpg Table 4. Results of heterogeneity analysis. https://www.frontiersin.org/files/Articles/1785881/fpubh-14-1785881-HTML-r3/image_m/fpubh-14-1785881-t005.jpg Table 5. Robustness test results. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1694379/full https://www.frontiersin.org/files/Articles/1694379/fpsyg-17-1694379-HTML/image_m/fpsyg-17-1694379-t001.jpg Table 1. The demographic characteristics and univariate analysis of psychological pain tolerance amo https://www.frontiersin.org/files/Articles/1694379/fpsyg-17-1694379-HTML/image_m/fpsyg-17-1694379-t002.jpg Table 2. The scores of psychological pain tolerance, psychological adaptation, quality of life, and https://www.frontiersin.org/files/Articles/1694379/fpsyg-17-1694379-HTML/image_m/fpsyg-17-1694379-t003.jpg Table 3. The correlations among psychological pain tolerance, psychological adaptation, quality of l https://www.frontiersin.org/files/Articles/1694379/fpsyg-17-1694379-HTML/image_m/fpsyg-17-1694379-t004.jpg Table 4. Independent variable assignment (n = 506). https://www.frontiersin.org/files/Articles/1694379/fpsyg-17-1694379-HTML/image_m/fpsyg-17-1694379-t005.jpg Table 5. Multiple linear regression analysis of psychological pain tolerance among hospitalized pati https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1827693/full https://www.frontiersin.org/files/Articles/1827693/fphys-17-1827693-HTML/image_m/fphys-17-1827693-g001.jpg Figure 1. Flowchart. https://www.frontiersin.org/files/Articles/1827693/fphys-17-1827693-HTML/image_m/fphys-17-1827693-t001.jpg Table 1. Descriptive data. https://www.frontiersin.org/files/Articles/1827693/fphys-17-1827693-HTML/image_m/fphys-17-1827693-g002.jpg Figure 2. Comparison of dominant and non-dominant SLHTs. SH, single leg hop for distance; TH, triple https://www.frontiersin.org/files/Articles/1827693/fphys-17-1827693-HTML/image_m/fphys-17-1827693-g003.jpg Figure 3. Vertical jump, sprint, and agility test results of the participants. CMJ, countermovement https://www.frontiersin.org/files/Articles/1827693/fphys-17-1827693-HTML/image_m/fphys-17-1827693-g004.jpg Figure 4. The relationship between jump tests and sprint and agility tests. *p<0.05; **p<0.01; r, re https://www.frontiersin.org/files/Articles/1827693/fphys-17-1827693-HTML/image_m/fphys-17-1827693-g005.jpg Figure 5. The relationship between dominant side SLHTs and sprint and agility tests.*p<0.05; **p<0.0 https://www.frontiersin.org/files/Articles/1827693/fphys-17-1827693-HTML/image_m/fphys-17-1827693-g006.jpg Figure 6. The relationship between non-dominant side SLHTs and sprint and agility tests. *p<0.05; ** https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1795118/full https://www.frontiersin.org/files/Articles/1795118/fphys-17-1795118-HTML/image_m/fphys-17-1795118-g001.jpg Figure 1. Flow chart showing results of screening for this systematic reviews in accordance with the https://www.frontiersin.org/files/Articles/1795118/fphys-17-1795118-HTML/image_m/fphys-17-1795118-t001.jpg Table 1. Inclusion and exclusion criteria. https://www.frontiersin.org/files/Articles/1795118/fphys-17-1795118-HTML/image_m/fphys-17-1795118-t002.jpg Table 2. Summary of entropy-based HRV studies, highlighting their advantages, limitations, and uses. https://www.frontiersin.org/files/Articles/1795118/fphys-17-1795118-HTML/image_m/fphys-17-1795118-t003.jpg Table 3. Summary of four HRV assessment indices (LHR, SSR, MSE, and BEI), highlighting their advanta https://www.frontiersin.org/files/Articles/1795118/fphys-17-1795118-HTML/image_m/fphys-17-1795118-t004.jpg Table 4. Quantitative improvements in diagnostic accuracy across various medical fields using entrop https://www.frontiersin.org/files/Articles/1795118/fphys-17-1795118-HTML/image_m/fphys-17-1795118-g002.jpg Figure 2. Distribution of entropy methods used in heart rate variability (HRV) studies included in t https://www.frontiersin.org/files/Articles/1795118/fphys-17-1795118-HTML/image_m/fphys-17-1795118-g003.jpg Figure 3. Clinical domains represented in the studies included in this review. https://www.frontiersin.org/files/Articles/1795118/fphys-17-1795118-HTML/image_m/fphys-17-1795118-g004.jpg Figure 4. Distribution of physiological signal sources used for entropy-based analysis in the review https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1792805/full https://www.frontiersin.org/files/Articles/1792805/fpubh-14-1792805-HTML/image_m/fpubh-14-1792805-g001.jpg Figure 1. Hypothesis model. https://www.frontiersin.org/files/Articles/1792805/fpubh-14-1792805-HTML/image_m/fpubh-14-1792805-t001.jpg Table 1. Participants’ characteristics and the median of the nurse safety behavior (n = 589). https://www.frontiersin.org/files/Articles/1792805/fpubh-14-1792805-HTML/image_m/fpubh-14-1792805-t002.jpg Table 2. Correlation between moral sensitivity, professional identity, nursing work environment, and https://www.frontiersin.org/files/Articles/1792805/fpubh-14-1792805-HTML/image_m/fpubh-14-1792805-g002.jpg Figure 2. Pathway relations and effect values for the hypothetical model. The solid lines represent https://www.frontiersin.org/files/Articles/1792805/fpubh-14-1792805-HTML/image_m/fpubh-14-1792805-t003.jpg Table 3. The mediating effect of professional identity between risk perception, moral sensitivity, n https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1709059/full https://www.frontiersin.org/files/Articles/1709059/fonc-15-1709059-HTML-r3/image_m/fonc-15-1709059-g001.jpg Figure 1. A large, round-like mass is visible in the right lobe of the liver, measuring approximatel https://www.frontiersin.org/files/Articles/1709059/fonc-15-1709059-HTML-r3/image_m/fonc-15-1709059-g002.jpg Figure 2. (a) The tumor shows significantly increased cellular density, predominantly composed of sp https://www.frontiersin.org/files/Articles/1709059/fonc-15-1709059-HTML-r3/image_m/fonc-15-1709059-g003.jpg Figure 3. (a) A round-like signal shadow, measuring approximately 3.2 cm × 3.5 cm, is seen near the https://www.frontiersin.org/files/Articles/1709059/fonc-15-1709059-HTML-r3/image_m/fonc-15-1709059-t001.jpg Table 1. Chemotherapy cases post-surgery for adult UESL. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1760865/full https://www.frontiersin.org/files/Articles/1760865/fpsyg-17-1760865-HTML/image_m/fpsyg-17-1760865-t001.jpg Table 1. Means, standard deviations, and correlations for all variables. https://www.frontiersin.org/files/Articles/1760865/fpsyg-17-1760865-HTML/image_m/fpsyg-17-1760865-t002.jpg Table 2. Mediation hypotheses testing results. https://www.frontiersin.org/files/Articles/1760865/fpsyg-17-1760865-HTML/image_m/fpsyg-17-1760865-t003.jpg Table 3. Moderated mediation effects results. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1603876/full https://www.frontiersin.org/files/Articles/1603876/fsurg-12-1603876-HTML/image_m/fsurg-12-1603876-g001.jpg Figure 1. Axial time-of-flight (TOF) MR angiography (A), coronal T2-weighted MRI (B), demonstrating https://www.frontiersin.org/files/Articles/1603876/fsurg-12-1603876-HTML/image_m/fsurg-12-1603876-g002.jpg Figure 2. Digital subtraction angiography (A) and schematic illustration (B) of the left vertebral a https://www.frontiersin.org/files/Articles/1603876/fsurg-12-1603876-HTML/image_m/fsurg-12-1603876-g003.jpg Figure 3. Digital subtraction angiography (A) and schematic illustration (B) showing telescopic plac https://www.frontiersin.org/files/Articles/1603876/fsurg-12-1603876-HTML/image_m/fsurg-12-1603876-g004.jpg Figure 4. Follow-up at 3 months. Axial time-of-flight (TOF) MR angiography (A) showing aneurysm thro https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1817866/full https://www.frontiersin.org/files/Articles/1817866/fpsyg-17-1817866-HTML/image_m/fpsyg-17-1817866-g001.jpg Figure 1. Pre- and post-intervention maximal elbow flexion (EF) force across groups (mean ± SD). Val https://www.frontiersin.org/files/Articles/1817866/fpsyg-17-1817866-HTML/image_m/fpsyg-17-1817866-g002.jpg Figure 2. Pre- and post-intervention co-contraction index (CCI) across groups (mean ± SD). Values ar https://www.frontiersin.org/files/Articles/1817866/fpsyg-17-1817866-HTML/image_m/fpsyg-17-1817866-g003.jpg Figure 3. Pre- and post-intervention antagonist muscle co-activation (AMCA) across groups (mean ± SD https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1780928/full https://www.frontiersin.org/files/Articles/1780928/fpls-17-1780928-HTML/image_m/fpls-17-1780928-t001.jpg Table 1. Variability in agronomic and physiological indices of sweet potato germplasm seedlings unde https://www.frontiersin.org/files/Articles/1780928/fpls-17-1780928-HTML/image_m/fpls-17-1780928-t002.jpg Table 2. PCA results for traits under nutrient deficiency: weighted coefficients, eigenvalues, varia https://www.frontiersin.org/files/Articles/1780928/fpls-17-1780928-HTML/image_m/fpls-17-1780928-t003.jpg Table 3. Correlation analysis of low nutrient tolerance indices with comprehensive evaluation value https://www.frontiersin.org/files/Articles/1780928/fpls-17-1780928-HTML/image_m/fpls-17-1780928-g001.jpg Figure 1. Cluster analysis of 35 genotypes of sweet potato evaluated for tolerance indices under con https://www.frontiersin.org/files/Articles/1780928/fpls-17-1780928-HTML/image_m/fpls-17-1780928-g002.jpg Figure 2. Scatter plots of comprehensive evaluation values for nitrogen (A), phosphorus (B), and pot https://www.frontiersin.org/files/Articles/1780928/fpls-17-1780928-HTML/image_m/fpls-17-1780928-g003.jpg Figure 3. Correlation matrices of agronomic and physiological indicators under low and high levels o https://www.frontiersin.org/files/Articles/1780928/fpls-17-1780928-HTML/image_m/fpls-17-1780928-t004.jpg Table 4. Yield performance and nitrogen use efficiency indices under low and high nitrogen treatment https://www.frontiersin.org/files/Articles/1780928/fpls-17-1780928-HTML/image_m/fpls-17-1780928-t005.jpg Table 5. Yield performance and phosphorus efficiency indices under low and high phosphorus treatment https://www.frontiersin.org/files/Articles/1780928/fpls-17-1780928-HTML/image_m/fpls-17-1780928-t006.jpg Table 6. Yield performance and potassium efficiency indices under low and high potassium treatments. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1771944/full https://www.frontiersin.org/files/Articles/1771944/fsurg-13-1771944-HTML/image_m/fsurg-13-1771944-g001.jpg Figure 1. CONSORT flow diagram. https://www.frontiersin.org/files/Articles/1771944/fsurg-13-1771944-HTML/image_m/fsurg-13-1771944-g002.jpg Figure 2. (A) Patient positioning and ultrasound probe orientation to perform the procedures. (B) Ul https://www.frontiersin.org/files/Articles/1771944/fsurg-13-1771944-HTML/image_m/fsurg-13-1771944-g003.jpg Figure 3. Ultrasound anatomy of the shoulder's anterior approach. ↑: SSP tendon insertion point on t https://www.frontiersin.org/files/Articles/1771944/fsurg-13-1771944-HTML/image_m/fsurg-13-1771944-t001.jpg Table 1. Ultrasound assessment for grading structural tendon changes in supraspinatus tendinopathy b https://www.frontiersin.org/files/Articles/1771944/fsurg-13-1771944-HTML/image_m/fsurg-13-1771944-t002.jpg Table 2. Baseline demographic and clinical characteristics for each group. https://www.frontiersin.org/files/Articles/1771944/fsurg-13-1771944-HTML/image_m/fsurg-13-1771944-g004.jpg Figure 4. CMS score trend through the different follow-ups. https://www.frontiersin.org/files/Articles/1771944/fsurg-13-1771944-HTML/image_m/fsurg-13-1771944-g005.jpg Figure 5. DASH score trend through the different follow-ups. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1789413/full https://www.frontiersin.org/files/Articles/1789413/fpubh-14-1789413-HTML/image_m/fpubh-14-1789413-t001.jpg Table 1. Framework of effectiveness criteria. https://www.frontiersin.org/files/Articles/1789413/fpubh-14-1789413-HTML/image_m/fpubh-14-1789413-g001.jpg Figure 1. PRISMA flow diagram of article selection. https://www.frontiersin.org/files/Articles/1789413/fpubh-14-1789413-HTML/image_m/fpubh-14-1789413-t002.jpg Table 2. Public health measures implemented for sporting mass gatherings effectiveness results. https://www.frontiersin.org/files/Articles/1789413/fpubh-14-1789413-HTML/image_m/fpubh-14-1789413-t003.jpg Table 3. Effectiveness of protocols to mitigate respiratory disease spread by MG. https://www.frontiersin.org/files/Articles/1789413/fpubh-14-1789413-HTML/image_m/fpubh-14-1789413-t004.jpg Table 4. Number of articles with various public health measures by reported effectiveness of interve https://www.frontiersin.org/files/Articles/1789413/fpubh-14-1789413-HTML/image_m/fpubh-14-1789413-t005.jpg Table 5. Number of articles with various public health measures by MG event era. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1580436/full https://www.frontiersin.org/files/Articles/1580436/fimmu-16-1580436-HTML/image_m/fimmu-16-1580436-t001.jpg Table 1. Primers used to construct recombinant plasmids. https://www.frontiersin.org/files/Articles/1580436/fimmu-16-1580436-HTML/image_m/fimmu-16-1580436-t002.jpg Table 2. Primers designed for qRT-PCR (16). https://www.frontiersin.org/files/Articles/1580436/fimmu-16-1580436-HTML/image_m/fimmu-16-1580436-t003.jpg Table 3. Details on the 10 predicted membrane lipoproteins as determined by bioinformatics analyses. https://www.frontiersin.org/files/Articles/1580436/fimmu-16-1580436-HTML/image_m/fimmu-16-1580436-g001.jpg Figure 1. Purification of membrane lipoproteins and immunogenicity test. (A) Purification of eight p https://www.frontiersin.org/files/Articles/1580436/fimmu-16-1580436-HTML/image_m/fimmu-16-1580436-g002.jpg Figure 2. Mouse anti-recombinant proteins polyclonal antibody preparation flow chart and antibody ti https://www.frontiersin.org/files/Articles/1580436/fimmu-16-1580436-HTML/image_m/fimmu-16-1580436-g003.jpg Figure 3. Secretory verification and subcellular localization. (A) Supernatants (S) and whole cell l https://www.frontiersin.org/files/Articles/1580436/fimmu-16-1580436-HTML/image_m/fimmu-16-1580436-g004.jpg Figure 4. Removal of endotoxin from recombinant proteins. (A) SDS-PAGE was used to detect the protei https://www.frontiersin.org/files/Articles/1580436/fimmu-16-1580436-HTML/image_m/fimmu-16-1580436-g005.jpg Figure 5. Recombinant proteins induced the expression of pro-inflammatory cytokines in epithelial ce https://www.frontiersin.org/files/Articles/1580436/fimmu-16-1580436-HTML/image_m/fimmu-16-1580436-g006.jpg Figure 6. Recombinant proteins induced the expression of pro-inflammatory cytokines in macrophages. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1668222/full https://www.frontiersin.org/files/Articles/1668222/fimmu-16-1668222-HTML/image_m/fimmu-16-1668222-t001.jpg Table 1. Alterations in the gut microbiota of patients with anti-NMDAR encephalitis. https://www.frontiersin.org/files/Articles/1668222/fimmu-16-1668222-HTML/image_m/fimmu-16-1668222-g001.jpg Figure 1. Altered gut microbiota composition contributes to intestinal barrier disruption and exacer https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1791572/full https://www.frontiersin.org/files/Articles/1791572/feduc-11-1791572-HTML-r1/image_m/feduc-11-1791572-t001.jpg Table 1. Average intra-item correlations—universal framework 2.0. https://www.frontiersin.org/files/Articles/1791572/feduc-11-1791572-HTML-r1/image_m/feduc-11-1791572-t002.jpg Table 2. CFA for the 8 essential skills. https://www.frontiersin.org/files/Articles/1791572/feduc-11-1791572-HTML-r1/image_m/feduc-11-1791572-t003.jpg Table 3. Inter-skill correlation matrix—universal framework 2.0. https://www.frontiersin.org/files/Articles/1791572/feduc-11-1791572-HTML-r1/image_m/feduc-11-1791572-t004.jpg Table 4. Average step correlation by distance. https://www.frontiersin.org/files/Articles/1791572/feduc-11-1791572-HTML-r1/image_m/feduc-11-1791572-g001.jpg Figure 1. Graph of trend of average correlation as distance between Skill steps increase. https://www.frontiersin.org/files/Articles/1791572/feduc-11-1791572-HTML-r1/image_m/feduc-11-1791572-t005.jpg Table 5. Regression analysis for wage premium—2025 data [model Fit statistics: observations 640, wal https://www.frontiersin.org/files/Articles/1791572/feduc-11-1791572-HTML-r1/image_m/feduc-11-1791572-t006.jpg Table 6. Regression analysis for wage premium—2023 data [model Fit statistics: observations 866, wal https://www.frontiersin.org/files/Articles/1791572/feduc-11-1791572-HTML-r1/image_m/feduc-11-1791572-t007.jpg Table 7. Regression analysis of skill score for AI usage [model Fit statistics: observations 1,515; https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1789646/full https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-g001.jpg Figure 1. The research framework. https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-t001.jpg Table 1. Respondents' profile. https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-t002.jpg Table 2. Means, standard deviations, and correlations between variables. https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-t003.jpg Table 3. Measurement model. https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-t004.jpg Table 4. Cronbach's Alpha, composite reliability and convergent validity of the constructs. https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-t005.jpg Table 5. Discriminant validity of the constructs based on HTMT. https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-t006.jpg Table 6. Coefficient of determination (R2). https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-g002.jpg Figure 2. Measurement and structural model results (SmartPLS output). https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-t007.jpg Table 7. PLS-SEM inner model (direct effects). https://www.frontiersin.org/files/Articles/1789646/fpsyg-17-1789646-HTML/image_m/fpsyg-17-1789646-t008.jpg Table 8. Specific indirect effects (Mediation). https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1713412/full https://www.frontiersin.org/files/Articles/1713412/fmed-13-1713412-HTML/image_m/fmed-13-1713412-t001.jpg Table 1. General characteristics of included literature. https://www.frontiersin.org/files/Articles/1713412/fmed-13-1713412-HTML/image_m/fmed-13-1713412-t002.jpg Table 2. Newcastle-Ottawa quality assessment scale results of cohort studies. https://www.frontiersin.org/files/Articles/1713412/fmed-13-1713412-HTML/image_m/fmed-13-1713412-g001.jpg Figure 1. Forest plot comparing the filter lifespan between the nafamostat mesylate and heparin grou https://www.frontiersin.org/files/Articles/1713412/fmed-13-1713412-HTML/image_m/fmed-13-1713412-g002.jpg Figure 2. Forest plot comparing the effective rate of anticoagulation between the nafamostat mesylat https://www.frontiersin.org/files/Articles/1713412/fmed-13-1713412-HTML/image_m/fmed-13-1713412-g003.jpg Figure 3. Forest plot comparing bleeding events between the nafamostat mesylate and heparin groups. https://www.frontiersin.org/files/Articles/1713412/fmed-13-1713412-HTML/image_m/fmed-13-1713412-g004.jpg Figure 4. Forest plot comparing length of hospital stay between the nafamostat mesylate and heparin https://www.frontiersin.org/files/Articles/1713412/fmed-13-1713412-HTML/image_m/fmed-13-1713412-g005.jpg Figure 5. Forest plot comparing coagulation indicators between the nafamostat mesylate and heparin g https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1780086/full https://www.frontiersin.org/files/Articles/1780086/fphar-17-1780086-HTML-r2/image_m/fphar-17-1780086-t001.jpg Table 1. Primer sequences used for qPCR analysis. https://www.frontiersin.org/files/Articles/1780086/fphar-17-1780086-HTML-r2/image_m/fphar-17-1780086-g001.jpg Figure 1. Survival rate of chicken embryos treated with chalcone derivatives OH17 and OH25. Kaplan–M https://www.frontiersin.org/files/Articles/1780086/fphar-17-1780086-HTML-r2/image_m/fphar-17-1780086-g002.jpg Figure 2. Morphological assessment of chicken embryos following treatment with chalcone derivatives https://www.frontiersin.org/files/Articles/1780086/fphar-17-1780086-HTML-r2/image_m/fphar-17-1780086-g003.jpg Figure 3. Quantitative real-time PCR analysis of apoptosis- and angiogenesis-related gene expression https://www.frontiersin.org/files/Articles/1780086/fphar-17-1780086-HTML-r2/image_m/fphar-17-1780086-g004.jpg Figure 4. Angiogenesis assay of the CAM in chicken embryos. The embryos were treated with 2 µM of bo https://www.frontiersin.org/files/Articles/1780086/fphar-17-1780086-HTML-r2/image_m/fphar-17-1780086-g005.jpg Figure 5. Viability of primary embryonic fibroblasts (NEFCs) following treatment with chalcone deriv https://www.frontiersin.org/files/Articles/1780086/fphar-17-1780086-HTML-r2/image_m/fphar-17-1780086-g006.jpg Figure 6. Morphological assessment of primary embryonic fibroblasts (NEFCs) following treatment with https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1646327/full https://www.frontiersin.org/files/Articles/1646327/fspor-07-1646327-HTML/image_m/fspor-07-1646327-g001.jpg Figure 1. Preferred reporting items for systematic reviews and meta-analyses flow chart of the liter https://www.frontiersin.org/files/Articles/1646327/fspor-07-1646327-HTML/image_m/fspor-07-1646327-t001.jpg Table 1. Overview of participants' characteristics in the included studies (k = 11). https://www.frontiersin.org/files/Articles/1646327/fspor-07-1646327-HTML/image_m/fspor-07-1646327-t002.jpg Table 2. Overview of intervention and executive function assessment characteristics in the included https://www.frontiersin.org/files/Articles/1646327/fspor-07-1646327-HTML/image_m/fspor-07-1646327-g002.jpg Figure 2. Cochrane RoB 2.0 risk assessment summary. https://www.frontiersin.org/files/Articles/1646327/fspor-07-1646327-HTML/image_m/fspor-07-1646327-g003.jpg Figure 3. Forest plot of exercise effects on executive function. RE, Model random-effects model; wks https://www.frontiersin.org/files/Articles/1646327/fspor-07-1646327-HTML/image_m/fspor-07-1646327-t003.jpg Table 3. Moderator analyses of effects of exercise on executive function outcomes. https://www.frontiersin.org/files/Articles/1646327/fspor-07-1646327-HTML/image_m/fspor-07-1646327-g004.jpg Figure 4. Funnel plot for potential publication bias. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1657241/full https://www.frontiersin.org/files/Articles/1657241/fmed-12-1657241-HTML/image_m/fmed-12-1657241-t001.jpg Table 1. Characteristics of the included studies. https://www.frontiersin.org/files/Articles/1657241/fmed-12-1657241-HTML/image_m/fmed-12-1657241-g001.jpg Figure 1. Flowchart of the study selection process. https://www.frontiersin.org/files/Articles/1657241/fmed-12-1657241-HTML/image_m/fmed-12-1657241-g002.jpg Figure 2. Forest plot of the sum total. https://www.frontiersin.org/files/Articles/1657241/fmed-12-1657241-HTML/image_m/fmed-12-1657241-g003.jpg Figure 3. Activation of fMRI signals in cortical and subcortical structures in the acupuncture group https://www.frontiersin.org/files/Articles/1657241/fmed-12-1657241-HTML/image_m/fmed-12-1657241-g004.jpg Figure 4. Activation of fMRI signals in cortical and subcortical structures in the control group. (A https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2026.1719829/full https://www.frontiersin.org/files/Articles/1719829/fagro-08-1719829-HTML/image_m/fagro-08-1719829-g001.jpg Figure 1. Location of the Kef–Siliana transect in northwestern Tunisia. (A) the location of Le Kef a https://www.frontiersin.org/files/Articles/1719829/fagro-08-1719829-HTML/image_m/fagro-08-1719829-t001.jpg Table 1. Context module: household demographics, livelihoods, environmental perceptions, and access https://www.frontiersin.org/files/Articles/1719829/fagro-08-1719829-HTML/image_m/fagro-08-1719829-t002.jpg Table 2. Household land, crop, and livestock profiles across six communities (n=167). https://www.frontiersin.org/files/Articles/1719829/fagro-08-1719829-HTML/image_m/fagro-08-1719829-g002.jpg Figure 2. Community-level scores of the 13 agroecology principles (Agroecology Adherence module) gro https://www.frontiersin.org/files/Articles/1719829/fagro-08-1719829-HTML/image_m/fagro-08-1719829-g003.jpg Figure 3. Across communities scores of the 13 agroecology principles (Agroecology Adherence module a https://www.frontiersin.org/files/Articles/1719829/fagro-08-1719829-HTML/image_m/fagro-08-1719829-g004.jpg Figure 4. Heat map of average scores for the 13 agroecology principles (Agroecology Adherence module https://www.frontiersin.org/files/Articles/1719829/fagro-08-1719829-HTML/image_m/fagro-08-1719829-g005.jpg Figure 5. Performance assessment of farming households across six communities in the Kef–Siliana tra https://www.frontiersin.org/files/Articles/1719829/fagro-08-1719829-HTML/image_m/fagro-08-1719829-g006.jpg Figure 6. Mean performance scores across the six communities: KPI ranked from highest to lowest. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1721579/full https://www.frontiersin.org/files/Articles/1721579/fnut-12-1721579-HTML/image_m/fnut-12-1721579-t001.jpg Table 1. Clinical and metabolic parameters for critically ill patients according to quartiles of TyG https://www.frontiersin.org/files/Articles/1721579/fnut-12-1721579-HTML/image_m/fnut-12-1721579-t002.jpg Table 2. Cox proportional hazard ratios (HR) for early-onset AKI (≤48 h) across categories of TyG-BM https://www.frontiersin.org/files/Articles/1721579/fnut-12-1721579-HTML/image_m/fnut-12-1721579-g001.jpg Figure 1. Restricted cubic spline analysis of the dose-response relationship between TyG-BMI and (A) https://www.frontiersin.org/files/Articles/1721579/fnut-12-1721579-HTML/image_m/fnut-12-1721579-t003.jpg Table 3. Association between TyG-BMI and severity of early-onset AKI (≤48 h) in critically ill patie https://www.frontiersin.org/files/Articles/1721579/fnut-12-1721579-HTML/image_m/fnut-12-1721579-t004.jpg Table 4. Diagnostic performance and optimal threshold analysis of TyG-BMI for predicting AKI at diff https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1761491/full https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-g001.jpg Figure 1. Representative images of Huayu 23 (A–D, normal oleic) and Huayu 6317 (E–H, high oleic) pea https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-g002.jpg Figure 2. Two-dimensional graph (a), difference comparison graph (b), and fingerprint spectrum (c) o https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-t001.jpg Table 1. GC-IMS detection results: relative content of compounds in peanuts. https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-g003.jpg Figure 3. Circular clustering heatmap (a), peak volume bar chart (b), and principal component analys https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-t002.jpg Table 2. ROAV values of peanuts following different processing treatments. https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-g004.jpg Figure 4. GC-IMS detects the relative content of compounds, and OPLS-DA determines the analysis resu https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-g005.jpg Figure 5. The E-nose detection results of peanut samples with different cooking and processing metho https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-g006.jpg Figure 6. Results of E-tongue detection of peanut samples using different cooking and processing met https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-t003.jpg Table 3. Results of free amino acid analysis in peanuts treated by different methods (g/100 g). https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-t004.jpg Table 4. Amino acid TAV values for peanuts subjected to different treatments. https://www.frontiersin.org/files/Articles/1761491/fnut-13-1761491-HTML-r1/image_m/fnut-13-1761491-g007.jpg Figure 7. Correlation analysis between ROAV values of compounds and TAV values of free amino acids i https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1778778/full https://www.frontiersin.org/files/Articles/1778778/fvets-13-1778778-HTML-r1/image_m/fvets-13-1778778-g001.jpg Figure 1. Schematic representation of the experimental setup showing: (1) The dog’s estimated jump t https://www.frontiersin.org/files/Articles/1778778/fvets-13-1778778-HTML-r1/image_m/fvets-13-1778778-g002.jpg Figure 2. Measurement of jump distance. The helper remains positioned adjacent to the black referenc https://www.frontiersin.org/files/Articles/1778778/fvets-13-1778778-HTML-r1/image_m/fvets-13-1778778-g003.jpg Figure 3. Measurement of flight length, defined as the distance along the jump trajectory from the m https://www.frontiersin.org/files/Articles/1778778/fvets-13-1778778-HTML-r1/image_m/fvets-13-1778778-g004.jpg Figure 4. Determination of impact time. The impact phase was defined as the interval from first visi https://www.frontiersin.org/files/Articles/1778778/fvets-13-1778778-HTML-r1/image_m/fvets-13-1778778-g005.jpg Figure 5. Measurement of body angle during the jump phase. Body angle was defined as the angle betwe https://www.frontiersin.org/files/Articles/1778778/fvets-13-1778778-HTML-r1/image_m/fvets-13-1778778-g006.jpg Figure 6. Measurement of head–neck angle, defined as the angle between the line connecting the exter https://www.frontiersin.org/files/Articles/1778778/fvets-13-1778778-HTML-r1/image_m/fvets-13-1778778-t001.jpg Table 1. Descriptive statistics for morphological and kinematic variables measured during the long a https://www.frontiersin.org/files/Articles/1778778/fvets-13-1778778-HTML-r1/image_m/fvets-13-1778778-t002.jpg Table 2. Descriptive statistics for terminal and energetic variables measured during the long attack https://www.frontiersin.org/files/Articles/1778778/fvets-13-1778778-HTML-r1/image_m/fvets-13-1778778-t003.jpg Table 3. Descriptive statistics for mechanical variables measured during the long attack in Belgian https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1661386/full https://www.frontiersin.org/files/Articles/1661386/fimmu-16-1661386-HTML/image_m/fimmu-16-1661386-g001.jpg Figure 1. Pathophysiological mechanisms of gut microenvironment systemic dysregulation in inflammato https://www.frontiersin.org/files/Articles/1661386/fimmu-16-1661386-HTML/image_m/fimmu-16-1661386-g002.jpg Figure 2. Risk factors driving the dysregulation of gut microbiota and their metabolites in IBD path https://www.frontiersin.org/files/Articles/1661386/fimmu-16-1661386-HTML/image_m/fimmu-16-1661386-g003.jpg Figure 3. Mechanism of intestinal barrier dysfunction and ISCs regulation in IBD pathogenesis. https://www.frontiersin.org/files/Articles/1661386/fimmu-16-1661386-HTML/image_m/fimmu-16-1661386-g004.jpg Figure 4. Dysregulation of innate and adaptive immune responses in IBD pathogenesis. https://www.frontiersin.org/files/Articles/1661386/fimmu-16-1661386-HTML/image_m/fimmu-16-1661386-t001.jpg Table 1. Therapeutic targeting multicomponents of the intestinal microenvironment. https://www.frontiersin.org/files/Articles/1661386/fimmu-16-1661386-HTML/image_m/fimmu-16-1661386-t002.jpg Table 2. NDDS in inflammatory bowel disease: classification and representative studies. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1688642/full https://www.frontiersin.org/files/Articles/1688642/fonc-15-1688642-HTML/image_m/fonc-15-1688642-t001.jpg Table 1. Global distribution of breast cancer (BC) among women aged 20–54 years. https://www.frontiersin.org/files/Articles/1688642/fonc-15-1688642-HTML/image_m/fonc-15-1688642-t002.jpg Table 2. Global distribution of breast cancer (BC) among women aged 55 years and older. https://www.frontiersin.org/files/Articles/1688642/fonc-15-1688642-HTML/image_m/fonc-15-1688642-g001.jpg Figure 1. Global prevalence of BC: a world map overview. (A) Global Prevalence of BC among women Age https://www.frontiersin.org/files/Articles/1688642/fonc-15-1688642-HTML/image_m/fonc-15-1688642-g002.jpg Figure 2. Changes in BC burden rates across SDI regions (1990-2021). (A) Prevalence of BC among wome https://www.frontiersin.org/files/Articles/1688642/fonc-15-1688642-HTML/image_m/fonc-15-1688642-g003.jpg Figure 3. Joinpoint regression analysis of burden rates of BC across age groups (20–54 years vs. 55+ https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1779092/full https://www.frontiersin.org/files/Articles/1779092/fvets-13-1779092-HTML-r1/image_m/fvets-13-1779092-g001.jpg Figure 1. T2-weighted slightly parasagittal (A,C) and transverse (B,D) MRI of the cervical spine. Mo https://www.frontiersin.org/files/Articles/1779092/fvets-13-1779092-HTML-r1/image_m/fvets-13-1779092-g002.jpg Figure 2. Transverse T1-weighted post-contrast MRI at the level of C3-C4 (A) and C4-C5 (B). Homogene https://www.frontiersin.org/files/Articles/1779092/fvets-13-1779092-HTML-r1/image_m/fvets-13-1779092-g003.jpg Figure 3. Intraoperative picture of both completed ventral slots at C3-C4 and C4-C5 levels without ( https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2026.1775533/full https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-g001.jpg Figure 1. Flow of the process. https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-t001.jpg Table 1. Properties of the factors. https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-g002.jpg Figure 2. Random forest’s basic structure. https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-t003.jpg Algorithm 1. https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-t002.jpg Table 2. Performance comparison of ML models for predicting viscosity. https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-g003.jpg Figure 3. RMSE. https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-g004.jpg Figure 4. MAE. https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-g005.jpg Figure 5. MSE. https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-g006.jpg Figure 6. R2. https://www.frontiersin.org/files/Articles/1775533/fphy-14-1775533-HTML/image_m/fphy-14-1775533-g007.jpg Figure 7. Percentage of accuracy precision (PAP). https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1720975/full https://www.frontiersin.org/files/Articles/1720975/fnut-13-1720975-HTML-r2/image_m/fnut-13-1720975-t001.jpg Table 1. Demographic, lifestyle, and clinical characteristics of 3,196 Coronary Artery Risk Developm https://www.frontiersin.org/files/Articles/1720975/fnut-13-1720975-HTML-r2/image_m/fnut-13-1720975-g001.jpg Figure 1. Associations of plasma pentadecanoic acid with blood pressure, pulse pressure, and resting https://www.frontiersin.org/files/Articles/1720975/fnut-13-1720975-HTML-r2/image_m/fnut-13-1720975-g002.jpg Figure 2. Risks of incident hypertension and CVD per SD of plasma pentadecanoic acid among CARDIA an https://www.frontiersin.org/files/Articles/1720975/fnut-13-1720975-HTML-r2/image_m/fnut-13-1720975-t002.jpg Table 2. Results for the two-sample Mendelian randomization analysis of plasma pentadecanoic acid wi https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2026.1720101/full https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-g001.jpg Figure 1. Flowchart of study scope. *Only computed for Germany and USA. https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-t001.jpg Table 1. Properties of LEV considered. https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-g002.jpg Figure 2. Modal split of trips in selected countries, trip purpose abbreviation - AP: All purposes, https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-g003.jpg Figure 3. Cumulative trips and mileage by trip length bin in the USA, India, and Germany undertaken https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-g004.jpg Figure 4. Maximum mileage substitution by LEVs in selected regions in 2030. https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-g005.jpg Figure 5. Tailpipe or TTW emission reduction potential of LEVs in selected regions in 2030 with cont https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-g006.jpg Figure 6. LCA-based emissions for LEVs, BEVs, and conventional passenger cars in Germany 2030 with 2 https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-g007.jpg Figure 7. Total ownership cost per kilometer for LEVs and EVs in India and Germany (adjusted for PPP https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-t002.jpg Table 2. LEVs and gender related mobility aspects. https://www.frontiersin.org/files/Articles/1720101/frsc-08-1720101-HTML/image_m/frsc-08-1720101-t003.jpg Table 3. LEV policy recommendations by region type. https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2025.1719495/full https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-t001.jpg Table 1. A summary of proposed car dependence archetypes, alongside a description and defining chara https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-g001.jpg Figure 1. A schematic to show the four proposed car dependence archetypes and how they populate each https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-g002.jpg Figure 2. Bivariate choropleth maps showing how the proportion of people traveling to work by car va https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-g003.jpg Figure 3. Maps illustrating the spatial distribution of LSOA-level vulnerability to each proposed ca https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-t002.jpg Table 2. A summary table of rural-urban classification percentages observed for each archetype and t https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-g004.jpg Figure 4. Radar diagrams representing the relative levels of demographics across each archetype subp https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-t003.jpg Table 3. A summary table of socio-economic classification percentages observed for each archetype an https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-t004.jpg Table 4. A summary table of broad ethnic group percentages observed for each archetype and the total https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-t005.jpg Table 5. A summary table of people with disabilities that cause major and minor impacts on daily act https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-g005.jpg Figure 5. A bar chart showing levels of disability in each archetype subpopulation and the total pop https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-t006.jpg Table 6. A summary table of age band percentages observed for each archetype and the total populatio https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-t007.jpg Table 7. A summary table of gender proportion observed for each archetype and the total population. https://www.frontiersin.org/files/Articles/1719495/frsc-07-1719495-HTML-r1/image_m/frsc-07-1719495-t008.jpg Table 8. A summary of odds ratios (OR) and average marginal effects (AME) results from logistic regr https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2026.1797353/full https://www.frontiersin.org/files/Articles/1797353/frsus-07-1797353-HTML/image_m/frsus-07-1797353-g001.jpg Figure 1. Structure of legal norms in Romania. https://www.frontiersin.org/files/Articles/1797353/frsus-07-1797353-HTML/image_m/frsus-07-1797353-t001.jpg Table 1. The main subject of legislative documents. https://www.frontiersin.org/files/Articles/1797353/frsus-07-1797353-HTML/image_m/frsus-07-1797353-t002.jpg Table 2. International conventions that Romania has joined. https://www.frontiersin.org/files/Articles/1797353/frsus-07-1797353-HTML/image_m/frsus-07-1797353-g002.jpg Figure 2. Timeline of normative acts related to protected areas in Romania (1990–2024), classified b https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2026.1677774/full https://www.frontiersin.org/files/Articles/1677774/frsus-07-1677774-HTML-r1/image_m/frsus-07-1677774-g001.jpg Figure 1. WEF challenges and the environment. CC: climate change. HWC: household water challenges. H https://www.frontiersin.org/files/Articles/1677774/frsus-07-1677774-HTML-r1/image_m/frsus-07-1677774-t001.jpg Table 1. Total variance explained by factors. https://www.frontiersin.org/files/Articles/1677774/frsus-07-1677774-HTML-r1/image_m/frsus-07-1677774-t002.jpg Table 2. Rotated factor matrix. https://www.frontiersin.org/files/Articles/1677774/frsus-07-1677774-HTML-r1/image_m/frsus-07-1677774-t003.jpg Table 3. Composite reliability, convergent validity and discriminant validity. https://www.frontiersin.org/files/Articles/1677774/frsus-07-1677774-HTML-r1/image_m/frsus-07-1677774-g002.jpg Figure 2. SEM model—testing hypotheses. https://www.frontiersin.org/files/Articles/1677774/frsus-07-1677774-HTML-r1/image_m/frsus-07-1677774-t004.jpg Table 4. SEM path coefficients. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1697872/full https://www.frontiersin.org/files/Articles/1697872/fvets-12-1697872-HTML-r4/image_m/fvets-12-1697872-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1697872/fvets-12-1697872-HTML-r4/image_m/fvets-12-1697872-t001.jpg Table 1. The 44 antimicrobial resistance genes identified by next-generation sequencing, consistentl https://www.frontiersin.org/files/Articles/1697872/fvets-12-1697872-HTML-r4/image_m/fvets-12-1697872-g001.jpg Figure 1. Classification of the identified antimicrobial resistance genes by antimicrobial class and https://www.frontiersin.org/files/Articles/1697872/fvets-12-1697872-HTML-r4/image_m/fvets-12-1697872-g002.jpg Figure 2. Average nucleotide identity (ANI) analysis between the genomes of the 0 × and 1,000 × pote https://www.frontiersin.org/files/Articles/1697872/fvets-12-1697872-HTML-r4/image_m/fvets-12-1697872-t002.jpg Table 2. Effect of increasing potentiated sulphonamide concentrations on the minimum inhibitory conc https://www.frontiersin.org/files/Articles/1697872/fvets-12-1697872-HTML-r4/image_m/fvets-12-1697872-t003.jpg Table 3. Results of the extended-spectrum β-lactamase (ESBL) test using ceftazidime (CTZ), cefotaxim https://www.frontiersin.org/files/Articles/1697872/fvets-12-1697872-HTML-r4/image_m/fvets-12-1697872-t004.jpg Table 4. Contig quality metrics based on QUAST software analysis for potentiated sulphonamide-expose https://www.frontiersin.org/files/Articles/1697872/fvets-12-1697872-HTML-r4/image_m/fvets-12-1697872-t005.jpg Table 5. Number of total and identified mutations per sample, classified by mutation type. https://www.frontiersin.org/files/Articles/1697872/fvets-12-1697872-HTML-r4/image_m/fvets-12-1697872-t006.jpg Table 6. Deletions, inversions, and single nucleotide polymorphisms (SNPs) detected in genes relevan https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1780266/full https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-g001.jpg Figure 1. (A) Cultures of isolates LTG2003(1–3) grown on 7H10 + PANTA medium (upper panel). (B) Colo https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-t001.jpg Table 1. Phenotypic and biochemical characteristics of the three isolates (Kent et al., 1985). https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-t002.jpg Table 2. Results of MALDI-TOF mass spectrometry analysis of isolates LTG2003(1), (2), and (3), perfo https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-g002.jpg Figure 2. Phylogenetic tree inferred from GBDP distances based on 16S rRNA gene sequences of LTG2003 https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-g003.jpg Figure 3. Phylogenetic tree of the LTG2003 strains (1, 2, and 3) using neighborhood-joining statisti https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-g004.jpg Figure 4. Phylogenetic tree of LTG2003 strains (1–3) using neighborhood-joining statistical methods https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-t003.jpg Table 3. In vitro antibiotic susceptibility profile of the Mycobacterium isolates as determined with https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-t004.jpg Table 4. Genome assembly and annotation quality metrics. https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-t005.jpg Table 5. Average nucleotide identity (ANI) results obtained using the NCBI-developed automated proka https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-t006.jpg Table 6. Pairwise comparisons of LTG2003 (1, 2, and 3) - genomes versus type strain genomes from the https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-g005.jpg Figure 5. Phylogenetic tree inferred with FastME 2.1.6.1 from GBDP distances calculated using the ge https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-g006.jpg Figure 6. Phylogenetic tree generated using the bioinformatics tools Mashtree and iTOL, comparing th https://www.frontiersin.org/files/Articles/1780266/fmicb-17-1780266-HTML/image_m/fmicb-17-1780266-g007.jpg Figure 7. The genome of strain LTG2003(1) was annotated using the RAST server. Key assembly statisti https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1719551/full https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-g001.jpg Figure 1. Step-wedge positioned on the breast-support table at (A) first and (B) second positions. T https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-t001.jpg Table 1. Printing parameter settings for each 3D printer and material. Not specified parameters were https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-g002.jpg Figure 2. μeff versus thickness of reference (filled circle symbols; dashed lines) and 3D materials https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-g003.jpg Figure 3. Dependence of μeff values for (A) different kVp (keeping W/Rh w/grid), (B) filtration (kee https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-g004.jpg Figure 4. Relative differences (%) between μeff of the selected material versus the kVp values, for https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-g005.jpg Figure 5. μeff versus thickness of reference (filled symbols; dashed lines) and 3D (open symbols; do https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-g006.jpg Figure 6. μ0 and k values versus kVp for (A) 3D and (B) reference materials obtained for M1 (Lines a https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-g007.jpg Figure 7. Heat map with cell values corresponding to the µ0 relative differences between reference a https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-g008.jpg Figure 8. Equivalent glandular proportions of the 3D materials for (A) w/and (B) w/o grid cases. Lin https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-t002.jpg Table 2. Equivalent glandular proportions (%) corresponding to the 3D-printed materials obtained fro https://www.frontiersin.org/files/Articles/1719551/fbioe-13-1719551-HTML/image_m/fbioe-13-1719551-g009.jpg Figure 9. (A) 3D-printed 11-mm-thick slab printed with PLA for gland + skin and ABS for fat, followi https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1738833/full https://www.frontiersin.org/files/Articles/1738833/fdgth-08-1738833-HTML/image_m/fdgth-08-1738833-t001.jpg Table 1. Sample risk profile identified by an unsupervised AI system. https://www.frontiersin.org/files/Articles/1738833/fdgth-08-1738833-HTML/image_m/fdgth-08-1738833-t002.jpg Table 2. Sample performance metrics of a supervised AI risk predictive model. https://www.frontiersin.org/files/Articles/1738833/fdgth-08-1738833-HTML/image_m/fdgth-08-1738833-t003.jpg Table 3. Risk predictive model for the CGPA score. https://www.frontiersin.org/files/Articles/1738833/fdgth-08-1738833-HTML/image_m/fdgth-08-1738833-t004.jpg Table 4. Risk predictive model for assessing attendance and student regularity. https://www.frontiersin.org/files/Articles/1738833/fdgth-08-1738833-HTML/image_m/fdgth-08-1738833-g001.jpg Figure 1. Overview of the intended workflow. https://www.frontiersin.org/files/Articles/1738833/fdgth-08-1738833-HTML/image_m/fdgth-08-1738833-t005.jpg Table 5. Risk mapping. https://www.frontiersin.org/files/Articles/1738833/fdgth-08-1738833-HTML/image_m/fdgth-08-1738833-t006.jpg Table 6. Intervention logic and escalation framework. https://www.frontiersin.org/files/Articles/1738833/fdgth-08-1738833-HTML/image_m/fdgth-08-1738833-t007.jpg Table 7. Glossary of key terms. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1812671/full https://www.frontiersin.org/files/Articles/1812671/fpubh-14-1812671-HTML/image_m/fpubh-14-1812671-t001.jpg Table 1. Socio-economic characteristics of households by site of residence. https://www.frontiersin.org/files/Articles/1812671/fpubh-14-1812671-HTML/image_m/fpubh-14-1812671-t002.jpg Table 2. Housing characteristics and dwelling conditions. https://www.frontiersin.org/files/Articles/1812671/fpubh-14-1812671-HTML/image_m/fpubh-14-1812671-t003.jpg Table 3. Distribution of substances among households in the study population. https://www.frontiersin.org/files/Articles/1812671/fpubh-14-1812671-HTML/image_m/fpubh-14-1812671-t004.jpg Table 4. Bivariable association between socio-economic indicators and household substance use. https://www.frontiersin.org/files/Articles/1812671/fpubh-14-1812671-HTML/image_m/fpubh-14-1812671-t005.jpg Table 5. Multivariable logistic regression of socio-economic factors associated with household subst https://www.frontiersin.org/journals/genome-editing/articles/10.3389/fgeed.2025.1649993/full https://www.frontiersin.org/files/Articles/1649993/fgeed-07-1649993-HTML/image_m/fgeed-07-1649993-g001.jpg Figure 1. Raji-Luc-WT stably express functional luciferase-mCherry and retain parental features. (A) https://www.frontiersin.org/files/Articles/1649993/fgeed-07-1649993-HTML/image_m/fgeed-07-1649993-g002.jpg Figure 2. Genomic deletion of target antigens in Raji-Luc lines does not alter proliferation or viab https://www.frontiersin.org/files/Articles/1649993/fgeed-07-1649993-HTML/image_m/fgeed-07-1649993-g003.jpg Figure 3. Raji knockout lines generated via CRISPR-Cas9 exhibit complete loss of CD19, CD20, and CD2 https://www.frontiersin.org/files/Articles/1649993/fgeed-07-1649993-HTML/image_m/fgeed-07-1649993-g004.jpg Figure 4. Raji KO lines model antigen loss and resist CAR-T cell–mediated cytotoxicity. (A) Schemati https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1640747/full https://www.frontiersin.org/files/Articles/1640747/fped-13-1640747-HTML/image_m/fped-13-1640747-t001.jpg Table 1. Demographic and vaccination data among the study population. https://www.frontiersin.org/files/Articles/1640747/fped-13-1640747-HTML/image_m/fped-13-1640747-t002.jpg Table 2. Clinical characteristics of long COVID and sex differences among the study population. https://www.frontiersin.org/files/Articles/1640747/fped-13-1640747-HTML/image_m/fped-13-1640747-t003.jpg Table 3. Clinical characteristics of long COVID and sex differences among patients under 12 years of https://www.frontiersin.org/files/Articles/1640747/fped-13-1640747-HTML/image_m/fped-13-1640747-t004.jpg Table 4. Clinical characteristics of long COVID and sex differences among patients 12 years of age o https://www.frontiersin.org/files/Articles/1640747/fped-13-1640747-HTML/image_m/fped-13-1640747-g001.jpg Figure 1. Individual symptom trajectories over time with model-based mean estimates. Each gray line https://www.frontiersin.org/files/Articles/1640747/fped-13-1640747-HTML/image_m/fped-13-1640747-g002.jpg Figure 2. Model-based estimates of change in number of symptoms over time since onset. The red line https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1745388/full https://www.frontiersin.org/files/Articles/1745388/fpsyt-17-1745388-HTML-r1/image_m/fpsyt-17-1745388-t001.jpg Table 1. Demographic and clinical characteristics of the study population. https://www.frontiersin.org/files/Articles/1745388/fpsyt-17-1745388-HTML-r1/image_m/fpsyt-17-1745388-t002.jpg Table 2. Means, standard deviations, effect sizes, and p values for main effects and interaction eff https://www.frontiersin.org/files/Articles/1745388/fpsyt-17-1745388-HTML-r1/image_m/fpsyt-17-1745388-g001.jpg Figure 1. Bar graphs of means and standard deviations for measures of depression (IDS-C), generalize https://www.frontiersin.org/files/Articles/1745388/fpsyt-17-1745388-HTML-r1/image_m/fpsyt-17-1745388-t003.jpg Table 3. Change from Visit 1 summary for behavioral variables from linear mixed model analyses (Tabl https://www.frontiersin.org/files/Articles/1745388/fpsyt-17-1745388-HTML-r1/image_m/fpsyt-17-1745388-g002.jpg Figure 2. Change from Visit 1 and 95% CI for behavioral variables estimated by linear mixed model an https://www.frontiersin.org/files/Articles/1745388/fpsyt-17-1745388-HTML-r1/image_m/fpsyt-17-1745388-g003.jpg Figure 3. Target distribution across right and left DLPFC. Color coding of targets indicates change https://www.frontiersin.org/files/Articles/1745388/fpsyt-17-1745388-HTML-r1/image_m/fpsyt-17-1745388-g004.jpg Figure 4. Average induced electric field strength of stimulation (|E|; V/m) for left and right DLPFC https://www.frontiersin.org/files/Articles/1745388/fpsyt-17-1745388-HTML-r1/image_m/fpsyt-17-1745388-g005.jpg Figure 5. Connectivity values between nodes of the default mode (blue labels), salience (red labels) https://www.frontiersin.org/files/Articles/1745388/fpsyt-17-1745388-HTML-r1/image_m/fpsyt-17-1745388-t004.jpg Table 4. Significant changes in network domain connectivity in left versus right side stimulation gr https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1674145/full https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-g001.jpg Figure 1. Structural model of university classroom silence. https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-t001.jpg Table 1. Reliability metrics by group (China and Korea). https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-t002.jpg Table 2-1. Exploratory factor analysis results (Six-factor solution: top five items per dimension). https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-t003.jpg Table 2-2. Reliability and convergent validity of latent constructs. https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-t004.jpg Table 2-3. Fornell–Larcker criterion and inter-construct correlations. https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-t005.jpg Table 3. Participants’ demographics. https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-t006.jpg Table 4. Correlations among the six latent dimensions of classroom silence. https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-t007.jpg Table 5. Standardized path coefficients and indirect effects in the baseline SEM model. https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-g002.jpg Figure 2. Standardized structural paths by country (China vs. Korea) from the multi-group SEM. https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-g003.jpg Figure 3. Cross-national differences (Δβ) in standardized structural paths between the Chinese and K https://www.frontiersin.org/files/Articles/1674145/fpsyg-16-1674145-HTML-r1/image_m/fpsyg-16-1674145-g004.jpg Figure 4. Effects of gender, grade, and discipline on classroom silence (SP). https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1712611/full https://www.frontiersin.org/files/Articles/1712611/fonc-15-1712611-HTML/image_m/fonc-15-1712611-t001.jpg Table 1. Characteristics of the participating centers. https://www.frontiersin.org/files/Articles/1712611/fonc-15-1712611-HTML/image_m/fonc-15-1712611-g001.jpg Figure 1. Map of participating EVAT Project centers by World Bank income level. https://www.frontiersin.org/files/Articles/1712611/fonc-15-1712611-HTML/image_m/fonc-15-1712611-t002.jpg Table 2. Characteristics of clinical deterioration events. https://www.frontiersin.org/files/Articles/1712611/fonc-15-1712611-HTML/image_m/fonc-15-1712611-t003.jpg Table 3. Comparisons of clinical deterioration events before and during/after PEWS implementation. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1657290/full https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g001.jpg Figure 1. Overall workflow of this study. ROI, region of interest; LASSO, least absolute shrinkage a https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g002.jpg Figure 2. Inclusion and exclusion flowchart for patients. https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g003.jpg Figure 3. ROI segmentation of brain metastasis, habitat generation, peritumoral region expansion, an https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-t001.jpg Table 1. Baseline characteristic of cohorts. https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-t002.jpg Table 2. Univariable and multivariable analysis of clinical features. https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g004.jpg Figure 4. Evaluation of clustering performance and visualization of habitat clusters. (A) Cluster va https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g005.jpg Figure 5. SHAP summary plots (A, B) quantify feature contributions to model predictions. Force plots https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g006.jpg Figure 6. ROC curves of predictive models across different cohorts. The AUC values and their 95% CIs https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-t003.jpg Table 3. Performance of different models in each cohorts. https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g007.jpg Figure 7. Calibration curves of predictive models across different cohorts. Calibration curves compa https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g008.jpg Figure 8. Delong test results comparing model performance across different feature sets and cohorts. https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g009.jpg Figure 9. DCA of predictive models across different cohorts. DCA curves show the net clinical benefi https://www.frontiersin.org/files/Articles/1657290/fonc-15-1657290-HTML-r1/image_m/fonc-15-1657290-g010.jpg Figure 10. Prognostic nomogram integrating clinical variables, peri-1mm imaging characteristics, and https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1785128/full https://www.frontiersin.org/files/Articles/1785128/fmed-13-1785128-HTML/image_m/fmed-13-1785128-g001.jpg Figure 1. Treatment timeline of the patient. https://www.frontiersin.org/files/Articles/1785128/fmed-13-1785128-HTML/image_m/fmed-13-1785128-g002.jpg Figure 2. (a) X-ray examination shows no obvious abnormalities; (b) MRI reveals a mass measuring app https://www.frontiersin.org/files/Articles/1785128/fmed-13-1785128-HTML/image_m/fmed-13-1785128-g003.jpg Figure 3. (a) Granulomatous inflammation of the left gluteal region and iliac fossa with extensive n https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1743290/full https://www.frontiersin.org/files/Articles/1743290/fmed-13-1743290-HTML/image_m/fmed-13-1743290-t001.jpg Table 1. Baseline characteristics of 389 hospitalized patients with respiratory diseases, grouped by https://www.frontiersin.org/files/Articles/1743290/fmed-13-1743290-HTML/image_m/fmed-13-1743290-t002.jpg Table 2. Univariate analysis of factors associated with respiratory recovery among 389 hospitalized https://www.frontiersin.org/files/Articles/1743290/fmed-13-1743290-HTML/image_m/fmed-13-1743290-t003.jpg Table 3. Multivariable logistic regression analysis of independent predictors of respiratory recover https://www.frontiersin.org/files/Articles/1743290/fmed-13-1743290-HTML/image_m/fmed-13-1743290-t004.jpg Table 4. Model performance and validation metrics. https://www.frontiersin.org/files/Articles/1743290/fmed-13-1743290-HTML/image_m/fmed-13-1743290-g001.jpg Figure 1. Receiver operating characteristic (ROC) curve of the prediction model. The ROC curve demon https://www.frontiersin.org/files/Articles/1743290/fmed-13-1743290-HTML/image_m/fmed-13-1743290-g002.jpg Figure 2. Calibration plot of the prediction model. The calibration plot compares the predicted and https://www.frontiersin.org/files/Articles/1743290/fmed-13-1743290-HTML/image_m/fmed-13-1743290-g003.jpg Figure 3. Nomogram for predicting respiratory recovery. The nomogram was constructed based on indepe https://www.frontiersin.org/files/Articles/1743290/fmed-13-1743290-HTML/image_m/fmed-13-1743290-g004.jpg Figure 4. Decision curve analysis (DCA) for clinical utility of the model. The DCA curve illustrates https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1727494/full https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-g001.jpg Figure 1. Research framework diagram. https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t001.jpg Table 1. Variable operationalization and measurement framework. https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t002.jpg Table 2. Sample characteristics (N = 500). https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t003.jpg Table 3. Descriptive statistics and correlation matrix (N = 500). https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t004.jpg Table 4. Measurement results. https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t005.jpg Table 5. Aggregation and multilevel model prerequisites. https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t006.jpg Table 6. Level-1 main effects (OLS). https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-g002.jpg Figure 2. Fixed effects estimates with 95% confidence intervals from multilevel models. https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t007.jpg Table 7. Level-2 main effects (OLS). https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t008.jpg Table 8. Mediation test (Bootstrap, 200 samples). https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-g003.jpg Figure 3. Structural path diagram of mediation model. https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t009.jpg Table 9. Cross-level interaction effects (OLS). https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-g004.jpg Figure 4. Johnson-Neyman significance region for cross-level interaction. https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t010.jpg Table 10. Robustness check (OLS with controls). https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t011.jpg Table 11. Heterogeneity analysis (urban vs. rural). https://www.frontiersin.org/files/Articles/1727494/fpubh-14-1727494-HTML-r1/image_m/fpubh-14-1727494-t012.jpg Table A-1. Core notation and variable description. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1679248/full https://www.frontiersin.org/files/Articles/1679248/fendo-16-1679248-HTML/image_m/fendo-16-1679248-g001.jpg Figure 1. Experimental design. Ovarian tissue from 5 patients was transplanted to the renal capsule https://www.frontiersin.org/files/Articles/1679248/fendo-16-1679248-HTML/image_m/fendo-16-1679248-g002.jpg Figure 2. CD31, CD34 immunostaining in the medullary and cortical sides of human ovarian xenograft a https://www.frontiersin.org/files/Articles/1679248/fendo-16-1679248-HTML/image_m/fendo-16-1679248-g003.jpg Figure 3. Quantification of CD31-positive and CD34-positive vessel density in the medullary and cort https://www.frontiersin.org/files/Articles/1679248/fendo-16-1679248-HTML/image_m/fendo-16-1679248-t001.jpg Table 1. The no of CD31 (+), CD34 (+) vessels/mm2, percentage of Ki67 (+) cells, and percentage of T https://www.frontiersin.org/files/Articles/1679248/fendo-16-1679248-HTML/image_m/fendo-16-1679248-g004.jpg Figure 4. (A) Ki67 immunostaining in the medullary and cortical sides of human ovarian xenograft aft https://www.frontiersin.org/files/Articles/1679248/fendo-16-1679248-HTML/image_m/fendo-16-1679248-g005.jpg Figure 5. (A) TUNEL immunostaining in the medullary and cortical sides of human ovarian xenograft af https://www.frontiersin.org/files/Articles/1679248/fendo-16-1679248-HTML/image_m/fendo-16-1679248-g006.jpg Figure 6. E2 and FSH concentration in mice’s plasma. ***P < 0.001. OVX, bilateral ovariectomy; OTCT, https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1641781/full https://www.frontiersin.org/files/Articles/1641781/fneur-16-1641781-HTML/image_m/fneur-16-1641781-g001.jpg Figure 1. Anticipated CONSORT flow diagram for participant recruitment and allocation. https://www.frontiersin.org/files/Articles/1641781/fneur-16-1641781-HTML/image_m/fneur-16-1641781-t001.jpg Table 1. Schedule of assessments and outcome measures across study timepoints. https://www.frontiersin.org/files/Articles/1641781/fneur-16-1641781-HTML/image_m/fneur-16-1641781-g002.jpg Figure 2. Electrode placement strategies for upper limb muscles based on Brunnstrom stages III–V. https://www.frontiersin.org/files/Articles/1641781/fneur-16-1641781-HTML/image_m/fneur-16-1641781-t002.jpg Table 2. Electrode placement strategy by Brunnstrom Stage. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1648841/full https://www.frontiersin.org/files/Articles/1648841/fneur-16-1648841-HTML/image_m/fneur-16-1648841-t001.jpg Table 1. The general characteristics of the included literature. https://www.frontiersin.org/files/Articles/1648841/fneur-16-1648841-HTML/image_m/fneur-16-1648841-g001.jpg Figure 1. Flow diagram of literature search. https://www.frontiersin.org/files/Articles/1648841/fneur-16-1648841-HTML/image_m/fneur-16-1648841-t002.jpg Table 2. Guide quality evaluation results. https://www.frontiersin.org/files/Articles/1648841/fneur-16-1648841-HTML/image_m/fneur-16-1648841-t003.jpg Table 3. Meta-analysis quality evaluation result. https://www.frontiersin.org/files/Articles/1648841/fneur-16-1648841-HTML/image_m/fneur-16-1648841-t004.jpg Table 4. Expert consensus quality evaluation result. https://www.frontiersin.org/files/Articles/1648841/fneur-16-1648841-HTML/image_m/fneur-16-1648841-t005.jpg Table 5. Results of literature extraction and grading of evidence. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1737823/full https://www.frontiersin.org/files/Articles/1737823/fpubh-14-1737823-HTML/image_m/fpubh-14-1737823-t001.jpg Table 1. Methodological quality appraisal results based on the AHRQ tool for each study. https://www.frontiersin.org/files/Articles/1737823/fpubh-14-1737823-HTML/image_m/fpubh-14-1737823-t002.jpg Table 2. Methodological quality appraisal results based on the NOS tool for each study. https://www.frontiersin.org/files/Articles/1737823/fpubh-14-1737823-HTML/image_m/fpubh-14-1737823-g001.jpg Figure 1. PRISMA flowchart showing the selection process of observational studies for inclusion in t https://www.frontiersin.org/files/Articles/1737823/fpubh-14-1737823-HTML/image_m/fpubh-14-1737823-t003.jpg Table 3. Characteristics of the included studies. https://www.frontiersin.org/files/Articles/1737823/fpubh-14-1737823-HTML/image_m/fpubh-14-1737823-g002.jpg Figure 2. Forest plot of pooled the association between frailty and depression in CHD patients. https://www.frontiersin.org/files/Articles/1737823/fpubh-14-1737823-HTML/image_m/fpubh-14-1737823-t004.jpg Table 4. Subgroup analyses stratified by region, frailty instrument, and depression instrument. https://www.frontiersin.org/files/Articles/1737823/fpubh-14-1737823-HTML/image_m/fpubh-14-1737823-g003.jpg Figure 3. Forest plot of sensitivity analysis of the association between frailty and depression in C https://www.frontiersin.org/files/Articles/1737823/fpubh-14-1737823-HTML/image_m/fpubh-14-1737823-g004.jpg Figure 4. Funnel plot of standard error by log odds ratio. Funnel plot of standard error by log odds https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1765169/full https://www.frontiersin.org/files/Articles/1765169/fpsyg-17-1765169-HTML-r1/image_m/fpsyg-17-1765169-t001.jpg Table 1. Scores of learning engagement, academic emotions, and academic self-efficacy among vocation https://www.frontiersin.org/files/Articles/1765169/fpsyg-17-1765169-HTML-r1/image_m/fpsyg-17-1765169-t002.jpg Table 2. Fit indices of latent profile analysis for learning engagement among vocational nursing stu https://www.frontiersin.org/files/Articles/1765169/fpsyg-17-1765169-HTML-r1/image_m/fpsyg-17-1765169-g001.jpg Figure 1. Latent profile analysis of learning engagement among vocational nursing students. https://www.frontiersin.org/files/Articles/1765169/fpsyg-17-1765169-HTML-r1/image_m/fpsyg-17-1765169-t003.jpg Table 3. Univariate analysis of latent classes in learning engagement among vocational nursing stude https://www.frontiersin.org/files/Articles/1765169/fpsyg-17-1765169-HTML-r1/image_m/fpsyg-17-1765169-t004.jpg Table 4. Multivariate analysis of factors associated with learning engagement profiles among vocatio https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1700758/full https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-g001.jpg Figure 1. Schematic diagrams of the anterior and posterior views of the lumbar-pelvic finite element https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-t001.jpg Table 1. Material properties of each material in the lumbar-pelvic three-dimensional finite element https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-g002.jpg Figure 2. Anterior views of lumbar transitional vertebrae with different types (A–H). https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-g003.jpg Figure 3. Schematic diagram of boundary loading conditions for the finite element model. https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-g004.jpg Figure 4. (A) The ROM for flexion–extension, lateral bending, and axial rotation in the three-dimens https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-g005.jpg Figure 5. Comparison of the overall displacement of different models under various loading condition https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-t002.jpg Table 2. Overall displacement values (mm) and percentage changes versus the normal model under vario https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-g006.jpg Figure 6. (A) Comparison of the maximum Mises stresses in the intervertebral discs of different mode https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-t003.jpg Table 3. Maximum mises stress values of intervertebral discs (MPa) and percentage changes versus the https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-t004.jpg Table 4. Maximum mises stress values of left sacroiliac joint (MPa) and percentage changes versus th https://www.frontiersin.org/files/Articles/1700758/fbioe-13-1700758-HTML/image_m/fbioe-13-1700758-t005.jpg Table 5. Maximum mises stress values of right sacroiliac joint (MPa) and percentage changes versus t https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1737196/full https://www.frontiersin.org/files/Articles/1737196/fpubh-14-1737196-HTML/image_m/fpubh-14-1737196-g001.jpg Figure 1. Relationship between SES, EE, EM, and MVPA. SES, family socioeconomic status; EE, exercise https://www.frontiersin.org/files/Articles/1737196/fpubh-14-1737196-HTML/image_m/fpubh-14-1737196-t001.jpg Table 1. Demographics. https://www.frontiersin.org/files/Articles/1737196/fpubh-14-1737196-HTML/image_m/fpubh-14-1737196-t002.jpg Table 2. Correlation between variables. https://www.frontiersin.org/files/Articles/1737196/fpubh-14-1737196-HTML/image_m/fpubh-14-1737196-t003.jpg Table 3. Regression results of the chain mediation model. https://www.frontiersin.org/files/Articles/1737196/fpubh-14-1737196-HTML/image_m/fpubh-14-1737196-t004.jpg Table 4. Standardized direct and indirect pathways. https://www.frontiersin.org/files/Articles/1737196/fpubh-14-1737196-HTML/image_m/fpubh-14-1737196-g002.jpg Figure 2. Chain mediation effect. SES, family socioeconomic status; EE, exercise environment; EM, ex https://www.frontiersin.org/files/Articles/1737196/fpubh-14-1737196-HTML/image_m/fpubh-14-1737196-t005.jpg Table 5. Stratified analysis. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1720648/full https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-g001.jpg Figure 1. Theoretical framework. https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-t001.jpg Table 1. Results of confirmatory factor analysis. https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-t002.jpg Table 2. Descriptive statistics and correlations. https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-t003.jpg Table 3. Testing for the mediation effect of anti-frustration ability. https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-g002.jpg Figure 2. AFA as the mediator between AM and CT. AFA, Anti-Frustration Ability; AM, Achievement Moti https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-t004.jpg Table 4. Mediation model effect. https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-t005.jpg Table 5. Testing for the moderating effect of student competence. https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-t006.jpg Table 6. The moderating effects at different levels of student competence. https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-g003.jpg Figure 3. SC as the moderator for the AC-AFA. AFA, anti-frustration ability; AC, achievement motivat https://www.frontiersin.org/files/Articles/1720648/fpsyg-16-1720648-HTML/image_m/fpsyg-16-1720648-t007.jpg Table 7. Conditional indirect effects. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1726761/full https://www.frontiersin.org/files/Articles/1726761/fpsyg-16-1726761-HTML/image_m/fpsyg-16-1726761-t001.jpg Table 1. Demographic differences in physical activity, resilience, and depression. https://www.frontiersin.org/files/Articles/1726761/fpsyg-16-1726761-HTML/image_m/fpsyg-16-1726761-t002.jpg Table 2. Correlation coefficients of physical activity, resilience, and depression. https://www.frontiersin.org/files/Articles/1726761/fpsyg-16-1726761-HTML/image_m/fpsyg-16-1726761-t003.jpg Table 3. Linear regression analysis of physical activity and resilience on depression. https://www.frontiersin.org/files/Articles/1726761/fpsyg-16-1726761-HTML/image_m/fpsyg-16-1726761-g001.jpg Figure 1. The mediating model diagram of resilience between physical activity and depression. ** p < https://www.frontiersin.org/files/Articles/1726761/fpsyg-16-1726761-HTML/image_m/fpsyg-16-1726761-t004.jpg Table 4. Decomposition of the path relationship of physical activity and depression. https://www.frontiersin.org/files/Articles/1726761/fpsyg-16-1726761-HTML/image_m/fpsyg-16-1726761-t005.jpg Table 5. Decomposition of the path relationship of physical activity and depression in male college https://www.frontiersin.org/files/Articles/1726761/fpsyg-16-1726761-HTML/image_m/fpsyg-16-1726761-g002.jpg Figure 2. The mediating model of resilience between physical activity and depression in male college https://www.frontiersin.org/files/Articles/1726761/fpsyg-16-1726761-HTML/image_m/fpsyg-16-1726761-t006.jpg Table 6. Decomposition of the path relationship of physical activity and depression in female colleg https://www.frontiersin.org/files/Articles/1726761/fpsyg-16-1726761-HTML/image_m/fpsyg-16-1726761-g003.jpg Figure 3. The mediating model of resilience between physical activity and depression in female colle https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1696673/full https://www.frontiersin.org/files/Articles/1696673/fnins-19-1696673-HTML/image_m/fnins-19-1696673-t001.jpg Table 1. Major molecular pathways by which exercise promotes Aβ clearance. https://www.frontiersin.org/files/Articles/1696673/fnins-19-1696673-HTML/image_m/fnins-19-1696673-g001.jpg Figure 1. The effect of exercise on melatonin at different time periods. https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2026.1772887/full https://www.frontiersin.org/files/Articles/1772887/fpain-07-1772887-HTML/image_m/fpain-07-1772887-g001.jpg Figure 1. Participant flowchart. https://www.frontiersin.org/files/Articles/1772887/fpain-07-1772887-HTML/image_m/fpain-07-1772887-t001.jpg Table 1. Participants’ characteristics. https://www.frontiersin.org/files/Articles/1772887/fpain-07-1772887-HTML/image_m/fpain-07-1772887-g002.jpg Figure 2. Box plot with scatter of the CoVAS mean score of pain rate before and after cold pressor t https://www.frontiersin.org/files/Articles/1772887/fpain-07-1772887-HTML/image_m/fpain-07-1772887-g003.jpg Figure 3. Box plot with scatter of the corticospinal excitability measurements for the anterior delt https://www.frontiersin.org/files/Articles/1772887/fpain-07-1772887-HTML/image_m/fpain-07-1772887-g004.jpg Figure 4. Correlation between CPM responses and plateau of IO curve for the anterior deltoid muscle. https://www.frontiersin.org/files/Articles/1772887/fpain-07-1772887-HTML/image_m/fpain-07-1772887-g005.jpg Figure 5. Correlation between CPM responses (delta score) and pain-induced changes in IO slopes for https://www.frontiersin.org/files/Articles/1772887/fpain-07-1772887-HTML/image_m/fpain-07-1772887-t002.jpg Table 2. Comparison of spearman correlations between CPM responses and corticospinal excitability am https://www.frontiersin.org/files/Articles/1772887/fpain-07-1772887-HTML/image_m/fpain-07-1772887-t003.jpg Table 3. Comparison of spearman correlations between CPM responses and corticospinal excitability am https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1776069/full https://www.frontiersin.org/files/Articles/1776069/fnagi-18-1776069-HTML/image_m/fnagi-18-1776069-g001.jpg Figure 1. CONSORT diagram showing the number of patients in the treatment groups. https://www.frontiersin.org/files/Articles/1776069/fnagi-18-1776069-HTML/image_m/fnagi-18-1776069-t001.jpg Table 1. Demographic characteristics of participants. https://www.frontiersin.org/files/Articles/1776069/fnagi-18-1776069-HTML/image_m/fnagi-18-1776069-t002.jpg Table 2. Comparison of clinical outcomes between two study groups. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1672010/full https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-t001.jpg Table 1. Key search words and synonyms used for each search field. https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-g001.jpg Figure 1. PRISMA flow diagram illustrating the study selection process at different stages. https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-g002.jpg Figure 2. (A) Risk of bias graph. (B) Risk of bias summary. https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-g003.jpg Figure 3. (A) Meta-analysis of the effect of exercise on pain scale in CNSLBP patients. (B) Meta-ana https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-g004.jpg Figure 4. Meta-analysis of the effects of different exercise types on pain scales in CNSLBP patients https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-t002.jpg Table 2. Meta-regression: Core resistance Training Parameters and Pain Scale Improvements. https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-t003.jpg Table 3. Meta-regression: Core strength training parameters and pain scale improvements. https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-t004.jpg Table 4. Meta-regression: Pilates training parameters and pain scale improvements. https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-g005.jpg Figure 5. Meta-analysis of the effect of exercise on ODI in CNSLBP patients. https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-g006.jpg Figure 6. Meta-analysis of the effect of exercise on RMDQ in CNSLBP patients. https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-g007.jpg Figure 7. Meta-analysis of the effect of exercise on ODI in CNSLBP patients (subgroup analysis). https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-g008.jpg Figure 8. Meta-analysis of the effect of exercise on two dimensions of SF-36 in CNSLBP patients. https://www.frontiersin.org/files/Articles/1672010/fphys-16-1672010-HTML/image_m/fphys-16-1672010-g009.jpg Figure 9. Mechanisms of three core-based therapies in chronic non-specific low back pain relief. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1758708/full https://www.frontiersin.org/files/Articles/1758708/fmed-13-1758708-HTML/image_m/fmed-13-1758708-t001.jpg Table 1. Key regulatory contrasts between US and EU approaches to lifecycle governance of adaptive m https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1748293/full https://www.frontiersin.org/files/Articles/1748293/fpsyt-17-1748293-HTML/image_m/fpsyt-17-1748293-g001.jpg Figure 1. Flow diagram of the searching and screening. https://www.frontiersin.org/files/Articles/1748293/fpsyt-17-1748293-HTML/image_m/fpsyt-17-1748293-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1748293/fpsyt-17-1748293-HTML/image_m/fpsyt-17-1748293-g002.jpg Figure 2. Forest plot of incidence of insomnia in patients with coronary heart disease. https://www.frontiersin.org/files/Articles/1748293/fpsyt-17-1748293-HTML/image_m/fpsyt-17-1748293-t002.jpg Table 2. Subgroup analyses of the pooled prevalence of insomnia in patients with coronary heart dise https://www.frontiersin.org/files/Articles/1748293/fpsyt-17-1748293-HTML/image_m/fpsyt-17-1748293-t003.jpg Table 3. Pooled risk factors of insomnia in patients with coronary heart disease. https://www.frontiersin.org/files/Articles/1748293/fpsyt-17-1748293-HTML/image_m/fpsyt-17-1748293-g003.jpg Figure 3. Sensitivity analysis of the pooled prevalence of insomnia in patients with coronary heart https://www.frontiersin.org/files/Articles/1748293/fpsyt-17-1748293-HTML/image_m/fpsyt-17-1748293-t004.jpg Table 4. Sensitivity analysis of risk factors for insomnia in patients with coronary heart disease. https://www.frontiersin.org/files/Articles/1748293/fpsyt-17-1748293-HTML/image_m/fpsyt-17-1748293-g004.jpg Figure 4. Publication bias in the prevalence of insomnia in patients with coronary heart disease. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1811373/full https://www.frontiersin.org/files/Articles/1811373/fphar-17-1811373-HTML/image_m/fphar-17-1811373-g001.jpg Figure 1. Dexmedetomidine attenuates Aβ1-42-induced apoptosis in neuronal cells. (A) Representative https://www.frontiersin.org/files/Articles/1811373/fphar-17-1811373-HTML/image_m/fphar-17-1811373-g002.jpg Figure 2. Dex suppresses Aβ1-42-triggered ROS generation and modulates the XIAP-MDM2-p53 axis. (A) R https://www.frontiersin.org/files/Articles/1811373/fphar-17-1811373-HTML/image_m/fphar-17-1811373-g003.jpg Figure 3. Pharmacological blockade with yohimbine abolishes the anti-oxidative and anti-apoptotic ef https://www.frontiersin.org/files/Articles/1811373/fphar-17-1811373-HTML/image_m/fphar-17-1811373-g004.jpg Figure 4. Knockdown of XIAP abolishes the anti-oxidative and anti-apoptotic effects of Dexmedetomidi https://www.frontiersin.org/files/Articles/1811373/fphar-17-1811373-HTML/image_m/fphar-17-1811373-g005.jpg Figure 5. Dexmedetomidine improves spatial learning and memory in 5×FAD mice. (A) Representative swi https://www.frontiersin.org/files/Articles/1811373/fphar-17-1811373-HTML/image_m/fphar-17-1811373-g006.jpg Figure 6. Dex reduces amyloid pathology and restores XIAP expression in the cortex and hippocampus o https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1596976/full https://www.frontiersin.org/files/Articles/1596976/fendo-16-1596976-HTML/image_m/fendo-16-1596976-g001.jpg Figure 1. Flow diagram of patient selection and inclusion in the study. Abbreviations: GH, growth ho https://www.frontiersin.org/files/Articles/1596976/fendo-16-1596976-HTML/image_m/fendo-16-1596976-t001.jpg Table 1. Baseline anthropometric and parental characteristics of the study groups. https://www.frontiersin.org/files/Articles/1596976/fendo-16-1596976-HTML/image_m/fendo-16-1596976-t002.jpg Table 2. Baseline IGF-1 and IGFBP-3 levels and z-scores. https://www.frontiersin.org/files/Articles/1596976/fendo-16-1596976-HTML/image_m/fendo-16-1596976-t003.jpg Table 3. Longitudinal changes in height z-score across study groups. https://www.frontiersin.org/files/Articles/1596976/fendo-16-1596976-HTML/image_m/fendo-16-1596976-t004.jpg Table 4. Growth velocity, GH dose, and pubertal status before and during rhGH treatment. https://www.frontiersin.org/files/Articles/1596976/fendo-16-1596976-HTML/image_m/fendo-16-1596976-g002.jpg Figure 2. Longitudinal changes in height z-scores by study group. Abbreviations are as in Table 1. https://www.frontiersin.org/files/Articles/1596976/fendo-16-1596976-HTML/image_m/fendo-16-1596976-t005.jpg Table 5. Annual and cumulative Δ height z-score. https://www.frontiersin.org/files/Articles/1596976/fendo-16-1596976-HTML/image_m/fendo-16-1596976-g003.jpg Figure 3. Annual and cumulative changes in height z-scores by group. Abbreviations are as in Table 1 https://www.frontiersin.org/files/Articles/1596976/fendo-16-1596976-HTML/image_m/fendo-16-1596976-t006.jpg Table 6. Comparison of target and observed height z-scores at baseline, year 3, and final height. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1757820/full https://www.frontiersin.org/files/Articles/1757820/fsufs-10-1757820-HTML/image_m/fsufs-10-1757820-g001.jpg Figure 1. The impact of political identity on chemical fertilizer substitution. https://www.frontiersin.org/files/Articles/1757820/fsufs-10-1757820-HTML/image_m/fsufs-10-1757820-t001.jpg Table 1. Definition of variables and results of descriptive statistics. https://www.frontiersin.org/files/Articles/1757820/fsufs-10-1757820-HTML/image_m/fsufs-10-1757820-t002.jpg Table 2. The results of the benchmark regression. https://www.frontiersin.org/files/Articles/1757820/fsufs-10-1757820-HTML/image_m/fsufs-10-1757820-t003.jpg Table 3. Heterogeneity test. https://www.frontiersin.org/files/Articles/1757820/fsufs-10-1757820-HTML/image_m/fsufs-10-1757820-t004.jpg Table 4. Regression results for external financing constraints. https://www.frontiersin.org/files/Articles/1757820/fsufs-10-1757820-HTML/image_m/fsufs-10-1757820-t005.jpg Table 5. Regression results for ecological perceptions. https://www.frontiersin.org/files/Articles/1757820/fsufs-10-1757820-HTML/image_m/fsufs-10-1757820-t006.jpg Table 6. Bootstrap test results for mediating effects (5,000 resamples). https://www.frontiersin.org/files/Articles/1757820/fsufs-10-1757820-HTML/image_m/fsufs-10-1757820-t007.jpg Table 7. TSLS regression: grassroots governance and organic fertilizer substitution. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1763833/full https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-g005.jpg Graphical Abstract https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-g001.jpg Figure 1. Sources and functions of kaempferol (48). https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t001.jpg Table 1. Composition and nutrient levels of basal diets (air-dried basis). https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t002.jpg Table 2. Composition and nutrient levels of basal diets (air-dried basis). https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-g002.jpg Figure 2. Liver section (40X) (A) Control Group, (B) Experimental Group I, (C) Experimental Group II https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t003.jpg Table 3. Effect of kaempferol on total cholesterol in egg yolks during late laying hens. https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-g003.jpg Figure 3. Effects of kaempferol on serum lipid metabolism in late laying hens. In the same row, diff https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-g004.jpg Figure 4. Effects of kaempferol on antioxidant-related enzymes in serum of late-laying hens. In the https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t004.jpg Table 4. Effects of kaempferol on antioxidant-related indicators in the liver of late-laying hens. https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t005.jpg Table 5. Effects of kaempferol on the expression of antioxidant-related genes in the uterus of late- https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t006.jpg Table 6. Effects of kaempferol on the expression of antioxidant-related genes in the ovaries of late https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t007.jpg Table 7. Effects of kaempferol on cytokines in serum of late-laying hens. https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t008.jpg Table 8. Effect of kaempferol on serum immunoglobulin levels in late-laying hens. https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t009.jpg Table 9. Effects of kaempferol on cytokine expression in the ovaries of late-laying hens. https://www.frontiersin.org/files/Articles/1763833/fvets-13-1763833-HTML/image_m/fvets-13-1763833-t010.jpg Table 10. Effects of kaempferol on cytokine expression in the uterine region of late-laying hens. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1632103/full https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-t001.jpg Table 1. Global burden of CRC attributable to low whole grains intake in 1990 and 2021, with Average https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g001.jpg Figure 1. ASMR (A) and ASDR (B) for CRC attributable to low whole grains intake in 204 countries in https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g002.jpg Figure 2. Temporal trends of CRC ASMR and ASDR attributed to low whole grains intake by joinpoint re https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g003.jpg Figure 3. Comparison of full-age cases ASMR and ASDR of CRC attributable to low whole grains intake https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g004.jpg Figure 4. Comparison of the burden of CRC attributable to low whole grains intake in different age g https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g005.jpg Figure 5. The correlation between ASMR for CRC attributable to low whole grains intake in 21 GBD reg https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g006.jpg Figure 6. The correlation between ASMR for CRC attributable to low whole grains intake in 204 countr https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g007.jpg Figure 7. The correlation between the AAPC of ASMR for CRC attributable to low whole grains intake a https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g008.jpg Figure 8. Frontier analysis results of CRC attributable to low whole grains intake ASMR and ASDR. Fr https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g009.jpg Figure 9. Age-period-cohort model analysis of CRC attributable to low whole grains intake. (A–C) Age https://www.frontiersin.org/files/Articles/1632103/fnut-12-1632103-HTML/image_m/fnut-12-1632103-g010.jpg Figure 10. Global trends in ASRs of CRC attributable to low whole grains intake by gender from 1990 https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1726210/full https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-g001.jpg Figure 1. Schematic illustration of iron metabolism dysregulation in drug-resistant cancer cells lea https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-t001.jpg Table 1. Ferroptosis-sensitive phenotypic features of drug-resistant cancers. https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-t002.jpg Table 2. Summary of currently used ferroptosis-inducing agents across cancer types. https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-g002.jpg Figure 2. Synergistic mechanisms of PD-1/PD-L1 inhibitors combined with ferroptosis inducers (and ch https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-t003.jpg Table 3. Combination therapeutic strategies targeting ferroptosis. https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-g003.jpg Figure 3. Clinical workflow for ferroptosis-targeted therapy in drug-resistant cancer: from initial https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-t004.jpg Table 4. Comparative analysis of ferroptosis-targeted therapeutic strategies. https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-t005.jpg Table 5. Nanoparticle-based delivery systems for ferroptosis inducers. https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-t006.jpg Table 6. Crosstalk between ferroptosis and other regulated cell death pathways in drug-resistant can https://www.frontiersin.org/files/Articles/1726210/fimmu-16-1726210-HTML/image_m/fimmu-16-1726210-t007.jpg Table 7. Impact of tumor microenvironment (TME) on ferroptosis in drug-resistant cancers. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1664627/full https://www.frontiersin.org/files/Articles/1664627/frai-08-1664627-HTML-r1/image_m/frai-08-1664627-g001.jpg Figure 1. The distribution of HF phenotype classification displayed from final study participants wi https://www.frontiersin.org/files/Articles/1664627/frai-08-1664627-HTML-r1/image_m/frai-08-1664627-t001.jpg Table 1. Comparison of characteristics among HF patients with preserved, mildly-reduced, and reduced https://www.frontiersin.org/files/Articles/1664627/frai-08-1664627-HTML-r1/image_m/frai-08-1664627-t002.jpg Table 2. Performance of HF phenotype prediction models using different data configurations. https://www.frontiersin.org/files/Articles/1664627/frai-08-1664627-HTML-r1/image_m/frai-08-1664627-g002.jpg Figure 2. Performance of random forests for HF phenotype classification using different data configu https://www.frontiersin.org/files/Articles/1664627/frai-08-1664627-HTML-r1/image_m/frai-08-1664627-t003.jpg Table 3. Performance of HF phenotype prediction models with reduced features using different data co https://www.frontiersin.org/files/Articles/1664627/frai-08-1664627-HTML-r1/image_m/frai-08-1664627-t004.jpg Table 4. Performance of HF phenotype prediction models with varying feature quantities using combine https://www.frontiersin.org/files/Articles/1664627/frai-08-1664627-HTML-r1/image_m/frai-08-1664627-g003.jpg Figure 3. Performance of random forests for HF phenotype classification with varying feature quantit https://www.frontiersin.org/files/Articles/1664627/frai-08-1664627-HTML-r1/image_m/frai-08-1664627-g004.jpg Figure 4. SHAP (SHapley Additive exPlanations) value distributions for top features in predicting HF https://www.frontiersin.org/files/Articles/1664627/frai-08-1664627-HTML-r1/image_m/frai-08-1664627-g005.jpg Figure 5. Mean SHAP (SHapley Additive exPlanations) values for top features across HF phenotype. Thi https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1776218/full https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-g001.jpg Figure 1. Gini coefficient for per capita disposable income among residents in China. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-g002.jpg Figure 2. Policy levers for sustainable transformation: an overall framework for assessing the syner https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t001.jpg Table 1. Green technology empowerment achievements in shandong province. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t002.jpg Table 2. An evaluation index system for digital inclusive finance using the entropy method. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t003.jpg Table 3. An index system for evaluating the efficiency of public resource allocation using the entro https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-g003.jpg Figure 3. Scatter plots of fitted curves across 32 provinces. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t004.jpg Table 4. Two-way fixed effects panel regression model data analysis. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t005.jpg Table 5. Robustness test. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t006.jpg Table 6. Since 2012, 32 provinces have jointly pursued common prosperity and ecological environmenta https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t007.jpg Table 7. Spatial econometric model specification and selection tests. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t008.jpg Table 8. Direct effects, indirect effects and total effects estimated by the spatial Durbin model. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t009.jpg Table 9. Robustness tests with alternative spatial weight matrices. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t010.jpg Table 10. Testing the mediating effect of digital technology (GTI). https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t011.jpg Table 11. Testing the mediating effects of digital financial inclusion (DFI). https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t012.jpg Table 12. Testing the mediating effect of digital governance level (DGL). https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t013.jpg Table 13. H1-5 bootstrap indirect effects. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t014.jpg Table 14. Relative mediation contribution across channels. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t015.jpg Table 15. Interaction effects among mediating channels. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t016.jpg Table 16. Smallholder inclusion and cooperative-based heterogeneity. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t017.jpg Table 17. Construction of the multidimensional common prosperity index. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t018.jpg Table 18. Regional variability test. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t019.jpg Table 19. Industry heterogeneity test. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t020.jpg Table 20. Heterogeneous effects of DVS across agri-food subsectors. https://www.frontiersin.org/files/Articles/1776218/fsufs-10-1776218-HTML/image_m/fsufs-10-1776218-t021.jpg Table 21. Testing for differences in enterprise types. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1774733/full https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g001.jpg Figure 1. Spatial distribution of the three major functional zones in China's major grain-producing https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-t001.jpg Table 1. Characteristics of the three functional zones comprising China's 13 major grain-producing p https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-t002.jpg Table 2. Evaluation index system for agricultural green development in major grain-producing areas. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-t003.jpg Table 3. Carbon emission coefficients and data sources for major agricultural inputs. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-t004.jpg Table 4. Weights of criteria and indicator layers. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g002.jpg Figure 2. Temporal evolution of agricultural green development in China's major grain-producing area https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g003.jpg Figure 3. Temporal evolution of each dimension of agricultural green development. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g004.jpg Figure 4. Temporal evolution of agricultural green development in each region (2006–2022). https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g005.jpg Figure 5. Spatiotemporal evolution of agricultural green development levels in China's major grain-p https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g006.jpg Figure 6. Temporal evolution of the overall and intra-regional Gini coefficients of agricultural gre https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g007.jpg Figure 7. Changes in inter-regional differences in agricultural green development level. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g008.jpg Figure 8. Sources of differences in agricultural green development level. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g009.jpg Figure 9. Temporal evolution of agricultural green development in each province of major grain-produ https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-t005.jpg Table 5. Cluster analysis of agricultural green development in major grain-producing areas. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-t006.jpg Table 6. Provincial rankings of agricultural green development in major grain-producing areas. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-t007.jpg Table 7. Rankings of provincial scores in each dimension of agricultural green development in 2022. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g010.jpg Figure 10. Average obstacle degree of each criterion layer in agricultural green development. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-t008.jpg Table 8. Obstacle degree of criterion layer (2006–2022; unit: %). https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-t009.jpg Table 9. Obstacle degree of indicator layer (2006–2022; unit: %). https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g011.jpg Figure 11. Obstacle degree of resource conservation indicators. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g012.jpg Figure 12. Obstacle degree of environmentally friendliness indicators. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g013.jpg Figure 13. Obstacle degree of ecological conservation indicators. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g014.jpg Figure 14. Obstacle degree of supply security indicators. https://www.frontiersin.org/files/Articles/1774733/fsufs-10-1774733-HTML/image_m/fsufs-10-1774733-g015.jpg Figure 15. Obstacle degree of economic growth indicators. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1773740/full https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-g001.jpg Figure 1. Trend of TFP. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-t001.jpg Table 1. Results of baseline regression. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-g002.jpg Figure 2. Parallel trend test. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-t002.jpg Table 2. Initial characteristic difference test. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-t003.jpg Table 3. PSM-DID. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-t004.jpg Table 4. BD-DID. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-t005.jpg Table 5. Add fixed effects. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-t006.jpg Table 6. Change dependent variable. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-t007.jpg Table 7. Sample sensitivity tests. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-g003.jpg Figure 3. Placebo test. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-t008.jpg Table 8. Mechanism analysis. https://www.frontiersin.org/files/Articles/1773740/fsufs-10-1773740-HTML/image_m/fsufs-10-1773740-t009.jpg Table 9. Heterogeneity analysis. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1762008/full https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-g001.jpg Figure 1. Typical stylized facts on China’s food security and agricultural technology innovation. (a https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-g002.jpg Figure 2. The Moran scatterplot of per-capita agricultural patent applications in 2021 based on a ge https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-g003.jpg Figure 3. The achievements of the “Broadband China” strategy. (a) Changes in the number of urban and https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t001.jpg Table 1. The impact of the “Broadband China” pilot policy on the level of digital infrastructure con https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-g004.jpg Figure 4. The spatial distribution of “Broadband China” pilot cities and their impact on agricultura https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-g005.jpg Figure 5. Mechanism of digital infrastructure in shaping agricultural technological innovation. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t002.jpg Table 2. Definitions and descriptive statistics of main variables. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t003.jpg Table 3. The impact of digital infrastructure construction on agricultural technology innovation: ba https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-g006.jpg Figure 6. Pre-treatment parallel trend test. (a) Test results based on the two-way fixed effects mod https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t004.jpg Table 4. The test for the relative exogeneity of treatment group selection. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t005.jpg Table 5. The validity test of identification strategy. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-g007.jpg Figure 7. The placebo test. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t006.jpg Table 6. Robustness test: Instrumental variable regression. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t007.jpg Table 7. Robustness test: adjusting the measurement of variables. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t008.jpg Table 8. Robustness test: adjusting the sample selection scope. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t009.jpg Table 9. Mechanism analysis. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t010.jpg Table 10. Excluding other possible mechanisms. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t011.jpg Table 11. Heterogeneity analysis I: heterogeneity in resource endowments. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t012.jpg Table 12. Heterogeneity analysis II: heterogeneity in entrepreneurial environment. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t013.jpg Table 13. Heterogeneity analysis III: heterogeneity in legal environment. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t014.jpg Table 14. Spatial spillover effects of policy shocks. https://www.frontiersin.org/files/Articles/1762008/fsufs-10-1762008-HTML/image_m/fsufs-10-1762008-t015.jpg Table 15. Downstream effects of agricultural technology innovation. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1747859/full https://www.frontiersin.org/files/Articles/1747859/fsufs-10-1747859-HTML/image_m/fsufs-10-1747859-g001.jpg Figure 1. Theoretical conceptual model. https://www.frontiersin.org/files/Articles/1747859/fsufs-10-1747859-HTML/image_m/fsufs-10-1747859-t001.jpg Table 1. Sample demographic characteristics. https://www.frontiersin.org/files/Articles/1747859/fsufs-10-1747859-HTML/image_m/fsufs-10-1747859-t002.jpg Table 2. Structural model fit indices. https://www.frontiersin.org/files/Articles/1747859/fsufs-10-1747859-HTML/image_m/fsufs-10-1747859-t003.jpg Table 3. Hypothesis testing results summary. https://www.frontiersin.org/files/Articles/1747859/fsufs-10-1747859-HTML/image_m/fsufs-10-1747859-t004.jpg Table 4. Mediation effect testing results. https://www.frontiersin.org/files/Articles/1747859/fsufs-10-1747859-HTML/image_m/fsufs-10-1747859-g002.jpg Figure 2. Final validated theoretical model. ***p < 0.001, **p < 0.01, *p < 0.05. Standardized path https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1714007/full https://www.frontiersin.org/files/Articles/1714007/fsurg-12-1714007-HTML/image_m/fsurg-12-1714007-t001.jpg Table 1. Preoperative characteristics of the patients. https://www.frontiersin.org/files/Articles/1714007/fsurg-12-1714007-HTML/image_m/fsurg-12-1714007-g001.jpg Figure 1. Intraoperative image of bentall procedure with carboSeal valsalva™ composite conduits. https://www.frontiersin.org/files/Articles/1714007/fsurg-12-1714007-HTML/image_m/fsurg-12-1714007-t002.jpg Table 2. Perioperative characteristics. https://www.frontiersin.org/files/Articles/1714007/fsurg-12-1714007-HTML/image_m/fsurg-12-1714007-t003.jpg Table 3. Early and late results. https://www.frontiersin.org/files/Articles/1714007/fsurg-12-1714007-HTML/image_m/fsurg-12-1714007-g002.jpg Figure 2. The results of the kaplan–meier analysis regarding freedom from all-causes death at 5 and https://www.frontiersin.org/files/Articles/1714007/fsurg-12-1714007-HTML/image_m/fsurg-12-1714007-g003.jpg Figure 3. The kaplan–meier curves showed event-free survival at 5 and 12 years 98% and 82%, respecti https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1748821/full https://www.frontiersin.org/files/Articles/1748821/fmolb-13-1748821-HTML-r1/image_m/fmolb-13-1748821-g001.jpg Figure 1. Transcriptomic profiling identifies reproducible DEGs in OSCC. (A) UMAP clustering of GSE3 https://www.frontiersin.org/files/Articles/1748821/fmolb-13-1748821-HTML-r1/image_m/fmolb-13-1748821-g002.jpg Figure 2. Network-based hub gene screening and expression validation in OSCC. (A) Top 10 hub candida https://www.frontiersin.org/files/Articles/1748821/fmolb-13-1748821-HTML-r1/image_m/fmolb-13-1748821-g003.jpg Figure 3. Validation and characterization of hub genes in TCGA cohort. (A–C) mRNA expression of CCNA https://www.frontiersin.org/files/Articles/1748821/fmolb-13-1748821-HTML-r1/image_m/fmolb-13-1748821-g004.jpg Figure 4. Prognostic nomogram and diagnostic performance of CCNA2, CD44 and STAT1. (A) Nomogram mode https://www.frontiersin.org/files/Articles/1748821/fmolb-13-1748821-HTML-r1/image_m/fmolb-13-1748821-g005.jpg Figure 5. Experimental validation of CCNA2, CD44 and STAT1 expression in OSCC tissues and cell lines https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1728196/full https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-g001.jpg Figure 1. Sampling locations of the contemporary (n=69) and historical (n=69) period. https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-g002.jpg Figure 2. Values of (A) the Menhinick Index across sampling periods (historical period: 1990–2004 an https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-t001.jpg Table 1. Stomach contents of common dolphins stranded around the Irish coastline from 2017 – 2019 (n https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-t002.jpg Table 2. Stomach contents of common dolphins stranded around the Irish coastline from 1990 – 2004 (n https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-g003.jpg Figure 3. Costello diagram with prey-specific abundance plotted against the frequency of occurrence https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-g004.jpg Figure 4. Full model (with samples from 1990–2004 and 2017-2019, optimal model based on lowest AIC) https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-g005.jpg Figure 5. Sub-model (with samples from 2017-2019) predictions of diet composition by number across ( https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-g006.jpg Figure 6. TROPH values across (A) seasons, (B) total body lengths and (C) the interaction of sex and https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-t003.jpg Table 3. Daily basal metabolic rate (BMR, kJ d-1), average daily metabolic requirements (ADMR, kJ d- https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-g007.jpg Figure 7. Daily rations (R) across (A) nutritional status, (B) sex, (C) sexual maturity status and ( https://www.frontiersin.org/files/Articles/1728196/fmars-13-1728196-HTML-r1/image_m/fmars-13-1728196-g008.jpg Figure 8. Gross calorific stomach contents across COD of common dolphins. The number of observations https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1768773/full https://www.frontiersin.org/files/Articles/1768773/fsufs-10-1768773-HTML/image_m/fsufs-10-1768773-t001.jpg Table 1. Development of NAOs in rural China (village level). https://www.frontiersin.org/files/Articles/1768773/fsufs-10-1768773-HTML/image_m/fsufs-10-1768773-t002.jpg Table 2. Features of land rental transactions. https://www.frontiersin.org/files/Articles/1768773/fsufs-10-1768773-HTML/image_m/fsufs-10-1768773-t003.jpg Table 3. Variable definitions and summary statistics. https://www.frontiersin.org/files/Articles/1768773/fsufs-10-1768773-HTML/image_m/fsufs-10-1768773-t004.jpg Table 4. Baseline regression results (Heckman two-step model). https://www.frontiersin.org/files/Articles/1768773/fsufs-10-1768773-HTML/image_m/fsufs-10-1768773-t005.jpg Table 5. Robustness checks: policy controls and alternative measure. https://www.frontiersin.org/files/Articles/1768773/fsufs-10-1768773-HTML/image_m/fsufs-10-1768773-t006.jpg Table 6. Addressing endogeneity (entropy balancing & IV-Heckman). https://www.frontiersin.org/files/Articles/1768773/fsufs-10-1768773-HTML/image_m/fsufs-10-1768773-t007.jpg Table 7. Mechanism analysis: lessee substitution and spillovers. https://www.frontiersin.org/files/Articles/1768773/fsufs-10-1768773-HTML/image_m/fsufs-10-1768773-t008.jpg Table 8. Heterogeneity by clan strength. https://www.frontiersin.org/files/Articles/1768773/fsufs-10-1768773-HTML/image_m/fsufs-10-1768773-t009.jpg Table A1. Results of entropy balancing. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1799063/full https://www.frontiersin.org/files/Articles/1799063/fpubh-14-1799063-HTML/image_m/fpubh-14-1799063-g001.jpg Figure 1. Study design flowchart. https://www.frontiersin.org/files/Articles/1799063/fpubh-14-1799063-HTML/image_m/fpubh-14-1799063-t001.jpg Table 1. Participant characteristics in the intervention and control groups. https://www.frontiersin.org/files/Articles/1799063/fpubh-14-1799063-HTML/image_m/fpubh-14-1799063-t002.jpg Table 2. Differences in CRS outcomes between control and intervention groups pre-intervention. https://www.frontiersin.org/files/Articles/1799063/fpubh-14-1799063-HTML/image_m/fpubh-14-1799063-t003.jpg Table 3. Differences in CRS outcomes between control and intervention groups post-intervention. https://www.frontiersin.org/files/Articles/1799063/fpubh-14-1799063-HTML/image_m/fpubh-14-1799063-t004.jpg Table 4. Logistic regression results for factors influencing CRS ownership rates. https://www.frontiersin.org/files/Articles/1799063/fpubh-14-1799063-HTML/image_m/fpubh-14-1799063-t005.jpg Table 5. Logistic regression results for factors influencing the rates of consistent CRS use. https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2025.1695375/full https://www.frontiersin.org/files/Articles/1695375/fnbeh-19-1695375-HTML/image_m/fnbeh-19-1695375-g001.jpg Figure 1. Adolescent mice show lower social context preference. (A) A schematic representation of th https://www.frontiersin.org/files/Articles/1695375/fnbeh-19-1695375-HTML/image_m/fnbeh-19-1695375-g002.jpg Figure 2. Age has no significant effect on cocaine conditioned place preference. (A) A schematic rep https://www.frontiersin.org/files/Articles/1695375/fnbeh-19-1695375-HTML/image_m/fnbeh-19-1695375-g003.jpg Figure 3. Palatable food preference is lower in adolescent mice. (A) A schematic representation of t https://www.frontiersin.org/files/Articles/1695375/fnbeh-19-1695375-HTML/image_m/fnbeh-19-1695375-g004.jpg Figure 4. Adolescent mice show decreased reward place preference compared to adults. (A) Comparison https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1691666/full https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-t001.jpg Table 1. Forms of fuzzy numbers. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-g001.jpg Figure 1. Urban community resilience for PHEs framework curve. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-t002.jpg Table 2. Framework of influencing factors for urban community resilience for PHEs. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-t003.jpg Table 3. DEMATEL analysis of social influencing factors of urban community resilience for PHEs. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-g002.jpg Figure 2. Critical impact path between variables of resilience in social dimension. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-g003.jpg Figure 3. BN structure of urban community resilience for PHEs. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-t004.jpg Table 4. Community basic information. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-t005.jpg Table 5. Prior probabilities of root nodes in the BN of urban community resilience for PHEs. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-t006.jpg Table 6. Conditional probability table of the non-root node “SocR5.” https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-g004.jpg Figure 4. Reverse diagnosis of BN for urban community resilience for PHEs. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-g005.jpg Figure 5. Sensitivity analysis of the parent node of urban community resilience for PHEs. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-g006.jpg Figure 6. The strength level of social resilience sensitive sources. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-t007.jpg Table 7. State transition probability matrix of DBN. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-t008.jpg Table 8. Design simulation scenarios for DBN. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-g007.jpg Figure 7. Dynamic inference simulation of urban community resilience for PHEs. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-g008.jpg Figure 8. Dynamic changes in CI of some influencing factors. https://www.frontiersin.org/files/Articles/1691666/fpubh-13-1691666-HTML-r1/image_m/fpubh-13-1691666-g009.jpg Figure 9. Importance analysis results of community epidemic resilience factors. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1724229/full https://www.frontiersin.org/files/Articles/1724229/fpubh-14-1724229-HTML/image_m/fpubh-14-1724229-t001.jpg Table 1. Variable coding for model analyses. https://www.frontiersin.org/files/Articles/1724229/fpubh-14-1724229-HTML/image_m/fpubh-14-1724229-t002.jpg Table 2. Comparison of variable characteristics between different mental workload. https://www.frontiersin.org/files/Articles/1724229/fpubh-14-1724229-HTML/image_m/fpubh-14-1724229-g001.jpg Figure 1. Variable selection process using LASSO regression. (A) LASSO coefficient profiles of the 1 https://www.frontiersin.org/files/Articles/1724229/fpubh-14-1724229-HTML/image_m/fpubh-14-1724229-t003.jpg Table 3. Binary logistic regression analysis of mental workload among nurses. https://www.frontiersin.org/files/Articles/1724229/fpubh-14-1724229-HTML/image_m/fpubh-14-1724229-g002.jpg Figure 2. Nomogram predicting the probability of high mental workload among nurses (training cohort, https://www.frontiersin.org/files/Articles/1724229/fpubh-14-1724229-HTML/image_m/fpubh-14-1724229-g003.jpg Figure 3. ROC curve and calibration curve for the nomogram based on the training cohort and validati https://www.frontiersin.org/files/Articles/1724229/fpubh-14-1724229-HTML/image_m/fpubh-14-1724229-g004.jpg Figure 4. Ten-Fold Cross-Validation performance of the mental workload risk prediction nomogram (n = https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1786178/full https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-g001.jpg Figure 1. Flowchart of the study design and experimental protocol. https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-g002.jpg Figure 2. Schematic diagram of the experimental protocol. The study paradigm consisted of three cons https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-g003.jpg Figure 3. Methodological illustration of ultrasound assessment for diaphragmatic function. (A) B-mod https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-g004.jpg Figure 4. Configuration of fNIRS optodes and spatial registration. (A) 3D cortical projection: The s https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-t001.jpg Table 1. Channel definitions for regions of interest (ROIs). https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-t002.jpg Table 2. Baseline demographic and clinical characteristics of participants. https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-t003.jpg Table 3. Changes in diaphragm excursion (DE) across time points. https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-g005.jpg Figure 5. Temporal dynamic changes in diaphragm excursion (DE) across groups. The line graph display https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-t004.jpg Table 4. Changes in oxy-hemoglobin concentration [HbO] across all ROIs. https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-g006.jpg Figure 6. Cortical activation in the ischemic stroke (IS) group. (A) Violin plots showing the distri https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-g007.jpg Figure 7. Cortical activation in the intracerebral hemorrhage (ICH) group. (A) Violin plots of mean https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-t005.jpg Table 5. Correlation between diaphragm excursion improvement and cortical activation intensity. https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-g008.jpg Figure 8. Cortical activation in the healthy control (HC) group. (A) Violin plots of mean (Δ[HbO]) d https://www.frontiersin.org/files/Articles/1786178/fneur-17-1786178-HTML/image_m/fneur-17-1786178-t006.jpg Table 6. Analysis of temporal dynamics and interaction effects (IS vs. ICH). https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1754952/full https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-g001.jpg Figure 1. Principle of attention mechanism. https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-g002.jpg Figure 2. Temperature retrieval RMSE of five algorithms (yellow, orange, and blue represent the retr https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-g003.jpg Figure 3. Temperature retrieval R2 of five algorithms (yellow, orange, and blue represent the retrie https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-g004.jpg Figure 4. Relative humidity retrieval RMSE of five algorithms (yellow, orange, and blue represent th https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-g005.jpg Figure 5. Relative humidity retrieval R2 of five algorithms (yellow, orange, and blue represent the https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-g006.jpg Figure 6. Temperature retrieval RMSE of WPA-SVM compared with SVM and MLP. https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-g007.jpg Figure 7. Temperature retrieval R² of WPA-SVM compared with SVM and MLP. https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-t001.jpg Table 1. Optimized parameters of SVM at different altitude levels in Haikou Region. https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-g008.jpg Figure 8. RMSE comparison for humidity profile retrieval before and after introducing the attention https://www.frontiersin.org/files/Articles/1754952/fmars-13-1754952-HTML/image_m/fmars-13-1754952-g009.jpg Figure 9. R² comparison for humidity profile retrieval before and after introducing the attention me https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1729222/full https://www.frontiersin.org/files/Articles/1729222/fimmu-16-1729222-HTML/image_m/fimmu-16-1729222-t001.jpg Table 1. Primers were used in this study. https://www.frontiersin.org/files/Articles/1729222/fimmu-16-1729222-HTML/image_m/fimmu-16-1729222-g001.jpg Figure 1. Inhibitory effect of CXCL13 on RANKL-induced osteoclastogenesis. (A) RAW264.7 cells were e https://www.frontiersin.org/files/Articles/1729222/fimmu-16-1729222-HTML/image_m/fimmu-16-1729222-g002.jpg Figure 2. CXCL13 induces cleaved caspase-3-mediated apoptosis in mature osteoclasts. (A) RAW264.7 ce https://www.frontiersin.org/files/Articles/1729222/fimmu-16-1729222-HTML/image_m/fimmu-16-1729222-g003.jpg Figure 3. CXCL13 suppresses RANKL-induced activation of the MAPK signaling pathway. (A) RAW264.7 cel https://www.frontiersin.org/files/Articles/1729222/fimmu-16-1729222-HTML/image_m/fimmu-16-1729222-g004.jpg Figure 4. CXCL13 suppresses RANKL-induced activation of the NF-κB signaling pathway. (A) RAW264.7 ce https://www.frontiersin.org/files/Articles/1729222/fimmu-16-1729222-HTML/image_m/fimmu-16-1729222-g005.jpg Figure 5. CXCL13 disrupts RANKL-induced p65 translocation to the nucleus. (A, B) Immunofluorescence https://www.frontiersin.org/files/Articles/1729222/fimmu-16-1729222-HTML/image_m/fimmu-16-1729222-g006.jpg Figure 6. Inhibition of CXCR5 did not effectively reverse the inhibitory effect of CXCL13 on RANKL-i https://www.frontiersin.org/files/Articles/1729222/fimmu-16-1729222-HTML/image_m/fimmu-16-1729222-g007.jpg Figure 7. Predicted molecular interaction between CXCL13 and RANK. (A) The virtual screening process https://www.frontiersin.org/files/Articles/1729222/fimmu-16-1729222-HTML/image_m/fimmu-16-1729222-g008.jpg Figure 8. Schematic illustration of signaling pathways involved in CXCL13-inhibited osteoclast forma https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1708106/full https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-g001.jpg Figure 1. The proportion of different startification of laparoscopic surgeries. https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-g002.jpg Figure 2. Flow chart of the study design. https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-t001.jpg Table 1. Co-occurrence counts of top 3 connections in network analysis. https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-g003.jpg Figure 3. Frequency of auricular acupoints for treating gastrointestinal dysfunction after laparosco https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-t002.jpg Table 2. Frequency of auricular acupoints for treating gastrointestinal dysfunction after laparoscop https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-t003.jpg Table 3. Second-order association rules of auricular acupoints for treating gastrointestinal dysfunc https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-t004.jpg Table 4. Third-order association rules of auricular acupoints for treating gastrointestinal dysfunct https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-g004.jpg Figure 4. Cluster analysis of auricular acupoints for treating gastrointestinal dysfunction after la https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-g005.jpg Figure 5. Network analysis of auricular acupoints for treating gastrointestinal dysfunction after la https://www.frontiersin.org/files/Articles/1708106/fmed-13-1708106-HTML/image_m/fmed-13-1708106-t005.jpg Table 5. Fourth-order association rules of auricular acupoints for treating gastrointestinal dysfunc https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1715403/full https://www.frontiersin.org/files/Articles/1715403/fneur-17-1715403-HTML/image_m/fneur-17-1715403-g001.jpg Figure 1. Patient selection process. https://www.frontiersin.org/files/Articles/1715403/fneur-17-1715403-HTML/image_m/fneur-17-1715403-t001.jpg Table 1. General characteristics of the study population (N = 19). https://www.frontiersin.org/files/Articles/1715403/fneur-17-1715403-HTML/image_m/fneur-17-1715403-t002.jpg Table 2. Comparison of the initial characteristics of patients’ response to VNS. https://www.frontiersin.org/files/Articles/1715403/fneur-17-1715403-HTML/image_m/fneur-17-1715403-t003.jpg Table 3. Seizure outcome evaluated by modified Engel and McHugh classification at last follow-up (>1 https://www.frontiersin.org/files/Articles/1715403/fneur-17-1715403-HTML/image_m/fneur-17-1715403-t004.jpg Table 4. ASM and VNS parameters at the last follow-up. https://www.frontiersin.org/journals/hematology/articles/10.3389/frhem.2026.1794624/full https://www.frontiersin.org/files/Articles/1794624/frhem-05-1794624-HTML-r2/image_m/frhem-05-1794624-t001.jpg Table 1. Socio-demographics of the study participants. Note respondents may not have answered every https://www.frontiersin.org/files/Articles/1794624/frhem-05-1794624-HTML-r2/image_m/frhem-05-1794624-g001.jpg Figure 1. Symptoms experienced by people with CLL before diagnosis of the condition. Respondents cou https://www.frontiersin.org/files/Articles/1794624/frhem-05-1794624-HTML-r2/image_m/frhem-05-1794624-t002.jpg Table 2. HM-PRO Part-A score by active monitoring status and knowledge about immunity. https://www.frontiersin.org/journals/conservation-science/articles/10.3389/fcosc.2025.1653827/full https://www.frontiersin.org/files/Articles/1653827/fcosc-06-1653827-HTML/image_m/fcosc-06-1653827-t001.jpg Table 1. Cryopreservation of orchid species. https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2026.1752758/full https://www.frontiersin.org/files/Articles/1752758/frph-08-1752758-HTML/image_m/frph-08-1752758-g001.jpg Figure 1. Global distribution of male infertility. https://www.frontiersin.org/files/Articles/1752758/frph-08-1752758-HTML/image_m/frph-08-1752758-g002.jpg Figure 2. Schematic representation of the testicular immune microenvironment and the blood-testis ba https://www.frontiersin.org/files/Articles/1752758/frph-08-1752758-HTML/image_m/frph-08-1752758-g003.jpg Figure 3. Molecular architecture of the blood-testis barrier between adjacent sertoli cells. https://www.frontiersin.org/files/Articles/1752758/frph-08-1752758-HTML/image_m/frph-08-1752758-g004.jpg Figure 4. Signaling pathways regulating blood-testis barrier dynamics: the interplay between mTORC1 https://www.frontiersin.org/files/Articles/1752758/frph-08-1752758-HTML/image_m/frph-08-1752758-g005.jpg Figure 5. Different diseases leading to male infertility: the underlying mechanisms. https://www.frontiersin.org/files/Articles/1752758/frph-08-1752758-HTML/image_m/frph-08-1752758-t001.jpg Table 1. Immunological mechanisms of male infertility. https://www.frontiersin.org/files/Articles/1752758/frph-08-1752758-HTML/image_m/frph-08-1752758-g006.jpg Figure 6. Types of antioxidants. https://www.frontiersin.org/files/Articles/1752758/frph-08-1752758-HTML/image_m/frph-08-1752758-g007.jpg Figure 7. Integrative schematic of immune pathways in male infertility: from diverse etiologies to c https://www.frontiersin.org/files/Articles/1752758/frph-08-1752758-HTML/image_m/frph-08-1752758-t002.jpg Table 2. Integration of treatment approaches for different diseases. https://www.frontiersin.org/journals/toxicology/articles/10.3389/ftox.2025.1645842/full https://www.frontiersin.org/files/Articles/1645842/ftox-07-1645842-HTML-r2/image_m/ftox-07-1645842-t001.jpg Table 1. Compounds screened in the ReproTracker assay. https://www.frontiersin.org/files/Articles/1645842/ftox-07-1645842-HTML-r2/image_m/ftox-07-1645842-g001.jpg Figure 1. Differentiation of hiPSCs toward neural rosette-like cells. (A) Schematic representation o https://www.frontiersin.org/files/Articles/1645842/ftox-07-1645842-HTML-r2/image_m/ftox-07-1645842-g002.jpg Figure 2. Effect of saccharin, retinoic acid and tacrolimus on the differentiation of hiPSCs into ne https://www.frontiersin.org/files/Articles/1645842/ftox-07-1645842-HTML-r2/image_m/ftox-07-1645842-g003.jpg Figure 3. Effect of reference compounds in trilineage differentiation of the ReproTracker assay. Alt https://www.frontiersin.org/files/Articles/1645842/ftox-07-1645842-HTML-r2/image_m/ftox-07-1645842-g004.jpg Figure 4. Correlation between the lowest observed adverse effect level (LOAEL) of teratogens in Repr https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1719701/full https://www.frontiersin.org/files/Articles/1719701/feduc-11-1719701-HTML/image_m/feduc-11-1719701-t001.jpg Table 1. Characteristics of the participants. https://www.frontiersin.org/files/Articles/1719701/feduc-11-1719701-HTML/image_m/feduc-11-1719701-t002.jpg Table 2. Model fit evaluation criteria. https://www.frontiersin.org/files/Articles/1719701/feduc-11-1719701-HTML/image_m/feduc-11-1719701-t003.jpg Table 3. Aiken's V for the evaluation of the relevance, representativeness, and clarity of the items https://www.frontiersin.org/files/Articles/1719701/feduc-11-1719701-HTML/image_m/feduc-11-1719701-t004.jpg Table 4. Factor loadings of the standardized solution of the exploratory factor analysis. https://www.frontiersin.org/files/Articles/1719701/feduc-11-1719701-HTML/image_m/feduc-11-1719701-t005.jpg Table 5. Factor loadings of the standardized solution of the confirmatory factor analysis for the re https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1763283/full https://www.frontiersin.org/files/Articles/1763283/fneur-17-1763283-HTML/image_m/fneur-17-1763283-g001.jpg Figure 1. PRISMA 2020 flow diagram for study selection. https://www.frontiersin.org/files/Articles/1763283/fneur-17-1763283-HTML/image_m/fneur-17-1763283-g002.jpg Figure 2. The proposed pathophysiological cascade in seropositive Bickerstaff brainstem encephalitis https://www.frontiersin.org/files/Articles/1763283/fneur-17-1763283-HTML/image_m/fneur-17-1763283-g003.jpg Figure 3. The proposed clinico-pathophysiological spectrum of the anti-GQ1b antibody syndrome. This https://www.frontiersin.org/files/Articles/1763283/fneur-17-1763283-HTML/image_m/fneur-17-1763283-t001.jpg Table 1. Clinical and paraclinical differentiation of the anti-GQ1b antibody spectrum phenotypes. https://www.frontiersin.org/files/Articles/1763283/fneur-17-1763283-HTML/image_m/fneur-17-1763283-g004.jpg Figure 4. Proposed diagnostic algorithm for suspected Bickerstaff brainstem encephalitis (BBE). This https://www.frontiersin.org/files/Articles/1763283/fneur-17-1763283-HTML/image_m/fneur-17-1763283-t002.jpg Table 2. Proposed clinical criteria for Bickerstaff Brainstem Encephalitis (BBE) spectrum disorders. https://www.frontiersin.org/files/Articles/1763283/fneur-17-1763283-HTML/image_m/fneur-17-1763283-g005.jpg Figure 5. A proposed risk-stratified treatment algorithm for Bickerstaff brainstem encephalitis (BBE https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1654425/full https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-g001.jpg Figure 1. (A) Forest plot of univariable hazard ratios for overall survival (OS). Points represent H https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-t001.jpg Table 1. Baseline patient and demographic and disease characteristics. https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-t002.jpg Table 2. The dynamics of tumor markers. https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-t003.jpg Table 3. The dynamics of inflammation-related hematological indices. https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-t004.jpg Table 4. Tumor responses. https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-g002.jpg Figure 2. Treatment response and survival analysis. (A) Duration of responses of patients in the ITT https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-t005.jpg Table 5. Treatment related adverse events (TRAE) ≥10%. https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-g003.jpg Figure 3. Association between peripheral blood biomarkers and treatment response. (A, B) The Kaplan– https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-g004.jpg Figure 4. Kaplan–Meier analysis of survival and pretreatment inflammatory markers. OS and PFS based https://www.frontiersin.org/files/Articles/1654425/fimmu-16-1654425-HTML/image_m/fimmu-16-1654425-t006.jpg Table 6. Statistical associations between blood indicators and tumor response. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1748742/full https://www.frontiersin.org/files/Articles/1748742/fmicb-17-1748742-HTML-r1/image_m/fmicb-17-1748742-g001.jpg Figure 1. Integrated precision bacteriophage therapy framework for drug-resistant bacterial infectio https://www.frontiersin.org/files/Articles/1748742/fmicb-17-1748742-HTML-r1/image_m/fmicb-17-1748742-t001.jpg Table 1. Representative bacteriophage therapeutics evaluated against antimicrobial-resistant bacteri https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1778061/full https://www.frontiersin.org/files/Articles/1778061/fcvm-13-1778061-HTML/image_m/fcvm-13-1778061-g001.jpg Figure 1. Panel (A) graph: hemodynamic forces (HDF) in a healthy subject. The red line represents th https://www.frontiersin.org/files/Articles/1778061/fcvm-13-1778061-HTML/image_m/fcvm-13-1778061-g002.jpg Figure 2. The spatial relationship of the cardiac long axes with its short axis. Three views are acq https://www.frontiersin.org/files/Articles/1778061/fcvm-13-1778061-HTML/image_m/fcvm-13-1778061-g003.jpg Figure 3. Panels (A1,A2): slightly skewed long axis lines on basal short axis views. Dashed lines re https://www.frontiersin.org/files/Articles/1778061/fcvm-13-1778061-HTML/image_m/fcvm-13-1778061-t001.jpg Table 1. Main differences between 4D flow and сine CMR. https://www.frontiersin.org/files/Articles/1778061/fcvm-13-1778061-HTML/image_m/fcvm-13-1778061-g004.jpg Figure 4. Flow chart of the steps required for HDF analysis. Images are acquired using non-contrast https://www.frontiersin.org/files/Articles/1778061/fcvm-13-1778061-HTML/image_m/fcvm-13-1778061-t002.jpg Table 2. Studies on HDF derived from cine CMR imaging. https://www.frontiersin.org/files/Articles/1778061/fcvm-13-1778061-HTML/image_m/fcvm-13-1778061-t003.jpg Table 3. Hemodynamic force parameters (modified from Pedrizzetti et al., EHJ-CVI 2025). https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1789919/full https://www.frontiersin.org/files/Articles/1789919/fmed-13-1789919-HTML/image_m/fmed-13-1789919-t001.jpg Table 1. Domain wise mean stress score across all the academic years (N = 311). https://www.frontiersin.org/files/Articles/1789919/fmed-13-1789919-HTML/image_m/fmed-13-1789919-g001.jpg Figure 1. Domain-wise trends in mean stress scores across academic years among MBBS students (N = 31 https://www.frontiersin.org/files/Articles/1789919/fmed-13-1789919-HTML/image_m/fmed-13-1789919-t002.jpg Table 2. Severity grading of stress across different stress domains in the study population (N = 311 https://www.frontiersin.org/files/Articles/1789919/fmed-13-1789919-HTML/image_m/fmed-13-1789919-g002.jpg Figure 2. Stacked distribution of mild, moderate, high, and severe stress across six stress domains https://www.frontiersin.org/files/Articles/1789919/fmed-13-1789919-HTML/image_m/fmed-13-1789919-t003.jpg Table 3. Coping mechanisms (mean score) across academic years. https://www.frontiersin.org/files/Articles/1789919/fmed-13-1789919-HTML/image_m/fmed-13-1789919-g003.jpg Figure 3. Comparison of coping mechanisms and mean stress scores across academic years among MBBS st https://www.frontiersin.org/files/Articles/1789919/fmed-13-1789919-HTML/image_m/fmed-13-1789919-g004.jpg Figure 4. Distribution of participants according to their average coping scores. https://www.frontiersin.org/files/Articles/1789919/fmed-13-1789919-HTML/image_m/fmed-13-1789919-t004.jpg Table 4. Summary of inferential statistical analyses examining stress domains and coping mechanisms https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1817264/full https://www.frontiersin.org/files/Articles/1817264/fmicb-17-1817264-HTML/image_m/fmicb-17-1817264-g001.jpg Figure 1. Illustration of the systematic laboratory workflow of the DNA microarray assay designed to https://www.frontiersin.org/files/Articles/1817264/fmicb-17-1817264-HTML/image_m/fmicb-17-1817264-t001.jpg Table 1. Distribution of clonal complexes and corresponding strain classifications among MRSA isolat https://www.frontiersin.org/files/Articles/1817264/fmicb-17-1817264-HTML/image_m/fmicb-17-1817264-t002.jpg Table 2. Detection of antibiotic resistance genes among MRSA isolated from retail chicken meat in th https://www.frontiersin.org/files/Articles/1817264/fmicb-17-1817264-HTML/image_m/fmicb-17-1817264-t003.jpg Table 3. Detection of virulence and enterotoxin genes among MRSA isolated from retail chicken meat i https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1689745/full https://www.frontiersin.org/files/Articles/1689745/fmed-12-1689745-HTML/image_m/fmed-12-1689745-g001.jpg Figure 1. Timeline of milestones in biomarker discovery for prediction and diagnosis of preeclampsia https://www.frontiersin.org/files/Articles/1689745/fmed-12-1689745-HTML/image_m/fmed-12-1689745-t001.jpg Table 1. Diagnostic criteria for hypertensive pregnancy disorders from various gynecology and obstet https://www.frontiersin.org/files/Articles/1689745/fmed-12-1689745-HTML/image_m/fmed-12-1689745-g002.jpg Figure 2. Antiangiogenic and proangiogenic state and factors in normal gestation and preeclampsia. I https://www.frontiersin.org/files/Articles/1689745/fmed-12-1689745-HTML/image_m/fmed-12-1689745-g003.jpg Figure 3. Overview of PE hallmarks and associated biomarkers, organized according to the two-stage m https://www.frontiersin.org/files/Articles/1689745/fmed-12-1689745-HTML/image_m/fmed-12-1689745-t002.jpg Table 2. GWAS-identified genes associated with preeclampsia. https://www.frontiersin.org/files/Articles/1689745/fmed-12-1689745-HTML/image_m/fmed-12-1689745-t003.jpg Table 3. Key miRNAs associated with the development of preeclampsia. https://www.frontiersin.org/files/Articles/1689745/fmed-12-1689745-HTML/image_m/fmed-12-1689745-t004.jpg Table 4. Proteomic biomarkers linked to preeclampsia. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1739236/full https://www.frontiersin.org/files/Articles/1739236/fcvm-13-1739236-HTML-r1/image_m/fcvm-13-1739236-g001.jpg Figure 1. Effects of patient-derived IgGs on endothelial junctions and cell and nuclear morphology. https://www.frontiersin.org/files/Articles/1739236/fcvm-13-1739236-HTML-r1/image_m/fcvm-13-1739236-g002.jpg Figure 2. Effects of patient-derived IgGs on cytoskeletal organization. Representative immunofluores https://www.frontiersin.org/files/Articles/1739236/fcvm-13-1739236-HTML-r1/image_m/fcvm-13-1739236-g003.jpg Figure 3. Autoantibody-induced endothelial activation (VCAM-1 expression) and mitochondrial dysfunct https://www.frontiersin.org/files/Articles/1739236/fcvm-13-1739236-HTML-r1/image_m/fcvm-13-1739236-g004.jpg Figure 4. Autoantibody-induced redistribution of AT1R in hcMVECs. Representative immunofluorescence https://www.frontiersin.org/files/Articles/1739236/fcvm-13-1739236-HTML-r1/image_m/fcvm-13-1739236-g005.jpg Figure 5. Autoantibody-induced redistribution of ETAR in hcMVECs. Representative immunofluorescence https://www.frontiersin.org/files/Articles/1739236/fcvm-13-1739236-HTML-r1/image_m/fcvm-13-1739236-g006.jpg Figure 6. Effects of patient-derived IgGs on hcMVEC viability and cytotoxicity. Cell viability was a https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1699992/full https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-t001.jpg Table 1. Participants’ demographic characteristics and related clinical COVID-19 status. https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-g001.jpg Figure 1. Effects of COVID-19 severity on the anti-SARS-CoV-2 serum IgG levels in recovered patients https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-g002.jpg Figure 2. Effects of COVID-19 on the B cell compartment. The frequencies of memory and naïve B cells https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-g003.jpg Figure 3. Effects of COVID-19 on the T cell compartment. Frequencies of central memory (Tcm), effect https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-g004.jpg Figure 4. Effects of COVID-19 on the T cell responses to SEB stimulation. The frequencies of CD4 (A) https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-g005.jpg Figure 5. Effects of COVID-19 on the NK cell compartment. Frequencies of the six NK cell subsets in https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-g006.jpg Figure 6. HLA polymorphism analysis. COVID-19 clinical presentation and the distribution of the 267 https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-t002.jpg Table 2. Alleles found exclusively in the severe COVID-19 recovered patients. https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-t003.jpg Table 3. Alleles found exclusively in the non-severe COVID-19 recovered patients. https://www.frontiersin.org/files/Articles/1699992/fimmu-17-1699992-HTML/image_m/fimmu-17-1699992-g007.jpg Figure 7. A schematic summary of the main study findings and conclusions. The antibody and nucleic a https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1733747/full https://www.frontiersin.org/files/Articles/1733747/feduc-11-1733747-HTML-r1/image_m/feduc-11-1733747-t001.jpg Table 1. Descriptive statistics of the estimation sample by major and teacher status. https://www.frontiersin.org/files/Articles/1733747/feduc-11-1733747-HTML-r1/image_m/feduc-11-1733747-t002.jpg Table 2. Logistic regression results: marginal effects on probability of becoming a teacher. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1720309/full https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-g001.jpg Figure 1. Video example of a “tiro y arrastre” competition (https://www.youtube.com/watch?v=QSZ4ib_r https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-t001.jpg Table 1. Behavioral indicators related to equine welfare. https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-t002.jpg Table 2. Health indicators related to equine welfare. https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-t003.jpg Table 3. Distribution (% and total number) of evaluated horses (n = 160) among the different categor https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-t004.jpg Table 4. Results of the questionnaire completed by the owners (n = 67). https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-t005.jpg Table 5. Frequency (%) and total number (n) of behavioral parameters observed in the evaluated equin https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-t006.jpg Table 6. Mean ± standard deviation values temperature (T °C), heart rate (HR bpm) and respiratory ra https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-t007.jpg Table 7. Mean and standard deviation of hematological values in sampled horses (n = 25) and referenc https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-t008.jpg Table 8. Frequency (%) and total number (n) of the animal welfare indicators assessed in the evaluat https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-t009.jpg Table 9. Correlations between aggregated behavioral scores and analyzed health parameters. https://www.frontiersin.org/files/Articles/1720309/fvets-13-1720309-HTML-r5/image_m/fvets-13-1720309-g002.jpg Figure 2. Body contact points with harnesses, headstall, and girth on the draft horse. https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2025.1679445/full https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t001.jpg Table 1. Demographics of respondents. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t002.jpg Table 2. Responses for Image 1. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t003.jpg Table 3. Responses for Image 2. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t004.jpg Table 4. Responses for Image 3. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t005.jpg Table 5. Responses for Image 4. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t006.jpg Table 6. Responses for Image 5. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t007.jpg Table 7. Responses for Image 6. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t008.jpg Table 8. Responses for Image 7. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t009.jpg Table 9. Responses for Image 8. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t010.jpg Table 10. Effect of first responder profession on statement #1. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t011.jpg Table 11. Effect of first responder profession on statement #2. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t012.jpg Table 12. Effect of first responder profession on statement #3. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t013.jpg Table 13. Effect of first responder profession on statement #4. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t014.jpg Table 14. Effect of first responder profession on statement #5. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t015.jpg Table 15. Effect of years’ worked on statement #1. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t016.jpg Table 16. Effect of years’ worked on statement #2. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t017.jpg Table 17. Effect of years’ worked on statement #3. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t018.jpg Table 18. Effect of years’ worked on statement #4. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t019.jpg Table 19. Effect of years’ worked on statement #5. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t020.jpg Table 20. Responses to statements on effective advertising. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t021.jpg Table 21. Rankings for terminology. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t022.jpg Table 22. Effect of first responder profession on terminology responses. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t023.jpg Table 23. Rankings for effective reach. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t024.jpg Table 24. Effect of first responder profession on reach responses. https://www.frontiersin.org/files/Articles/1679445/fcomm-10-1679445-HTML-r3/image_m/fcomm-10-1679445-t025.jpg Table 25. Effect of years’ worked on terminology responses. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1685864/full https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g001.jpg Figure 1. Flow diagram of the study selection process following the preferred reporting items for sy https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-t001.jpg Table 1. Main characteristics of included studies. https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g002.jpg Figure 2. Risk of bias and applicability concerns graph. The horizontal axis shows the percentage (0 https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g003.jpg Figure 3. Risk of bias and applicability concerns summary. The horizontal axis presents the four QUA https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g004.jpg Figure 4. Forest plots of the pooled sensitivity. Each circle represents the point estimate of sensi https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g005.jpg Figure 5. Forest plots of the pooled specificity. Each circle represents the study-specific point es https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g006.jpg Figure 6. Summary receiver operating characteristic (SROC) curve. Horizontal axis (X): 1 − Specifici https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g007.jpg Figure 7. Forest plot of diagnostic odds ratio. Each circle represents the study-specific diagnostic https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g008.jpg Figure 8. Forest plot of negative likelihood ratio. Each circle represents the study-specific negati https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g009.jpg Figure 9. Forest plot of positive likelihood ratio. Each circle represents the study-specific positi https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g010.jpg Figure 10. Forest plot of diagnostic odds ratio(After removing Yuri E.). After excluding this study, https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-g011.jpg Figure 11. Funnel plot for publication bias. The horizontal axis is 1 divided by the square root of https://www.frontiersin.org/files/Articles/1685864/fonc-15-1685864-HTML/image_m/fonc-15-1685864-t002.jpg Table 2. Results of sensitivity analysis. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1797222/full https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-g001.jpg Figure 1. Map of the Emilia Romagna region. In the red square, the area where the samples of Chelon https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-t001.jpg Table 1. Mathematical formulas for each parameter and descriptor calculated in this study. https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-g002.jpg Figure 2. Relationships between sagitta biometric parameters and fish length (TL). Curves and their https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-g003.jpg Figure 3. Relationships between sagitta shape descriptors and fish length (TL). Curves and their res https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-g004.jpg Figure 4. Relationships between sagitta structural parameters and fish length (TL). Curves and their https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-g005.jpg Figure 5. Representative thermogravimetric analysis (TGA) profile of the otolith of a male individua https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-t002.jpg Table 2. Average OM% for the three different sex groups: not determined (ND), males (M), and females https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-g006.jpg Figure 6. Diffractogram resulting from XRD analysis on the sagitta otolith of Chelon ramada. The dif https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-t003.jpg Table 3. Average values of air temperature, water temperature, unit of pH, and alkalinity of the hyd https://www.frontiersin.org/files/Articles/1797222/fmars-13-1797222-HTML/image_m/fmars-13-1797222-t004.jpg Table 4. Average values of HCO3−, Ω of aragonite, CO2, and pCO2 from the hydrometric station of Volt https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1652042/full https://www.frontiersin.org/files/Articles/1652042/fneur-16-1652042-HTML-r1/image_m/fneur-16-1652042-g001.jpg Figure 1. Flow chart of the study population. https://www.frontiersin.org/files/Articles/1652042/fneur-16-1652042-HTML-r1/image_m/fneur-16-1652042-t001.jpg Table 1. General characteristics of the study population according to ALI before and after propensit https://www.frontiersin.org/files/Articles/1652042/fneur-16-1652042-HTML-r1/image_m/fneur-16-1652042-t002.jpg Table 2. The association between ALI and ischemic stroke. https://www.frontiersin.org/files/Articles/1652042/fneur-16-1652042-HTML-r1/image_m/fneur-16-1652042-g002.jpg Figure 2. The forest plot between ALI and ischemic stroke in subgroups with AF. https://www.frontiersin.org/files/Articles/1652042/fneur-16-1652042-HTML-r1/image_m/fneur-16-1652042-g003.jpg Figure 3. Calculation of unmeasured confounding E-values for ALI grouped ORs. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1730328/full https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-g001.jpg Figure 1. Conceptual framework. https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-t001.jpg Table 1. Demographic and usage profile of respondents (n = 420). https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-t002.jpg Table 2. Item weights and loadings of formative second-order constructs. https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-t003.jpg Table 3. Convergent validity (after deleted items). https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-g002.jpg Figure 2. Result of PLS. https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-g003.jpg Figure 3. Results of structural model-research hypotheses. https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-t004.jpg Table 4. Results of structural model-research hypotheses significant at **p ≤ 0.01, *p < 0.05. https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-t005.jpg Table 5. Summary of PLS results. https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-t006.jpg Table 6. Results of PLS. https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-t007.jpg Table 7. Table of importance-performance map analysis (IPMA). https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-g004.jpg Figure 4. IPMA for gratification obtained from WeChat sticker use model. https://www.frontiersin.org/files/Articles/1730328/fpsyg-17-1730328-HTML/image_m/fpsyg-17-1730328-t008.jpg Table 8. Total effects from the PLS-SEM analysis and necessity effect sizes (CE-FDH ceiling line). https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1596178/full https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t001.jpg Table 1. Comparison of the basic information per cycle between pregnant and non-pregnant groups. https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t002.jpg Table 2. Comparison of the number of embryos transferred per cycle between pregnant and non-pregnant https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t003.jpg Table 3. Number of high-quality embryos transferred in pregnancy group and non-pregnancy group. https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t004.jpg Table 4. Influence of maternal age on pregnancy outcome. https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t005.jpg Table 5. Influence of transferred embryos numbers on pregnancy outcome. https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t006.jpg Table 6. Influence of transferred high-quality embryos numbers on pregnancy outcome. https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-g001.jpg Figure 1. ROC curves for predicting clinical pregnancy based on maternal age, the number of embryos https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t007.jpg Table 7. Influence of transferred 7–9 cell embryos numbers on pregnancy outcome. https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t008.jpg Table 8. Influence of transferred grade 1 embryos numbers on pregnancy outcome. https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t009.jpg Table 9. Influence of fragmented embryos numbers on pregnancy outcome. https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-t010.jpg Table 10. Influence of uneven embryos numbers on pregnancy outcome. https://www.frontiersin.org/files/Articles/1596178/fendo-16-1596178-HTML/image_m/fendo-16-1596178-g002.jpg Figure 2. ROC curves for predicting clinical pregnancy based on embryo morphological parameters. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1803995/full https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g001.jpg Figure 1. Schematic representation - Division of study groups and biomaterials used (A) Bone defect https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g002.jpg Figure 2. (A) Skin incision on the rat’s hind limb (B) Medial para-patellar incision (C) Exposure of https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-t001.jpg Table 1. Denaturation temperature (Td, °C) for the different matrices. https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g003.jpg Figure 3. SEM micrograph of elastin matrix after alkaline treatment. Magnification of (A) ×200; (B) https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g004.jpg Figure 4. SEM micrograph of porcine serosa matrix after alkaline treatment. Magnification of (A) ×20 https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g005.jpg Figure 5. Graph of the morphometric analysis of groups G1 to G7. Demonstration of the percentage of https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g006.jpg Figure 6. Graph of the statistical analysis of groups G1 to G7. Statistical differences among groups https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g007.jpg Figure 7. Graph of the biomechanical analysis of groups G1 to G7. Demonstration of the maximum load https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g008.jpg Figure 8. Graph of the statistical analysis of the maximum load of groups G1 to G7. https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g009.jpg Figure 9. Photomacrography and radiology of the study groups. On the left, macroscopic images of the https://www.frontiersin.org/files/Articles/1803995/fbioe-14-1803995-HTML/image_m/fbioe-14-1803995-g010.jpg Figure 10. Photomicrographs of Groups G1 to G7 (A) Masson’s Trichrome Staining at ×40 magnification https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1745222/full https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-t001.jpg Table 1. Socio-demographic distribution of participants. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-t002.jpg Table 2. Themes and ıllustrative content derived from participants’ responses. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g001.jpg Figure 1. Family and inter generational meaning. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g002.jpg Figure 2. Profession and economic independence. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g003.jpg Figure 3. Lifestyle pride. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g004.jpg Figure 4. Moral and spiritual fulfillment. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g005.jpg Figure 5. Missing out on life opportunities. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g006.jpg Figure 6. Relationship and familial ruptures. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g007.jpg Figure 7. Healthy and body regrets. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g008.jpg Figure 8. Faith-based acceptance and surrender. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g009.jpg Figure 9. Wishes and expectations about manner of death. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g010.jpg Figure 10. Peacefulness and readiness for death. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g011.jpg Figure 11. Fears and perception of uncertainty about death. https://www.frontiersin.org/files/Articles/1745222/fpsyg-17-1745222-HTML/image_m/fpsyg-17-1745222-g012.jpg Figure 12. Commitment to life and feeling of incompleteness. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1680983/full https://www.frontiersin.org/files/Articles/1680983/fpsyt-16-1680983-HTML/image_m/fpsyt-16-1680983-g001.jpg Figure 1. PRISMA flowchart for the literature search strategy. The figure illustrates the search and https://www.frontiersin.org/files/Articles/1680983/fpsyt-16-1680983-HTML/image_m/fpsyt-16-1680983-t001.jpg Table 1. Summary of study characteristics. https://www.frontiersin.org/files/Articles/1680983/fpsyt-16-1680983-HTML/image_m/fpsyt-16-1680983-t002.jpg Table 2. Patients demographics. https://www.frontiersin.org/files/Articles/1680983/fpsyt-16-1680983-HTML/image_m/fpsyt-16-1680983-t003.jpg Table 3. Quality assessment of the included studies by using the Newcastle-Ottawa Scale (NOS) tools https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2026.1803262/full https://www.frontiersin.org/files/Articles/1803262/fncel-20-1803262-HTML/image_m/fncel-20-1803262-g001.jpg Figure 1. Backpropagating action potentials (bAPs) are unreliable electrical signals. (A) Schematic https://www.frontiersin.org/files/Articles/1803262/fncel-20-1803262-HTML/image_m/fncel-20-1803262-g002.jpg Figure 2. Probability gates in cortical information processing. (A) Schematic of a pyramidal neuron https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1743952/full https://www.frontiersin.org/files/Articles/1743952/fgene-17-1743952-HTML/image_m/fgene-17-1743952-g001.jpg Figure 1. Three Pinus species (Pd, Pk, Pt) are susceptible while the other two Pinus species and one https://www.frontiersin.org/files/Articles/1743952/fgene-17-1743952-HTML/image_m/fgene-17-1743952-g002.jpg Figure 2. Percentage of green pixels and PWN density of five Pinus species and one hybrid. (A) Perce https://www.frontiersin.org/files/Articles/1743952/fgene-17-1743952-HTML/image_m/fgene-17-1743952-t001.jpg Table 1. Statistics of De novo transcriptome assembly of five Pinus species and one hybrid. https://www.frontiersin.org/files/Articles/1743952/fgene-17-1743952-HTML/image_m/fgene-17-1743952-g003.jpg Figure 3. Overview of the DEGs in response to PWN inoculation in two PWD-resistant Pinus species and https://www.frontiersin.org/files/Articles/1743952/fgene-17-1743952-HTML/image_m/fgene-17-1743952-g004.jpg Figure 4. GO analysis of upregulated DEGs at 2 and 4 weeks post-inoculation of PWN compared to 0 wee https://www.frontiersin.org/files/Articles/1743952/fgene-17-1743952-HTML/image_m/fgene-17-1743952-g005.jpg Figure 5. GO analysis of downregulated DEGs at 2 and 4 weeks post-inoculation of PWN compared to 0 w https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1797225/full https://www.frontiersin.org/files/Articles/1797225/fmed-13-1797225-HTML-r1/image_m/fmed-13-1797225-t001.jpg Table 1. Evaluation of the certainty of evidence using GRADE framework. https://www.frontiersin.org/files/Articles/1797225/fmed-13-1797225-HTML-r1/image_m/fmed-13-1797225-g001.jpg Figure 1. PRISMA flowchart of study inclusion. https://www.frontiersin.org/files/Articles/1797225/fmed-13-1797225-HTML-r1/image_m/fmed-13-1797225-t002.jpg Table 2. Description of the included studies. https://www.frontiersin.org/files/Articles/1797225/fmed-13-1797225-HTML-r1/image_m/fmed-13-1797225-g002.jpg Figure 2. Forest plot of the pooled prevalence of asymptomatic meibomian gland dysfunction. https://www.frontiersin.org/files/Articles/1797225/fmed-13-1797225-HTML-r1/image_m/fmed-13-1797225-g003.jpg Figure 3. Heterogeneity assessment of the pooled prevalence of asymptomatic meibomian gland dysfunct https://www.frontiersin.org/files/Articles/1797225/fmed-13-1797225-HTML-r1/image_m/fmed-13-1797225-g004.jpg Figure 4. Meta-regression of asymptomatic Meibomian gland dysfunction by. (A) Proportion of female p https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1766876/full https://www.frontiersin.org/files/Articles/1766876/feduc-11-1766876-HTML-r1/image_m/feduc-11-1766876-t001.jpg Table 1. Description of the participants. https://www.frontiersin.org/files/Articles/1766876/feduc-11-1766876-HTML-r1/image_m/feduc-11-1766876-t002.jpg Table 2. Theme–RQ mapping. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1805941/full https://www.frontiersin.org/files/Articles/1805941/fneur-17-1805941-HTML-r1/image_m/fneur-17-1805941-g001.jpg Figure 1. Ultrasound acquisition and measurement of carotid bifurcation geometry. Representative ult https://www.frontiersin.org/files/Articles/1805941/fneur-17-1805941-HTML-r1/image_m/fneur-17-1805941-g002.jpg Figure 2. Schematic illustration of carotid bifurcation geometry and measurement locations. Diameter https://www.frontiersin.org/files/Articles/1805941/fneur-17-1805941-HTML-r1/image_m/fneur-17-1805941-t001.jpg Table 1. Baseline characteristics of the study population are stratified by ethnicity. https://www.frontiersin.org/files/Articles/1805941/fneur-17-1805941-HTML-r1/image_m/fneur-17-1805941-t002.jpg Table 2. Model-based estimated marginal means of carotid bifurcation geometry ratios by ethnicity (e https://www.frontiersin.org/files/Articles/1805941/fneur-17-1805941-HTML-r1/image_m/fneur-17-1805941-t003.jpg Table 3. Association between ethnicity and carotid bifurcation geometry ratios assessed by linear mi https://www.frontiersin.org/files/Articles/1805941/fneur-17-1805941-HTML-r1/image_m/fneur-17-1805941-g003.jpg Figure 3. Summary of the magnitude and direction of ethnicity-associated differences in carotid bifu https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1744450/full https://www.frontiersin.org/files/Articles/1744450/fneur-17-1744450-HTML/image_m/fneur-17-1744450-g001.jpg Figure 1. Rodent brain regions and neural circuits involved in the regulation of social behavior, so https://www.frontiersin.org/files/Articles/1744450/fneur-17-1744450-HTML/image_m/fneur-17-1744450-g002.jpg Figure 2. BDNF–TrkB signaling pathways. BDNF is synthesized in excitatory neurons as a precursor (pr https://www.frontiersin.org/journals/natural-products/articles/10.3389/fntpr.2025.1764266/full https://www.frontiersin.org/files/Articles/1764266/fntpr-04-1764266-HTML/image_m/fntpr-04-1764266-g001.jpg Figure 1. Proportion of live cells within explants conditioned with TURsim (A) or TTsim (B) (T1: 7.6 https://www.frontiersin.org/files/Articles/1764266/fntpr-04-1764266-HTML/image_m/fntpr-04-1764266-g002.jpg Figure 2. Media Nitric Oxide (NO) concentration from explants conditioned with TURsim or TTsim (T1: https://www.frontiersin.org/files/Articles/1764266/fntpr-04-1764266-HTML/image_m/fntpr-04-1764266-g003.jpg Figure 3. Media Prostaglandin E2 (PGE2) concentration from explants conditioned with TURsim or TTsim https://www.frontiersin.org/files/Articles/1764266/fntpr-04-1764266-HTML/image_m/fntpr-04-1764266-g004.jpg Figure 4. Media Glycosaminoglycan (GAG) concentration from explants conditioned with TURsim or TTsim https://www.frontiersin.org/files/Articles/1764266/fntpr-04-1764266-HTML/image_m/fntpr-04-1764266-g005.jpg Figure 5. Cartilage glycosaminoglycan (GAG) content of explants conditioned with TURsim (A) and TTsi https://www.frontiersin.org/files/Articles/1764266/fntpr-04-1764266-HTML/image_m/fntpr-04-1764266-g006.jpg Figure 6. Glycosaminoglycan Retention Index by explants conditioned with TURsim (A) and TTsim (B) at https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1727493/full https://www.frontiersin.org/files/Articles/1727493/fbinf-06-1727493-HTML-r2/image_m/fbinf-06-1727493-g001.jpg Figure 1. Overview of the ChickpeaOmicsR package, highlighting its capabilities in integrating genet https://www.frontiersin.org/files/Articles/1727493/fbinf-06-1727493-HTML-r2/image_m/fbinf-06-1727493-t001.jpg Table 1. Overview of RNA-seq experimental datasets used for gene expression analysis in chickpea, re https://www.frontiersin.org/files/Articles/1727493/fbinf-06-1727493-HTML-r2/image_m/fbinf-06-1727493-t002.jpg Table 2. Summary of the ChickpeaOmicsR database components, describing the integrated genomics, tran https://www.frontiersin.org/files/Articles/1727493/fbinf-06-1727493-HTML-r2/image_m/fbinf-06-1727493-g002.jpg Figure 2. Overview of the ChickpeaOmicsR Shiny web application and its multi-omics analysis tools. ( https://www.frontiersin.org/files/Articles/1727493/fbinf-06-1727493-HTML-r2/image_m/fbinf-06-1727493-t003.jpg Table 3. Summary of differentially expressed genes (DEGs) identified across multiple stress conditio https://www.frontiersin.org/files/Articles/1727493/fbinf-06-1727493-HTML-r2/image_m/fbinf-06-1727493-g003.jpg Figure 3. The gene intersection and multiomics analysis revealed a significant association between g https://www.frontiersin.org/files/Articles/1727493/fbinf-06-1727493-HTML-r2/image_m/fbinf-06-1727493-g004.jpg Figure 4. Gene enrichment analysis of significantly differentially expressed genes (DEGs) associated https://www.frontiersin.org/files/Articles/1727493/fbinf-06-1727493-HTML-r2/image_m/fbinf-06-1727493-g005.jpg Figure 5. Protein-Protein Interaction (PPI) Network: (A) PPI network of the top differentially expre https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1782889/full https://www.frontiersin.org/files/Articles/1782889/fped-14-1782889-HTML/image_m/fped-14-1782889-t001.jpg Table 1. Risk factors and mechanisms of neonatal hyperbilirubinemia in high-altitude regions. https://www.frontiersin.org/files/Articles/1782889/fped-14-1782889-HTML/image_m/fped-14-1782889-g001.jpg Figure 1. Diagram illustrating bilirubin metabolism, showing hemoglobin breakdown in macrophages, tr https://www.frontiersin.org/files/Articles/1782889/fped-14-1782889-HTML/image_m/fped-14-1782889-g002.jpg Figure 2. Diagram illustrating the mechanism of UGT1A1 Enzyme in Bilirubin Metabolism. The breakdown https://www.frontiersin.org/files/Articles/1782889/fped-14-1782889-HTML/image_m/fped-14-1782889-g003.jpg Figure 3. Diagram shows the mechanism of action of phototherapy for neonatal hyperbilirubinemia. Upo https://www.frontiersin.org/files/Articles/1782889/fped-14-1782889-HTML/image_m/fped-14-1782889-t002.jpg Table 2. Laboratory indicators of hyperbilirubinemia. https://www.frontiersin.org/files/Articles/1782889/fped-14-1782889-HTML/image_m/fped-14-1782889-t003.jpg Table 3. Multidimensional comparison of different serum bilirubin assay methods. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1688206/full https://www.frontiersin.org/files/Articles/1688206/fmicb-16-1688206-HTML/image_m/fmicb-16-1688206-g001.jpg Figure 1. Map of Norway, Sweden, and Finland Showing the geographical locations of three Norwegian R https://www.frontiersin.org/files/Articles/1688206/fmicb-16-1688206-HTML/image_m/fmicb-16-1688206-t001.jpg Table 1. Overview of positive and total counts for ear tissue pools (n = 10/pool) and Individual Ear https://www.frontiersin.org/files/Articles/1688206/fmicb-16-1688206-HTML/image_m/fmicb-16-1688206-g002.jpg Figure 2. Phylogenetic analysis of the 5′-untranslated region (5’-UTR) for reference pestivirus isol https://www.frontiersin.org/files/Articles/1688206/fmicb-16-1688206-HTML/image_m/fmicb-16-1688206-g003.jpg Figure 3. Representative gel image comparing reference Pestivirus strains (BVDV1b AU526, BVDV2a PI28 https://www.frontiersin.org/files/Articles/1688206/fmicb-16-1688206-HTML/image_m/fmicb-16-1688206-t002.jpg Table 2. Performance assessment of commercial pestivirus antigen capture ELISA (ACE) (IDEXX ELISA BV https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1613622/full https://www.frontiersin.org/files/Articles/1613622/fimmu-16-1613622-HTML/image_m/fimmu-16-1613622-g001.jpg Figure 1. Mechanisms of target cell apoptosis induced by CAR-T cells. CAR-T cells eliminate target c https://www.frontiersin.org/files/Articles/1613622/fimmu-16-1613622-HTML/image_m/fimmu-16-1613622-t001.jpg Table 1. Published AIDs treatments by B-cell-targeting CAR-T cells. https://www.frontiersin.org/files/Articles/1613622/fimmu-16-1613622-HTML/image_m/fimmu-16-1613622-g002.jpg Figure 2. Strategies for CAR-T cell generation: in vitro engineering and in vivo delivery. (A) Ex vi https://www.frontiersin.org/files/Articles/1613622/fimmu-16-1613622-HTML/image_m/fimmu-16-1613622-g003.jpg Figure 3. Controllable CAR-T cells with inducible safety switches. (A) Drug-inducible suicide switch https://www.frontiersin.org/files/Articles/1613622/fimmu-16-1613622-HTML/image_m/fimmu-16-1613622-g004.jpg Figure 4. Engineered CAR-T cells and CAR-Tregs for autoimmune disease treatment. (A) Transient CAR-T https://www.frontiersin.org/files/Articles/1613622/fimmu-16-1613622-HTML/image_m/fimmu-16-1613622-t002.jpg Table 2. Clinical trials using CAR-based cells for AIDs therapy. https://www.frontiersin.org/files/Articles/1613622/fimmu-16-1613622-HTML/image_m/fimmu-16-1613622-g005.jpg Figure 5. Logic-gated CAR-T cells for precise targeting of autoreactive cells. (A) “OR” gate design: https://www.frontiersin.org/files/Articles/1613622/fimmu-16-1613622-HTML/image_m/fimmu-16-1613622-g006.jpg Figure 6. SynNotch-regulated CAR-NK cells for conditional IL-15 expression. (A) Conventional CAR-NK https://www.frontiersin.org/files/Articles/1613622/fimmu-16-1613622-HTML/image_m/fimmu-16-1613622-t003.jpg Table 3. Engineered CAR-immune cells in AIDs therapies. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1666858/full https://www.frontiersin.org/files/Articles/1666858/fpsyg-16-1666858-HTML/image_m/fpsyg-16-1666858-g001.jpg Figure 1. The proposed moderated mediation model. https://www.frontiersin.org/files/Articles/1666858/fpsyg-16-1666858-HTML/image_m/fpsyg-16-1666858-t001.jpg Table 1. Variable discrimination validity test results. https://www.frontiersin.org/files/Articles/1666858/fpsyg-16-1666858-HTML/image_m/fpsyg-16-1666858-t002.jpg Table 2. Descriptive statistics and correlation matrix results. https://www.frontiersin.org/files/Articles/1666858/fpsyg-16-1666858-HTML/image_m/fpsyg-16-1666858-g002.jpg Figure 2. Structural equation model results. https://www.frontiersin.org/files/Articles/1666858/fpsyg-16-1666858-HTML/image_m/fpsyg-16-1666858-t003.jpg Table 3. Bootstrap analysis of mediating effect of chain mediation model. https://www.frontiersin.org/journals/horticulture/articles/10.3389/fhort.2026.1813573/full https://www.frontiersin.org/files/Articles/1813573/fhort-05-1813573-HTML/image_m/fhort-05-1813573-g007.jpg Figure 7. The quality of tomato fruits. (A) total soluble solids (TSS), (B) lycopene, (C) GABA, and https://www.frontiersin.org/journals/horticulture/articles/10.3389/fhort.2026.1746049/full https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-t001.jpg Table 1. Electric conductance and ion compositions of raw methane digestate and irrigation solution https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-g001.jpg Figure 1. The daily average environmental data (temperature inside the greenhouse, outside radiation https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-g002.jpg Figure 2. Daily nitrate-nitrogen (NO3-N) ion concentrations in the MPMdigestate and CF treatments su https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-g003.jpg Figure 3. Irrigation and drainage EC and pH of both treatments during the cultivation period. (A) ir https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-t002.jpg Table 2. Electric conductance and ion compositions of drainage solutions 84 days after transplant. https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-t003.jpg Table 3. Plant aboveground biomass under chemical (CF) and MPM-processed methane fermentation digest https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-g004.jpg Figure 4. Visual representation of plant development. MPM-digestate represents MPM-processed methane https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-g005.jpg Figure 5. Distribution of dry matter to the stem + leaf and fruit. (A) Distribution of dry matter in https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-g006.jpg Figure 6. The hierarchy of yield components and related traits in tomato from the total stage of cul https://www.frontiersin.org/files/Articles/1746049/fhort-05-1746049-HTML-r1/image_m/fhort-05-1746049-g007.jpg Figure 7. The quality of tomato fruits. (A) total soluble solids (TSS), (B) lycopene, (C) GABA, and https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1684264/full https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-g001.jpg Figure 1. Workflow of this study. Workflow of this study for Predicting CK19 Expression and Recurren https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-t001.jpg Table 1. Clinical characteristics of patients with HCC. https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-t002.jpg Table 2. Radiologic characteristics of patients with HCC. https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-t003.jpg Table 3. Explore the predictors with binary logistic regression analysis. https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-t004.jpg Table 4. Comparison of performance of DL-HR nomogram and clinical-radiologic model. https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-g002.jpg Figure 2. Visualization of habitat and deep learning features in CK19+ HCC. (a) Axial MRI image show https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-g003.jpg Figure 3. The utilization of the nomogram to predict the CK19 expression. The nomogram incorporates https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-g004.jpg Figure 4. Receiver operating characteristic curves, calibration curves, and decision curve of the DL https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-g005.jpg Figure 5. Confusion matrices of DL-HR nomogram model. The matrices display model performance in clas https://www.frontiersin.org/files/Articles/1684264/fonc-15-1684264-HTML-r1/image_m/fonc-15-1684264-g006.jpg Figure 6. Kaplan-Meier survival curve of recurrence-free survival (RFS). DL-HR nomogram-predicted CK https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1707432/full https://www.frontiersin.org/files/Articles/1707432/fpubh-13-1707432-HTML-r1/image_m/fpubh-13-1707432-g001.jpg Figure 1. Algorithm for HIV diagnosis in China. https://www.frontiersin.org/files/Articles/1707432/fpubh-13-1707432-HTML-r1/image_m/fpubh-13-1707432-g002.jpg Figure 2. Flowchart of study design. POCT, point-of-care HIV-1 viral load test; LABT, laboratory-bas https://www.frontiersin.org/files/Articles/1707432/fpubh-13-1707432-HTML-r1/image_m/fpubh-13-1707432-t001.jpg Table 1. Diagnostic performance of POCT and LABT compared to follow-up Western blot results. https://www.frontiersin.org/files/Articles/1707432/fpubh-13-1707432-HTML-r1/image_m/fpubh-13-1707432-t002.jpg Table 2. Agreement between POCT and LABT results in parallel-tested samples. https://www.frontiersin.org/files/Articles/1707432/fpubh-13-1707432-HTML-r1/image_m/fpubh-13-1707432-g003.jpg Figure 3. Pearson correlation and Bland–Altman analyses between the POCT and the LABT. (A) Pearson c https://www.frontiersin.org/files/Articles/1707432/fpubh-13-1707432-HTML-r1/image_m/fpubh-13-1707432-t003.jpg Table 3. Comparison of service delivery outcomes between POCT and LABT. https://www.frontiersin.org/files/Articles/1707432/fpubh-13-1707432-HTML-r1/image_m/fpubh-13-1707432-g004.jpg Figure 4. Kaplan–Meier curve comparing time from initial screening to NAT result reporting between P https://www.frontiersin.org/files/Articles/1707432/fpubh-13-1707432-HTML-r1/image_m/fpubh-13-1707432-g005.jpg Figure 5. Laboratory staff perceptions of POCT versus LABT platforms. (A) Perceived simplicity of PO https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1667172/full https://www.frontiersin.org/files/Articles/1667172/fmed-12-1667172-HTML-r2/image_m/fmed-12-1667172-t001.jpg Table 1. Evidence synthesis and appraisal methodology for hypoxemia prevention and management in eld https://www.frontiersin.org/files/Articles/1667172/fmed-12-1667172-HTML-r2/image_m/fmed-12-1667172-t002.jpg Table 2. Comparison of baseline characteristics between groups. https://www.frontiersin.org/files/Articles/1667172/fmed-12-1667172-HTML-r2/image_m/fmed-12-1667172-t003.jpg Table 3. Repeated measures analysis of variance on oxygen saturation levels at different time points https://www.frontiersin.org/files/Articles/1667172/fmed-12-1667172-HTML-r2/image_m/fmed-12-1667172-g001.jpg Figure 1. Group-based effect analysis. https://www.frontiersin.org/files/Articles/1667172/fmed-12-1667172-HTML-r2/image_m/fmed-12-1667172-g002.jpg Figure 2. Effect analysis based on time points. https://www.frontiersin.org/files/Articles/1667172/fmed-12-1667172-HTML-r2/image_m/fmed-12-1667172-t004.jpg Table 4. Comparison of perioperative SpO2 levels (mean ± SD, %) between groups. https://www.frontiersin.org/files/Articles/1667172/fmed-12-1667172-HTML-r2/image_m/fmed-12-1667172-t005.jpg Table 5. Comparison of postoperative hypoxemia incidence between groups (n = 104). https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1667307/full https://www.frontiersin.org/files/Articles/1667307/fpubh-13-1667307-HTML/image_m/fpubh-13-1667307-g001.jpg Figure 1. Literature screening flowchart. https://www.frontiersin.org/files/Articles/1667307/fpubh-13-1667307-HTML/image_m/fpubh-13-1667307-t001.jpg Table 1. Basic characteristics of literature inclusion. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1756195/full https://www.frontiersin.org/files/Articles/1756195/fpls-17-1756195-HTML/image_m/fpls-17-1756195-g001.jpg Figure 1. Monthly mean temperature and precipitation at the experimental site from January 2023 to D https://www.frontiersin.org/files/Articles/1756195/fpls-17-1756195-HTML/image_m/fpls-17-1756195-g002.jpg Figure 2. Spatial distribution of experimental materials. https://www.frontiersin.org/files/Articles/1756195/fpls-17-1756195-HTML/image_m/fpls-17-1756195-t001.jpg Table 1. Sources of experimental materials. https://www.frontiersin.org/files/Articles/1756195/fpls-17-1756195-HTML/image_m/fpls-17-1756195-g003.jpg Figure 3. Agronomic traits and yield performance of E. sibiricus. (a) Plant height, (b) Stem diamete https://www.frontiersin.org/files/Articles/1756195/fpls-17-1756195-HTML/image_m/fpls-17-1756195-t002.jpg Table 2. Two-way ANOVA of the effects of elevation and years on phenotypic traits and yield in E. si https://www.frontiersin.org/files/Articles/1756195/fpls-17-1756195-HTML/image_m/fpls-17-1756195-g004.jpg Figure 4. Mantel analysis of correlations between yield and agronomic traits in E. sibiricus. https://www.frontiersin.org/files/Articles/1756195/fpls-17-1756195-HTML/image_m/fpls-17-1756195-g005.jpg Figure 5. K-means cluster analysis of key traits in E. sibiricus across an elevational gradient. https://www.frontiersin.org/files/Articles/1756195/fpls-17-1756195-HTML/image_m/fpls-17-1756195-g006.jpg Figure 6. Technique for order preference by similarity to ideal solution comprehensive evaluation of https://www.frontiersin.org/files/Articles/1756195/fpls-17-1756195-HTML/image_m/fpls-17-1756195-g007.jpg Figure 7. Structural equation modeling and the standardized effect values of various factors. (a, c) https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1730206/full https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-g001.jpg Figure 1. 26S proteasome (As shown in the figure, the 26S proteasome consists of two parts, the core https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-g002.jpg Figure 2. Ubiquitination degradation process and deubiquitination process (The ubiquitination proces https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-g003.jpg Figure 3. Aβ and UPS (In AD, APP is cleaved by β-secretase and γ-secretase to form Aβ, especially Aβ https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-g004.jpg Figure 4. Tau and UPS (In pathological conditions, Tau proteins are abnormally phosphorylated and fo https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-g005.jpg Figure 5. Synapse and UPS (UPS can regulate synaptic protein turnover by ubiquitination of mislabele https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-t001.jpg Table 1. Experimental study of targeting the proteasome in the treatment of AD. https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-t002.jpg Table 2. Experimental study of targeting ubiquitin ligase in the treatment of AD. https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-t003.jpg Table 3. Experimental study of targeted deubiquitinase in the treatment of AD. https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-t004.jpg Table 4. Experimental study on the treatment of AD by regulating UPS. https://www.frontiersin.org/files/Articles/1730206/fnagi-17-1730206-HTML/image_m/fnagi-17-1730206-t005.jpg Table 5. Experimental study of PROTACs in the treatment of AD. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1736340/full https://www.frontiersin.org/files/Articles/1736340/fphys-16-1736340-HTML/image_m/fphys-16-1736340-g001.jpg Figure 1. Polarization and Functions of Macrophages. LPS, IFN-γ, ox-LDL, HMGB1, Cav-1 and GM-CSF dri https://www.frontiersin.org/files/Articles/1736340/fphys-16-1736340-HTML/image_m/fphys-16-1736340-t001.jpg Table 1. Metabolic and lipid re-programming of macrophages in asthma. https://www.frontiersin.org/files/Articles/1736340/fphys-16-1736340-HTML/image_m/fphys-16-1736340-g002.jpg Figure 2. Glycolysis–dependent macrophage polarization and airway remodeling. M1-polarized macrophag https://www.frontiersin.org/files/Articles/1736340/fphys-16-1736340-HTML/image_m/fphys-16-1736340-g003.jpg Figure 3. Lipid metabolic reprogramming linking macrophage polarization to asthma. In classically ac https://www.frontiersin.org/files/Articles/1736340/fphys-16-1736340-HTML/image_m/fphys-16-1736340-t002.jpg Table 2. Applications of strategies targeting macrophage metabolic pathways in asthma therapy. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1760792/full https://www.frontiersin.org/files/Articles/1760792/fmed-13-1760792-HTML-r1/image_m/fmed-13-1760792-g001.jpg Figure 1. Literature review flowchart. COPD, chronic obstructive pulmonary disease; CT, concurrent t https://www.frontiersin.org/files/Articles/1760792/fmed-13-1760792-HTML-r1/image_m/fmed-13-1760792-g002.jpg Figure 2. Forest plot of 6-minute walk distance (6MWD). https://www.frontiersin.org/files/Articles/1760792/fmed-13-1760792-HTML-r1/image_m/fmed-13-1760792-t001.jpg Table 1. Pairwise meta-analysis for all outcomes. https://www.frontiersin.org/files/Articles/1760792/fmed-13-1760792-HTML-r1/image_m/fmed-13-1760792-g003.jpg Figure 3. Forest plot of St. George's Respiratory Questionnaire (SGRQ). https://www.frontiersin.org/files/Articles/1760792/fmed-13-1760792-HTML-r1/image_m/fmed-13-1760792-g004.jpg Figure 4. Dose–response relationship between concurrent training and 6-min walk distance (6MWD). https://www.frontiersin.org/files/Articles/1760792/fmed-13-1760792-HTML-r1/image_m/fmed-13-1760792-t002.jpg Table 2. CT dose recommendation. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1774008/full https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-t001.jpg Table 1. Quantitative traits and the associated test methods used in the experiment. https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-t002.jpg Table 2. Qualitative traits and the associated test methods used in the experiment. https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-t003.jpg Table 3. Analysis of quantitative traits of angled luffa. https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-t004.jpg Table 4. Analysis of quantitative traits of angled luffa. https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-t005.jpg Table 5. Variance analysis of quantitative traits in 209 angled luffa accessions from different year https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-g001.jpg Figure 1. Correlation analysis between phenotypic traits of angled luffa resources. Correlation anal https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-g002.jpg Figure 2. Cluster analysis of phenotypic traits based on angled luffa germplasm resources. https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-g003.jpg Figure 3. Comparison of important quantitative traits of 6 groups of angled luffa. Different letters https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-t006.jpg Table 6. Principle component analysis of phenotypic traits of angled luffa accessions. https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-g004.jpg Figure 4. The principal component analysis of the 209 angled luffa accessions. Red represents quanti https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-t007.jpg Table 7. Information on important agronomic traits of excellent angled luffa germplasm. https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-t008.jpg Table 8. Variation of genetic parameters in 209 populations of angled luffa. https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-t009.jpg Table 9. Paired Fst values of 6 subgroups of 209 angled luffa accessions. https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-g005.jpg Figure 5. Analysis of population structure of 209 angled luffa varieties. (A) CV error distribution https://www.frontiersin.org/files/Articles/1774008/fpls-17-1774008-HTML/image_m/fpls-17-1774008-g006.jpg Figure 6. Phylogenetic tree of 209 angled luffa varieties constructed by the neighbor-joining method https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1776694/full https://www.frontiersin.org/files/Articles/1776694/fimmu-17-1776694-HTML-r1/image_m/fimmu-17-1776694-g001.jpg Figure 1. Metabolic reprogramming orchestrates immune cell differentiation and functional plasticity https://www.frontiersin.org/files/Articles/1776694/fimmu-17-1776694-HTML-r1/image_m/fimmu-17-1776694-g002.jpg Figure 2. The interplay between immunosuppression-induced metabolic disorders and targeted therapeut https://www.frontiersin.org/files/Articles/1776694/fimmu-17-1776694-HTML-r1/image_m/fimmu-17-1776694-g003.jpg Figure 3. Sequential immunometabolic mechanisms and therapeutic interventions in Hepatic Ischemia-Re https://www.frontiersin.org/files/Articles/1776694/fimmu-17-1776694-HTML-r1/image_m/fimmu-17-1776694-t001.jpg Table 1. Overview of immunometabolic therapeutic targets in liver transplantation: efficacy, risks, https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1665408/full https://www.frontiersin.org/files/Articles/1665408/fpubh-13-1665408-HTML/image_m/fpubh-13-1665408-t001.jpg Table 1. PAMQ scores of ICU nurses (n = 366). https://www.frontiersin.org/files/Articles/1665408/fpubh-13-1665408-HTML/image_m/fpubh-13-1665408-t002.jpg Table 2. General characteristics of ICU nurses and univariate analysis of their PAMQ scores (n = 366 https://www.frontiersin.org/files/Articles/1665408/fpubh-13-1665408-HTML/image_m/fpubh-13-1665408-t003.jpg Table 3. Correlation analysis of PAMQ, presenteeism behavior scale, and PSSS in ICU nurses (r; n = 3 https://www.frontiersin.org/files/Articles/1665408/fpubh-13-1665408-HTML/image_m/fpubh-13-1665408-t004.jpg Table 4. Results of multivariate linear regression analysis for PAMQ in ICU nurses (n = 366). https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1681431/full https://www.frontiersin.org/files/Articles/1681431/fmed-13-1681431-HTML/image_m/fmed-13-1681431-g001.jpg Figure 1. Cross-departmental patient admission process under the “One Bed” hospital-wide model. https://www.frontiersin.org/files/Articles/1681431/fmed-13-1681431-HTML/image_m/fmed-13-1681431-t001.jpg Table 1. Risk values of Failure Mode and Effects Analysis before and after implementation. https://www.frontiersin.org/files/Articles/1681431/fmed-13-1681431-HTML/image_m/fmed-13-1681431-g002.jpg Figure 2. Failure Mode and Effects Analysis (FMEA)-based implementation phases and key activities. https://www.frontiersin.org/files/Articles/1681431/fmed-13-1681431-HTML/image_m/fmed-13-1681431-g003.jpg Figure 3. Pre- and post-intervention RPNs for key failure modes under the hospital-wide “One Bed” mo https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1671279/full https://www.frontiersin.org/files/Articles/1671279/fpubh-13-1671279-HTML/image_m/fpubh-13-1671279-g001.jpg Figure 1. The screening process of the research subjects. https://www.frontiersin.org/files/Articles/1671279/fpubh-13-1671279-HTML/image_m/fpubh-13-1671279-t001.jpg Table 1. Demographic characteristics of unintentional injuries among hospitalized children from 2015 https://www.frontiersin.org/files/Articles/1671279/fpubh-13-1671279-HTML/image_m/fpubh-13-1671279-t002.jpg Table 2. The variation in the ranking of causes of unintentional injuries among hospitalized childre https://www.frontiersin.org/files/Articles/1671279/fpubh-13-1671279-HTML/image_m/fpubh-13-1671279-t003.jpg Table 3. The distribution of unintentional injuries by gender, age, time, and place among hospitaliz https://www.frontiersin.org/files/Articles/1671279/fpubh-13-1671279-HTML/image_m/fpubh-13-1671279-t004.jpg Table 4. The relationship between unintentional injury levels and gender, age, time, place, and inju https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1752273/full https://www.frontiersin.org/files/Articles/1752273/fpubh-14-1752273-HTML/image_m/fpubh-14-1752273-g001.jpg Figure 1. Flowchart of the sampling procedure and participant selection process. https://www.frontiersin.org/files/Articles/1752273/fpubh-14-1752273-HTML/image_m/fpubh-14-1752273-t001.jpg Table 1. Univariate analysis of factors included in the final multivariate model (n = 2,984). https://www.frontiersin.org/files/Articles/1752273/fpubh-14-1752273-HTML/image_m/fpubh-14-1752273-t002.jpg Table 2. Multivariate binary logistic regression analysis of factors associated with adequate health https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1770404/full https://www.frontiersin.org/files/Articles/1770404/fmed-13-1770404-HTML/image_m/fmed-13-1770404-t001.jpg Table 1. Description of the nursing research order-service model using the TiDIER framework. https://www.frontiersin.org/files/Articles/1770404/fmed-13-1770404-HTML/image_m/fmed-13-1770404-t002.jpg Table 2. Demographic characteristics of clinical nurses and graduate nursing students. https://www.frontiersin.org/files/Articles/1770404/fmed-13-1770404-HTML/image_m/fmed-13-1770404-t003.jpg Table 3. Pre- and post-intervention scores of self-assessed research competence, research self-effic https://www.frontiersin.org/files/Articles/1770404/fmed-13-1770404-HTML/image_m/fmed-13-1770404-t004.jpg Table 4. Pre- and post-intervention scores of self-assessed research competence, research self-effic https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1688025/full https://www.frontiersin.org/files/Articles/1688025/fmed-12-1688025-HTML/image_m/fmed-12-1688025-t001.jpg Table 1. The clinical characteristics of samples. https://www.frontiersin.org/files/Articles/1688025/fmed-12-1688025-HTML/image_m/fmed-12-1688025-g001.jpg Figure 1. Identifying the heterogeneity of AMS microenvironment by scRNA-seq. (A) Schematic workflow https://www.frontiersin.org/files/Articles/1688025/fmed-12-1688025-HTML/image_m/fmed-12-1688025-g002.jpg Figure 2. Identification of AMS-Associated genes by differential gene expression analysis of bulk RN https://www.frontiersin.org/files/Articles/1688025/fmed-12-1688025-HTML/image_m/fmed-12-1688025-g003.jpg Figure 3. Identification of AMS-Associated genes by WGCNA of bulk RNA-Seq Data. (A) The correlation https://www.frontiersin.org/files/Articles/1688025/fmed-12-1688025-HTML/image_m/fmed-12-1688025-g004.jpg Figure 4. Development, validation, and assessment of the AMS Diagnostic Model. (A) The combination o https://www.frontiersin.org/files/Articles/1688025/fmed-12-1688025-HTML/image_m/fmed-12-1688025-g005.jpg Figure 5. Exploring the potential mechanism of AMS associated signatures. (A) Spearman correlation o https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1723098/full https://www.frontiersin.org/files/Articles/1723098/fnut-13-1723098-HTML-r1/image_m/fnut-13-1723098-t001.jpg Table 1. Patient demographics and baseline characteristics. https://www.frontiersin.org/files/Articles/1723098/fnut-13-1723098-HTML-r1/image_m/fnut-13-1723098-g001.jpg Figure 1. Univariate analysis of influencing factors (logistic regression). https://www.frontiersin.org/files/Articles/1723098/fnut-13-1723098-HTML-r1/image_m/fnut-13-1723098-g002.jpg Figure 2. Multivariate analysis of influencing factors (logistic regression). https://www.frontiersin.org/files/Articles/1723098/fnut-13-1723098-HTML-r1/image_m/fnut-13-1723098-g003.jpg Figure 3. Association between UAHDL and CMBCD with the RCS function. Model with 5 knots located at 5 https://www.frontiersin.org/files/Articles/1723098/fnut-13-1723098-HTML-r1/image_m/fnut-13-1723098-t002.jpg Table 2. Subgroup analysis. https://www.frontiersin.org/files/Articles/1723098/fnut-13-1723098-HTML-r1/image_m/fnut-13-1723098-g004.jpg Figure 4. Nomogram for predicting CMBCD risk and its validation. (A) Nomogram for predicting individ https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1611730/full https://www.frontiersin.org/files/Articles/1611730/feduc-10-1611730-HTML/image_m/feduc-10-1611730-g001.jpg Figure 1. The PLC for MathemaTIC program; (a) the organization into six sessions, with the first par https://www.frontiersin.org/files/Articles/1611730/feduc-10-1611730-HTML/image_m/feduc-10-1611730-g002.jpg Figure 2. (a) The team that developed and delivered the training; (b) the process of designing and p https://www.frontiersin.org/files/Articles/1611730/feduc-10-1611730-HTML/image_m/feduc-10-1611730-g003.jpg Figure 3. Barriers and benefits coded based on teacher reports. N = 29 teachers, 6 groups (3 groups https://www.frontiersin.org/files/Articles/1611730/feduc-10-1611730-HTML/image_m/feduc-10-1611730-g004.jpg Figure 4. N = 29 teachers, 6 groups (3 groups of 4 teachers, 2 groups of 5 teachers, 1 group of 7 te https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1785410/full https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-g001.jpg Figure 1. Conceptual framework based on the social-ecological model. https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-t001.jpg Table 1. Descriptive statistics of the sample (N = 2,132). https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-t002.jpg Table 2. Fit statistics for latent class models (1-−4 classes). https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-g002.jpg Figure 2. Elbow plot. https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-t003.jpg Table 3. Sample distribution and predicted probabilities of community lifestyles. https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-g003.jpg Figure 3. Latent class profile plot. https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-t004.jpg Table 4. Sample distribution and predicted probabilities of community lifestyles (N = 2,132). https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-g004.jpg Figure 4. Common support histogram of propensity scores before and after matching. https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-t005.jpg Table 5. Sample balance test of propensity score matching. https://www.frontiersin.org/files/Articles/1785410/fpubh-14-1785410-HTML/image_m/fpubh-14-1785410-t006.jpg Table 6. Multilevel logit regression analysis of neighborhood disadvantage, exposure, and self-rated https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1765272/full https://www.frontiersin.org/files/Articles/1765272/fpubh-14-1765272-HTML/image_m/fpubh-14-1765272-t001.jpg Table 1. Questionnaire content table. https://www.frontiersin.org/files/Articles/1765272/fpubh-14-1765272-HTML/image_m/fpubh-14-1765272-t002.jpg Table 2. Distribution of the number of older adults in different exercise behavior stages. https://www.frontiersin.org/files/Articles/1765272/fpubh-14-1765272-HTML/image_m/fpubh-14-1765272-t003.jpg Table 3. Characteristics of exercise behavior preferences among different types of older adults. https://www.frontiersin.org/files/Articles/1765272/fpubh-14-1765272-HTML/image_m/fpubh-14-1765272-t004.jpg Table 4. Variance test of sports cognition in different exercise behavior stages. https://www.frontiersin.org/files/Articles/1765272/fpubh-14-1765272-HTML/image_m/fpubh-14-1765272-t005.jpg Table 5. Variance test of related factors in different exercise behavior stages. https://www.frontiersin.org/files/Articles/1765272/fpubh-14-1765272-HTML/image_m/fpubh-14-1765272-t006.jpg Table 6. Variance test of exercise demand in different exercise behavior stages. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1770159/full https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t001.jpg Table 1. Data table of China’s National Fitness policy documents (1949–2021) (partial). https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t002.jpg Table 2. Intensity of National Fitness policies and scoring criteria. https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-g001.jpg Figure 1. Theme consistency score. https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t003.jpg Table 3. Policy tool types. https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-g002.jpg Figure 2. Annual changes in the number of China’s National Fitness policies in China and policy effe https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-g003.jpg Figure 3. Changes in the number of China’s National Fitness policies in China and average policy eff https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t004.jpg Table 4. Statistical table on the composition of policy document types for China’s National Fitness https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t005.jpg Table 5. Statistics on the composition of policy-issuing bodies in China’s National Fitness policies https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t006.jpg Table 6. Number of jointly issued National Fitness policy documents in China. https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t007.jpg Table 7. Number of National Fitness policy documents issued by leading departments in China. https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-g004.jpg Figure 4. Network diagram of cooperation between policy-making bodies for National Fitness policies https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t008.jpg Table 8. Centrality indicators of collaborative actors in China’s National Fitness policies. https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-g005.jpg Figure 5. Results of LDA topic model analysis of China’s National Fitness policy texts. https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t009.jpg Table 9. Keywords and relevance of thematic content in China’s National Fitness policies. https://www.frontiersin.org/files/Articles/1770159/fpubh-14-1770159-HTML-r1/image_m/fpubh-14-1770159-t010.jpg Table 10. Statistical table on the use of policy tool types in China’s National Fitness policies. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1683082/full https://www.frontiersin.org/files/Articles/1683082/fmed-12-1683082-HTML-r1/image_m/fmed-12-1683082-t001.jpg Table 1. Donor characteristics of kidney transplants with and without donor AKI. https://www.frontiersin.org/files/Articles/1683082/fmed-12-1683082-HTML-r1/image_m/fmed-12-1683082-t002.jpg Table 2. Recipient and transplant characteristics of kidney transplants with and without donor AKI. https://www.frontiersin.org/files/Articles/1683082/fmed-12-1683082-HTML-r1/image_m/fmed-12-1683082-g001.jpg Figure 1. Recipient eGFR (based on CKD-EPI formula) up to 5 years after kidney transplantation from https://www.frontiersin.org/files/Articles/1683082/fmed-12-1683082-HTML-r1/image_m/fmed-12-1683082-t003.jpg Table 3. Short- and long-term outcomes of kidney transplants with and without donor AKI. https://www.frontiersin.org/files/Articles/1683082/fmed-12-1683082-HTML-r1/image_m/fmed-12-1683082-g002.jpg Figure 2. Kaplan-Meier estimates for death-censored graft survival (A), overall graft survival (B) a https://www.frontiersin.org/files/Articles/1683082/fmed-12-1683082-HTML-r1/image_m/fmed-12-1683082-g003.jpg Figure 3. Kaplan-Meier estimates for death-censored graft survival (A), overall graft survival (B) a https://www.frontiersin.org/files/Articles/1683082/fmed-12-1683082-HTML-r1/image_m/fmed-12-1683082-t004.jpg Table 4. Fixed effects of multivariable Cox regression of death-censored graft loss and mortality of https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1789178/full https://www.frontiersin.org/files/Articles/1789178/fpubh-14-1789178-HTML/image_m/fpubh-14-1789178-t001.jpg Table 1. Basic characteristics of study subjects (N = 170). https://www.frontiersin.org/files/Articles/1789178/fpubh-14-1789178-HTML/image_m/fpubh-14-1789178-t002.jpg Table 2. Distribution of occupations. https://www.frontiersin.org/files/Articles/1789178/fpubh-14-1789178-HTML/image_m/fpubh-14-1789178-t003.jpg Table 3. Results of auditory examination. https://www.frontiersin.org/files/Articles/1789178/fpubh-14-1789178-HTML/image_m/fpubh-14-1789178-t004.jpg Table 4. Cognitive function assessment (MoCA) in study subjects. https://www.frontiersin.org/files/Articles/1789178/fpubh-14-1789178-HTML/image_m/fpubh-14-1789178-t005.jpg Table 5. Correlation analysis between auditory and ABR indices and MoCA total score (including left https://www.frontiersin.org/files/Articles/1789178/fpubh-14-1789178-HTML/image_m/fpubh-14-1789178-t006.jpg Table 6. Pearson correlation coefficients among variables. https://www.frontiersin.org/files/Articles/1789178/fpubh-14-1789178-HTML/image_m/fpubh-14-1789178-t007.jpg Table 7. Results of multivariate linear regression analysis with multiple models for factors influen https://www.frontiersin.org/files/Articles/1789178/fpubh-14-1789178-HTML/image_m/fpubh-14-1789178-t008.jpg Table 8. Mediating effect analysis of years of exposure to hazard on MoCA total score. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1722134/full https://www.frontiersin.org/files/Articles/1722134/fimmu-17-1722134-HTML/image_m/fimmu-17-1722134-g001.jpg Figure 1. Schematic illustrating how gut dysbiosis promotes atherosclerosis via metabolic (TMAO/SCFA https://www.frontiersin.org/files/Articles/1722134/fimmu-17-1722134-HTML/image_m/fimmu-17-1722134-t001.jpg Table 1. Summary of key probiotic strains and their anti-atherosclerotic effects. https://www.frontiersin.org/files/Articles/1722134/fimmu-17-1722134-HTML/image_m/fimmu-17-1722134-t002.jpg Table 2. Clinical trials investigating probiotics and atherosclerosis/related cardiovascular risk. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1732682/full https://www.frontiersin.org/files/Articles/1732682/frai-08-1732682-HTML/image_m/frai-08-1732682-t001.jpg Table 1. Core characteristics and optimization strategies of models used in this study. https://www.frontiersin.org/files/Articles/1732682/frai-08-1732682-HTML/image_m/frai-08-1732682-g001.jpg Figure 1. Word frequency statistics of teachers’ report texts. The (a) presents the word cloud gener https://www.frontiersin.org/files/Articles/1732682/frai-08-1732682-HTML/image_m/frai-08-1732682-t002.jpg Table 2. Distribution of depression among adolescents. https://www.frontiersin.org/files/Articles/1732682/frai-08-1732682-HTML/image_m/frai-08-1732682-g002.jpg Figure 2. The recall rates (recall for Class 1) of the models. https://www.frontiersin.org/files/Articles/1732682/frai-08-1732682-HTML/image_m/frai-08-1732682-t003.jpg Table 3. Evaluation metrics of the models. https://www.frontiersin.org/files/Articles/1732682/frai-08-1732682-HTML/image_m/frai-08-1732682-t004.jpg Table 4. The confusion matrices of the random forest model. https://www.frontiersin.org/files/Articles/1732682/frai-08-1732682-HTML/image_m/frai-08-1732682-t005.jpg Table 5. Top 5 most important features and their mean SHAP values for the four models. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1771225/full https://www.frontiersin.org/files/Articles/1771225/fneur-17-1771225-HTML-r2/image_m/fneur-17-1771225-g001.jpg Figure 1. Flowchart of the study design. MPP, Mycoplasma pneumoniae pneumonia; CI, cerebral infarcti https://www.frontiersin.org/files/Articles/1771225/fneur-17-1771225-HTML-r2/image_m/fneur-17-1771225-t001.jpg Table 1. Mycoplasma pneumoniae pneumonia (MPP) cohort. https://www.frontiersin.org/files/Articles/1771225/fneur-17-1771225-HTML-r2/image_m/fneur-17-1771225-g002.jpg Figure 2. Radiologic manifestations in nine children with cerebral infarction (CI) complicating Myco https://www.frontiersin.org/files/Articles/1771225/fneur-17-1771225-HTML-r2/image_m/fneur-17-1771225-g003.jpg Figure 3. Associations and discriminatory performance of inflammatory and coagulation biomarkers for https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1749473/full https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-g001.jpg Figure 1. Study area of the Qingdao (Map Review Number: GS (2024) 0650). https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-t001.jpg Table 1. Data sources and description. https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-t002.jpg Table 2. Carbon density values for different land-use types (t·ha-1). https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-t003.jpg Table 3. Land-use conversion constraint matrix. https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-t004.jpg Table 4. Neighborhood weights. https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-g002.jpg Figure 2. Land-use structure from 2010 to 2020. https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-g003.jpg Figure 3. Development potential of each land-use type at the grid-cell level. (A) Farm land. (B) For https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-g004.jpg Figure 4. Comparison between forecast and real land-use patterns. (A) Forecast. (B) Real. https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-g005.jpg Figure 5. Land-use transition matrix. https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-t005.jpg Table 5. Carbon storage transition matrix among land-use types during 2010-2015. (unit: 105 t). https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-t006.jpg Table 6. Carbon storage transition matrix among land-use types during 2015-2020. (unit: 105 t). https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-t007.jpg Table 7. Contribution of each driving factor to the expansion of different land-use types. https://www.frontiersin.org/files/Articles/1749473/fmars-13-1749473-HTML/image_m/fmars-13-1749473-g006.jpg Figure 6. Spatial distribution of carbon storage in Qingdao from 2010 to 2020. (A) Carbon storage in https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1750045/full https://www.frontiersin.org/files/Articles/1750045/fnins-20-1750045-HTML/image_m/fnins-20-1750045-g001.jpg Figure 1. Screenshot of the NeoNaid GUI. The graphs show the predicted sleep hypnogram and the brain https://www.frontiersin.org/files/Articles/1750045/fnins-20-1750045-HTML/image_m/fnins-20-1750045-g002.jpg Figure 2. Rejection rates due to quality control in the two datasets. Rejected %: the total percenta https://www.frontiersin.org/files/Articles/1750045/fnins-20-1750045-HTML/image_m/fnins-20-1750045-g003.jpg Figure 3. FBA performance for different simulated data lengths (1,000 repetitions per window, per re https://www.frontiersin.org/files/Articles/1750045/fnins-20-1750045-HTML/image_m/fnins-20-1750045-g004.jpg Figure 4. Sleep performance with (robust) and without (naive) artifact rejection and heuristic postp https://www.frontiersin.org/files/Articles/1750045/fnins-20-1750045-HTML/image_m/fnins-20-1750045-g005.jpg Figure 5. Per-recording performance per channel. “All” refers to attention-weighted average of singl https://www.frontiersin.org/files/Articles/1750045/fnins-20-1750045-HTML/image_m/fnins-20-1750045-g006.jpg Figure 6. The relation between the performance and postmenstrual age (PMA). Left: internal Dataset A https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1743520/full https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-g001.jpg Figure 1. Hypothetical model: the hypothesis is different types of mandala coloring influence operat https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-t001.jpg Table 1. Operation of “gamified coloring group” versus “sliding coloring group”. https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-g002.jpg Figure 2. Examples of participants’ coloring results. https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-g003.jpg Figure 3. Structure flowchart. https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-t002.jpg Table 2. Descriptive statistics for each dependent variable by group and measurement time (M ± SD). https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-g004.jpg Figure 4. Means on the state anxiety, Ln skin conductance level, and heart rate in experiment 1 at T https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-t003.jpg Table 3. Repeated measures analysis of variance (ANOVA) for SA, SCL and HR (Experiment 1). https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-t004.jpg Table 4. Descriptive statistics for each dependent variable by group and measurement time (M ± SD). https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-g005.jpg Figure 5. Means on the state anxiety, Ln skin conductance level, and heart rate in Experiment 2 at T https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-t005.jpg Table 5. Repeated measures analysis of variance (ANOVA) for SA, SCL and HR (Experiment 2). https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-t006.jpg Table 6. Correlation analysis among variables. https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-t007.jpg Table 7. Testing the mediating effect of flow state between groups and state anxiety. https://www.frontiersin.org/files/Articles/1743520/fpubh-14-1743520-HTML/image_m/fpubh-14-1743520-g006.jpg Figure 6. Flow state fully mediated the relationship between groups and state anxiety. The asterisk https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1773060/full https://www.frontiersin.org/files/Articles/1773060/fonc-16-1773060-HTML/image_m/fonc-16-1773060-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1773060/fonc-16-1773060-HTML/image_m/fonc-16-1773060-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1773060/fonc-16-1773060-HTML/image_m/fonc-16-1773060-g002.jpg Figure 2. Primary outcomes of included studies. Blue:quality of life indicators, yellow: exercise ca https://www.frontiersin.org/files/Articles/1773060/fonc-16-1773060-HTML/image_m/fonc-16-1773060-g003.jpg Figure 3. Secondary outcomes of included studies. Blue:social function indicators, red: body composi https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1778784/full https://www.frontiersin.org/files/Articles/1778784/fpubh-14-1778784-HTML/image_m/fpubh-14-1778784-g001.jpg Figure 1. PRISMA diagram illustrating the process of identification, screening and inclusion of stud https://www.frontiersin.org/files/Articles/1778784/fpubh-14-1778784-HTML/image_m/fpubh-14-1778784-t001.jpg Table 1. Characteristics of included studies classified by transmission mode and level of outcome as https://www.frontiersin.org/files/Articles/1778784/fpubh-14-1778784-HTML/image_m/fpubh-14-1778784-t002.jpg Table 2. Synthesized relationships between climate variables and infectious disease outcomes identif https://www.frontiersin.org/files/Articles/1778784/fpubh-14-1778784-HTML/image_m/fpubh-14-1778784-t003.jpg Table 3. Governance domains to mitigate climate-sensitive infectious disease risks. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1724204/full https://www.frontiersin.org/files/Articles/1724204/fonc-16-1724204-HTML/image_m/fonc-16-1724204-g001.jpg Figure 1. Timeline of the patient's case. https://www.frontiersin.org/files/Articles/1724204/fonc-16-1724204-HTML/image_m/fonc-16-1724204-g002.jpg Figure 2. Histopathological examinations. (a) H&E staining: The tumor cells of the SMARCA4-deficient https://www.frontiersin.org/files/Articles/1724204/fonc-16-1724204-HTML/image_m/fonc-16-1724204-g003.jpg Figure 3. Efficacy of initial radiotherapy. (A) On initial presentation, enhanced CT revealed a mass https://www.frontiersin.org/files/Articles/1724204/fonc-16-1724204-HTML/image_m/fonc-16-1724204-g004.jpg Figure 4. Treatment strategies administered at each disease progression. (A) First Progression: The https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1583794/full https://www.frontiersin.org/files/Articles/1583794/fpsyg-16-1583794-HTML-r1/image_m/fpsyg-16-1583794-g001.jpg Figure 1. Proposed mediation model. https://www.frontiersin.org/files/Articles/1583794/fpsyg-16-1583794-HTML-r1/image_m/fpsyg-16-1583794-t001.jpg Table 1. Descriptive statistics for data collection tools. https://www.frontiersin.org/files/Articles/1583794/fpsyg-16-1583794-HTML-r1/image_m/fpsyg-16-1583794-t002.jpg Table 2. Correlations between scale scores. https://www.frontiersin.org/files/Articles/1583794/fpsyg-16-1583794-HTML-r1/image_m/fpsyg-16-1583794-g002.jpg Figure 2. Mediation models with path coefficients. https://www.frontiersin.org/files/Articles/1583794/fpsyg-16-1583794-HTML-r1/image_m/fpsyg-16-1583794-t003.jpg Table 3. Bootstrap results of indirect effect. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1771697/full https://www.frontiersin.org/files/Articles/1771697/fonc-16-1771697-HTML/image_m/fonc-16-1771697-g001.jpg Figure 1. Visualization of single-cell RNA-sequencing data from human and mouse brain regions, color https://www.frontiersin.org/files/Articles/1771697/fonc-16-1771697-HTML/image_m/fonc-16-1771697-g002.jpg Figure 2. Context-dependent roles of EMILIN-1 in primary brain tumors and neuroblastoma. In glioblas https://www.frontiersin.org/files/Articles/1771697/fonc-16-1771697-HTML/image_m/fonc-16-1771697-t001.jpg Table 1. Clinical and translational landscape of EMILIN-1 in nervous system tumors. https://www.frontiersin.org/files/Articles/1771697/fonc-16-1771697-HTML/image_m/fonc-16-1771697-g003.jpg Figure 3. Pathophysiological mechanisms defining the function of EMILIN-1 in tumor development and p https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1749851/full https://www.frontiersin.org/files/Articles/1749851/fnins-20-1749851-HTML/image_m/fnins-20-1749851-g001.jpg Figure 1. tDCS montage (anode over P2 and cathode over AF3) and region of interest (ROIs) from which https://www.frontiersin.org/files/Articles/1749851/fnins-20-1749851-HTML/image_m/fnins-20-1749851-g002.jpg Figure 2. Workflow of the extraction of the structural connectome from DWI and T1w MRI for each subj https://www.frontiersin.org/files/Articles/1749851/fnins-20-1749851-HTML/image_m/fnins-20-1749851-g003.jpg Figure 3. Structural connectivity matrix showing the connections among the 379 parcels defined by th https://www.frontiersin.org/files/Articles/1749851/fnins-20-1749851-HTML/image_m/fnins-20-1749851-g004.jpg Figure 4. Normalized number of streamlines between the anode and right-hemisphere parcels, sorted by https://www.frontiersin.org/files/Articles/1749851/fnins-20-1749851-HTML/image_m/fnins-20-1749851-g005.jpg Figure 5. Normalized number of streamlines between the anode and left-hemisphere parcels, sorted by https://www.frontiersin.org/files/Articles/1749851/fnins-20-1749851-HTML/image_m/fnins-20-1749851-g006.jpg Figure 6. EF distribution on coronal and axial slices of white and grey matter for DTI and NoDTI sim https://www.frontiersin.org/files/Articles/1749851/fnins-20-1749851-HTML/image_m/fnins-20-1749851-t001.jpg Table 1. EF distribution in NoDTI and DTI simulations in each ROI. https://www.frontiersin.org/files/Articles/1749851/fnins-20-1749851-HTML/image_m/fnins-20-1749851-t002.jpg Table 2. EF spread in NoDTI and DTI simulations in each ROI. https://www.frontiersin.org/files/Articles/1749851/fnins-20-1749851-HTML/image_m/fnins-20-1749851-g007.jpg Figure 7. V50 linear behaviour with respect to the connectivity strength between cortical area under https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1808541/full https://www.frontiersin.org/files/Articles/1808541/fimmu-17-1808541-HTML/image_m/fimmu-17-1808541-g001.jpg Figure 1. Healthy and sarcopenic muscle immune landscapes. In healthy skeletal muscle, type I interf https://www.frontiersin.org/files/Articles/1808541/fimmu-17-1808541-HTML/image_m/fimmu-17-1808541-g002.jpg Figure 2. Immune landscape of healthy muscle regeneration. In healthy skeletal muscle, neutrophils i https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2025.1597223/full https://www.frontiersin.org/files/Articles/1597223/fsoc-10-1597223-HTML/image_m/fsoc-10-1597223-g001.jpg Figure 1. Districts where study was conducted. https://www.frontiersin.org/files/Articles/1597223/fsoc-10-1597223-HTML/image_m/fsoc-10-1597223-t001.jpg Table 1. Sociodemographic and associations with discrimination, understanding and acceptance of LGBT https://www.frontiersin.org/files/Articles/1597223/fsoc-10-1597223-HTML/image_m/fsoc-10-1597223-t002.jpg Table 2. Unadjusted and adjusted beta coefficients of factors influencing LGBTQ+ acceptance and perc https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1715880/full https://www.frontiersin.org/files/Articles/1715880/fimmu-17-1715880-HTML-r2/image_m/fimmu-17-1715880-t001.jpg Table 1. Maternal and child demographic characteristics among participants with maternal inflammatio https://www.frontiersin.org/files/Articles/1715880/fimmu-17-1715880-HTML-r2/image_m/fimmu-17-1715880-g001.jpg Figure 1. Flowchart of at-birth, third-trimester, and restricted temporal proximity study samples. https://www.frontiersin.org/files/Articles/1715880/fimmu-17-1715880-HTML-r2/image_m/fimmu-17-1715880-g002.jpg Figure 2. Box plots of high vs. low maternal inflammatory levels markers at birth and in the third t https://www.frontiersin.org/files/Articles/1715880/fimmu-17-1715880-HTML-r2/image_m/fimmu-17-1715880-t002.jpg Table 2. Median log2-transformed maternal inflammatory marker levels across the at-birth and third-t https://www.frontiersin.org/files/Articles/1715880/fimmu-17-1715880-HTML-r2/image_m/fimmu-17-1715880-g003.jpg Figure 3. Volcano plots illustrating the associations between continuous maternal inflammation level https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1735601/full https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-g001.jpg Figure 1. Conceptual framework. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t001.jpg Table 1. Demographics of the respondents. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t002.jpg Table 2. Model summary: regression analysis. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t003.jpg Table 3. ANOVA results: regression analysis. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t004.jpg Table 4. Coefficients: regression analysis. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t005.jpg Table 5. Correlation analysis for role of social media engagement in driving brand loyalty. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t006.jpg Table 6. ANOVA test. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t007.jpg Table 7. Chi-square test for Instagram/Facebook. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t008.jpg Table 8. Chi-square test for Brand’s mobile app. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t009.jpg Table 9. Chi-square test for third-party apps. https://www.frontiersin.org/files/Articles/1735601/fsufs-09-1735601-HTML-r2/image_m/fsufs-09-1735601-t010.jpg Table 10. Chi-square test for brand’s official website. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1779435/full https://www.frontiersin.org/files/Articles/1779435/fimmu-17-1779435-HTML/image_m/fimmu-17-1779435-g001.jpg Figure 1. Trial profile. A total of 5,410 potential participants were screened, with a total of 601 https://www.frontiersin.org/files/Articles/1779435/fimmu-17-1779435-HTML/image_m/fimmu-17-1779435-t001.jpg Table 1. Baseline characteristics by study group allocation. https://www.frontiersin.org/files/Articles/1779435/fimmu-17-1779435-HTML/image_m/fimmu-17-1779435-g002.jpg Figure 2. Binding and sVNT responses up to 12 months following fractional (orange) or standard (blue https://www.frontiersin.org/files/Articles/1779435/fimmu-17-1779435-HTML/image_m/fimmu-17-1779435-g003.jpg Figure 3. T cell memory responses by AIM assay up to 12 months following fractional (orange) or stan https://www.frontiersin.org/files/Articles/1779435/fimmu-17-1779435-HTML/image_m/fimmu-17-1779435-g004.jpg Figure 4. T cell memory responses by ICS assay up to 12 months following fractional (orange) or stan https://www.frontiersin.org/files/Articles/1779435/fimmu-17-1779435-HTML/image_m/fimmu-17-1779435-g005.jpg Figure 5. IFN-γ producing cell responses by ELISpot up to 12 months following fractional (orange) or https://www.frontiersin.org/files/Articles/1779435/fimmu-17-1779435-HTML/image_m/fimmu-17-1779435-g006.jpg Figure 6. Multiplex cytokine analysis up to 12 months following fractional (orange) or standard (blu https://www.frontiersin.org/files/Articles/1779435/fimmu-17-1779435-HTML/image_m/fimmu-17-1779435-t002.jpg Table 2. Undocumented SARS-CoV-2 infections assessed by ≥1.2-fold change increase in anti-Spike IgG https://www.frontiersin.org/files/Articles/1779435/fimmu-17-1779435-HTML/image_m/fimmu-17-1779435-g007.jpg Figure 7. Vaccine-induced immunity (blue) and hybrid-immunity (pink) responses following fractional https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1743752/full https://www.frontiersin.org/files/Articles/1743752/fimmu-17-1743752-HTML/image_m/fimmu-17-1743752-g001.jpg Figure 1. (A, B) Temporal changes in serum tumor marker levels, generated using GraphPad Prism softw https://www.frontiersin.org/files/Articles/1743752/fimmu-17-1743752-HTML/image_m/fimmu-17-1743752-g002.jpg Figure 2. Timeline of the therapeutic intervention and image changes. https://www.frontiersin.org/files/Articles/1743752/fimmu-17-1743752-HTML/image_m/fimmu-17-1743752-t001.jpg Table 1. Timeline of diagnosis and treatment. https://www.frontiersin.org/files/Articles/1743752/fimmu-17-1743752-HTML/image_m/fimmu-17-1743752-t002.jpg Table 2. Prevalence of MSI-H/d-MMR and PD-L1 (CPS) in tumors types where MMR/MSI testing is guidelin https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1715455/full https://www.frontiersin.org/files/Articles/1715455/fimmu-16-1715455-HTML/image_m/fimmu-16-1715455-g001.jpg Figure 1. Definition of paradigm: the central concept of acoustic immune remodeling (Created in http https://www.frontiersin.org/files/Articles/1715455/fimmu-16-1715455-HTML/image_m/fimmu-16-1715455-g002.jpg Figure 2. Multifunctional roles of ultrasound in acoustic immune reprogramming (Created in https://B https://www.frontiersin.org/files/Articles/1715455/fimmu-16-1715455-HTML/image_m/fimmu-16-1715455-t001.jpg Table 1. Optimal ultrasound parameters for immune reprogramming in different disease contexts. https://www.frontiersin.org/files/Articles/1715455/fimmu-16-1715455-HTML/image_m/fimmu-16-1715455-t002.jpg Table 2. Comparison of characteristics and immune remodeling applications of major ultrasound-respon https://www.frontiersin.org/files/Articles/1715455/fimmu-16-1715455-HTML/image_m/fimmu-16-1715455-g003.jpg Figure 3. Programmability and bidirectional regulation across a spectrum of diseases (Created in htt https://www.frontiersin.org/files/Articles/1715455/fimmu-16-1715455-HTML/image_m/fimmu-16-1715455-t003.jpg Table 3. Summary of core therapeutic strategies and representative studies in acoustic immune remode https://www.frontiersin.org/files/Articles/1715455/fimmu-16-1715455-HTML/image_m/fimmu-16-1715455-g004.jpg Figure 4. The key challenges and paths leading to clinical transformation (Created in https://BioRen https://www.frontiersin.org/journals/ophthalmology/articles/10.3389/fopht.2026.1795898/full https://www.frontiersin.org/files/Articles/1795898/fopht-06-1795898-HTML/image_m/fopht-06-1795898-t001.jpg Table 1. Performance metrics for each system and fragment. https://www.frontiersin.org/files/Articles/1795898/fopht-06-1795898-HTML/image_m/fopht-06-1795898-t002.jpg Table 2. Comparison of CDE values for UNITY 4D and CENTURION torsional modes at defined power and ac https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1792856/full https://www.frontiersin.org/files/Articles/1792856/fphar-17-1792856-HTML-r1/image_m/fphar-17-1792856-g001.jpg Figure 1. Experimental design. At different time points, twenty-one female C57BL/6J mice were weighe https://www.frontiersin.org/files/Articles/1792856/fphar-17-1792856-HTML-r1/image_m/fphar-17-1792856-g002.jpg Figure 2. (A) mice body weight at the different time points (Sham; BLM; BLM+). (B–G) standard quanti https://www.frontiersin.org/files/Articles/1792856/fphar-17-1792856-HTML-r1/image_m/fphar-17-1792856-g003.jpg Figure 3. Refined segmentation strategy. Novel method for measuring respiratory parenchymal aeration https://www.frontiersin.org/files/Articles/1792856/fphar-17-1792856-HTML-r1/image_m/fphar-17-1792856-g004.jpg Figure 4. Segmented representative images. Three-dimensional and axial images of segmented mouse lun https://www.frontiersin.org/files/Articles/1792856/fphar-17-1792856-HTML-r1/image_m/fphar-17-1792856-g005.jpg Figure 5. Histological analysis. Representative images of Masson’s trichrome stained mice lung slide https://www.frontiersin.org/files/Articles/1792856/fphar-17-1792856-HTML-r1/image_m/fphar-17-1792856-g006.jpg Figure 6. TGF-β/Smad pathway activation in BLM mice. (A) representative Western blot analysis of cyt https://www.frontiersin.org/files/Articles/1792856/fphar-17-1792856-HTML-r1/image_m/fphar-17-1792856-g007.jpg Figure 7. Inflammasome pathway activation in BLM mice. (A) Representative Western blot analysis of N https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1694363/full https://www.frontiersin.org/files/Articles/1694363/fnut-13-1694363-HTML/image_m/fnut-13-1694363-t001.jpg Table 1. Characteristics of the study population. https://www.frontiersin.org/files/Articles/1694363/fnut-13-1694363-HTML/image_m/fnut-13-1694363-t002.jpg Table 2. Characteristics of study population and anthropometric measurements. https://www.frontiersin.org/files/Articles/1694363/fnut-13-1694363-HTML/image_m/fnut-13-1694363-t003.jpg Table 3. Participants’ energy, macro- and micronutrient intakes. https://www.frontiersin.org/files/Articles/1694363/fnut-13-1694363-HTML/image_m/fnut-13-1694363-t004.jpg Table 4. Participants’ PSS-10 and HIT-6 classification. https://www.frontiersin.org/files/Articles/1694363/fnut-13-1694363-HTML/image_m/fnut-13-1694363-t005.jpg Table 5. Participants’ PSS-10 and HIT-6 scores. https://www.frontiersin.org/files/Articles/1694363/fnut-13-1694363-HTML/image_m/fnut-13-1694363-t006.jpg Table 6. Correlation between age, dietary magnesium intake, height, body weight, BMI values, HIT-6, https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1773976/full https://www.frontiersin.org/files/Articles/1773976/fsufs-10-1773976-HTML/image_m/fsufs-10-1773976-g001.jpg Figure 1. Theoretical framework. https://www.frontiersin.org/files/Articles/1773976/fsufs-10-1773976-HTML/image_m/fsufs-10-1773976-t001.jpg Table 1. Description of variables. https://www.frontiersin.org/files/Articles/1773976/fsufs-10-1773976-HTML/image_m/fsufs-10-1773976-t002.jpg Table 2. Baseline regression results. https://www.frontiersin.org/files/Articles/1773976/fsufs-10-1773976-HTML/image_m/fsufs-10-1773976-t003.jpg Table 3. Robustness and endogeneity tests. https://www.frontiersin.org/files/Articles/1773976/fsufs-10-1773976-HTML/image_m/fsufs-10-1773976-t004.jpg Table 4. Heterogeneity tests by dimension. https://www.frontiersin.org/files/Articles/1773976/fsufs-10-1773976-HTML/image_m/fsufs-10-1773976-t005.jpg Table 5. Mediating effect of agricultural mechanization. https://www.frontiersin.org/files/Articles/1773976/fsufs-10-1773976-HTML/image_m/fsufs-10-1773976-t006.jpg Table 6. Mediating effect of farmer fixed-asset investment. https://www.frontiersin.org/files/Articles/1773976/fsufs-10-1773976-HTML/image_m/fsufs-10-1773976-t007.jpg Table 7. Moderating effect results. https://www.frontiersin.org/files/Articles/1773976/fsufs-10-1773976-HTML/image_m/fsufs-10-1773976-g002.jpg Figure 2. The digital financial inclusion index and its sub-dimensions in Tiandong County, Guangxi ( https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1757799/full https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-g001.jpg Figure 1. Theoretical framework: regional green food industry resilience and sustainable development https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-t001.jpg Table 1. Data sources, variable construction, and descriptive statistics. https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-t002.jpg Table 2. Variable definitions, measurement indicators, and calculation methods. https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-t003.jpg Table 3. Endogeneity tests and robustness checks. https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-t004.jpg Table 4. Descriptive statistics and correlation matrix. https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-t005.jpg Table 5. Baseline and mediation results. https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-g002.jpg Figure 2. Mediation model: export competitiveness as mediator. https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-t006.jpg Table 6. Regional heterogeneity and moderation analysis. https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-t007.jpg Table 7. Summary of differentiated regional policy strategies. https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-g003.jpg Figure 3. Regional heterogeneity in mediation effects. https://www.frontiersin.org/files/Articles/1757799/fsufs-10-1757799-HTML/image_m/fsufs-10-1757799-g004.jpg Figure 4. Moderating effect of policy support intensity on the relationship between export competiti https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1747418/full https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-g001.jpg Figure 1. Theoretical logic of the DE affecting IAUR. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t001.jpg Table 1. Construction of the index system for the development of IAUR. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t002.jpg Table 2. Construction of DE development index indicator system. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t003.jpg Table 3. Descriptive statistics of variables. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-g002.jpg Figure 2. Spatial distribution of the level of IAUR development in Chinese cities (2012, 2015, 2019, https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-g003.jpg Figure 3. Spatial distribution of the level of DE development in Chinese cities (2012, 2015, 2019, 2 https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-g004.jpg Figure 4. Kernel density of the development level of IUAR and DE in Chinese cities. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t004.jpg Table 4. Benchmark regression results. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t005.jpg Table 5. Dimensional regression results. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t006.jpg Table 6. Robustness and endogeneity tests results. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t007.jpg Table 7. Heterogeneity analysis by geographical location. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t008.jpg Table 8. Heterogeneity analysis by city administrative hierarchy. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t009.jpg Table 9. Heterogeneity analysis by DE development level. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t010.jpg Table 10. Threshold interval test results. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t011.jpg Table 11. Threshold effects regression results. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t012.jpg Table 12. Global Moran’s I of the IAUR and DE, 2012–2023. https://www.frontiersin.org/files/Articles/1747418/fsufs-09-1747418-HTML/image_m/fsufs-09-1747418-t013.jpg Table 13. SDM regression results and effects decomposition. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1743757/full https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-g001.jpg Figure 1. Cumulative distribution of dietary diversity scores among rural residents. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-g002.jpg Figure 2. Annual trends in dietary diversity scores among rural residents. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-t001.jpg Table 1. Descriptive statistics. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-t002.jpg Table 2. Baseline regression. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-t003.jpg Table 3. Robustness test. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-t004.jpg Table 4. Endogeneity tests. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-t005.jpg Table 5. Heterogeneity analysis: individual heterogeneity. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-t006.jpg Table 6. Heterogeneity analysis: Household heterogeneity. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-t007.jpg Table 7. Heterogeneity analysis: community heterogeneity. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-t008.jpg Table 8. Heterogeneity analysis: food source heterogeneity. https://www.frontiersin.org/files/Articles/1743757/fsufs-10-1743757-HTML/image_m/fsufs-10-1743757-t009.jpg Table 9. Mechanism analysis. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1743006/full https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-g001.jpg Figure 1. Hypothesized structural equation model. https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-t001.jpg Table 1. Sample composition and data sources. https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-t002.jpg Table 2. Baseline characteristics and balance test (farmers). https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-t003.jpg Table 3. Baseline characteristics and balance test (students). https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-t004.jpg Table 4. Variable definitions and measurements. https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-t005.jpg Table 5. Student entrepreneurship project incubation outcomes. https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-t006.jpg Table 6. Impact of blockchain traceability on consumer perceptions and purchase behavior. https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-g002.jpg Figure 2. Student entrepreneurship success rate comparison. https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-t007.jpg Table 7. Student entrepreneurship competency development (experimental group, N = 60). https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-g003.jpg Figure 3. Monthly revenue growth trend of student entrepreneurship projects. https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-t008.jpg Table 8. Farmer household income changes (2023–2024). https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-g004.jpg Figure 4. Farmer household income changes: project participants vs. control group. https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-t009.jpg Table 9. Sustainable development comprehensive assessment. https://www.frontiersin.org/files/Articles/1743006/fsufs-09-1743006-HTML-r1/image_m/fsufs-09-1743006-g005.jpg Figure 5. Structural equation model path analysis results. https://www.frontiersin.org/journals/epidemiology/articles/10.3389/fepid.2026.1710531/full https://www.frontiersin.org/files/Articles/1710531/fepid-06-1710531-HTML/image_m/fepid-06-1710531-g001.jpg Figure 1. Health-seeking behavior and interventions. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1790229/full https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-g001.jpg Figure 1. PRISMA flow diagram. PRISMA flow diagram delineates the systematic process of identifying https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-t001.jpg Table 1. Characteristics of the studies included in the meta-analysis. https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-t002.jpg Table 2. Characteristics of the studies included in the meta-analysis. https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-g002.jpg Figure 2. Forest plot comparing IL-6: no appendicitis versus appendicitis. https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-g003.jpg Figure 3. Forest plot comparing IL-6: uncomplicated appendicitis versus complicated appendicitis. UA https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-g004.jpg Figure 4. Funnel plot of the comparisons of IL-6: uncomplicated appendicitis versus complicated appe https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-g005.jpg Figure 5. Subgroup forest plot comparing IL-6: no appendicitis versus appendicitis. https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-g006.jpg Figure 6. Subgroup forest plots of the comparisons of IL-6: uncomplicated appendicitis versus compli https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-g007.jpg Figure 7. Leave−one−out sensitivity analysis plot for studies comparing non−appendicitis versus appe https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-g008.jpg Figure 8. Leave−one−out sensitivity analysis plot for studies comparing non−complicated appendicitis https://www.frontiersin.org/files/Articles/1790229/fimmu-17-1790229-HTML/image_m/fimmu-17-1790229-g009.jpg Figure 9. Forest plot of the independent predictive value of IL-6 in complicated appendicitis. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1813865/full https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-t001.jpg Table 1. Anthropometric data, CPET workload protocols and performance outcomes for each study partic https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-t002.jpg Table 2. Parameters measured during the study and their corresponding measurement time points used i https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-g001.jpg Figure 1. Schematic representation of the CPET protocol. https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-t003.jpg Table 3. Mean values (standard deviation) of metabolic and respiratory system parameters measured du https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-t004.jpg Table 4. Results of the ANOVA test for effect of time for metabolic, respiratory system, and HR/HRV https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-g002.jpg Figure 2. Time course of the ventilatory equivalent for oxygen (VE/V̇O2) throughout the measurement https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-t005.jpg Table 5. t test statistic, degrees of freedom, p value and effect sizes of parameters, measured only https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-t006.jpg Table 6. HRV parameters for both study groups during resting (REST) and recovery (REC) phase. Data a https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-t007.jpg Table 7. Mean values (standard deviation) for parameters associated with microcirculation in healthy https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-g003.jpg Figure 3. Box plot of skin blood flow measured in the finger during resting phase (REST) and during https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-g004.jpg Figure 4. Box plot of cutaneous vascular conductance measured in the finger during resting phase (RE https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-g005.jpg Figure 5. Box plot of skin temperature measured in the finger during resting phase (REST) and during https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-t008.jpg Table 8. Blood glucose (GC) and lactate concentrations (LC) measured during resting phase and upon r https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-g006.jpg Figure 6. Box plot of blood glucose concentrations (GC) at the time of resting phase (REST) and imme https://www.frontiersin.org/files/Articles/1813865/fendo-17-1813865-HTML/image_m/fendo-17-1813865-g007.jpg Figure 7. Box plot of blood lactate concentrations (LC) at the time of resting phase (REST) and imme https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1708575/full https://www.frontiersin.org/files/Articles/1708575/fonc-15-1708575-HTML/image_m/fonc-15-1708575-g001.jpg Figure 1. A patient with epidermal growth factor receptor-mutated non-small cell lung carcinoma. Com https://www.frontiersin.org/files/Articles/1708575/fonc-15-1708575-HTML/image_m/fonc-15-1708575-g002.jpg Figure 2. Comparison of 12-lead electrocardiograms (ECG) at baseline and after treatment. (A) Baseli https://www.frontiersin.org/files/Articles/1708575/fonc-15-1708575-HTML/image_m/fonc-15-1708575-t001.jpg Table 1. The changes in serum cardiac markers over time with treatment. https://www.frontiersin.org/files/Articles/1708575/fonc-15-1708575-HTML/image_m/fonc-15-1708575-g003.jpg Figure 3. Time course of this case. https://www.frontiersin.org/files/Articles/1708575/fonc-15-1708575-HTML/image_m/fonc-15-1708575-g004.jpg Figure 4. Detection of myocardial injury using cardiac imaging. (A) T1 mapping showed no clear eleva https://www.frontiersin.org/journals/clinical-diabetes-and-healthcare/articles/10.3389/fcdhc.2026.1760534/full https://www.frontiersin.org/files/Articles/1760534/fcdhc-07-1760534-HTML/image_m/fcdhc-07-1760534-g001.jpg Figure 1. Selection criteria. https://www.frontiersin.org/files/Articles/1760534/fcdhc-07-1760534-HTML/image_m/fcdhc-07-1760534-t001.jpg Table 1. Participants’ characteristics. https://www.frontiersin.org/files/Articles/1760534/fcdhc-07-1760534-HTML/image_m/fcdhc-07-1760534-t002.jpg Table 2. The ocular symptoms and ocular status of participants stratified by gender. https://www.frontiersin.org/files/Articles/1760534/fcdhc-07-1760534-HTML/image_m/fcdhc-07-1760534-t003.jpg Table 3. Overall prevalence of clinically diagnosed dry eye DED stratified by age group, gender, mar https://www.frontiersin.org/files/Articles/1760534/fcdhc-07-1760534-HTML/image_m/fcdhc-07-1760534-t004.jpg Table 4. The difference between the quality and the quantity of tears amongst the participants using https://www.frontiersin.org/files/Articles/1760534/fcdhc-07-1760534-HTML/image_m/fcdhc-07-1760534-t005.jpg Table 5. Determinants of Clinically Significant DED. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1678974/full https://www.frontiersin.org/files/Articles/1678974/fpsyt-17-1678974-HTML/image_m/fpsyt-17-1678974-t001.jpg Table 1. Demographic and clinical characteristics of study population (n=68). https://www.frontiersin.org/files/Articles/1678974/fpsyt-17-1678974-HTML/image_m/fpsyt-17-1678974-t002.jpg Table 2. Diagnoses of the study sample at discharge. https://www.frontiersin.org/files/Articles/1678974/fpsyt-17-1678974-HTML/image_m/fpsyt-17-1678974-g001.jpg Figure 1. Distribution of diagnostic categories by age group. The figure illustrates the distributio https://www.frontiersin.org/files/Articles/1678974/fpsyt-17-1678974-HTML/image_m/fpsyt-17-1678974-t003.jpg Table 3. Clinical characteristics of psychosis of the study population on admission. https://www.frontiersin.org/files/Articles/1678974/fpsyt-17-1678974-HTML/image_m/fpsyt-17-1678974-t004.jpg Table 4A. Medical investigation. Abnormal findings and diagnostic investigations. https://www.frontiersin.org/files/Articles/1678974/fpsyt-17-1678974-HTML/image_m/fpsyt-17-1678974-t005.jpg Table 4B. Quantifiable laboratory values. https://www.frontiersin.org/journals/environmental-archaeology/articles/10.3389/fearc.2026.1816569/full https://www.frontiersin.org/files/Articles/1816569/fearc-05-1816569-HTML/image_m/fearc-05-1816569-g001.jpg Figure 1. Conceptual framework of the toponymic research methodology. https://www.frontiersin.org/files/Articles/1816569/fearc-05-1816569-HTML/image_m/fearc-05-1816569-t001.jpg Table 1. Toponyms indicating landscape changes (Ibn Fadlan, 1939a,b; ; Kaimuldinova, 2022; Maksheev, https://www.frontiersin.org/files/Articles/1816569/fearc-05-1816569-HTML/image_m/fearc-05-1816569-g002.jpg Figure 2. Landscape transformation: spatial representation. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1680854/full https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-t001.jpg Table 1. ASRs of opioid use disorders in the China and total in 1990 and 2021, and the temporal tren https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-t002.jpg Table 2. Number of incident cases and incidence rate of opioid use disorders in the China and total https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-t003.jpg Table 3. Number of prevalent cases and prevalence rate of opioid use disorders in the China and tota https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-t004.jpg Table 4. Number of death cases and mortality rate of opioid use disorders in the China and total in https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-t005.jpg Table 5. Number of DALYs cases and DALYs rate of opioid use disorders in the China and total in 1990 https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-g001.jpg Figure 1. Trends in the burden of opioid use disorder (OUD) in China and globally. (A,E) Show the ag https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-g002.jpg Figure 2. Age, period, and cohort effects on the incidence and prevalence of OUD in China. (A,B) Sho https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-t006.jpg Table 6. OUD prevalence and mortality relative risks due to age, period, and cohort effects. https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-g003.jpg Figure 3. Trends in age-standardized burden indicators and their annual percentage change (APC) for https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-g004.jpg Figure 4. Health inequality trends in OUD-related DALYs across countries, 1990–2019. (A) Shows healt https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-g005.jpg Figure 5. Forecasted trends in age-standardized rates of OUD in China, 2020–2035. (A) ASIR; (B) ASPR https://www.frontiersin.org/files/Articles/1680854/fpubh-13-1680854-HTML/image_m/fpubh-13-1680854-g006.jpg Figure 6. Predicted trends of the burden of opioid use disorder (OUD) in China based on the BAPC mod https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1695935/full https://www.frontiersin.org/files/Articles/1695935/fmed-12-1695935-HTML-r2/image_m/fmed-12-1695935-g001.jpg Figure 1. Selection procedure for the study population. Participants were drawn from the 2017 to 201 https://www.frontiersin.org/files/Articles/1695935/fmed-12-1695935-HTML-r2/image_m/fmed-12-1695935-t001.jpg Table 1. Distribution and characteristics of U.S. adults aged 18–59 years by body composition group https://www.frontiersin.org/files/Articles/1695935/fmed-12-1695935-HTML-r2/image_m/fmed-12-1695935-t002.jpg Table 2. Association between body composition and odds of elevated high-sensitivity C-reactive prote https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1674846/full https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-g001.jpg Figure 1. Trend of irrigation during the experiment. https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-t001.jpg Table 1. Total and aboveground dry weight (g plant−1), root-to-shoot ratio (R/S), total fruit number https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-g002.jpg Figure 2. Interaction effects of water content and biostimulant doses on total (A) and aboveground ( https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-t002.jpg Table 2. Color parameters (L*: brightness; a*: green intensity, chroma component ranging from green https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-g003.jpg Figure 3. Interaction effects of water content and biostimulant doses on brightness (L*) (A) and chr https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-t003.jpg Table 3. Tomato fruit quality traits as affected by water content (100%WC and 50%WC) and biostimulan https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-g004.jpg Figure 4. Interaction effects of water content and biostimulant application on the total soluble sol https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-g005.jpg Figure 5. Interaction effects of water content and biostimulant application on the lycopene content https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-g006.jpg Figure 6. Mean effects of water content and biostimulant doses on net photosynthesis (A) and stomata https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-g007.jpg Figure 7. Mean effects of water content and biostimulant doses on Fv/Fm (A) and SPAD index (B) of to https://www.frontiersin.org/files/Articles/1674846/fsufs-10-1674846-HTML/image_m/fsufs-10-1674846-g008.jpg Figure 8. Principal component loading and score plot of principal component analysis (PCA) of qualit https://www.frontiersin.org/journals/epigenetics-and-epigenomics/articles/10.3389/freae.2026.1755829/full https://www.frontiersin.org/files/Articles/1755829/freae-04-1755829-HTML/image_m/freae-04-1755829-g001.jpg Figure 1. Summary figure of the relationship between lncRNAs and drug resistance and discovery. Bioi https://www.frontiersin.org/files/Articles/1755829/freae-04-1755829-HTML/image_m/freae-04-1755829-t001.jpg Table 1. Summary table of the mechanisms through which lncRNAs can regulate drug resistance. https://www.frontiersin.org/files/Articles/1755829/freae-04-1755829-HTML/image_m/freae-04-1755829-g002.jpg Figure 2. Schematic overview of the role of epigenetic modifications in regulating cancer cell respo https://www.frontiersin.org/files/Articles/1755829/freae-04-1755829-HTML/image_m/freae-04-1755829-t002.jpg Table 2. Summary table of epigenetic inhibitors or approved drugs and their metabolic targets. https://www.frontiersin.org/files/Articles/1755829/freae-04-1755829-HTML/image_m/freae-04-1755829-g003.jpg Figure 3. Schematic representation of the bidirectional interplay between cellular metabolism and ep https://www.frontiersin.org/files/Articles/1755829/freae-04-1755829-HTML/image_m/freae-04-1755829-t003.jpg Table 3. Summary table of the potential usefulness of bioinformatic tools in the context of drug dis https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1732193/full https://www.frontiersin.org/files/Articles/1732193/fimmu-16-1732193-HTML/image_m/fimmu-16-1732193-g001.jpg Figure 1. Overview of the immune-related adverse outcome pathway of cytokine release syndrome, devel https://www.frontiersin.org/files/Articles/1732193/fimmu-16-1732193-HTML/image_m/fimmu-16-1732193-t001.jpg Table 1. Traditional in vitro models for studying individual or multiple events of CRS. https://www.frontiersin.org/files/Articles/1732193/fimmu-16-1732193-HTML/image_m/fimmu-16-1732193-t002.jpg Table 2. Several advanced in vitro models for studying individual or multiple events of CRS. https://www.frontiersin.org/files/Articles/1732193/fimmu-16-1732193-HTML/image_m/fimmu-16-1732193-t003.jpg Table 3. Suggested advantages and disadvantages of previously mentioned models. https://www.frontiersin.org/files/Articles/1732193/fimmu-16-1732193-HTML/image_m/fimmu-16-1732193-g002.jpg Figure 2. Decision tree to aid appropriate model selections. Created with BioRender.com. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1562648/full https://www.frontiersin.org/files/Articles/1562648/fvets-12-1562648-HTML-r1/image_m/fvets-12-1562648-t001.jpg Table 1. Results from two sets of questions on methods of communicating with farmers about biosecuri https://www.frontiersin.org/files/Articles/1562648/fvets-12-1562648-HTML-r1/image_m/fvets-12-1562648-t002.jpg Table 2. Details of focus groups and materials used for analysis. https://www.frontiersin.org/files/Articles/1562648/fvets-12-1562648-HTML-r1/image_m/fvets-12-1562648-g001.jpg Figure 1. Post-it notes and flipcharts created by focus groups 1 and 4. https://www.frontiersin.org/files/Articles/1562648/fvets-12-1562648-HTML-r1/image_m/fvets-12-1562648-t003.jpg Table 3. Themes and subthemes developed from the content analysis. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1716676/full https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-t001.jpg Table 1. Protocol optimization for hydroponic-based phenotyping (list of macro- and micro-nutrient c https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-g001.jpg Figure 1. Weekly minimum and maximum temperature, Cumulative Growing Degree Days (GDD), and WDS peri https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-g002.jpg Figure 2. Screening of maize inbred lines under water deficit stress conditions. (a) Hydroponic plat https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-t002.jpg Table 2. Analysis of variance of maize stock for root traits under hydroponics phenotyping platform. https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-t003.jpg Table 3. Analysis of variance of maize stock for yield and its contributing traits under field condi https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-g003.jpg Figure 3. Boxplots showing variation in traits under control (c) and water-deficit stress (t) treatm https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-g004.jpg Figure 4. Correlation matrix of maize traits: a visual representation of the linear relationships (R https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-g005.jpg Figure 5. Principal component analysis (PCA) biplot summarizing the variability among maize genotype https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-t004.jpg Table 4. Top contributing root traits to grain yield (GY) under stress: PCA contribution and correla https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-t005.jpg Table 5. Comparison of morpho-physiological and yield-related traits in tolerant maize lines under W https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-t006.jpg Table 6. Comparative evaluation of maize inbreds using drought tolerance indices based on RT and TL. https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-g006.jpg Figure 6. Correlation of genotype performance ranks between hydroponics and field environments. This https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-g007.jpg Figure 7. Visual assessment of root and shoot performance of maize inbreds under hydroponic screenin https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-g008.jpg Figure 8. High-resolution root scans of maize inbreds at 60 DAS under hydroponic screening. Root ima https://www.frontiersin.org/files/Articles/1716676/fpls-16-1716676-HTML/image_m/fpls-16-1716676-g009.jpg Figure 9. High-resolution root scans of maize introgressed lines at 60 DAS under hydroponic screenin https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1819270/full https://www.frontiersin.org/files/Articles/1819270/fgene-17-1819270-HTML/image_m/fgene-17-1819270-g001.jpg Figure 1. Architectural models of public genomic data infrastructures. (A) Centralized archive model https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1798841/full https://www.frontiersin.org/files/Articles/1798841/fmed-13-1798841-HTML/image_m/fmed-13-1798841-t001.jpg Table 1. Baseline characteristics of the study population stratified by preoperative anxiety status https://www.frontiersin.org/files/Articles/1798841/fmed-13-1798841-HTML/image_m/fmed-13-1798841-t002.jpg Table 2. Univariate analysis of factors associated with preoperative anxiety among surgical patients https://www.frontiersin.org/files/Articles/1798841/fmed-13-1798841-HTML/image_m/fmed-13-1798841-t003.jpg Table 3. Multivariable logistic regression analysis identifying independent predictors of preoperati https://www.frontiersin.org/files/Articles/1798841/fmed-13-1798841-HTML/image_m/fmed-13-1798841-g001.jpg Figure 1. Receiver operating characteristic (ROC) curve of the multivariable prediction model for pr https://www.frontiersin.org/files/Articles/1798841/fmed-13-1798841-HTML/image_m/fmed-13-1798841-g002.jpg Figure 2. Calibration plot of the multivariable prediction model for preoperative anxiety. The calib https://www.frontiersin.org/files/Articles/1798841/fmed-13-1798841-HTML/image_m/fmed-13-1798841-g003.jpg Figure 3. Nomogram for individualized prediction of preoperative anxiety. The nomogram was construct https://www.frontiersin.org/files/Articles/1798841/fmed-13-1798841-HTML/image_m/fmed-13-1798841-t004.jpg Table 4. Discriminative performance of the predictive model for preoperative anxiety. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1742248/full https://www.frontiersin.org/files/Articles/1742248/fpubh-14-1742248-HTML/image_m/fpubh-14-1742248-g001.jpg Figure 1. Schematic representation of the step-by-step process of co-designing the WASH intervention https://www.frontiersin.org/files/Articles/1742248/fpubh-14-1742248-HTML/image_m/fpubh-14-1742248-t001.jpg Table 1. The composition of the TWG for WASH interventions. https://www.frontiersin.org/files/Articles/1742248/fpubh-14-1742248-HTML/image_m/fpubh-14-1742248-t002.jpg Table 2. CodeBook for qualitative analysis (IDI and FGD). https://www.frontiersin.org/files/Articles/1742248/fpubh-14-1742248-HTML/image_m/fpubh-14-1742248-t003.jpg Table 3. Socio-demographic characteristics of the respondents. https://www.frontiersin.org/files/Articles/1742248/fpubh-14-1742248-HTML/image_m/fpubh-14-1742248-t004.jpg Table 4. Prioritization outcomes of WASH interventions by the stakeholders during the co-designing w https://www.frontiersin.org/files/Articles/1742248/fpubh-14-1742248-HTML/image_m/fpubh-14-1742248-t005.jpg Table 5. Outputs of co-designed implementation strategies for water interventions developed by the s https://www.frontiersin.org/files/Articles/1742248/fpubh-14-1742248-HTML/image_m/fpubh-14-1742248-t006.jpg Table 6. Outputs of co-designed implementation strategies for sanitation interventions developed by https://www.frontiersin.org/files/Articles/1742248/fpubh-14-1742248-HTML/image_m/fpubh-14-1742248-t007.jpg Table 7. Outputs of co-designed implementation strategies for hygiene interventions developed by the https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1768286/full https://www.frontiersin.org/files/Articles/1768286/fpsyg-17-1768286-HTML/image_m/fpsyg-17-1768286-g001.jpg Figure 1. The job-demand resources (JDR) model (Demerouti et al., 2001). https://www.frontiersin.org/files/Articles/1768286/fpsyg-17-1768286-HTML/image_m/fpsyg-17-1768286-g002.jpg Figure 2. Conceptual model linking colleague absenteeism to burnout in primary healthcare workers (b https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1763407/full https://www.frontiersin.org/files/Articles/1763407/fpsyt-17-1763407-HTML-r1/image_m/fpsyt-17-1763407-t001.jpg Table 1. Moral injury group psychotherapy phase overview. https://www.frontiersin.org/files/Articles/1763407/fpsyt-17-1763407-HTML-r1/image_m/fpsyt-17-1763407-t002.jpg Table 2. Moral injury group psychotherapy session overview. https://www.frontiersin.org/files/Articles/1763407/fpsyt-17-1763407-HTML-r1/image_m/fpsyt-17-1763407-t003.jpg Table 3. Hypothesized change agents, mechanisms, and facilitators of change. https://www.frontiersin.org/files/Articles/1763407/fpsyt-17-1763407-HTML-r1/image_m/fpsyt-17-1763407-g001.jpg Figure 1. The hypothesized model of therapeutic change in the depth-oriented group psychotherapy for https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1807180/full https://www.frontiersin.org/files/Articles/1807180/fpls-17-1807180-HTML/image_m/fpls-17-1807180-g001.jpg Figure 1. Representative image of cassava root slices for postharvest physiological deterioration (P https://www.frontiersin.org/files/Articles/1807180/fpls-17-1807180-HTML/image_m/fpls-17-1807180-g002.jpg Figure 2. Distribution of PPD severity via human visual and AI-powered phenotyping. Histograms with https://www.frontiersin.org/files/Articles/1807180/fpls-17-1807180-HTML/image_m/fpls-17-1807180-g003.jpg Figure 3. Population structure of 298 cassava genotypes used in genome-wide association studies (GWA https://www.frontiersin.org/files/Articles/1807180/fpls-17-1807180-HTML/image_m/fpls-17-1807180-g004.jpg Figure 4. Population structure of 298 cassava genotypes used in genome-wide association studies (GWA https://www.frontiersin.org/files/Articles/1807180/fpls-17-1807180-HTML/image_m/fpls-17-1807180-t001.jpg Table 1. Significant SNPs identified from human visual and AI-powered PPD scoring methods using DArT https://www.frontiersin.org/files/Articles/1807180/fpls-17-1807180-HTML/image_m/fpls-17-1807180-g005.jpg Figure 5. Manhattan and quantile–quantile (Q-Q) plots of significant markers for PPD from the GWAS a https://www.frontiersin.org/files/Articles/1807180/fpls-17-1807180-HTML/image_m/fpls-17-1807180-t002.jpg Table 2. Significant SNPs identified from human visual and AI-powered PPD scoring methods using GBS https://www.frontiersin.org/files/Articles/1807180/fpls-17-1807180-HTML/image_m/fpls-17-1807180-g006.jpg Figure 6. Manhattan and quantile–quantile (Q-Q) plots of significant markers for PPD from the GWAS a https://www.frontiersin.org/files/Articles/1807180/fpls-17-1807180-HTML/image_m/fpls-17-1807180-t003.jpg Table 3. Putative gene annotations of significant SNPs directly mapped to annotated genes using DArT https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1709865/full https://www.frontiersin.org/files/Articles/1709865/fpubh-13-1709865-HTML-r1/image_m/fpubh-13-1709865-g001.jpg Figure 1. Flow diagram of sample selection showing inclusion criteria for girls aged 12–23 months. https://www.frontiersin.org/files/Articles/1709865/fpubh-13-1709865-HTML-r1/image_m/fpubh-13-1709865-t001.jpg Table 1. Description of independent variables used in the study. https://www.frontiersin.org/files/Articles/1709865/fpubh-13-1709865-HTML-r1/image_m/fpubh-13-1709865-t002.jpg Table 2. Description of sequential adjustment models used in the analysis. https://www.frontiersin.org/files/Articles/1709865/fpubh-13-1709865-HTML-r1/image_m/fpubh-13-1709865-t003.jpg Table 3. Distribution of vaccination status among girl children aged 12–23 months by background char https://www.frontiersin.org/files/Articles/1709865/fpubh-13-1709865-HTML-r1/image_m/fpubh-13-1709865-g002.jpg Figure 2. Prevalence of full vaccination among girl children by maternal son preference (N = 20,899) https://www.frontiersin.org/files/Articles/1709865/fpubh-13-1709865-HTML-r1/image_m/fpubh-13-1709865-t004.jpg Table 4. Generalized linear model estimates of maternal son preference and girl child vaccination in https://www.frontiersin.org/files/Articles/1709865/fpubh-13-1709865-HTML-r1/image_m/fpubh-13-1709865-t005.jpg Table 5. Fairlie decomposition analysis showing the contribution of child, maternal, and household c https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1773668/full https://www.frontiersin.org/files/Articles/1773668/fpsyt-17-1773668-HTML/image_m/fpsyt-17-1773668-t001.jpg Table 1. Demographic and clinical characteristics. Values are presented as mean ± SD (standard devia https://www.frontiersin.org/files/Articles/1773668/fpsyt-17-1773668-HTML/image_m/fpsyt-17-1773668-t002.jpg Table 2. Adjusted marginal means (and standard errors) of the total completion time and number of er https://www.frontiersin.org/files/Articles/1773668/fpsyt-17-1773668-HTML/image_m/fpsyt-17-1773668-g001.jpg Figure 1. Adjusted marginal means and 95% confidence intervals for (A) total completion time (second https://www.frontiersin.org/files/Articles/1773668/fpsyt-17-1773668-HTML/image_m/fpsyt-17-1773668-g002.jpg Figure 2. (A, B) show the relationship between the number of errors in TMT-B and B-A and the route o https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1672792/full https://www.frontiersin.org/files/Articles/1672792/fped-13-1672792-HTML/image_m/fped-13-1672792-t001.jpg Table 1. Percentage of patients vaccinated for influenza and/or COVID-19 in the four groups. https://www.frontiersin.org/files/Articles/1672792/fped-13-1672792-HTML/image_m/fped-13-1672792-t002.jpg Table 2. The association between the flu and the COVID-19 vaccinations and treatment. https://www.frontiersin.org/files/Articles/1672792/fped-13-1672792-HTML/image_m/fped-13-1672792-t003.jpg Table 3. Relationship between age at the diagnosis and one of the two vaccinations under study (the https://www.frontiersin.org/files/Articles/1672792/fped-13-1672792-HTML/image_m/fped-13-1672792-t004.jpg Table 4. Adherence to vaccinations for influenza and/or COVID-19 in correlation with treatment. https://www.frontiersin.org/journals/ophthalmology/articles/10.3389/fopht.2026.1812277/full https://www.frontiersin.org/files/Articles/1812277/fopht-06-1812277-HTML/image_m/fopht-06-1812277-t001.jpg Table 1. Calculated estimates of workforce numbers per 10,000 children and young people. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1792092/full https://www.frontiersin.org/files/Articles/1792092/fpsyt-17-1792092-HTML/image_m/fpsyt-17-1792092-t001.jpg Table 1. Comparison of stress correlates between participants with and without anxiety. https://www.frontiersin.org/files/Articles/1792092/fpsyt-17-1792092-HTML/image_m/fpsyt-17-1792092-t002.jpg Table 2. Distribution of anxiety severity levels among Chinese primary school teachers (N = 3,199). https://www.frontiersin.org/files/Articles/1792092/fpsyt-17-1792092-HTML/image_m/fpsyt-17-1792092-t003.jpg Table 3. Binary logistic regression analyses of correlates of anxiety in primary school teachers. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1783636/full https://www.frontiersin.org/files/Articles/1783636/fcvm-13-1783636-HTML-r1/image_m/fcvm-13-1783636-g001.jpg Figure 1. Flowchart of patient selection and data preprocessing for model development. Stepwise excl https://www.frontiersin.org/files/Articles/1783636/fcvm-13-1783636-HTML-r1/image_m/fcvm-13-1783636-g002.jpg Figure 2. Variable selection using LASSO and boruta. (A) cross-validation curve of binomial deviance https://www.frontiersin.org/files/Articles/1783636/fcvm-13-1783636-HTML-r1/image_m/fcvm-13-1783636-g003.jpg Figure 3. Discrimination of eight machine-learning models and performance of the best classifier. (A https://www.frontiersin.org/files/Articles/1783636/fcvm-13-1783636-HTML-r1/image_m/fcvm-13-1783636-t001.jpg Table 1. Baseline characteristics of training set. https://www.frontiersin.org/files/Articles/1783636/fcvm-13-1783636-HTML-r1/image_m/fcvm-13-1783636-g004.jpg Figure 4. SHAP-based interpretation of the optimal predictive model.(A) Beeswarm plot showing the di https://www.frontiersin.org/files/Articles/1783636/fcvm-13-1783636-HTML-r1/image_m/fcvm-13-1783636-g005.jpg Figure 5. Calibration and clinical utility of the prediction models in the validation set. (A) Calib https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1752369/full https://www.frontiersin.org/files/Articles/1752369/fmed-13-1752369-HTML/image_m/fmed-13-1752369-g001.jpg Figure 1. Electronic universal testing machine. Photograph of the mechanical testing apparatus used https://www.frontiersin.org/files/Articles/1752369/fmed-13-1752369-HTML/image_m/fmed-13-1752369-g002.jpg Figure 2. The bending plate experiments. Schematic illustration of the three plate bending configura https://www.frontiersin.org/files/Articles/1752369/fmed-13-1752369-HTML/image_m/fmed-13-1752369-g003.jpg Figure 3. The influence of drilled an invaild hole in clavicle experiments. (A) Distal-empty-hole gr https://www.frontiersin.org/files/Articles/1752369/fmed-13-1752369-HTML/image_m/fmed-13-1752369-t001.jpg Table 1. The force data of bend plate groups. https://www.frontiersin.org/files/Articles/1752369/fmed-13-1752369-HTML/image_m/fmed-13-1752369-t002.jpg Table 2. The force data of screw type and location groups. https://www.frontiersin.org/files/Articles/1752369/fmed-13-1752369-HTML/image_m/fmed-13-1752369-t003.jpg Table 3. The force data of invalid hole in the clavicle. https://www.frontiersin.org/files/Articles/1752369/fmed-13-1752369-HTML/image_m/fmed-13-1752369-g004.jpg Figure 4. The maximum force of these groups were measured in the figures, when the clavicles were fr https://www.frontiersin.org/files/Articles/1752369/fmed-13-1752369-HTML/image_m/fmed-13-1752369-g005.jpg Figure 5. The screws type and location experiments. Comparison of screw types used at the proximal h https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1717082/full https://www.frontiersin.org/files/Articles/1717082/fcimb-15-1717082-HTML/image_m/fcimb-15-1717082-g001.jpg Figure 1. Schematic representation outlining the composite hydrogel and its potential application in https://www.frontiersin.org/files/Articles/1717082/fcimb-15-1717082-HTML/image_m/fcimb-15-1717082-g002.jpg Figure 2. Characterization of the BP-QS/TGF-β3 hydrogel. (A) (1) The light-yellow lyophilized BP-QS/ https://www.frontiersin.org/files/Articles/1717082/fcimb-15-1717082-HTML/image_m/fcimb-15-1717082-g003.jpg Figure 3. Biocompatibility evaluations. (A) Both the TGF-β3 and BP-QS/TGF-β3 groups significantly re https://www.frontiersin.org/files/Articles/1717082/fcimb-15-1717082-HTML/image_m/fcimb-15-1717082-g004.jpg Figure 4. Cell scratch assay. (A) Representative microscopic images of cell scratch wound healing in https://www.frontiersin.org/files/Articles/1717082/fcimb-15-1717082-HTML/image_m/fcimb-15-1717082-g005.jpg Figure 5. In vitro antibacterial assay. (A) Antibacterial activity test, (B) inhibition zones, and ( https://www.frontiersin.org/files/Articles/1717082/fcimb-15-1717082-HTML/image_m/fcimb-15-1717082-g006.jpg Figure 6. Macroscopic progression and quantitative analysis of wound healing in diabetic mice. (A) S https://www.frontiersin.org/files/Articles/1717082/fcimb-15-1717082-HTML/image_m/fcimb-15-1717082-g007.jpg Figure 7. Histological analysis of the wound tissues. (A) H&E staining of the wound sections at (1) https://www.frontiersin.org/files/Articles/1717082/fcimb-15-1717082-HTML/image_m/fcimb-15-1717082-g008.jpg Figure 8. Systemic toxicity analysis of the major organs in mice. Histological analysis of the major https://www.frontiersin.org/journals/environmental-engineering/articles/10.3389/fenve.2026.1760490/full https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-t001.jpg Table 1. Water reuse in metropolitan and urban areas: typical reuse opportunities, constraints, conc https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-g001.jpg Figure 1. Approximate locations of decentralized wastewater treatment facilities in New York City. https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-g002.jpg Figure 2. Illustration of the two types of satellite treatment systems: The extraction type satellit https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-g003.jpg Figure 3. Satellite and stand-alone wastewater treatment facilities operate by the City of Los Angel https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-g004.jpg Figure 4. Representative advanced primary technologies: (a) cloth disc filter, (b) Proteus primary f https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-t002.jpg Table 2. Abbreviated descriptions of the operational details of APT technologies. https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-t003.jpg Table 3. Typical performance data and aerial footprint of APT technologies compared to conventional https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-g005.jpg Figure 5. Long-term constituent removal performance data for the cloth disc filter at the Linda WRRF https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-t004.jpg Table 4. Typical aerial footprint of AST technologies compared to conventional secondary treatment w https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-g006.jpg Figure 6. Application of APT and AST technologies: (a) approximate footprint requirements for APT an https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-g007.jpg Figure 7. Schematic illustration of the different types of satellite treatment systems and reuse opp https://www.frontiersin.org/files/Articles/1760490/fenve-05-1760490-HTML-r1/image_m/fenve-05-1760490-g008.jpg Figure 8. Typical details illustrating the use of a dual piping system for landscape irrigation with https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1669036/full https://www.frontiersin.org/files/Articles/1669036/fmed-12-1669036-HTML-r2/image_m/fmed-12-1669036-t001.jpg Table 1. Cardiopulmonary exercise test results. https://www.frontiersin.org/files/Articles/1669036/fmed-12-1669036-HTML-r2/image_m/fmed-12-1669036-t002.jpg Table 2. Patients with chronic fatigue versus sedentary controls stratified by the presence or absen https://www.frontiersin.org/files/Articles/1669036/fmed-12-1669036-HTML-r2/image_m/fmed-12-1669036-g001.jpg Figure 1. (A) Normal and (B) dysfunctional respiratory patterns in two different ME/CFS cases, with https://www.frontiersin.org/files/Articles/1669036/fmed-12-1669036-HTML-r2/image_m/fmed-12-1669036-g002.jpg Figure 2. (A) PETCO2 at rest and 25 W on Day 1 and Day 2 of CPET in the patients with ME/CFS and per https://www.frontiersin.org/files/Articles/1669036/fmed-12-1669036-HTML-r2/image_m/fmed-12-1669036-g003.jpg Figure 3. Venn diagrams showing the overlap of dysfunctional breathing, persistent hyperventilation, https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1720324/full https://www.frontiersin.org/files/Articles/1720324/fpsyg-17-1720324-HTML/image_m/fpsyg-17-1720324-t001.jpg Table 1. Shows an 8-segment string trial. https://www.frontiersin.org/files/Articles/1720324/fpsyg-17-1720324-HTML/image_m/fpsyg-17-1720324-t002.jpg Table 2. Presents descriptive statistics (means, standard deviations) and measurement scales for all https://www.frontiersin.org/files/Articles/1720324/fpsyg-17-1720324-HTML/image_m/fpsyg-17-1720324-t003.jpg Table 3. Shows the correlations between NWS ADS reading and the variables under consideration. https://www.frontiersin.org/files/Articles/1720324/fpsyg-17-1720324-HTML/image_m/fpsyg-17-1720324-t004.jpg Table 4. Shows the correlations between NWS CDS reading and the variables under consideration. https://www.frontiersin.org/files/Articles/1720324/fpsyg-17-1720324-HTML/image_m/fpsyg-17-1720324-t005.jpg Table 5. Shows the regression model NWS ADS. https://www.frontiersin.org/files/Articles/1720324/fpsyg-17-1720324-HTML/image_m/fpsyg-17-1720324-t006.jpg Table 6. Shows the regression model NWS CDS. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1756518/full https://www.frontiersin.org/files/Articles/1756518/fcimb-16-1756518-HTML/image_m/fcimb-16-1756518-t001.jpg Table 1. Peptide names, molecular weights (kDa), and amino acid sequences (N- to C-terimuns) of CLEC https://www.frontiersin.org/files/Articles/1756518/fcimb-16-1756518-HTML/image_m/fcimb-16-1756518-g001.jpg Figure 1. Antibacterial activity of CLEC3A-derived peptides against K. pneumoniae and A. baumannii. https://www.frontiersin.org/files/Articles/1756518/fcimb-16-1756518-HTML/image_m/fcimb-16-1756518-g002.jpg Figure 2. Antifungal activity of CLEC3A-derived peptides against C. albicans, C. neoformans and C. a https://www.frontiersin.org/files/Articles/1756518/fcimb-16-1756518-HTML/image_m/fcimb-16-1756518-t002.jpg Table 2. MIC50 values (in µM), determined by viable count assay, of selected antimicrobial peptides https://www.frontiersin.org/files/Articles/1756518/fcimb-16-1756518-HTML/image_m/fcimb-16-1756518-g003.jpg Figure 3. Inhibition of biofilm formation. The effects of the CLEC3A-derived peptides HT-47 and WRK- https://www.frontiersin.org/files/Articles/1756518/fcimb-16-1756518-HTML/image_m/fcimb-16-1756518-g004.jpg Figure 4. Scanning electron microscopy of fungi after peptide treatment. C. albicans, C. neoformans, https://www.frontiersin.org/files/Articles/1756518/fcimb-16-1756518-HTML/image_m/fcimb-16-1756518-g005.jpg Figure 5. Transmission electron microscopy of Candida auris after peptide treatment. Representative https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1699693/full https://www.frontiersin.org/files/Articles/1699693/fcell-13-1699693-HTML/image_m/fcell-13-1699693-g001.jpg Figure 1. The total paradigm of the searching strategy of the present study. https://www.frontiersin.org/files/Articles/1699693/fcell-13-1699693-HTML/image_m/fcell-13-1699693-t001.jpg Table 1. Genes that are affected by microplastics (MPs) in various types of cancer cells. https://www.frontiersin.org/files/Articles/1699693/fcell-13-1699693-HTML/image_m/fcell-13-1699693-t002.jpg Table 2. Detailed information about gene function and molecular mechanisms that are affected by MPs, https://www.frontiersin.org/files/Articles/1699693/fcell-13-1699693-HTML/image_m/fcell-13-1699693-t003.jpg Table 3. Various effects of MPs on the anti-cancer agents and the final effect caused by these impac https://www.frontiersin.org/files/Articles/1699693/fcell-13-1699693-HTML/image_m/fcell-13-1699693-g002.jpg Figure 2. Detailed data about molecular interactions between microRNAs with anti-cancer activity and https://www.frontiersin.org/files/Articles/1699693/fcell-13-1699693-HTML/image_m/fcell-13-1699693-g003.jpg Figure 3. MicroRNAs with the in-silico capability to suppress genes in MPs-based cancers and their p https://www.frontiersin.org/files/Articles/1699693/fcell-13-1699693-HTML/image_m/fcell-13-1699693-t004.jpg Table 4. Detailed information about the binding affinity of microRNAs and genes in cancer affected b https://www.frontiersin.org/files/Articles/1699693/fcell-13-1699693-HTML/image_m/fcell-13-1699693-g004.jpg Figure 4. MicroRNAs with the in silico capability to suppress genes in MPs-based cancers and their p https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1677983/full https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-t001.jpg Table 1. Codes were used to merge Scopus and Web of Science exported data in RStudio. https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-t002.jpg Table 2. Queries for selecting records in the bibliometric analysis of post-COVID-19 complications i https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-t003.jpg Table 3. Queries for selecting records in the bibliometric analysis of post-COVID-19 complications i https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-g001.jpg Figure 1. Flowchart for selecting records in the bibliometric analysis of post-COVID-19 complication https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-g002.jpg Figure 2. (A) Countries’ scientific production on children's complications in long COVID; (B) Top 5 https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-g003.jpg Figure 3. (A) Top 10 most productive journals on pediatric complications in post-COVID; (B) Top 10 j https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-g004.jpg Figure 4. (A) Top 10 most prolific authors of articles in the field of pediatric complications in po https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-t004.jpg Table 4. Top 10 most cited articles within the post-COVID condition corpus retrieved using pediatric https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-g005.jpg Figure 5. (A) A tree map representing 20 most relevant keywords found in the articles on pediatric c https://www.frontiersin.org/files/Articles/1677983/fped-14-1677983-HTML/image_m/fped-14-1677983-g006.jpg Figure 6. A three-fields plot illustrating the interconnections between the top ten journals, author https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1814403/full https://www.frontiersin.org/files/Articles/1814403/fphar-17-1814403-HTML/image_m/fphar-17-1814403-g001.jpg Figure 1. Clinical characteristics of reported cases of malignant tumor progression. (A) Heat map of https://www.frontiersin.org/files/Articles/1814403/fphar-17-1814403-HTML/image_m/fphar-17-1814403-g002.jpg Figure 2. Classification of top 50 drugs associated with the malignant tumor progression. https://www.frontiersin.org/files/Articles/1814403/fphar-17-1814403-HTML/image_m/fphar-17-1814403-g003.jpg Figure 3. The distribution of ROR signal intensity of the top 50 drugs related to the progression of https://www.frontiersin.org/files/Articles/1814403/fphar-17-1814403-HTML/image_m/fphar-17-1814403-t001.jpg Table 1. Drug information in FAERS and JADER. https://www.frontiersin.org/files/Articles/1814403/fphar-17-1814403-HTML/image_m/fphar-17-1814403-g004.jpg Figure 4. Clinical characteristics of reported cases of malignant tumor progression in Japan. (A) He https://www.frontiersin.org/files/Articles/1814403/fphar-17-1814403-HTML/image_m/fphar-17-1814403-g005.jpg Figure 5. The FAERS database corresponds to the distribution of ROR signal intensity associated with https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1677177/full https://www.frontiersin.org/files/Articles/1677177/fimmu-16-1677177-HTML/image_m/fimmu-16-1677177-t001.jpg Table 1. Overview of β cell-associated PRRs implicated in type 1 diabetes. https://www.frontiersin.org/files/Articles/1677177/fimmu-16-1677177-HTML/image_m/fimmu-16-1677177-g001.jpg Figure 1. Distinct signaling pathways of TLR2, TLR4, and ALPK1 in β cells. TLR2 forms heterodimers w https://www.frontiersin.org/files/Articles/1677177/fimmu-16-1677177-HTML/image_m/fimmu-16-1677177-g002.jpg Figure 2. Complementary and synergistic recognition of viral RNA by TLR3, MDA5, and RIG-I in pancrea https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1814485/full https://www.frontiersin.org/files/Articles/1814485/fpubh-14-1814485-HTML/image_m/fpubh-14-1814485-t001.jpg Table 1. Selected policy documents on the cultural inheritance of and innovations in traditional Chi https://www.frontiersin.org/files/Articles/1814485/fpubh-14-1814485-HTML/image_m/fpubh-14-1814485-t002.jpg Table 2. Content coding status of selected policy documents related to the cultural inheritance of a https://www.frontiersin.org/files/Articles/1814485/fpubh-14-1814485-HTML/image_m/fpubh-14-1814485-t003.jpg Table 3. Categories and definitions of policy instruments for the cultural inheritance of and innova https://www.frontiersin.org/files/Articles/1814485/fpubh-14-1814485-HTML/image_m/fpubh-14-1814485-t004.jpg Table 4. Categories and definitions of policy objectives. https://www.frontiersin.org/files/Articles/1814485/fpubh-14-1814485-HTML/image_m/fpubh-14-1814485-g001.jpg Figure 1. Two-dimensional analytical framework for the cultural inheritance of and innovations in tr https://www.frontiersin.org/files/Articles/1814485/fpubh-14-1814485-HTML/image_m/fpubh-14-1814485-t005.jpg Table 5. Distribution of basic policy instruments. https://www.frontiersin.org/files/Articles/1814485/fpubh-14-1814485-HTML/image_m/fpubh-14-1814485-t006.jpg Table 6. Distribution of policy objectives. https://www.frontiersin.org/files/Articles/1814485/fpubh-14-1814485-HTML/image_m/fpubh-14-1814485-t007.jpg Table 7. Two-dimensional cross-analysis results of “Policy Instruments–Policy Objectives” [n (%)]. https://www.frontiersin.org/files/Articles/1814485/fpubh-14-1814485-HTML/image_m/fpubh-14-1814485-g002.jpg Figure 2. Heatmap representation of policy instruments. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1678800/full https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-t001.jpg Table 1. Comparison of the specifications between this study and other references. https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-g001.jpg Figure 1. The flowchart of the subjective refraction based on the innovative refractive screening pl https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-g002.jpg Figure 2. The schematic diagram of measuring the ESD. https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-t002.jpg Table 2. The baseline clinical characteristics of the study population. https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-g003.jpg Figure 3. (a) The experimental scatter data is the function of the ID and the ESD and the correspond https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-g004.jpg Figure 4. Bland-Altman analysis of the agreement in S diopter measurements between the proposed meth https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-g005.jpg Figure 5. Bland-Altman analysis of the agreement in S diopter measurements. The analysis is presente https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-g006.jpg Figure 6. The statistical profiles of the α error for (a) teenagers, (b) adults, and (c) all subject https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-g007.jpg Figure 7. Bland-Altman analysis of the agreement in C diopter measurements between the two methods. https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-g008.jpg Figure 8. (a) Bland-Altman analysis showing the mean difference and 95% LOAs (left panel), and the c https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-t003.jpg Table 3. Bland-Altman agreement analysis and paired t-test comparison between smartphone and clinica https://www.frontiersin.org/files/Articles/1678800/fbioe-13-1678800-HTML-r1/image_m/fbioe-13-1678800-g009.jpg Figure 9. Threshold of −3.00 D < SER ≤ 0.00 D (red line) or −6.00 D < SER ≤ −3.00 D (blue line), dia https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1739512/full https://www.frontiersin.org/files/Articles/1739512/fneur-17-1739512-HTML/image_m/fneur-17-1739512-g001.jpg Figure 1. Flowchart for inclusion and exclusion. https://www.frontiersin.org/files/Articles/1739512/fneur-17-1739512-HTML/image_m/fneur-17-1739512-t001.jpg Table 1. Characteristics of participants grouped by cognitive impairment. https://www.frontiersin.org/files/Articles/1739512/fneur-17-1739512-HTML/image_m/fneur-17-1739512-t002.jpg Table 2. ORs and 95% CIs of cognitive impairment. https://www.frontiersin.org/files/Articles/1739512/fneur-17-1739512-HTML/image_m/fneur-17-1739512-t003.jpg Table 3. ORs and 95% CIs of cognitive impairment in non-albuminuria subgroup and albuminuria subgrou https://www.frontiersin.org/files/Articles/1739512/fneur-17-1739512-HTML/image_m/fneur-17-1739512-g002.jpg Figure 2. Nonlinear association of serum CysC with cognitive impairment across subgroups. Restricted https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2026.1823440/full https://www.frontiersin.org/files/Articles/1823440/feart-14-1823440-HTML-r1/image_m/feart-14-1823440-g001.jpg Figure 1. Temporal Governance Framework for Geological Carbon Storage: From Physical Mechanisms to I https://www.frontiersin.org/files/Articles/1823440/feart-14-1823440-HTML-r1/image_m/feart-14-1823440-t001.jpg Table 1. Temporal governance framework for geological carbon storage: Mechanisms, instruments, and i https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1631701/full https://www.frontiersin.org/files/Articles/1631701/fneur-16-1631701-HTML/image_m/fneur-16-1631701-t001.jpg Table 1. Basic information of patients. https://www.frontiersin.org/files/Articles/1631701/fneur-16-1631701-HTML/image_m/fneur-16-1631701-t002.jpg Table 2. Effect of revascularization on cognition among all patients. https://www.frontiersin.org/files/Articles/1631701/fneur-16-1631701-HTML/image_m/fneur-16-1631701-g001.jpg Figure 1. Estimated marginal means for different test items between two groups. (A) Shows the MoCA t https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1795551/full https://www.frontiersin.org/files/Articles/1795551/fsurg-13-1795551-HTML/image_m/fsurg-13-1795551-g001.jpg Figure 1. Ultrasound image showing a right popliteal artery aneurysm (4.1 cm) with a 15 mm mural thr https://www.frontiersin.org/files/Articles/1795551/fsurg-13-1795551-HTML/image_m/fsurg-13-1795551-g002.jpg Figure 2. CT angiography showing the aneurysm with a maximum diameter of 3.7 cm and moderate-to-seve https://www.frontiersin.org/files/Articles/1795551/fsurg-13-1795551-HTML/image_m/fsurg-13-1795551-g003.jpg Figure 3. Arrow indicates the medial head of the gastrocnemius muscle compressing the popliteal arte https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1740005/full https://www.frontiersin.org/files/Articles/1740005/fimmu-16-1740005-HTML/image_m/fimmu-16-1740005-g000.jpg Graphical Abstract. Schematic of predictors of adverse outcomes and targeted interventions in nasal https://www.frontiersin.org/files/Articles/1740005/fimmu-16-1740005-HTML/image_m/fimmu-16-1740005-t001.jpg Table 1. Key predictive markers of nasal inflammatory diseases. https://www.frontiersin.org/files/Articles/1740005/fimmu-16-1740005-HTML/image_m/fimmu-16-1740005-g001.jpg Figure 1. Role of type 2 and non-type 2 inflammatory factors, inflammatory cell markers, and microor https://www.frontiersin.org/files/Articles/1740005/fimmu-16-1740005-HTML/image_m/fimmu-16-1740005-g002.jpg Figure 2. Role of metabolic product predictive indicators in the adverse outcomes of nasal inflammat https://www.frontiersin.org/files/Articles/1740005/fimmu-16-1740005-HTML/image_m/fimmu-16-1740005-t002.jpg Table 2. Targeted monoclonal antibody drugs for nasal inflammatory diseases. https://www.frontiersin.org/files/Articles/1740005/fimmu-16-1740005-HTML/image_m/fimmu-16-1740005-t003.jpg Table 3. The most promising predictive markers. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1752995/full https://www.frontiersin.org/files/Articles/1752995/fpsyg-17-1752995-HTML/image_m/fpsyg-17-1752995-g001.jpg Figure 1. Hypothesized model. https://www.frontiersin.org/files/Articles/1752995/fpsyg-17-1752995-HTML/image_m/fpsyg-17-1752995-t001.jpg Table 1. Distribution of demographic variables among participants. https://www.frontiersin.org/files/Articles/1752995/fpsyg-17-1752995-HTML/image_m/fpsyg-17-1752995-t002.jpg Table 2. Correlation analysis. https://www.frontiersin.org/files/Articles/1752995/fpsyg-17-1752995-HTML/image_m/fpsyg-17-1752995-t003.jpg Table 3. Results of multiple linear regression analysis. https://www.frontiersin.org/files/Articles/1752995/fpsyg-17-1752995-HTML/image_m/fpsyg-17-1752995-t004.jpg Table 4. Results of the mediation pathway analysis. https://www.frontiersin.org/files/Articles/1752995/fpsyg-17-1752995-HTML/image_m/fpsyg-17-1752995-g002.jpg Figure 2. The chain mediation model of fitness app need support and women’s exercise adherence behav https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1802305/full https://www.frontiersin.org/files/Articles/1802305/fneur-17-1802305-HTML/image_m/fneur-17-1802305-g001.jpg Figure 1. Flowchart for literature screening. https://www.frontiersin.org/files/Articles/1802305/fneur-17-1802305-HTML/image_m/fneur-17-1802305-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1802305/fneur-17-1802305-HTML/image_m/fneur-17-1802305-g002.jpg Figure 2. Risk of bias assessment results of the included studies: (A) risk of bias graph and (B) ri https://www.frontiersin.org/files/Articles/1802305/fneur-17-1802305-HTML/image_m/fneur-17-1802305-t002.jpg Table 2. Pairwise meta-analysis. https://www.frontiersin.org/files/Articles/1802305/fneur-17-1802305-HTML/image_m/fneur-17-1802305-g003.jpg Figure 3. Network meta-analysis diagrams of eligible comparisons: (A) effective rate; (B) VFSS; (C) https://www.frontiersin.org/files/Articles/1802305/fneur-17-1802305-HTML/image_m/fneur-17-1802305-g004.jpg Figure 4. Network meta-analysis of head-to-head comparisons. Red and bold numbers are statistically https://www.frontiersin.org/files/Articles/1802305/fneur-17-1802305-HTML/image_m/fneur-17-1802305-g005.jpg Figure 5. Surface under the cumulative ranking curve (SUCRA) analysis for assessing the relative eff https://www.frontiersin.org/files/Articles/1802305/fneur-17-1802305-HTML/image_m/fneur-17-1802305-g006.jpg Figure 6. Funnel plot of publication bias. (A) Effective rate; (B) VFSS; (C) FOIS; (D) SSA. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1766561/full https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-g001.jpg Figure 1. Motivation types and subtypes in self-determination theory (Ryan and Deci, 2000, p.72). https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-g002.jpg Figure 2. Research model. https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-t001.jpg Table 1. Demographics of the sample. https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-t002.jpg Table 2. Measurement Items. https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-t003.jpg Table 3. Reliability and validity. https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-t004.jpg Table 4. Discriminant validity (HTMT ratio). https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-t005.jpg Table 5. Result of R2, adjusted R2 and Q2. https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-t006.jpg Table 6. Summary of the PLS-SEM analysis. https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-g003.jpg Figure 3. PLS-SEM results of the structural model. https://www.frontiersin.org/files/Articles/1766561/fpsyg-17-1766561-HTML/image_m/fpsyg-17-1766561-t007.jpg Table 7. Mediation Analysis. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1784966/full https://www.frontiersin.org/files/Articles/1784966/fnagi-18-1784966-HTML/image_m/fnagi-18-1784966-g001.jpg Figure 1. Flow diagram showing the results of the systematic search for the selected studies in the https://www.frontiersin.org/files/Articles/1784966/fnagi-18-1784966-HTML/image_m/fnagi-18-1784966-t001.jpg Table 1. List of included studies on structure and neural activity in ADD and MCID compared to ADND https://www.frontiersin.org/files/Articles/1784966/fnagi-18-1784966-HTML/image_m/fnagi-18-1784966-g002.jpg Figure 2. Abnormal regions identified in the activation likelihood estimate coordinate-based meta-an https://www.frontiersin.org/files/Articles/1784966/fnagi-18-1784966-HTML/image_m/fnagi-18-1784966-g003.jpg Figure 3. Subgroup analysis revealing abnormal regions identified in activation likelihood estimatio https://www.frontiersin.org/files/Articles/1784966/fnagi-18-1784966-HTML/image_m/fnagi-18-1784966-t002.jpg Table 2. Results of ALE coordinate-based meta-analysis in structural and neural activity alterations https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1815974/full https://www.frontiersin.org/files/Articles/1815974/fimmu-17-1815974-HTML/image_m/fimmu-17-1815974-t001.jpg Table 1. Summary of TIL and PBMC datasets used to train TRACE. https://www.frontiersin.org/files/Articles/1815974/fimmu-17-1815974-HTML/image_m/fimmu-17-1815974-g001.jpg Figure 1. Overview of model training and checkpointing. Single-cell gene expression and paired TCRαβ https://www.frontiersin.org/files/Articles/1815974/fimmu-17-1815974-HTML/image_m/fimmu-17-1815974-g002.jpg Figure 2. TRACE performance and experimental validation. (A) Performance of intermediate TRACE model https://www.frontiersin.org/files/Articles/1815974/fimmu-17-1815974-HTML/image_m/fimmu-17-1815974-t002.jpg Table 2. TRT prediction methods implemented and benchmarked against TRACE. https://www.frontiersin.org/files/Articles/1815974/fimmu-17-1815974-HTML/image_m/fimmu-17-1815974-g003.jpg Figure 3. TRACE performance relative to other TRT methods. (A, B) Performance (test set MCC) of TRAC https://www.frontiersin.org/files/Articles/1815974/fimmu-17-1815974-HTML/image_m/fimmu-17-1815974-g004.jpg Figure 4. TRACE identifies tumor reactive cells specifically in tumor contexts. (A) Application of T https://www.frontiersin.org/files/Articles/1815974/fimmu-17-1815974-HTML/image_m/fimmu-17-1815974-g005.jpg Figure 5. Large-scale survey of tumor reactivity in different cancer types using TRACE. (A) sTRACE s https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1796810/full https://www.frontiersin.org/files/Articles/1796810/fpubh-14-1796810-HTML-r1/image_m/fpubh-14-1796810-g001.jpg Figure 1. Flow chart diagram for identifying AI policy documents across ASPPH member schools and pro https://www.frontiersin.org/files/Articles/1796810/fpubh-14-1796810-HTML-r1/image_m/fpubh-14-1796810-t001.jpg Table 1. TF-IDF analysis. https://www.frontiersin.org/files/Articles/1796810/fpubh-14-1796810-HTML-r1/image_m/fpubh-14-1796810-t002.jpg Table 2. Content analysis. https://www.frontiersin.org/files/Articles/1796810/fpubh-14-1796810-HTML-r1/image_m/fpubh-14-1796810-t003.jpg Table 3. Thematic analysis. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1794241/full https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-g001.jpg Figure 1. System architecture showing data flow from sensor acquisition through pre-processing, phys https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-g002.jpg Figure 2. Neural architecture showing multi-sensor fusion, parallel temporal feature extraction, dif https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-t001.jpg Table 1. Dataset characteristics and distribution. https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-t002.jpg Table 2. Comparison with state-of-the-art methods. https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-g003.jpg Figure 3. Normalized confusion matrix for five-level fatigue classification. The diagonal elements r https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-t003.jpg Table 3. Per-class classification performance with 95% CI. https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-g004.jpg Figure 4. Regression analysis for asymmetry index prediction. (a) Scatter plot showing predicted vs. https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-t004.jpg Table 4. Ablation study on model components. https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-t005.jpg Table 5. Sensitivity analysis of physics constraint weights. https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-g005.jpg Figure 5. Physics constraint analysis. (a–d) Training and validation loss curves for individual cons https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-t006.jpg Table 6. Physics constraint satisfaction at convergence. https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-t007.jpg Table 7. Robustness analysis: performance under sensor noise and failure. https://www.frontiersin.org/files/Articles/1794241/fpubh-14-1794241-HTML/image_m/fpubh-14-1794241-t008.jpg Table 8. Cross-subject and cross-environment generalization. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1573279/full https://www.frontiersin.org/files/Articles/1573279/fonc-15-1573279-HTML/image_m/fonc-15-1573279-g001.jpg Figure 1. Functional landscape of coagulation-related genes in ESCC. (A) Oncoplot of somatic mutatio https://www.frontiersin.org/files/Articles/1573279/fonc-15-1573279-HTML/image_m/fonc-15-1573279-g002.jpg Figure 2. Prognostic analysis of coagulation-related genes in ESCC. (A) Two identified subtypes from https://www.frontiersin.org/files/Articles/1573279/fonc-15-1573279-HTML/image_m/fonc-15-1573279-g003.jpg Figure 3. Machine learning algorithms identifying coagulation-related genes and establishing predict https://www.frontiersin.org/files/Articles/1573279/fonc-15-1573279-HTML/image_m/fonc-15-1573279-g004.jpg Figure 4. Clinical feature analysis and immune microenvironment analysis of prognostic models. (A) T https://www.frontiersin.org/files/Articles/1573279/fonc-15-1573279-HTML/image_m/fonc-15-1573279-g005.jpg Figure 5. Single-cell analysis of ESCC. (A) t-SNE plot showing the five cell types after clustering. https://www.frontiersin.org/files/Articles/1573279/fonc-15-1573279-HTML/image_m/fonc-15-1573279-g006.jpg Figure 6. Coagulation feature genes analysis in ESCC immune cell subgroups and drug sensitivity. (A) https://www.frontiersin.org/files/Articles/1573279/fonc-15-1573279-HTML/image_m/fonc-15-1573279-g007.jpg Figure 7. Pan-cancer analysis of RINT1. (A) GSVA analysis heatmap for patients in high and low-risk https://www.frontiersin.org/files/Articles/1573279/fonc-15-1573279-HTML/image_m/fonc-15-1573279-g008.jpg Figure 8. Functional identification of RINT1 and in vitro validation of its expression levels. (A) D https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1585761/full https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g001.jpg Figure 1. Technology roadmap. DEG, differentially expressed genes; ExpDiff and ROC, expression diffe https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g002.jpg Figure 2. Dataset processing of combined datasets and GSE97760 (A) Distribution boxplot of the GEO d https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g003.jpg Figure 3. Differential gene expression analysis (A) Volcano plot of differentially expressed genes b https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g004.jpg Figure 4. Results of GO and KEGG enrichment analysis for GS&MetabolismRDEGs. (A) The results of GO a https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g005.jpg Figure 5. Diagnostic model of AD (A) Forest plot of the six GS&MetabolismRDEGs included in the logis https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g006.jpg Figure 6. Diagnostic and validation analysis of AD (A) Nomograms of hub genes in the combined GEO da https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g007.jpg Figure 7. ssGSEA score analysis (A) Comparison of the GS&Metabolism score between the control and AD https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g008.jpg Figure 8. Differential gene expression analysis and GSEA for combined datasets (A,B). Volcano plot ( https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g009.jpg Figure 9. Regulatory network of hub genes (A). The mRNA-TF regulatory network of hub genes. (B) The https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g010.jpg Figure 10. Differential expression validation and ROC curve analysis (A). Grouping comparison of hub https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g011.jpg Figure 11. Immune infiltration analysis of combined datasets using the CIBERSORT algorithm A-B. The https://www.frontiersin.org/files/Articles/1585761/fmolb-12-1585761-HTML/image_m/fmolb-12-1585761-g012.jpg Figure 12. Immune infiltration analysis of risk groups using the CIBERSORT algorithm (A). Grouping c https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1737738/full https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-t001.jpg Table 1. Cost differences for new buildings, second-hand sales, and chartering of different containe https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-t002.jpg Table 2. Benchmark equilibrium outcomes for second-hand ship transactions across different vessel ty https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-g001.jpg Figure 1. Benchmark equilibrium outcomes trend chart (the data has been normalized). https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-t003.jpg Table 3. Market channel differentiation outcomes. https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-g002.jpg Figure 2. Bar chart comparing buyer’s risk neutrality and risk aversion data results. https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-t004.jpg Table 4. Comparison of data results table of buyer’s risk neutrality and risk aversion. https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-g003.jpg Figure 3. Comparison chart of two types of discrete model results. https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-t005.jpg Table 5. Results of two types of discrete models. https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-t006.jpg Table 6. Reserve price ranges by ship type. https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-g004.jpg Figure 4. Comparison chart of trading probabilities before and after the price retention introductio https://www.frontiersin.org/files/Articles/1737738/fmars-12-1737738-HTML/image_m/fmars-12-1737738-t007.jpg Table 7. Retain the trading probability before and after the price introduction. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1678090/full https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-g001.jpg Figure 1. Random Soldier Kinematogram (RSK). This example frame shows an RSK with 75% motion coheren https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-g002.jpg Figure 2. Schematic overview of the study procedure. The type of stimuli (univalent, bivalent), situ https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-t001.jpg Table 1. Parametric conditions used in Experiment 1. https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-g003.jpg Figure 3. Motion and color discrimination performance for white and khaki uniform colors. (A) RT and https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-t002.jpg Table 2. Parametric conditions used in Experiment 2. https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-g004.jpg Figure 4. Random Soldier Kinematogram (RSK) with varying coherence parameters. Shown are RSKs with 7 https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-g005.jpg Figure 5. Target and distractor sensitivity to RSK stimuli. RT (red line, left axis) and accuracy (b https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-t003.jpg Table 3. Regression models and regression coefficients for log-transformed RT and arc-sine transform https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-g006.jpg Figure 6. Schematic illustration of the task-switching paradigm. (A) In single-task blocks, the part https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-g007.jpg Figure 7. Effects of task sequence, preparation time and congruency on performance. (A) RT and error https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-t004.jpg Table 4. Summary of costs induced by task sequence and their preparatory reduction (i.e., reduction https://www.frontiersin.org/files/Articles/1678090/fpsyg-17-1678090-HTML/image_m/fpsyg-17-1678090-g008.jpg Figure 8. Asymmetry of switch cost and error cost. Switch cost (Left) and error cost (Right) as a fu https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1742123/full https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-g001.jpg Figure 1. Overview of Installed solar energy capacity around the world. Data source: International R https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-g002.jpg Figure 2. Research & development spending as a share of GDP. Data source: Our World in Data (2025). https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-g003.jpg Figure 3. Cereal yields by region. Data source: Food and Agriculture Organization of the United Nati https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-g004.jpg Figure 4. Share of cereals allocated to human food globally. Data source: Food and Agriculture Organ https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-t001.jpg Table 1. Summary of the data. https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-t002.jpg Table 2. Descriptive statistics. https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-t003.jpg Table 3. The ADF and PP unit root test results. https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-t004.jpg Table 4. ARDL bounds test results. https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-t005.jpg Table 5. Long and short run results for Model 1: LFS. https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-t006.jpg Table 6. Robustness check for Model 1: LFS. https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-t007.jpg Table 7. Long and short run results for Model 2: LAGS. https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-t008.jpg Table 8. Robustness check for Model 2: LAGS. https://www.frontiersin.org/files/Articles/1742123/fsufs-10-1742123-HTML/image_m/fsufs-10-1742123-t009.jpg Table 9. Outcomes of diagnostic tests. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1737358/full https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-g001.jpg Figure 1. Theoretical framework diagram. https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-t001.jpg Table 1. System of new quality productivity indicators for food. https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-t002.jpg Table 2. Descriptive statistics. https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-t003.jpg Table 3. Benchmark regression results. https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-t004.jpg Table 4. Robustness test results. https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-t005.jpg Table 5. Mechanism test results. https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-t006.jpg Table 6. Threshold effect test results. https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-t007.jpg Table 7. Threshold effect regression results. https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-t008.jpg Table 8. Results of the heterogeneity test based on the division of the main grain producing areas. https://www.frontiersin.org/files/Articles/1737358/fsufs-09-1737358-HTML/image_m/fsufs-09-1737358-t009.jpg Table 9. Heterogeneity test results based on traditional financial level segmentation. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1732739/full https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-g001.jpg Figure 1. Conceptual model. https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-t001.jpg Table 1. Constructs and indicators. https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-t002.jpg Table 2. Characteristics of the sample. https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-g002.jpg Figure 2. Basic information of case samples. https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-t003.jpg Table 3. Measures of internal consistency reliability and convergent validity. https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-t004.jpg Table 4. Discriminant validity using average variance extracted (AVE). https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-t005.jpg Table 5. Discriminant validity using heterotrait-monotrait ratio (HTMT). https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-t006.jpg Table 6. Values for Stone-Geisser’s Q2 and adjusted R2. https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-g003.jpg Figure 3. Path coefficient analysis. https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-t007.jpg Table 7. Direct path analysis. https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-t008.jpg Table 8. Indirect path analysis. https://www.frontiersin.org/files/Articles/1732739/fsufs-10-1732739-HTML/image_m/fsufs-10-1732739-g004.jpg Figure A1. Structure of traditional rural supply chain and rural e-commerce supply chain. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1730243/full https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-g001.jpg Figure 1. Mechanism of impact of InsurTech on agricultural insurance development and income threshol https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-t001.jpg Table 1. Definition of main variables and data sources. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-t002.jpg Table 2. Descriptive statistics for the sample of major grain-producing areas. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-t003.jpg Table 3. Baseline regression results. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-t004.jpg Table 4. Robustness and endogeneity testing results. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-g002.jpg Figure 2. Standardized bias of variables after matching. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-g003.jpg Figure 3. Common support range of propensity scores. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-g004.jpg Figure 4. Kernel density distribution before and after matching. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-t005.jpg Table 5. Balance test results for matched variables. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-t006.jpg Table 6. PSM results for the impact of InsurTech on agricultural insurance. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-t007.jpg Table 7. Regional heterogeneity regression results. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-t008.jpg Table 8. Threshold effect existence test results. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-t009.jpg Table 9. Panel threshold model estimation results. https://www.frontiersin.org/files/Articles/1730243/fsufs-10-1730243-HTML/image_m/fsufs-10-1730243-g005.jpg Figure 5. LR statistic plot of the disposable income threshold effect for farmers. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1728761/full https://www.frontiersin.org/files/Articles/1728761/fsufs-09-1728761-HTML/image_m/fsufs-09-1728761-t001.jpg Table 1. Indicators for evaluating non-cognitive abilities. https://www.frontiersin.org/files/Articles/1728761/fsufs-09-1728761-HTML/image_m/fsufs-09-1728761-t002.jpg Table 2. Definition and descriptive statistics of main variables. https://www.frontiersin.org/files/Articles/1728761/fsufs-09-1728761-HTML/image_m/fsufs-09-1728761-t003.jpg Table 3. Digital literacy and income inequality among farmers: baseline regression results. https://www.frontiersin.org/files/Articles/1728761/fsufs-09-1728761-HTML/image_m/fsufs-09-1728761-t004.jpg Table 4. Digital literacy and income inequality among farmers: classified regression results. https://www.frontiersin.org/files/Articles/1728761/fsufs-09-1728761-HTML/image_m/fsufs-09-1728761-t005.jpg Table 5. Digital literacy and income inequality among farmers: endogeneity test results. https://www.frontiersin.org/files/Articles/1728761/fsufs-09-1728761-HTML/image_m/fsufs-09-1728761-t006.jpg Table 6. Digital literacy and income inequality among farmers: robustness test results. https://www.frontiersin.org/files/Articles/1728761/fsufs-09-1728761-HTML/image_m/fsufs-09-1728761-t007.jpg Table 7. Digital literacy and income inequality among farmers: results of heterogeneity analysis. https://www.frontiersin.org/files/Articles/1728761/fsufs-09-1728761-HTML/image_m/fsufs-09-1728761-t008.jpg Table 8. Digital literacy and income inequality among farmers: regression results for moderating eff https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1728693/full https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-g001.jpg Figure 1. Research framework. https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-t001.jpg Table 1. Variable definitions. https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-t002.jpg Table 2. Descriptive statistics and correlation analysis of variables. https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-t003.jpg Table 3. Results of digital transformation (DT) on supply chain efficiency (SCE). https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-t004.jpg Table 4. Results of endogeneity and robustness test. https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-t005.jpg Table 5. Results of moderating effect of contractual and relational governance. https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-t006.jpg Table 6. Heterogeneity test results of the DT–SCE relationship. https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-t007.jpg Table 7. The Z-values of the regression coefficient variations. https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-t008.jpg Table 8. Heterogeneity in the moderating role by organizational life cycle. https://www.frontiersin.org/files/Articles/1728693/fsufs-10-1728693-HTML-r1/image_m/fsufs-10-1728693-t009.jpg Table 9. Heterogeneity in the moderating role by regional business cooperation culture. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1768402/full https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-g001.jpg Figure 1. Schematic representation of the metabolism of DMT and HRM present in ayahuasca and their p https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-g002.jpg Figure 2. Flowchart illustrating the development, validation, and predictive applications of the PBP https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-t001.jpg Table 1. Simulated dosing regimens of inhibitors and substrates for DDI evaluation. https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-g003.jpg Figure 3. Plasma concentration-time profiles of DMT and HRM following oral administration of ayahuas https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-g004.jpg Figure 4. Simulation of plasma concentration–time profiles of DMT and HRM. https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-t002.jpg Table 2. Summary of clinical study data used for DMT and HRM. The table presents predicted and obser https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-g005.jpg Figure 5. Simulation of plasma concentration–time profiles of FL and NFL. Panel (A) shows the simula https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-t003.jpg Table 3. Summary of clinical study data used for FL and NFL. The table reports predicted and observe https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-g006.jpg Figure 6. Simulation of plasma concentration–time profiles of PR. https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-t004.jpg Table 4. Summary of clinical study data used for PR. The table reports predicted and observed data, https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-g007.jpg Figure 7. Plasma simulation of DMT and HRM in the presence of the inhibitor fluoxetine. Panel (A) sh https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-g008.jpg Figure 8. Plasma simulation of DMT and HRM in the presence of the inhibitor paroxetine. Panel (A) sh https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-t005.jpg Table 5. Simulated pharmacokinetic parameters for DMT and HRM in the absence and presence of the inh https://www.frontiersin.org/files/Articles/1768402/fmolb-13-1768402-HTML/image_m/fmolb-13-1768402-t006.jpg Table 6. Simulated pharmacokinetic parameters for DMT and HRM in the absence and presence of the inh https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1644169/full https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-g001.jpg Figure 1. Flowchart of overall methodology used to predict the anti-cancer effect of AG for TNBC. https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-g002.jpg Figure 2. Construction of target network and acquisition of key genes. (a) Structural formula of AG. https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-g003.jpg Figure 3. Functional enrichment analysis of key genes in TNBC. (a) Bar plot from the GO analysis. (b https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-g004.jpg Figure 4. Determining target hub genes through machine learning algorithms. (a) Error rate curves of https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-g005.jpg Figure 5. Molecular docking interactions of AG with hub genes. (a) Molecular docking interaction bet https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-t001.jpg Table 1. Molecular docking results of AG with hub genes (kcal/mol). https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-g006.jpg Figure 6. The binding mode of AG and SRC. (a) 3D binding mode of Arctigenin and 4MXO. (b) Analysis r https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-t002.jpg Table 2. The contributions of each energy term to the binding energy of AG with SRC (kcal/mol). https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-t003.jpg Table 3. SPR kinetic parameters for the interaction between immobilized SRC and AG. https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-g007.jpg Figure 7. Effects of AG on the proliferation of MDA-MB-453 cells. (a) Cell viability was determined https://www.frontiersin.org/files/Articles/1644169/fmolb-12-1644169-HTML/image_m/fmolb-12-1644169-g008.jpg Figure 8. Effects of AG on the apoptosis of MDA-MB-453 cells. (a) Cells were incubated with various https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1762743/full https://www.frontiersin.org/files/Articles/1762743/fmicb-17-1762743-HTML/image_m/fmicb-17-1762743-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1762743/fmicb-17-1762743-HTML/image_m/fmicb-17-1762743-g001.jpg Figure 1. The interactions among plant compartments highlight how microorganisms contribute to soil https://www.frontiersin.org/files/Articles/1762743/fmicb-17-1762743-HTML/image_m/fmicb-17-1762743-g002.jpg Figure 2. Assessment of microbial community structure and prediction of plant diseases, soil health, https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1797240/full https://www.frontiersin.org/files/Articles/1797240/fimmu-17-1797240-HTML/image_m/fimmu-17-1797240-g001.jpg Figure 1. Participant demographics. (A) number of participants in terms of gender the average age. ( https://www.frontiersin.org/files/Articles/1797240/fimmu-17-1797240-HTML/image_m/fimmu-17-1797240-g002.jpg Figure 2. Cumulative assessment of serum samples. Pseudovirus neutralization assay utilizing lentivi https://www.frontiersin.org/files/Articles/1797240/fimmu-17-1797240-HTML/image_m/fimmu-17-1797240-g003.jpg Figure 3. Stratification by previous infection status. Samples from 32 previously infected and 63 no https://www.frontiersin.org/files/Articles/1797240/fimmu-17-1797240-HTML/image_m/fimmu-17-1797240-g004.jpg Figure 4. Neutralization of variants. Samples that showed ≥ 50% reduction in infection of the Wuhan- https://www.frontiersin.org/files/Articles/1797240/fimmu-17-1797240-HTML/image_m/fimmu-17-1797240-g005.jpg Figure 5. Neutralization of variants stratified by vaccination and previous infection status. (A) Ne https://www.frontiersin.org/files/Articles/1797240/fimmu-17-1797240-HTML/image_m/fimmu-17-1797240-g006.jpg Figure 6. ADE of SARS-CoV-2 1.617 S pseudovirion infection. (A) Incubation of 10x diluted serum with https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1822931/full https://www.frontiersin.org/files/Articles/1822931/fcimb-16-1822931-HTML/image_m/fcimb-16-1822931-t001.jpg Table 1. Primer sequences used in this study. https://www.frontiersin.org/files/Articles/1822931/fcimb-16-1822931-HTML/image_m/fcimb-16-1822931-g001.jpg Figure 1. Construction of prokaryotic expression plasmid pET-28a vector and induced expression of BP https://www.frontiersin.org/files/Articles/1822931/fcimb-16-1822931-HTML/image_m/fcimb-16-1822931-g002.jpg Figure 2. Purification of the target protein, viral titer determination, and verification of the pol https://www.frontiersin.org/files/Articles/1822931/fcimb-16-1822931-HTML/image_m/fcimb-16-1822931-t002.jpg Table 2. Determination of P/N values based on iELISA checkerboard titration for optimizing protein c https://www.frontiersin.org/files/Articles/1822931/fcimb-16-1822931-HTML/image_m/fcimb-16-1822931-g003.jpg Figure 3. Optimization of the iELISA experimental conditions. (A) The optimal dilution of the polycl https://www.frontiersin.org/files/Articles/1822931/fcimb-16-1822931-HTML/image_m/fcimb-16-1822931-g004.jpg Figure 4. Normal distribution curve of OD450 values of negative serum samples. The curve illustrates https://www.frontiersin.org/files/Articles/1822931/fcimb-16-1822931-HTML/image_m/fcimb-16-1822931-g005.jpg Figure 5. Specificity and Sensitivity Analysis of the iELISA. (A) Specificity analysis of the iELISA https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1709943/full https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-g001.jpg Figure 1. Selected videos for the formal experiment. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-t001.jpg Table 1. Library of VR emotion-eliciting videos. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-t002.jpg Table 2. Pre-test User Questionnaire https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-g002.jpg Figure 2. SAM scale. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-t003.jpg Table 3. PAD emotion scale. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-g003.jpg Figure 3. Experiment procedure. (a) Multi-dimensional emotion elicitation scenario, (b) GSR sensor, https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-g004.jpg Figure 4. MMTED: multi-modal temporal emotion detector. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-t004.jpg Table 4. Performance Comparison in Experiment 1, 2, 3. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-g005.jpg Figure 5. Training accuracy iteration plot for MMTED. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-t005.jpg Table 5. Number of samples for each of the 8 categories. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-t006.jpg Table 6. Learning rate and loss function optimization. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-t007.jpg Table 7. 5-fold cross-validation of experiment 1, 2, 3. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-g006.jpg Figure 6. Training accuracy iteration plot for MMTED in 5-fold cross-validation. (a) Experiment 1: M https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-t008.jpg Table 8. Ablation study analysis. https://www.frontiersin.org/files/Articles/1709943/fpsyg-16-1709943-HTML/image_m/fpsyg-16-1709943-g007.jpg Figure 7. Confusion matrices of MMTED model vs. GSR-only models vs. eye-only models. (a) MMTED Model https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1791554/full https://www.frontiersin.org/files/Articles/1791554/fonc-16-1791554-HTML/image_m/fonc-16-1791554-t001.jpg Table 1. Characteristics of MM patients (n=73) with high(n=37) and low(n=36) GAS5 expression. https://www.frontiersin.org/files/Articles/1791554/fonc-16-1791554-HTML/image_m/fonc-16-1791554-g001.jpg Figure 1. Comparison of circulating GAS5 expression levels. Box-plot chart of relative GAS5 expressi https://www.frontiersin.org/files/Articles/1791554/fonc-16-1791554-HTML/image_m/fonc-16-1791554-g002.jpg Figure 2. Impact of GAS5 expression on progression-free survival (PFS) in multiple myeloma. Kaplan-M https://www.frontiersin.org/files/Articles/1791554/fonc-16-1791554-HTML/image_m/fonc-16-1791554-g003.jpg Figure 3. Kaplan–Meier analysis of overall survival (OS) between GAS5low (n=36; 18 censored) and GAS https://www.frontiersin.org/files/Articles/1791554/fonc-16-1791554-HTML/image_m/fonc-16-1791554-t002.jpg Table 2. Univariate and multivariate analysis with OS as dependent variable. https://www.frontiersin.org/files/Articles/1791554/fonc-16-1791554-HTML/image_m/fonc-16-1791554-t003.jpg Table 3. Univariate and multivariate analyses with PFS as dependent variable. https://www.frontiersin.org/files/Articles/1791554/fonc-16-1791554-HTML/image_m/fonc-16-1791554-g004.jpg Figure 4. Graphical abstract of overall impact and significance of the study. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1748426/full https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-t001.jpg Table 1. Closest relatives of bacterial isolates based on 16S rRNA gene sequence analysis. https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g001.jpg Figure 1. Neighbor-joining phylogenetic tree based on 16S rRNA gene sequences showing the relationsh https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g002.jpg Figure 2. Effects of bacterial isolates on soybean seed germination rate under a range of salinity l https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g003.jpg Figure 3. Effects of rhizobacterial isolates on soybean seed germination percentage (A) and germinat https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g004.jpg Figure 4. Response surface plots illustrating soybean seed germination response to bacterial concent https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g005.jpg Figure 5. Effects of bacterial isolates on soybean plant height under pulse salinity stress. Means ( https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g006.jpg Figure 6. Effects of bacterial isolates on soybean SPAD values under pulse salinity stress. Means (n https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g007.jpg Figure 7. Effects of bacterial isolates on soybean shoot DW under pulse salinity stress. Means (n = https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-t002.jpg Table 2. Effects of bacterial isolates on soybean traits under salinity stress conditions. https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g008.jpg Figure 8. Interaction of salinity × bacterial strain × CFU on total root length of soybean. Error ba https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g009.jpg Figure 9. Interaction of salinity × bacterial strain × CFU on number of root tips of soybean. Error https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-t003.jpg Table 3. Auxin production, phosphate solubilization index (PSI), siderophore production, and nitroge https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g010.jpg Figure 10. Changes in maximum growth (Max V [600]) of bacterial isolates 5 and 7 under varying salin https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g011.jpg Figure 11. Changes in lag time of bacterial isolates 5 and 7 under varying salinity levels. https://www.frontiersin.org/files/Articles/1748426/fpls-17-1748426-HTML/image_m/fpls-17-1748426-g012.jpg Figure 12. Growth curves of bacterial isolates 5 and 7 under different NaCl concentrations over 48 h https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1787980/full https://www.frontiersin.org/files/Articles/1787980/fpubh-14-1787980-HTML/image_m/fpubh-14-1787980-t001.jpg Table 1. Logical model of the problem based on the PRECEDE model (101)—weight loss and remission of https://www.frontiersin.org/files/Articles/1787980/fpubh-14-1787980-HTML/image_m/fpubh-14-1787980-t002.jpg Table 2. Matrix of change objectives for BO2. https://www.frontiersin.org/files/Articles/1787980/fpubh-14-1787980-HTML/image_m/fpubh-14-1787980-t003.jpg Table 3. Theory-based method and practical strategies for BO2. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1614803/full https://www.frontiersin.org/files/Articles/1614803/fpsyg-16-1614803-HTML/image_m/fpsyg-16-1614803-g001.jpg Figure 1. The hypothesized mediated model. https://www.frontiersin.org/files/Articles/1614803/fpsyg-16-1614803-HTML/image_m/fpsyg-16-1614803-g002.jpg Figure 2. Flow chart of the studied participants. https://www.frontiersin.org/files/Articles/1614803/fpsyg-16-1614803-HTML/image_m/fpsyg-16-1614803-t001.jpg Table 1. Socio-demographic and academic characteristics of the study participants (N = 217). https://www.frontiersin.org/files/Articles/1614803/fpsyg-16-1614803-HTML/image_m/fpsyg-16-1614803-t002.jpg Table 2. Distribution of the studied participants regarding their score in satisfaction with life, p https://www.frontiersin.org/files/Articles/1614803/fpsyg-16-1614803-HTML/image_m/fpsyg-16-1614803-t003.jpg Table 3. Multiple linear regression for the effect of psychological capital on satisfaction with lif https://www.frontiersin.org/files/Articles/1614803/fpsyg-16-1614803-HTML/image_m/fpsyg-16-1614803-t004.jpg Table 4. Mediation analysis for psychological capital on the effect of life satisfaction on psycholo https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1720731/full https://www.frontiersin.org/files/Articles/1720731/fmed-13-1720731-HTML/image_m/fmed-13-1720731-t001.jpg Table 1. Demographic and clinical characteristics of the 7 patients with familial hypobetalipoprotei https://www.frontiersin.org/files/Articles/1720731/fmed-13-1720731-HTML/image_m/fmed-13-1720731-t002.jpg Table 2. Genetic characteristics and identified mutations in the seven patients included in the case https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1798506/full https://www.frontiersin.org/files/Articles/1798506/fphar-17-1798506-HTML/image_m/fphar-17-1798506-g001.jpg Figure 1. Study design and sampling schedule for patients in the intermittent and continuous group. https://www.frontiersin.org/files/Articles/1798506/fphar-17-1798506-HTML/image_m/fphar-17-1798506-t001.jpg Table 1. Demographic and intraoperative data. https://www.frontiersin.org/files/Articles/1798506/fphar-17-1798506-HTML/image_m/fphar-17-1798506-g002.jpg Figure 2. Concentration-time profiles for ceftaroline in plasma and parasternal subcutaneous tissue https://www.frontiersin.org/files/Articles/1798506/fphar-17-1798506-HTML/image_m/fphar-17-1798506-t002.jpg Table 2. Pharmacokinetic parameters for ceftaroline in plasma and parasternal subcutaneous tissues f https://www.frontiersin.org/files/Articles/1798506/fphar-17-1798506-HTML/image_m/fphar-17-1798506-g003.jpg Figure 3. Proportion of participants reaching the target 50% fT>MIC and 100% fT>MIC. The proportion https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1735375/full https://www.frontiersin.org/files/Articles/1735375/fmicb-17-1735375-HTML/image_m/fmicb-17-1735375-t001.jpg Table 1. Demographic and clinical characteristics of study participants. https://www.frontiersin.org/files/Articles/1735375/fmicb-17-1735375-HTML/image_m/fmicb-17-1735375-g001.jpg Figure 1. Distinct microbial diversity and composition in saliva and plaque samples in children with https://www.frontiersin.org/files/Articles/1735375/fmicb-17-1735375-HTML/image_m/fmicb-17-1735375-g002.jpg Figure 2. Phylum-level taxonomic composition of oral microbiota in plaque and saliva samples: (A) St https://www.frontiersin.org/files/Articles/1735375/fmicb-17-1735375-HTML/image_m/fmicb-17-1735375-g003.jpg Figure 3. Machine learning performance and microbial feature selection in classifying T1D status usi https://www.frontiersin.org/files/Articles/1735375/fmicb-17-1735375-HTML/image_m/fmicb-17-1735375-t002.jpg Table 2. Classification performance of random forest models predicting T1D status using saliva and p https://www.frontiersin.org/files/Articles/1735375/fmicb-17-1735375-HTML/image_m/fmicb-17-1735375-g004.jpg Figure 4. UMAP visualization of microbial taxa differentiating T1D status in plaque and saliva sampl https://www.frontiersin.org/files/Articles/1735375/fmicb-17-1735375-HTML/image_m/fmicb-17-1735375-t003.jpg Table 3. Directionality of abundance and literature-based characterization of the top 10 salivary ba https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1534830/full https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-g001.jpg Figure 1. Proposed AI-based DIY framework for interstitial glucose level prediction up to 30 and 60  https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-g002.jpg Figure 2. Basis of the development of the LISO loss function design. (a) Conventional squared error https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-g003.jpg Figure 3. Comparison of metric performance for the proposed loss functions: MSE and LISO. Boxplots o https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-t001.jpg Table 1. Evaluation metrics in the validation folds for the proposed models for MSE and LISO loss fu https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-t002.jpg Table 2. Evaluation metrics in the validation folds for the proposed models for MSE and LISO loss fu https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-g004.jpg Figure 4. Boxplots representing subject-wise (n = 29) ISO-based prediction metrics (parkesAB and ISO https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-g005.jpg Figure 5. Boxplots representing subject-wise (n = 29) ISO-based prediction metrics (parkesAB and ISO https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-t003.jpg Table 3. Comparison of the proposed DIY framework with other state-of-the-art approaches. Results fo https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-g006.jpg Figure 6. RMSE, parkesAB, and ISOZone metrics for different test sets after training the personalize https://www.frontiersin.org/files/Articles/1534830/fdgth-07-1534830-HTML/image_m/fdgth-07-1534830-g007.jpg Figure 7. DIY module workflow from the user's perspective. Notice that the first use of the model im https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1690222/full https://www.frontiersin.org/files/Articles/1690222/fcimb-16-1690222-HTML/image_m/fcimb-16-1690222-g001.jpg Figure 1. Genome organization and comparative amino acid (aa) identities of chicken megrivirus strai https://www.frontiersin.org/files/Articles/1690222/fcimb-16-1690222-HTML/image_m/fcimb-16-1690222-t001.jpg Table 1. Nonsynonymous SNPs found in the avian rotavirus A strain AvRV-A/broiler/IN/A2323728-003/23/ https://www.frontiersin.org/files/Articles/1690222/fcimb-16-1690222-HTML/image_m/fcimb-16-1690222-g002.jpg Figure 2. Phylogenetic clustering of the Indian strain ChMeV-C/broiler/IN/A2323728-003/23 identified https://www.frontiersin.org/files/Articles/1690222/fcimb-16-1690222-HTML/image_m/fcimb-16-1690222-t002.jpg Table 2. Genome segment organization and assembly metrics of the Indian avian orthoreovirus strain R https://www.frontiersin.org/files/Articles/1690222/fcimb-16-1690222-HTML/image_m/fcimb-16-1690222-g003.jpg Figure 3. Phylogenetic placement of the Indian ARV strain Reo/broiler/IN/A2323728-003/23 identified https://www.frontiersin.org/files/Articles/1690222/fcimb-16-1690222-HTML/image_m/fcimb-16-1690222-t003.jpg Table 3. Sequence analysis of the Indian avian rotavirus A strain AvRV-A/broiler/IN/A2323728-003/23/ https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1780802/full https://www.frontiersin.org/files/Articles/1780802/fpsyg-17-1780802-HTML/image_m/fpsyg-17-1780802-g001.jpg Figure 1. Research workflow. https://www.frontiersin.org/files/Articles/1780802/fpsyg-17-1780802-HTML/image_m/fpsyg-17-1780802-t001.jpg Table 1. Baseline classifier scores. https://www.frontiersin.org/files/Articles/1780802/fpsyg-17-1780802-HTML/image_m/fpsyg-17-1780802-t002.jpg Table 2. Classification results. https://www.frontiersin.org/files/Articles/1780802/fpsyg-17-1780802-HTML/image_m/fpsyg-17-1780802-g002.jpg Figure 2. Sample main concepts for the generated ontologies. https://www.frontiersin.org/files/Articles/1780802/fpsyg-17-1780802-HTML/image_m/fpsyg-17-1780802-g003.jpg Figure 3. Root concepts in ontology from literature research. https://www.frontiersin.org/files/Articles/1780802/fpsyg-17-1780802-HTML/image_m/fpsyg-17-1780802-t003.jpg Table 3. Similarity between ontologies limited to 800 concepts. https://www.frontiersin.org/files/Articles/1780802/fpsyg-17-1780802-HTML/image_m/fpsyg-17-1780802-t004.jpg Table 4. Similarity between ontologies limited to 500 concepts. https://www.frontiersin.org/files/Articles/1780802/fpsyg-17-1780802-HTML/image_m/fpsyg-17-1780802-t005.jpg Table 5. Similarity between ontologies limited to 200 concepts. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1786232/full https://www.frontiersin.org/files/Articles/1786232/fpubh-14-1786232-HTML/image_m/fpubh-14-1786232-g001.jpg Figure 1. Occurrence of viral pathogens in treated and untreated samples of the 10 monitored ships ( https://www.frontiersin.org/files/Articles/1786232/fpubh-14-1786232-HTML/image_m/fpubh-14-1786232-g002.jpg Figure 2. Microbial concentration in untreated and treated samples, separately for each shipping com https://www.frontiersin.org/files/Articles/1786232/fpubh-14-1786232-HTML/image_m/fpubh-14-1786232-t001.jpg Table 1. Bacterial and viral indicator removal by microbial parameter and shipping company (results https://www.frontiersin.org/files/Articles/1786232/fpubh-14-1786232-HTML/image_m/fpubh-14-1786232-t002.jpg Table 2. Ship information and clinical surveillance. https://www.frontiersin.org/files/Articles/1786232/fpubh-14-1786232-HTML/image_m/fpubh-14-1786232-g003.jpg Figure 3. Categorization of syndromes of potential viral infectious origin identified during the sur https://www.frontiersin.org/files/Articles/1786232/fpubh-14-1786232-HTML/image_m/fpubh-14-1786232-t003.jpg Table 3. Qualitative comparison between SARS-CoV-2 detection in untreated wastewater and clinical CO https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1725730/full https://www.frontiersin.org/files/Articles/1725730/fimmu-17-1725730-HTML/image_m/fimmu-17-1725730-g001.jpg Figure 1. Roadmap. https://www.frontiersin.org/files/Articles/1725730/fimmu-17-1725730-HTML/image_m/fimmu-17-1725730-g002.jpg Figure 2. ES-62 mollecular mechanisms of action. https://www.frontiersin.org/files/Articles/1725730/fimmu-17-1725730-HTML/image_m/fimmu-17-1725730-t001.jpg Table 1. Overview of immunomodulatory mechanism of Ancylostoma species. https://www.frontiersin.org/files/Articles/1725730/fimmu-17-1725730-HTML/image_m/fimmu-17-1725730-g003.jpg Figure 3. AIP 1 and AIP 2 immunomodulatory properties. https://www.frontiersin.org/files/Articles/1725730/fimmu-17-1725730-HTML/image_m/fimmu-17-1725730-g004.jpg Figure 4. Immunomodulation properties of A. pegreffii. Adapted from: Zeng MH, Alsobaie S, Wang XX, L https://www.frontiersin.org/files/Articles/1725730/fimmu-17-1725730-HTML/image_m/fimmu-17-1725730-g005.jpg Figure 5. Al-CPI (Ascaris lumbricoides cystatin) immunomodulatory mechanisms. https://www.frontiersin.org/files/Articles/1725730/fimmu-17-1725730-HTML/image_m/fimmu-17-1725730-g006.jpg Figure 6. Immunomodulatory mechanisms of F. hepatica. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1791617/full https://www.frontiersin.org/files/Articles/1791617/fvets-13-1791617-HTML-r1/image_m/fvets-13-1791617-g001.jpg Figure 1. Swine semen deliveries from two distribution centers. Reproduced with permission. https://www.frontiersin.org/files/Articles/1791617/fvets-13-1791617-HTML-r1/image_m/fvets-13-1791617-t001.jpg Table 1. Veterinarians, CAHWs, and Farmers AI procedures. https://www.frontiersin.org/files/Articles/1791617/fvets-13-1791617-HTML-r1/image_m/fvets-13-1791617-t002.jpg Table 2. Base case results using GLM OR + literature-sourced price per piglet. https://www.frontiersin.org/files/Articles/1791617/fvets-13-1791617-HTML-r1/image_m/fvets-13-1791617-t003.jpg Table 3. Program costs. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1776343/full https://www.frontiersin.org/files/Articles/1776343/fpsyg-17-1776343-HTML/image_m/fpsyg-17-1776343-t001.jpg Table 1. Demographic characteristics of participants. https://www.frontiersin.org/files/Articles/1776343/fpsyg-17-1776343-HTML/image_m/fpsyg-17-1776343-g001.jpg Figure 1. CONSORT flow diagram of participant recruitment, randomization, follow-up, and analysis. https://www.frontiersin.org/files/Articles/1776343/fpsyg-17-1776343-HTML/image_m/fpsyg-17-1776343-t002.jpg Table 2. Kolmogorov–Smirnov test for normality (SOBI total). https://www.frontiersin.org/files/Articles/1776343/fpsyg-17-1776343-HTML/image_m/fpsyg-17-1776343-t003.jpg Table 3. Levene’s test for equality of error variances (Post_SOBI). https://www.frontiersin.org/files/Articles/1776343/fpsyg-17-1776343-HTML/image_m/fpsyg-17-1776343-t004.jpg Table 4. Descriptive indicators of SOBI total (pre- and post-test). https://www.frontiersin.org/files/Articles/1776343/fpsyg-17-1776343-HTML/image_m/fpsyg-17-1776343-t005.jpg Table 5. ANCOVA results for SOBI total. https://www.frontiersin.org/files/Articles/1776343/fpsyg-17-1776343-HTML/image_m/fpsyg-17-1776343-g002.jpg Figure 2. Mean changes in social belonging across groups. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1726221/full https://www.frontiersin.org/files/Articles/1726221/fbioe-13-1726221-HTML/image_m/fbioe-13-1726221-t001.jpg Table 1. Characteristics of the participants. https://www.frontiersin.org/files/Articles/1726221/fbioe-13-1726221-HTML/image_m/fbioe-13-1726221-g001.jpg Figure 1. Locations of surface markers and the personalized scaled musculoskeletal model. (A–C) the https://www.frontiersin.org/files/Articles/1726221/fbioe-13-1726221-HTML/image_m/fbioe-13-1726221-g002.jpg Figure 2. Gait phase division of the two progressive movements with reference to standardized walkin https://www.frontiersin.org/files/Articles/1726221/fbioe-13-1726221-HTML/image_m/fbioe-13-1726221-g003.jpg Figure 3. Lower-limb musculoskeletal model driven by the two progressive movements. BDG (8th) and TC https://www.frontiersin.org/files/Articles/1726221/fbioe-13-1726221-HTML/image_m/fbioe-13-1726221-t002.jpg Table 2. Comparison of general gait parameters. https://www.frontiersin.org/files/Articles/1726221/fbioe-13-1726221-HTML/image_m/fbioe-13-1726221-g004.jpg Figure 4. ROM trajectories of lower limb joints during two progressive movements compared to normal https://www.frontiersin.org/files/Articles/1726221/fbioe-13-1726221-HTML/image_m/fbioe-13-1726221-g005.jpg Figure 5. Joint reaction forces of lower limb joints and ground reaction forces trajectories during https://www.frontiersin.org/files/Articles/1726221/fbioe-13-1726221-HTML/image_m/fbioe-13-1726221-g006.jpg Figure 6. Hip and knee net joint moments trajectories during two progressive movements compared to n https://www.frontiersin.org/files/Articles/1726221/fbioe-13-1726221-HTML/image_m/fbioe-13-1726221-g007.jpg Figure 7. Average muscle activity trajectories (Hip abductors, extensors and Knee flexors) during tw https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1786529/full https://www.frontiersin.org/files/Articles/1786529/fpubh-14-1786529-HTML/image_m/fpubh-14-1786529-g001.jpg Figure 1. Distribution of students by body mass index (BMI) category. Bars represent the number of s https://www.frontiersin.org/files/Articles/1786529/fpubh-14-1786529-HTML/image_m/fpubh-14-1786529-t001.jpg Table 1. Descriptive characteristics of the study sample (n = 325). https://www.frontiersin.org/files/Articles/1786529/fpubh-14-1786529-HTML/image_m/fpubh-14-1786529-g002.jpg Figure 2. Multiple correspondence analysis biplot. Associations between social determinants, nutriti https://www.frontiersin.org/files/Articles/1786529/fpubh-14-1786529-HTML/image_m/fpubh-14-1786529-t002.jpg Table 2. Eigenvalues and variance explained by MCA dimensions. https://www.frontiersin.org/files/Articles/1786529/fpubh-14-1786529-HTML/image_m/fpubh-14-1786529-g003.jpg Figure 3. Distribution of students by cluster. Pie chart and bar chart showing cluster sizes and pro https://www.frontiersin.org/files/Articles/1786529/fpubh-14-1786529-HTML/image_m/fpubh-14-1786529-g004.jpg Figure 4. Cluster profiles heatmap. Characterization matrix showing favorability levels across key v https://www.frontiersin.org/files/Articles/1786529/fpubh-14-1786529-HTML/image_m/fpubh-14-1786529-t003.jpg Table 3. Cluster centroids in MCA factorial space. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1786958/full https://www.frontiersin.org/files/Articles/1786958/fcvm-13-1786958-HTML-r2/image_m/fcvm-13-1786958-t001.jpg Table 1. Baseline functional class, comorbidities, and clinical parameters (n = 110). https://www.frontiersin.org/files/Articles/1786958/fcvm-13-1786958-HTML-r2/image_m/fcvm-13-1786958-t002.jpg Table 2. Echocardiographic and hemodynamic parameters at baseline. https://www.frontiersin.org/files/Articles/1786958/fcvm-13-1786958-HTML-r2/image_m/fcvm-13-1786958-t003.jpg Table 3. Dynamic changes in clinical, echocardiographic, and hemodynamic parameters before and after https://www.frontiersin.org/files/Articles/1786958/fcvm-13-1786958-HTML-r2/image_m/fcvm-13-1786958-g001.jpg Figure 1. Kaplan–Meier survival curves comparing patients with chronic thromboembolic pulmonary hype https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1711597/full https://www.frontiersin.org/files/Articles/1711597/fonc-15-1711597-HTML-r1/image_m/fonc-15-1711597-g001.jpg Figure 1. Study design and patient flow. Between January 1, 2015, and December 31, 2021, a total of https://www.frontiersin.org/files/Articles/1711597/fonc-15-1711597-HTML-r1/image_m/fonc-15-1711597-t001.jpg Table 1. Demographics and baseline characteristics of trial participants. https://www.frontiersin.org/files/Articles/1711597/fonc-15-1711597-HTML-r1/image_m/fonc-15-1711597-g002.jpg Figure 2. Twelve-month changes in pain intensity, sleep disturbance, and QoL impairment related to p https://www.frontiersin.org/files/Articles/1711597/fonc-15-1711597-HTML-r1/image_m/fonc-15-1711597-t002.jpg Table 2. PDQ-7 sum score and individual symptom item scores at baseline, and after three and twelve https://www.frontiersin.org/files/Articles/1711597/fonc-15-1711597-HTML-r1/image_m/fonc-15-1711597-g003.jpg Figure 3. Twelve-month changes in health outcomes (physical and mental health, activity impairment, https://www.frontiersin.org/files/Articles/1711597/fonc-15-1711597-HTML-r1/image_m/fonc-15-1711597-g004.jpg Figure 4. Conversion of initial non-responders to responders with successive HCCP treatments. (A) Pa https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1725349/full https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-g001.jpg Figure 1. The historical development of DCs. The initial discovery of DCs, characterized by their st https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-t001.jpg Table 1. Major dendritic cell subsets in intestinal immunity. https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-g002.jpg Figure 2. The functional transition of DCs from an immature to a mature state. Immature DCs act as “ https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-g003.jpg Figure 3. The historical development of TFH Cells. TFH cells were first described in human lymphoid https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-g004.jpg Figure 4. Functional classification and defining markers of TFH-cell subsets. The TFH-cell family co https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-g005.jpg Figure 5. Adaptive immune activation initiates TFH differentiation and pro-inflammatory functions. A https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-g006.jpg Figure 6. DCs provide co-stimulatory signals to activate transcription factors and trigger TFH cell https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-g007.jpg Figure 7. DCs initiate signal transduction and promote TFH cell differentiation by secreting cytokin https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-g008.jpg Figure 8. TFH cells mediate the recruitment of mature DCs and synergistically drive the formation of https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-t002.jpg Table 2. Systematic comparison of TFH-B versus TFH-DC interactions: analyzing the pathogenic signifi https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-t003.jpg Table 3. Model-specific findings versus the clinical characteristics of IBD: analyzing translational https://www.frontiersin.org/files/Articles/1725349/fimmu-17-1725349-HTML/image_m/fimmu-17-1725349-t004.jpg Table 4. Treatment strategy of the DC-TFH cell interactions delays IBD progression. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1776973/full https://www.frontiersin.org/files/Articles/1776973/fnins-20-1776973-HTML-r2/image_m/fnins-20-1776973-g001.jpg Figure 1. Representation of the five coronal brain regions dissected for 1H MAS NMR experiments. https://www.frontiersin.org/files/Articles/1776973/fnins-20-1776973-HTML-r2/image_m/fnins-20-1776973-g002.jpg Figure 2. 1H NMR spectra of the murine adult brain tissue sample (water presaturation pulse, MAS = 4 https://www.frontiersin.org/files/Articles/1776973/fnins-20-1776973-HTML-r2/image_m/fnins-20-1776973-t001.jpg Table 1. Main effects for variables modeled as a four-way interaction between age, genotype, sex and https://www.frontiersin.org/files/Articles/1776973/fnins-20-1776973-HTML-r2/image_m/fnins-20-1776973-g003.jpg Figure 3. Differences in relative metabolite concentration between dcr (green) and control (black) m https://www.frontiersin.org/files/Articles/1776973/fnins-20-1776973-HTML-r2/image_m/fnins-20-1776973-g004.jpg Figure 4. The difference in the relative metabolite concentrations between control (male: black, fem https://www.frontiersin.org/files/Articles/1776973/fnins-20-1776973-HTML-r2/image_m/fnins-20-1776973-g005.jpg Figure 5. The relative concentration of metabolites that showed a significant main effect of brain r https://www.frontiersin.org/journals/plant-physiology/articles/10.3389/fphgy.2025.1652412/full https://www.frontiersin.org/files/Articles/1652412/fphgy-03-1652412-HTML/image_m/fphgy-03-1652412-g001.jpg Figure 1. A map showing the three study locations along with pictures of the landscapes. https://www.frontiersin.org/files/Articles/1652412/fphgy-03-1652412-HTML/image_m/fphgy-03-1652412-g002.jpg Figure 2. Correlation analysis between T50, Tleaf, Tcrit, and studied leaf traits. The colors repres https://www.frontiersin.org/files/Articles/1652412/fphgy-03-1652412-HTML/image_m/fphgy-03-1652412-g003.jpg Figure 3. Box plots showing variation in T50 among different growth forms. https://www.frontiersin.org/files/Articles/1652412/fphgy-03-1652412-HTML/image_m/fphgy-03-1652412-g004.jpg Figure 4. Graphical representation of thermal susceptibility for the 52 studied species. The black b https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1798135/full https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-t001.jpg Table 1. Quantitative summary and design philosophy of the evaluated CNN backbones. https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-g001.jpg Figure 1. Overview of the proposed MVBeetle model architecture (ResNet50 shown as an example). https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-g002.jpg Figure 2. Workflow of MVBeetle feature encoding, attention fusion, and Grad-CAM explainability analy https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-g003.jpg Figure 3. Examples of dorsal view images for the 43 investigated species. (a) Alticinae (Nos. 1–20) https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-g004.jpg Figure 4. Species-wise distribution of multi-view images in (a) Alticinae and (b) Galerucinae. https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-t002.jpg Table 2. Ablation comparison of baseline model, multi-view (Concat), and attention-based MVBeetle ac https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-t003.jpg Table 3. Dual-view ablation study performance (test accuracy, mean ± Std). https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-g005.jpg Figure 5. Normalized confusion matrix of the VGG16-based multi-view fusion model. https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-g006.jpg Figure 6. Grad-CAM visualization demonstrating robust background suppression across various specimen https://www.frontiersin.org/files/Articles/1798135/fpls-17-1798135-HTML/image_m/fpls-17-1798135-g007.jpg Figure 7. Grad-CAM interpretability analysis of Alticinae and Galerucinae. (a) Alticinae activation https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1741800/full https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-g001.jpg Figure 1. Figure represents the key features of CSCs. https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-t001.jpg Table 1. Hallmark features of CSCs and their functional significance. https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-g002.jpg Figure 2. Diagrammatic representation of TME and the interacting components including Immune cells ( https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-g003.jpg Figure 3. The figure depicts mitochondrial metabolic plasticity in CSCs and a dynamic metabolic swit https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-g004.jpg Figure 4. Mitochondrial metabolism regulates HIF-1α stabilization and epigenetic changes, promoting https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-t002.jpg Table 2. Comparative overview of key metabolic features distinguishing CSCs from NSCs, highlighting https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-g005.jpg Figure 5. Figure represents various regulators of mitochondrial fusion/fission and role of AMPK/PGC- https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-t003.jpg Table 3. Represents various molecular players and their mechanisms, impact on CSC function along wit https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-t004.jpg Table 4. A comprehensive table represents key regulators, their mechanisms of action and their impac https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-t005.jpg Table 5. Inhibitors of mitochondrial biogenesis and dynamics in cancer stem cells. https://www.frontiersin.org/files/Articles/1741800/fmolb-13-1741800-HTML/image_m/fmolb-13-1741800-t006.jpg Table 6. Combinatorial therapies incorporating mitochondrial-targeting agents with chemotherapy and https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2026.1801730/full https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-g001.jpg Figure 1. Steps for Study inclusion criteria. https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-g002.jpg Figure 2. Study selection flow. https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-g003.jpg Figure 3. Theme of research. https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-t001.jpg Table 1. Summary of key lighting parameters and typical ranges used in VR-based IEQ research. https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-g004.jpg Figure 4. Six major Indoor Environment Typologies are illustrated in the study: Educational, Office https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-t002.jpg Table 2. VR methodologies and IEQ–lighting variables investigated across diverse indoor environments https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-g005.jpg Figure 5. Classifications by Experimental Research Protocols. Note. Overview of the common VR–IEQ ex https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-g006.jpg Figure 6. Popular Tools used in VR-based IEQ simulation and evaluation, including modeling (Revit, R https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-g007.jpg Figure 7. Conceptual synthesis of findings. https://www.frontiersin.org/files/Articles/1801730/fbuil-12-1801730-HTML/image_m/fbuil-12-1801730-g008.jpg Figure 8. Conceptual network of VR-based lighting and indoor environmental quality (IEQ) research. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1797299/full https://www.frontiersin.org/files/Articles/1797299/fimmu-17-1797299-HTML/image_m/fimmu-17-1797299-t001.jpg Table 1. Evidence levels and validation status of key mechanisms in the co-evolution paradigm. https://www.frontiersin.org/files/Articles/1797299/fimmu-17-1797299-HTML/image_m/fimmu-17-1797299-g001.jpg Figure 1. Probabilistic spatiotemporal trajectory of the prostate cancer immune landscape. This conc https://www.frontiersin.org/files/Articles/1797299/fimmu-17-1797299-HTML/image_m/fimmu-17-1797299-g002.jpg Figure 2. Spatially organized functional niches within the mCRPC ecosystem. This schematic integrate https://www.frontiersin.org/files/Articles/1797299/fimmu-17-1797299-HTML/image_m/fimmu-17-1797299-g003.jpg Figure 3. A precision ecological intervention framework for metastatic castration-resistant prostate https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1708938/full https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g001.jpg Figure 1. The diagram illustrates three cultivation patterns: monoculture of M. officinalis, monocul https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-t001.jpg Table 1. Hay yield of different planting patterns and year of yield measurement. https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g002.jpg Figure 2. Metabolite abundance expression. The horizontal axis represents samples, and the vertical https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-t002.jpg Table 2. Abundance expression of differential metabolites. https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g003.jpg Figure 3. Venn diagram of metabolites. Different comparison groups are filled with distinct colors, https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g004.jpg Figure 4. OPLS-DA model metabolite loading plot. (a) Represents oat monoculture versus intercropping https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-t003.jpg Table 3. Expression of key differential metabolites in the loading plot of metabolites between monoc https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-t004.jpg Table 4. Expression of key differential metabolites in the loading plot of metabolites between monoc https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g005.jpg Figure 5. Volcano plot of differential primary metabolites analysis. (a) A. sativa vs. intercropping https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-t005.jpg Table 5. Volcano plot of differential primary metabolites analysis in A. sativa monoculture and inte https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-t006.jpg Table 6. Volcano plot of differential primary metabolites analysis in M. officinalis monoculture and https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g006.jpg Figure 6. VIP plot of metabolites. (a) Represents the comparison between A. sativa monoculture and i https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g007.jpg Figure 7. Map the differential metabolites to the KEGG database, classify and enrich the differentia https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g008.jpg Figure 8. Three replicates were set up for each sample. (a) Alpha diversity. The Pielou’s evenness i https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g009.jpg Figure 9. Statistical analysis of soil microbial metagenomic KEGG pathways. The left vertical axis r https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g010.jpg Figure 10. Statistical test for microbial species differences. The test model is Tukey–HSD. The bar https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g011.jpg Figure 11. Heatmap of correlations between differential metabolites and differential microorganisms. https://www.frontiersin.org/files/Articles/1708938/fmicb-16-1708938-HTML/image_m/fmicb-16-1708938-g012.jpg Figure 12. Microbial KEGG functional differential analysis–reporterscore plot. (a) represents the co https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1737854/full https://www.frontiersin.org/files/Articles/1737854/fnut-13-1737854-HTML-r1/image_m/fnut-13-1737854-g001.jpg Figure 1. The flow chart of identifying eligible subjects. PLCO, Prostate, Lung, Colorectal, and Ova https://www.frontiersin.org/files/Articles/1737854/fnut-13-1737854-HTML-r1/image_m/fnut-13-1737854-t001.jpg Table 1. Baseline characteristics of study population according to overall fat quality index. https://www.frontiersin.org/files/Articles/1737854/fnut-13-1737854-HTML-r1/image_m/fnut-13-1737854-g002.jpg Figure 2. Nonlinear dose–response analysis on the association of FQI score with the risk of HNC (A: https://www.frontiersin.org/files/Articles/1737854/fnut-13-1737854-HTML-r1/image_m/fnut-13-1737854-t002.jpg Table 2. Hazard ratios and 95% confidence interval of the association between FQI and head and neck https://www.frontiersin.org/files/Articles/1737854/fnut-13-1737854-HTML-r1/image_m/fnut-13-1737854-g003.jpg Figure 3. Subgroup analysis and Cox proportional hazards model. A forest plot was generated to asses https://www.frontiersin.org/files/Articles/1737854/fnut-13-1737854-HTML-r1/image_m/fnut-13-1737854-t003.jpg Table 3. Dietary fatty acids intake and the risk of head and neck cancer or subsites in the PLCO coh https://www.frontiersin.org/files/Articles/1737854/fnut-13-1737854-HTML-r1/image_m/fnut-13-1737854-g004.jpg Figure 4. The six combined exposure groups were formed based on the joint classification of FQI scor https://www.frontiersin.org/files/Articles/1737854/fnut-13-1737854-HTML-r1/image_m/fnut-13-1737854-t004.jpg Table 4. Sensitivity analyses on the between fat quality index and head and neck cancer incidence. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1807547/full https://www.frontiersin.org/files/Articles/1807547/fimmu-17-1807547-HTML-r1/image_m/fimmu-17-1807547-t001.jpg Table 1. Different conditions applied for the hexavalent formulation and resulting formulates charac https://www.frontiersin.org/files/Articles/1807547/fimmu-17-1807547-HTML-r1/image_m/fimmu-17-1807547-g001.jpg Figure 1. Hexavalent formulation (Hexa, green) tested in mice with and without Alhydrogel in compari https://www.frontiersin.org/files/Articles/1807547/fimmu-17-1807547-HTML-r1/image_m/fimmu-17-1807547-g002.jpg Figure 2. Hexavalent formulation (Hexa, green) tested in rats with and without Alhydrogel in compari https://www.frontiersin.org/files/Articles/1807547/fimmu-17-1807547-HTML-r1/image_m/fimmu-17-1807547-g003.jpg Figure 3. Hexavalent formulation (Hexa, green) tested in rabbits in comparison to bivalent Salmonell https://www.frontiersin.org/files/Articles/1807547/fimmu-17-1807547-HTML-r1/image_m/fimmu-17-1807547-g004.jpg Figure 4. S. sonnei GMMA-O:2 conjugate + Vi-CRM197 compared to S. sonnei GMMA physically mixed with https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1749994/full https://www.frontiersin.org/files/Articles/1749994/fendo-17-1749994-HTML/image_m/fendo-17-1749994-g001.jpg Figure 1. Physical, biochemical and renal functional parameters of mice. (A–C) Body weight, kidney w https://www.frontiersin.org/files/Articles/1749994/fendo-17-1749994-HTML/image_m/fendo-17-1749994-g002.jpg Figure 2. TSWN formula or valsartan treatment inhibited renal fibrosis in diabetic mice. (A) Images https://www.frontiersin.org/files/Articles/1749994/fendo-17-1749994-HTML/image_m/fendo-17-1749994-g003.jpg Figure 3. TSWN formula or valsartan treatment alleviated renal tubular injury of db/db mice. (A, B) https://www.frontiersin.org/files/Articles/1749994/fendo-17-1749994-HTML/image_m/fendo-17-1749994-g004.jpg Figure 4. TSWN formula or valsartan treatment decreased iron levels in the kidneys of db/db mice. (A https://www.frontiersin.org/files/Articles/1749994/fendo-17-1749994-HTML/image_m/fendo-17-1749994-g005.jpg Figure 5. TSWN formula or valsartan treatment elevated the p-AMPK/t-AMPK ratio, ameliorated oxidativ https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1662183/full https://www.frontiersin.org/files/Articles/1662183/fimmu-16-1662183-HTML/image_m/fimmu-16-1662183-g001.jpg Figure 1. Disease progression in WT and Csf2 KO mice following subcutaneous P. verrucosa infection. https://www.frontiersin.org/files/Articles/1662183/fimmu-16-1662183-HTML/image_m/fimmu-16-1662183-g002.jpg Figure 2. Histopathological features and fungal load in footpad tissue following subcutaneous infect https://www.frontiersin.org/files/Articles/1662183/fimmu-16-1662183-HTML/image_m/fimmu-16-1662183-g003.jpg Figure 3. Immune cell infiltration and cytokine levels in WT and Csf2 KO mice during P. verrucosa in https://www.frontiersin.org/files/Articles/1662183/fimmu-16-1662183-HTML/image_m/fimmu-16-1662183-g004.jpg Figure 4. Functional assessment of BMDMs from mice of different genotypes. BMDMs from WT and Csf2 KO https://www.frontiersin.org/files/Articles/1662183/fimmu-16-1662183-HTML/image_m/fimmu-16-1662183-g005.jpg Figure 5. Functional assessment of neutrophils from mice of different genotypes. Neutrophils from th https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1648802/full https://www.frontiersin.org/files/Articles/1648802/fendo-16-1648802-HTML/image_m/fendo-16-1648802-g001.jpg Figure 1. Drainage volume and pain scores following Pseudomonas aeruginosa injection (PAI). Mean 24- https://www.frontiersin.org/files/Articles/1648802/fendo-16-1648802-HTML/image_m/fendo-16-1648802-t001.jpg Table 1. Demographic, clinical, and outcome characteristics of patients undergoing Pseudomonas aerug https://www.frontiersin.org/files/Articles/1648802/fendo-16-1648802-HTML/image_m/fendo-16-1648802-t002.jpg Table 2. Drainage information of patients undergoing the injection of Pseudomonas aeruginosa (PAI). https://www.frontiersin.org/files/Articles/1648802/fendo-16-1648802-HTML/image_m/fendo-16-1648802-t003.jpg Table 3. Adverse events of patients undergoing the injection of Pseudomonas aeruginosa (PAI). https://www.frontiersin.org/files/Articles/1648802/fendo-16-1648802-HTML/image_m/fendo-16-1648802-g002.jpg Figure 2. Proposed tiered management algorithm for chyle fistula (CF). The application of Pseudomona https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1740643/full https://www.frontiersin.org/files/Articles/1740643/fimmu-17-1740643-HTML/image_m/fimmu-17-1740643-g001.jpg Figure 1. Flowchart of patient selection for the training and validation cohorts. https://www.frontiersin.org/files/Articles/1740643/fimmu-17-1740643-HTML/image_m/fimmu-17-1740643-t001.jpg Table 1. Baseline data of the patients in training group (n=144) and validation cohort (n=100). https://www.frontiersin.org/files/Articles/1740643/fimmu-17-1740643-HTML/image_m/fimmu-17-1740643-t002.jpg Table 2. Multi-modal assessment of the margin. https://www.frontiersin.org/files/Articles/1740643/fimmu-17-1740643-HTML/image_m/fimmu-17-1740643-g002.jpg Figure 2. Impact of histopathologic, tumor burden, molecular, and immune domains on locoregional con https://www.frontiersin.org/files/Articles/1740643/fimmu-17-1740643-HTML/image_m/fimmu-17-1740643-t003.jpg Table 3. Multivariable analysis of predictors for locoregional control (LRC) and distant metastasis https://www.frontiersin.org/files/Articles/1740643/fimmu-17-1740643-HTML/image_m/fimmu-17-1740643-t004.jpg Table 4. Comparison of Cox model performance for predicting locoregional control (LRC) and distant m https://www.frontiersin.org/files/Articles/1740643/fimmu-17-1740643-HTML/image_m/fimmu-17-1740643-g003.jpg Figure 3. Impact of margin risk index (MRIx) on locoregional control (LRC) and distant metastasis fr https://www.frontiersin.org/files/Articles/1740643/fimmu-17-1740643-HTML/image_m/fimmu-17-1740643-g004.jpg Figure 4. Accuracy of margin risk index (MRIx) in predicting locoregional control (LRC) and distant https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1803572/full https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-g001.jpg Figure 1. Overview of the methodological design and data integration process for dual-stage health r https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-g002.jpg Figure 2. Hybrid learning architecture and training strategy for health risk modeling. https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-t009.jpg Algorithm 1. https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-t001.jpg Table 1. Dataset composition and experimental scenarios. https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-t002.jpg Table 2. Comparison between the trivial baseline and the learning-based models across datasets. https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-t003.jpg Table 3. Correlation between the learned risk representation and the original risk score. https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-t004.jpg Table 4. Perturbation analysis of key variables and their impact on the learned risk representation. https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-t005.jpg Table 5. Behavior of risk stratification models under induced class construction. https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-g003.jpg Figure 3. ROC curves of the hybrid risk stratification scheme evaluated independently on NHANES and https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-t006.jpg Table 6. Performance on the derived integrated dataset. https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-g004.jpg Figure 4. Evaluation of the hybrid risk stratification scheme on the Integrated Public Health Datase https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-t007.jpg Table 7. Stability of performance metrics across cross-validation folds (TRAIN split). https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-g005.jpg Figure 5. Stability analysis using cross-validation. (a) Distribution of the distance to the optimum https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-t008.jpg Table 8. Top-20 risk attribution signals across datasets. https://www.frontiersin.org/files/Articles/1803572/fbinf-06-1803572-HTML/image_m/fbinf-06-1803572-g006.jpg Figure 6. Relative organization of attribution signals across datasets. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1704597/full https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-g001.jpg Figure 1. Study selection flow of the scoping review process: PRISMA-ScR flow chart. https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-g002.jpg Figure 2. Chronic metabolic diseases may be induced by insulin resistance, created in BioRender. Eld https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-t001.jpg Table 1. The most common genetic mutations and their associations to insulin resistance. https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-t002.jpg Table 2. The most common environmental factors and their associations with the development of insuli https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-t003.jpg Table 3. The subcellular organelles dysfunction and its associations with the development of insulin https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-t004.jpg Table 4. The role of intracellular stress factor pathways in the progression of insulin resistance. https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-t005.jpg Table 5. The interplay between ER stress and insulin resistance. https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-t006.jpg Table 6. The physiological and metabolic effects of the LCHF-KD. https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-t007.jpg Table 7. Mechanisms of the LCHF in alleviating ER stress-related insulin resistance. https://www.frontiersin.org/files/Articles/1704597/fnut-12-1704597-HTML/image_m/fnut-12-1704597-t008.jpg Table 8. Summary of experimental and clinical evidence on the effect of low-carbohydrate, high-fat k https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1738418/full https://www.frontiersin.org/files/Articles/1738418/fpubh-14-1738418-HTML/image_m/fpubh-14-1738418-t001.jpg Table 1. Model fit indices for latent profile analysis models. https://www.frontiersin.org/files/Articles/1738418/fpubh-14-1738418-HTML/image_m/fpubh-14-1738418-g001.jpg Figure 1. Distribution of characteristics across four latent profiles of ESN among rural older adult https://www.frontiersin.org/files/Articles/1738418/fpubh-14-1738418-HTML/image_m/fpubh-14-1738418-g002.jpg Figure 2. Radar chart of the five domains of ESN across four latent profiles. Each axis represents o https://www.frontiersin.org/files/Articles/1738418/fpubh-14-1738418-HTML/image_m/fpubh-14-1738418-t002.jpg Table 2. Multinomial logistic regression of factors associated with ESN profiles (n = 719). https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1713721/full https://www.frontiersin.org/files/Articles/1713721/fpls-16-1713721-HTML/image_m/fpls-16-1713721-g001.jpg Figure 1. Effects of different MT treatments on the contents of fresh weight (A), dry weight (B), ch https://www.frontiersin.org/files/Articles/1713721/fpls-16-1713721-HTML/image_m/fpls-16-1713721-g002.jpg Figure 2. Effects of different MT treatments on the contents of H2O2 (A), MDA (B), O2- (C), SOD (D), https://www.frontiersin.org/files/Articles/1713721/fpls-16-1713721-HTML/image_m/fpls-16-1713721-g003.jpg Figure 3. Effects of different MT treatments on the content of BSP (A), flavonoids (B), and saponins https://www.frontiersin.org/files/Articles/1713721/fpls-16-1713721-HTML/image_m/fpls-16-1713721-g004.jpg Figure 4. Metabolomics analysis of B. striata tube rs in different treatments. (A) The PCA analysis https://www.frontiersin.org/files/Articles/1713721/fpls-16-1713721-HTML/image_m/fpls-16-1713721-g005.jpg Figure 5. Transcriptome analysis of B. striata tubers in different treatments. (A) The PCA analysis https://www.frontiersin.org/files/Articles/1713721/fpls-16-1713721-HTML/image_m/fpls-16-1713721-g006.jpg Figure 6. BSP biosynthesis pathway. https://www.frontiersin.org/files/Articles/1713721/fpls-16-1713721-HTML/image_m/fpls-16-1713721-g007.jpg Figure 7. The correlation heatmap of DEGs and DEMs related to BSP synthesis. https://www.frontiersin.org/files/Articles/1713721/fpls-16-1713721-HTML/image_m/fpls-16-1713721-g008.jpg Figure 8. Coexpression network analysis of hub genes (green rhombus) and TFs (pink circles). The red https://www.frontiersin.org/files/Articles/1713721/fpls-16-1713721-HTML/image_m/fpls-16-1713721-g009.jpg Figure 9. qRT-PCR validation of genes related to BSP biosynthesis of B. striata at three different t https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1753615/full https://www.frontiersin.org/files/Articles/1753615/fpls-17-1753615-HTML-r1/image_m/fpls-17-1753615-g001.jpg Figure 1. Phylogenetic analysis of homologous proteins of O.sativa, A.thaliana, and G.max HATs and H https://www.frontiersin.org/files/Articles/1753615/fpls-17-1753615-HTML-r1/image_m/fpls-17-1753615-g002.jpg Figure 2. Gene structure, motif and domains of GmHAT (A) and GmHDAC (B). The phylogenetic trees, mot https://www.frontiersin.org/files/Articles/1753615/fpls-17-1753615-HTML-r1/image_m/fpls-17-1753615-g003.jpg Figure 3. Intraspecific and interspecific colinearity analysis of HDAC and HAT. (A) and (B) indicate https://www.frontiersin.org/files/Articles/1753615/fpls-17-1753615-HTML-r1/image_m/fpls-17-1753615-g004.jpg Figure 4. Cis-elements in the promoters of GmHAT and GmHDAC. Lines represent sequences 2kb upstream https://www.frontiersin.org/files/Articles/1753615/fpls-17-1753615-HTML-r1/image_m/fpls-17-1753615-g005.jpg Figure 5. Heatmap of expression levels of GmHAT and GmHDAC in different tissues. This circular heatm https://www.frontiersin.org/files/Articles/1753615/fpls-17-1753615-HTML-r1/image_m/fpls-17-1753615-g006.jpg Figure 6. Expression profiles of 16 GmHATs and GmHDACs in soybean seedlings under salt and drought s https://www.frontiersin.org/files/Articles/1753615/fpls-17-1753615-HTML-r1/image_m/fpls-17-1753615-g007.jpg Figure 7. Histone H3 and H4 acetylation levels under salt and drought treatments. (A) Immunoblot ana https://www.frontiersin.org/files/Articles/1753615/fpls-17-1753615-HTML-r1/image_m/fpls-17-1753615-g008.jpg Figure 8. Expression profiles of selected GmHATs and GmHDACs in soybean seedlings under five plant h https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1639959/full https://www.frontiersin.org/files/Articles/1639959/fpsyt-16-1639959-HTML-r1/image_m/fpsyt-16-1639959-g001.jpg Figure 1. Participant screening flowchart. https://www.frontiersin.org/files/Articles/1639959/fpsyt-16-1639959-HTML-r1/image_m/fpsyt-16-1639959-t001.jpg Table 1. GAD-7 score for university students. https://www.frontiersin.org/files/Articles/1639959/fpsyt-16-1639959-HTML-r1/image_m/fpsyt-16-1639959-t002.jpg Table 2. Distribution of the variables in rural and urban respondents. https://www.frontiersin.org/files/Articles/1639959/fpsyt-16-1639959-HTML-r1/image_m/fpsyt-16-1639959-t003.jpg Table 3. Distribution of variables among survey respondents the presence or absence of anxiety sympt https://www.frontiersin.org/files/Articles/1639959/fpsyt-16-1639959-HTML-r1/image_m/fpsyt-16-1639959-t004.jpg Table 4. Logistic regression results for sociodemographic characteristics associated with anxiety sy https://www.frontiersin.org/files/Articles/1639959/fpsyt-16-1639959-HTML-r1/image_m/fpsyt-16-1639959-t005.jpg Table 5. The Fairlie decomposition model of anxiety symptom status in urban and rural university stu https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1738196/full https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g001.jpg Figure 1. The implementation and application pipeline of this work. https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g002.jpg Figure 2. The architecture of the basic LPM of the complete cardiovascular system and LVAD. LVAD: le https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-t001.jpg Table 1. Parameter values of basic LPM model. TBV: total blood volume, LVAD: left ventricular assist https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-t002.jpg Table 2. The parameters Ka, Kb, and Kc of the HeartMate 3 and CorHeart 6. https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-t003.jpg Table 3. Categorization of parameters, along with their sources and whether they are patient-specifi https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g003.jpg Figure 3. The GSF analysis of the RCR model. (a) The model structure of RCR, (b) the inlet boundary https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g004.jpg Figure 4. The GSF analysis of the RCRR model. (a) The model structure of RCRR, (b) the inlet boundar https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-t004.jpg Table 4. Variables involved in dataset generation, along with their initial values and sampling rang https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g005.jpg Figure 5. Hemodynamic index in dataset calculated by LPM with annotated minimum, maximum, 25% and 75 https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-t005.jpg Table 5. Information on three patients included in this study. https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g006.jpg Figure 6. The structure of CLPM-Net. NN A and NN B predict the left and right ventricular end-systol https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g007.jpg Figure 7. (a) Network architecture with inputs of flow and EDV/ESV, producing Plves as output. (b) N https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g008.jpg Figure 8. The results of the ramp test for the basic LPM. The subgraph above shows the LVAD rotation https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g009.jpg Figure 9. The pressure-volume loop observed in two patients with HF in response to variations in the https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g010.jpg Figure 10. The results of traditional sensitivity analysis. https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g011.jpg Figure 11. The results of global sensitivity analysis. (a) GSF analysis across all ten model paramet https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-t006.jpg Table 6. The direct output of CLPM-Net and the corresponding errors. RMSE: Relative mean square erro https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g012.jpg Figure 12. The linear regression plot of CLPM-Net’s output and the ground truth. (a) EmaxLV and (b) https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g013.jpg Figure 13. The linear regression plot of CLPM-Net’s output and the ground truth for the six systemic https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g014.jpg Figure 14. The comparative NN prediction results of the AP, PAP, and PCWP with the selection of the https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g015.jpg Figure 15. The comparative NN prediction results of the AP, PAP, and PCWP with the selection of the https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g016.jpg Figure 16. The linear regression plot of non-hierarchical NN’s output and the ground truth. (A) Emax https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g017.jpg Figure 17. The linear regression plot of non-hierarchical NN’s output and the ground truth for the s https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-g018.jpg Figure 18. The relationship between prediction error and the size of the train dataset as it increas https://www.frontiersin.org/files/Articles/1738196/fphys-17-1738196-HTML/image_m/fphys-17-1738196-t007.jpg Table 7. Clinical validation results for three patients. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1788491/full https://www.frontiersin.org/files/Articles/1788491/fmicb-17-1788491-HTML/image_m/fmicb-17-1788491-g001.jpg Figure 1. Regulatory effects of strain treatments on the growth of Festuca sinensis cv. Qinghai. (A) https://www.frontiersin.org/files/Articles/1788491/fmicb-17-1788491-HTML/image_m/fmicb-17-1788491-g002.jpg Figure 2. KEGG pathway enrichment analysis of significantly differential metabolites (SDMs) in diffe https://www.frontiersin.org/files/Articles/1788491/fmicb-17-1788491-HTML/image_m/fmicb-17-1788491-g003.jpg Figure 3. K-means clustering, correlation analysis, and interaction networks of significantly differ https://www.frontiersin.org/files/Articles/1788491/fmicb-17-1788491-HTML/image_m/fmicb-17-1788491-g004.jpg Figure 4. Enrichment analysis of differentially expressed genes (DEGs) in different comparison group https://www.frontiersin.org/files/Articles/1788491/fmicb-17-1788491-HTML/image_m/fmicb-17-1788491-g005.jpg Figure 5. Expression trends of differentially expressed genes (DEGs) and significantly differential https://www.frontiersin.org/files/Articles/1788491/fmicb-17-1788491-HTML/image_m/fmicb-17-1788491-g006.jpg Figure 6. Correlation network analysis of key metabolites, core genes, and transcription factors in https://www.frontiersin.org/files/Articles/1788491/fmicb-17-1788491-HTML/image_m/fmicb-17-1788491-g007.jpg Figure 7. Schematic diagram of the molecular mechanisms underlying growth promotion in Festuca sinen https://www.frontiersin.org/journals/hematology/articles/10.3389/frhem.2026.1686097/full https://www.frontiersin.org/files/Articles/1686097/frhem-05-1686097-HTML/image_m/frhem-05-1686097-t001.jpg Table 1. Comparative characteristics of apoptosis and necroptosis based on the key molecular, morph https://www.frontiersin.org/files/Articles/1686097/frhem-05-1686097-HTML/image_m/frhem-05-1686097-g001.jpg Figure 1. Molecular mechanisms of extrinsic apoptosis and necroptosis. Death receptor activation can https://www.frontiersin.org/files/Articles/1686097/frhem-05-1686097-HTML/image_m/frhem-05-1686097-t002.jpg Table 2. Comprehensive overview of the molecular mechanisms, genetic determinants, and therapeutic r https://www.frontiersin.org/files/Articles/1686097/frhem-05-1686097-HTML/image_m/frhem-05-1686097-t003.jpg Table 3. Context-dependent factors influencing necroptosis outcomes in multiple myeloma. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1767899/full https://www.frontiersin.org/files/Articles/1767899/fpls-17-1767899-HTML/image_m/fpls-17-1767899-g001.jpg Figure 1. Chemical characterization of ZnO NPs. (A) NAADF imaging by transmission electron microscop https://www.frontiersin.org/files/Articles/1767899/fpls-17-1767899-HTML/image_m/fpls-17-1767899-g002.jpg Figure 2. Responses of Glycyrrhiza uralensis to salt and ZnO NPs application. (A) The morphology of https://www.frontiersin.org/files/Articles/1767899/fpls-17-1767899-HTML/image_m/fpls-17-1767899-g003.jpg Figure 3. Analysis of metabolomic changes under different treatments. (A) Principal component analys https://www.frontiersin.org/files/Articles/1767899/fpls-17-1767899-HTML/image_m/fpls-17-1767899-g004.jpg Figure 4. Transcriptional relationship and differentially expressed genes (DEGs) between different s https://www.frontiersin.org/files/Articles/1767899/fpls-17-1767899-HTML/image_m/fpls-17-1767899-g005.jpg Figure 5. Analysis of differentially expressed genes (DEGs) among different samples. (A) GO enrichme https://www.frontiersin.org/files/Articles/1767899/fpls-17-1767899-HTML/image_m/fpls-17-1767899-g006.jpg Figure 6. Schematic models of phenylpropane biosynthesis and flavonoid biosynthesis. heatmaps show r https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1781248/full https://www.frontiersin.org/files/Articles/1781248/fimmu-17-1781248-HTML/image_m/fimmu-17-1781248-g001.jpg Figure 1. Schematic representation of innate and adaptive immune responses in atherosclerosis. The d https://www.frontiersin.org/files/Articles/1781248/fimmu-17-1781248-HTML/image_m/fimmu-17-1781248-g002.jpg Figure 2. Mechanistic integration of endothelial dysfunction, oxidative stress, and NLRP3 inflammaso https://www.frontiersin.org/files/Articles/1781248/fimmu-17-1781248-HTML/image_m/fimmu-17-1781248-g003.jpg Figure 3. The immunopathogenesis of myocarditis progression: from viral entry to chronic remodeling. https://www.frontiersin.org/files/Articles/1781248/fimmu-17-1781248-HTML/image_m/fimmu-17-1781248-g004.jpg Figure 4. Integrated immunopathogenesis of systemic vasculitides across the arterial wall. While the https://www.frontiersin.org/files/Articles/1781248/fimmu-17-1781248-HTML/image_m/fimmu-17-1781248-t001.jpg Table 1. Summary of representative therapeutic agents targeting key immune hubs. https://www.frontiersin.org/files/Articles/1781248/fimmu-17-1781248-HTML/image_m/fimmu-17-1781248-g005.jpg Figure 5. Therapeutic targeting of shared mechanistic modules across cardiovascular inflammatory dis https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1818835/full https://www.frontiersin.org/files/Articles/1818835/fimmu-17-1818835-HTML/image_m/fimmu-17-1818835-g001.jpg Figure 1. Integrated immuno-inflammatory-metabolic network driving the development of cardiovascular https://www.frontiersin.org/files/Articles/1818835/fimmu-17-1818835-HTML/image_m/fimmu-17-1818835-g002.jpg Figure 2. The inflammation–thrombosis–coagulation axis linking immune activation to acute cardiovasc https://www.frontiersin.org/files/Articles/1818835/fimmu-17-1818835-HTML/image_m/fimmu-17-1818835-g003.jpg Figure 3. Lifestyle-induced trained immunity and immunometabolic reprogramming in cardiovascular ris https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2026.1819151/full https://www.frontiersin.org/files/Articles/1819151/fnbeh-20-1819151-HTML-r1/image_m/fnbeh-20-1819151-g001.jpg Figure 1. Hardware of the object recognition analysis system. https://www.frontiersin.org/files/Articles/1819151/fnbeh-20-1819151-HTML-r1/image_m/fnbeh-20-1819151-g002.jpg Figure 2. Markers used for DLC artificial neural network training. https://www.frontiersin.org/files/Articles/1819151/fnbeh-20-1819151-HTML-r1/image_m/fnbeh-20-1819151-t001.jpg Table 1. The results of correlation analysis between visual observation and NOR analysis system. https://www.frontiersin.org/files/Articles/1819151/fnbeh-20-1819151-HTML-r1/image_m/fnbeh-20-1819151-g003.jpg Figure 3. Analysis of traditional behavioral measures in the novel object recognition (NOR) task in https://www.frontiersin.org/files/Articles/1819151/fnbeh-20-1819151-HTML-r1/image_m/fnbeh-20-1819151-g004.jpg Figure 4. Analysis of fine-motor behavioral indicators in the novel object recognition (NOR) task in https://www.frontiersin.org/files/Articles/1819151/fnbeh-20-1819151-HTML-r1/image_m/fnbeh-20-1819151-g005.jpg Figure 5. Analysis of traditional behavioral measures in the object location recognition (OLR) task https://www.frontiersin.org/files/Articles/1819151/fnbeh-20-1819151-HTML-r1/image_m/fnbeh-20-1819151-g006.jpg Figure 6. Analysis of fine-motor behavioral indicators in the object location recognition (OLR) task https://www.frontiersin.org/files/Articles/1819151/fnbeh-20-1819151-HTML-r1/image_m/fnbeh-20-1819151-g007.jpg Figure 7. Analysis of traditional behavioral measures in the novel object recognition (NOR) task in https://www.frontiersin.org/files/Articles/1819151/fnbeh-20-1819151-HTML-r1/image_m/fnbeh-20-1819151-g008.jpg Figure 8. Analysis of fine-motor behavioral indicators in the novel object recognition (NOR) task in https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1796603/full https://www.frontiersin.org/files/Articles/1796603/fpsyg-17-1796603-HTML/image_m/fpsyg-17-1796603-g001.jpg Figure 1. Participant flow through the trial. FEMT, five-element music therapy; RT, relaxation train https://www.frontiersin.org/files/Articles/1796603/fpsyg-17-1796603-HTML/image_m/fpsyg-17-1796603-t001.jpg Table 1. Participant characteristics at baseline. https://www.frontiersin.org/files/Articles/1796603/fpsyg-17-1796603-HTML/image_m/fpsyg-17-1796603-t002.jpg Table 2. Comparison of scores on each scale between the two baseline groups. https://www.frontiersin.org/files/Articles/1796603/fpsyg-17-1796603-HTML/image_m/fpsyg-17-1796603-g002.jpg Figure 2. Trends in estimated mean changes in validated psychological distress thermometer scores fo https://www.frontiersin.org/files/Articles/1796603/fpsyg-17-1796603-HTML/image_m/fpsyg-17-1796603-t003.jpg Table 3. Within-group estimated means and effect sizes vDT. https://www.frontiersin.org/files/Articles/1796603/fpsyg-17-1796603-HTML/image_m/fpsyg-17-1796603-g003.jpg Figure 3. Estimated between-group differences in vDT scores (RT – FEMT) and their 90% confidence int https://www.frontiersin.org/files/Articles/1796603/fpsyg-17-1796603-HTML/image_m/fpsyg-17-1796603-t004.jpg Table 4. The estimated between-group mean difference in vDT between the two groups at the time of in https://www.frontiersin.org/files/Articles/1796603/fpsyg-17-1796603-HTML/image_m/fpsyg-17-1796603-t005.jpg Table 5. Intra-group changes in secondary outcome measures at each follow-up time point and standard https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1783732/full https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-t001.jpg Table 1. Results of sample characteristics. https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-t002.jpg Table 2. Comparison of model fit metrics. https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-t003.jpg Table 3. Variable descriptive statistics. https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-t004.jpg Table 4. Standardized path results of the association between community environment and depression o https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-t005.jpg Table 5. Non-standardized path results of the association between community environment and depressi https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-g001.jpg Figure 1. Standardized coefficients for full sample model. Note: To enhance the clarity of the visua https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-t006.jpg Table 6. Standardized path analysis results of the association between community environment and dep https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-t007.jpg Table 7. Non-standardized path analysis results of the association between community environment and https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-g002.jpg Figure 2. Standardization coefficients for younger-old adults model. https://www.frontiersin.org/files/Articles/1783732/fpubh-14-1783732-HTML/image_m/fpubh-14-1783732-g003.jpg Figure 3. Standardization coefficients for older-old adults model. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1700961/full https://www.frontiersin.org/files/Articles/1700961/fmed-12-1700961-HTML-r1/image_m/fmed-12-1700961-g001.jpg Figure 1. Trial exclusion and classification criteria. PRO: patient-reported outcome. https://www.frontiersin.org/files/Articles/1700961/fmed-12-1700961-HTML-r1/image_m/fmed-12-1700961-g002.jpg Figure 2. The number and age-standardized rate of incidence (A), deaths (B), and DALYs (C) of lung c https://www.frontiersin.org/files/Articles/1700961/fmed-12-1700961-HTML-r1/image_m/fmed-12-1700961-t001.jpg Table 1. Characteristics of all identified trials and patient-reported outcome (PRO) related trials. https://www.frontiersin.org/files/Articles/1700961/fmed-12-1700961-HTML-r1/image_m/fmed-12-1700961-g003.jpg Figure 3. Annual number of clinical trials analyzed. https://www.frontiersin.org/files/Articles/1700961/fmed-12-1700961-HTML-r1/image_m/fmed-12-1700961-t002.jpg Table 2. High-frequency of PRO instruments used in different outcomes. https://www.frontiersin.org/files/Articles/1700961/fmed-12-1700961-HTML-r1/image_m/fmed-12-1700961-g004.jpg Figure 4. Distribution of PRO-related trials analyzed. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1597686/full https://www.frontiersin.org/files/Articles/1597686/fimmu-16-1597686-HTML/image_m/fimmu-16-1597686-t001.jpg Table 1. Other potential mechanisms of immune escape in lung cancer. https://www.frontiersin.org/files/Articles/1597686/fimmu-16-1597686-HTML/image_m/fimmu-16-1597686-g001.jpg Figure 1. Mechanisms of immune escape in lung cancer. EGFR, epidermal growth factor receptor; ERK, e https://www.frontiersin.org/files/Articles/1597686/fimmu-16-1597686-HTML/image_m/fimmu-16-1597686-t002.jpg Table 2. Recent advances in clinical trials of ICIs for lung cancer. https://www.frontiersin.org/files/Articles/1597686/fimmu-16-1597686-HTML/image_m/fimmu-16-1597686-t003.jpg Table 3. Some ongoing clinical trials on emerging targets. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1812714/full https://www.frontiersin.org/files/Articles/1812714/fsurg-13-1812714-HTML/image_m/fsurg-13-1812714-g001.jpg Figure 1. The change in bronchial angle is defined as the preoperative angle minus the postoperative https://www.frontiersin.org/files/Articles/1812714/fsurg-13-1812714-HTML/image_m/fsurg-13-1812714-g002.jpg Figure 2. Three-dimensional lung volumetry was conducted semi-automatically with mimics research 21. https://www.frontiersin.org/files/Articles/1812714/fsurg-13-1812714-HTML/image_m/fsurg-13-1812714-t001.jpg Table 1. Baseline characters of patients between two group (pre-op). https://www.frontiersin.org/files/Articles/1812714/fsurg-13-1812714-HTML/image_m/fsurg-13-1812714-g003.jpg Figure 3. Flowchart of patient selection. https://www.frontiersin.org/files/Articles/1812714/fsurg-13-1812714-HTML/image_m/fsurg-13-1812714-t002.jpg Table 2. Comparison of complications and other clinical data. https://www.frontiersin.org/files/Articles/1812714/fsurg-13-1812714-HTML/image_m/fsurg-13-1812714-t003.jpg Table 3. Change in bronchial angles between two groups (°, x ± s). https://www.frontiersin.org/files/Articles/1812714/fsurg-13-1812714-HTML/image_m/fsurg-13-1812714-t004.jpg Table 4. Change in lung volume and pulmonary function between two groups. https://www.frontiersin.org/files/Articles/1812714/fsurg-13-1812714-HTML/image_m/fsurg-13-1812714-t005.jpg Table 5. The mean LCQ-MC score of two group. https://www.frontiersin.org/files/Articles/1812714/fsurg-13-1812714-HTML/image_m/fsurg-13-1812714-t006.jpg Table 6. Subgroup analysis of key outcomes in group P according to station 9 lymph node dissection s https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1794280/full https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t001.jpg Table 1. Port list by development status. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t002.jpg Table 2. Port list by lifecycle status. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-g001.jpg Figure 1. Overall distribution of ports along the belt and road corridor. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t003.jpg Table 3. Variable introduction. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-g002.jpg Figure 2. Development trend of ports along the silk road from 2014 to 2023. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t004.jpg Table 4. Results of the collinearity test. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t005.jpg Table 5. Results of the linear regression. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t006.jpg Table 6. Results of the nonlinear regression. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t007.jpg Table 7. Endogeneity test results. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t008.jpg Table 8. Robustness check results: including additional controls. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t009.jpg Table 9. Robustness check results: trimming outliers. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t010.jpg Table 10. Mediation diagnostics. https://www.frontiersin.org/files/Articles/1794280/fmars-13-1794280-HTML/image_m/fmars-13-1794280-t011.jpg Table 11. Inflection points for the mature ports and new/growth ports. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1765815/full https://www.frontiersin.org/files/Articles/1765815/fphar-16-1765815-HTML-r1/image_m/fphar-16-1765815-g001.jpg Figure 1. Placental perfusion and recovery of dulaglutide. Placenta perfusion profiles (A) obtained https://www.frontiersin.org/files/Articles/1765815/fphar-16-1765815-HTML-r1/image_m/fphar-16-1765815-g002.jpg Figure 2. Immunofluorescence visualisation of dulaglutide-AlexaFluor647 distribution in human placen https://www.frontiersin.org/files/Articles/1765815/fphar-16-1765815-HTML-r1/image_m/fphar-16-1765815-g003.jpg Figure 3. Dulaglutide-AlexaFluor647 and Rab5 co-localisation in perfused placental villous structure https://www.frontiersin.org/files/Articles/1765815/fphar-16-1765815-HTML-r1/image_m/fphar-16-1765815-g004.jpg Figure 4. Distribution of fluorescently labelled dulaglutide relative to CD34+ foetal vessels in per https://www.frontiersin.org/files/Articles/1765815/fphar-16-1765815-HTML-r1/image_m/fphar-16-1765815-g005.jpg Figure 5. Corrected apparent permeability of dulaglutide and reference substances across BeWo b30 ce https://www.frontiersin.org/files/Articles/1765815/fphar-16-1765815-HTML-r1/image_m/fphar-16-1765815-g006.jpg Figure 6. Influence of dulaglutide on human placental explant viability and functionality. Influence https://www.frontiersin.org/files/Articles/1765815/fphar-16-1765815-HTML-r1/image_m/fphar-16-1765815-g007.jpg Figure 7. Influence of dulaglutide on BeWo cells functionality. Influence of dulaglutide on placenta https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1793621/full https://www.frontiersin.org/files/Articles/1793621/fimmu-17-1793621-HTML/image_m/fimmu-17-1793621-t001.jpg Table 1. Selected observational studies on periodontitis and cardiovascular outcomes. https://www.frontiersin.org/files/Articles/1793621/fimmu-17-1793621-HTML/image_m/fimmu-17-1793621-g001.jpg Figure 1. Overview of the oral–vascular axis linking chronic periodontitis to ASCVD.The left panel s https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1797716/full https://www.frontiersin.org/files/Articles/1797716/fpubh-14-1797716-HTML/image_m/fpubh-14-1797716-t001.jpg Table 1. Demographic and HIV vulnerability profile of the sample by PrEP use**. https://www.frontiersin.org/files/Articles/1797716/fpubh-14-1797716-HTML/image_m/fpubh-14-1797716-g001.jpg Figure 1. PrEP awareness, offering, and uptake among study participants (n = 288). PrEP awareness at https://www.frontiersin.org/files/Articles/1797716/fpubh-14-1797716-HTML/image_m/fpubh-14-1797716-g002.jpg Figure 2. PrEP offering and uptake by baseline PrEP awareness. Bars represent the proportion of part https://www.frontiersin.org/files/Articles/1797716/fpubh-14-1797716-HTML/image_m/fpubh-14-1797716-t002.jpg Table 2. HIV follow-up testing outcomes (months-3 and/or 6-), by province and metropolitan/municipal https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1592656/full https://www.frontiersin.org/files/Articles/1592656/fimmu-17-1592656-HTML/image_m/fimmu-17-1592656-g001.jpg Figure 1. (A) PET/CT revealed six scattered hypermetabolic metastatic lesions in the left lobe. (B) https://www.frontiersin.org/files/Articles/1592656/fimmu-17-1592656-HTML/image_m/fimmu-17-1592656-g002.jpg Figure 2. Immunostaining (A) Positive immunostaining for α-inhibin in Adrenalcorticalcarcinoma (anti https://www.frontiersin.org/files/Articles/1592656/fimmu-17-1592656-HTML/image_m/fimmu-17-1592656-g003.jpg Figure 3. (A) Timeline of treatment administration From Episode of care. (B) Longitudinal changes in https://www.frontiersin.org/files/Articles/1592656/fimmu-17-1592656-HTML/image_m/fimmu-17-1592656-g004.jpg Figure 4. PET/CT scan (A) Pre PEF demonstrating 6 FDG avid lesions in the liver. (B) post PEF demons https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1782592/full https://www.frontiersin.org/files/Articles/1782592/fpsyg-17-1782592-HTML/image_m/fpsyg-17-1782592-t001.jpg Table 1. The results of descriptive analysis among three index categories. https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1696542/full https://www.frontiersin.org/files/Articles/1696542/fchem-13-1696542-HTML/image_m/fchem-13-1696542-g007.jpg Scheme 1. Schematic representation of the biosensor for the detection of CYFRA 21-1 DNA. https://www.frontiersin.org/files/Articles/1696542/fchem-13-1696542-HTML/image_m/fchem-13-1696542-g001.jpg Figure 1. TEM image (A), length distribution (B), and diameter distribution (C) of Fe3O4/α-Fe2O3 MHN https://www.frontiersin.org/files/Articles/1696542/fchem-13-1696542-HTML/image_m/fchem-13-1696542-g002.jpg Figure 2. XPS survey (A) and Fe 2p (B), C 1s (C), O 1s (D), and Au 4f (E) core-level spectra recorde https://www.frontiersin.org/files/Articles/1696542/fchem-13-1696542-HTML/image_m/fchem-13-1696542-g003.jpg Figure 3. CV (A) and EIS spectra (B) of [Fe(CN)6]3-/4- at various modified electrodes: (a) unmodifie https://www.frontiersin.org/files/Articles/1696542/fchem-13-1696542-HTML/image_m/fchem-13-1696542-g004.jpg Figure 4. DPV responses for the optimization of the Fe3O4/α-Fe2O3@Au concentration (A), the ssDNA co https://www.frontiersin.org/files/Articles/1696542/fchem-13-1696542-HTML/image_m/fchem-13-1696542-g005.jpg Figure 5. DPV curves of CYFRA 21-1 at different concentrations detected by the biosensor of Fe3O4/α- https://www.frontiersin.org/files/Articles/1696542/fchem-13-1696542-HTML/image_m/fchem-13-1696542-t001.jpg Table 1. Comparison of the reported electrochemical methods for CYFRA 21-1 detection. https://www.frontiersin.org/files/Articles/1696542/fchem-13-1696542-HTML/image_m/fchem-13-1696542-g006.jpg Figure 6. Selectivity (A), reproducibility (B), and stability (C) study of the biosensor using Fe3O4 https://www.frontiersin.org/files/Articles/1696542/fchem-13-1696542-HTML/image_m/fchem-13-1696542-t002.jpg Table 2. Determination of CYFRA 21-1 concentration in spiked human serum samples using Fe3O4/α-Fe2O3 https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1637091/full https://www.frontiersin.org/files/Articles/1637091/fmed-12-1637091-HTML/image_m/fmed-12-1637091-g001.jpg Figure 1. (A) Estimated probability and group membership using longitudinally self-reported AEs of a https://www.frontiersin.org/files/Articles/1637091/fmed-12-1637091-HTML/image_m/fmed-12-1637091-g002.jpg Figure 2. (A) Estimated probability and group membership using longitudinally self-reported AEs of a https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1756889/full https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-t001.jpg Table 1. Factor levels for a 23 factorial design. https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-t002.jpg Table 2. Box–Behnken design matrix of the three variables in coded units and the response values for https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-g001.jpg Figure 1. (a) Standardized Pareto chart for extraction efficiency and (b) effects of variations in t https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-g002.jpg Figure 2. (a) The response surface plots of the effect of temperature, liquid solid ratio and their https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-t003.jpg Table 3. ANOVA for the effect liquid to material ratio, temperature and irradiation time on the cont https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-g003.jpg Figure 3. HPLC analysis at 280 nm of the Periploca angustifolia phenolic extract. https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-g004.jpg Figure 4. 1H NMR spectra of caffeic acid and catechin (400 MHz, CD3OD). https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-g005.jpg Figure 5. 13C-NMR (broad-band and DEPT 135) spectra of PAPE fraction (400 MHz, CD3OD). https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-g006.jpg Figure 6. Representative HMBC spectra of PAPE fraction (400 MHz, CD3OD). https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-t004.jpg Table 4. Correlations of carbon/proton 2JC–H and 3JC–H of compounds 1 and 2 (400 MHz, CD3OD). https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-g007.jpg Figure 7. Scavenging effects of the Periploca angustifolia phenolic extract on (a) DPPH assay and (b https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-g008.jpg Figure 8. Effect of different PAPE concentrations on (a) lipid peroxidation and (b) protein glycatio https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-t005.jpg Table 5. Effect of PAPE extraction against Cd-induced damage in the antioxidant and pro-oxidant stat https://www.frontiersin.org/files/Articles/1756889/fphys-17-1756889-HTML/image_m/fphys-17-1756889-g009.jpg Figure 9. Histopathological examination of testicular sections of different groups of rats. Control https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1808355/full https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-g001.jpg Figure 1. Transmission mechanism linking environmental regulation and occupational health governance https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-t001.jpg Table 1. Descriptions of parameters. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-t002.jpg Table 2. Payoff matrix for the local government and high-pollution enterprises. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-t003.jpg Table 3. Local stability analysis of equilibrium points under SRSP. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-t004.jpg Table 4. Local stability analysis of equilibrium points under SRDP. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-t005.jpg Table 5. Local stability analysis of equilibrium points under DRSP. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-t006.jpg Table 6. Local stability analysis of equilibrium points under DRDP. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-t007.jpg Table 7. Initial parameter assignments for the two-party evolutionary game. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-g002.jpg Figure 2. (a) Evolutionary pathways of occupational health risk management strategies for polluting https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-g003.jpg Figure 3. Impact of benefit differential on occupational health management strategy. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-g004.jpg Figure 4. Impact of cost differential on occupational health management strategy. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-g005.jpg Figure 5. Impact of reward on occupational health management strategies. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-g006.jpg Figure 6. Impact of punishment on occupational health management strategies. https://www.frontiersin.org/files/Articles/1808355/fpubh-14-1808355-HTML/image_m/fpubh-14-1808355-g007.jpg Figure 7. Impact of monitoring probability on occupational health management strategies. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1559977/full https://www.frontiersin.org/files/Articles/1559977/fsurg-12-1559977-HTML/image_m/fsurg-12-1559977-g001.jpg Figure 1. The case of a ruptured right PTA aneurysm leading to a CCF. (A–C) Right ICA injection, lat https://www.frontiersin.org/files/Articles/1559977/fsurg-12-1559977-HTML/image_m/fsurg-12-1559977-t001.jpg Table 1. Summary of the 11 previous reported cases of PPTA aneurysm. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1745815/full https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-g001.jpg Figure 1. Measurement of prepontine anatomical parameters. All measurements were made by reconstruct https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-g002.jpg Figure 2. Flowchart of the nested model-selection and evaluation procedure. https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-g003.jpg Figure 3. (a) Boxplots of numerical features comparing the asymptomatic control and symptomatic iTN https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-t001.jpg Table 1. Summary of feature-level group comparisons (asymptomatic control = Class-0; symptomatic iTN https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-g004.jpg Figure 4. Confusion matrices averaged over 20 outer repetitions (mean ± std). https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-t002.jpg Table 2. Test performance of six classifiers across 20 independent outer repetitions [mean ± std (95 https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-g005.jpg Figure 5. Threshold-independent discrimination on the held-out test sets across 20 repetitions. (A) https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-t003.jpg Table 3. Pairwise Wilcoxon–Holm comparisons for F1 across 20 paired outer repetitions. https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-t004.jpg Table 4. Frequency of feature selection across 20 outer repetitions for each model. https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-g006.jpg Figure 6. Global feature attributions from SHAP beeswarm plots for the six classifiers (Bagging, KNN https://www.frontiersin.org/files/Articles/1745815/fmed-13-1745815-HTML/image_m/fmed-13-1745815-g007.jpg Figure 7. Local feature attributions from LIME explanations for the same test instance analyzed in t https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1813510/full https://www.frontiersin.org/files/Articles/1813510/fendo-17-1813510-HTML/image_m/fendo-17-1813510-t001.jpg Table 1. Cut-off values based on the International Consensus on Continuous Glucose Monitoring Metric https://www.frontiersin.org/files/Articles/1813510/fendo-17-1813510-HTML/image_m/fendo-17-1813510-t002.jpg Table 2. Descriptive characteristics of the study population. https://www.frontiersin.org/files/Articles/1813510/fendo-17-1813510-HTML/image_m/fendo-17-1813510-g001.jpg Figure 1. Distribution of CGM metrics with consensus-based clinical targets. Violin and boxplot repr https://www.frontiersin.org/files/Articles/1813510/fendo-17-1813510-HTML/image_m/fendo-17-1813510-g002.jpg Figure 2. Multivariable logistic regression models for isCGM-derived glycemic. Outcomes Forest plot https://www.frontiersin.org/journals/genome-editing/articles/10.3389/fgeed.2025.1667329/full https://www.frontiersin.org/files/Articles/1667329/fgeed-07-1667329-HTML/image_m/fgeed-07-1667329-g001.jpg Figure 1. Genome Editing for Treatment of Human Disease Network structure. COST Action 21113 is orga https://www.frontiersin.org/files/Articles/1667329/fgeed-07-1667329-HTML/image_m/fgeed-07-1667329-g002.jpg Figure 2. Cost Action and second GenE-HumDi annual meeting demographics. (A) Cost Action Members fro https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1725607/full https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-g001.jpg Figure 1. Analysis framework. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t001.jpg Table 1. Evaluation index system of rural digital commerce development level. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t002.jpg Table 2. Evaluation index system of rural industrial convergence. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t003.jpg Table 3. Evaluation index system of rural digital connectivity. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t004.jpg Table 4. Descriptive statistics of key variables. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-g002.jpg Figure 2. Quadratic fitted curve between the rural digital connectivity and urban–rural income inequ https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t005.jpg Table 5. Estimation results of the baseline regression models. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-g003.jpg Figure 3. Marginal effect of dig on Theil index with 95% confidence intervals. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t006.jpg Table 6. Estimation results of the robustness checks. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t007.jpg Table 7. Heterogeneity analysis: free shipping. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t008.jpg Table 8. Heterogeneity analysis: urbanization. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t009.jpg Table 9. Spatial autocorrelation test result of rural digital connectivity and urban–rural income in https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t010.jpg Table 10. Model selection tests. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t011.jpg Table 11. Direct, indirect, and total effects of spatial Durbin model estimation. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t012.jpg Table 12. Digital connectivity refines digital commerce. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t013.jpg Table 13. Digital connectivity influences rural industrial structure through digital commerce. https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t014.jpg Table 14. Digital connectivity influences urban–rural income inequality through the industrial struc https://www.frontiersin.org/files/Articles/1725607/fsufs-09-1725607-HTML/image_m/fsufs-09-1725607-t015.jpg Table 15. Digital connectivity influences urban–rural income inequality through the rural industrial https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1721160/full https://www.frontiersin.org/files/Articles/1721160/fsufs-10-1721160-HTML/image_m/fsufs-10-1721160-g001.jpg Figure 1. Theoretical framework diagram. https://www.frontiersin.org/files/Articles/1721160/fsufs-10-1721160-HTML/image_m/fsufs-10-1721160-t001.jpg Table 1. Control variables. https://www.frontiersin.org/files/Articles/1721160/fsufs-10-1721160-HTML/image_m/fsufs-10-1721160-t002.jpg Table 2. Descriptive statistics of variables. https://www.frontiersin.org/files/Articles/1721160/fsufs-10-1721160-HTML/image_m/fsufs-10-1721160-g002.jpg Figure 2. Time–space trend chart. https://www.frontiersin.org/files/Articles/1721160/fsufs-10-1721160-HTML/image_m/fsufs-10-1721160-t003.jpg Table 3. Benchmark regression results. https://www.frontiersin.org/files/Articles/1721160/fsufs-10-1721160-HTML/image_m/fsufs-10-1721160-t004.jpg Table 4. Endogeneity test. https://www.frontiersin.org/files/Articles/1721160/fsufs-10-1721160-HTML/image_m/fsufs-10-1721160-t005.jpg Table 5. Robustness test. https://www.frontiersin.org/files/Articles/1721160/fsufs-10-1721160-HTML/image_m/fsufs-10-1721160-t006.jpg Table 6. Mechanism test of GF on LT. https://www.frontiersin.org/files/Articles/1721160/fsufs-10-1721160-HTML/image_m/fsufs-10-1721160-t007.jpg Table 7. Heterogeneity regression. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1780040/full https://www.frontiersin.org/files/Articles/1780040/fmed-13-1780040-HTML/image_m/fmed-13-1780040-g001.jpg Figure 1. Literature search and selection process. https://www.frontiersin.org/files/Articles/1780040/fmed-13-1780040-HTML/image_m/fmed-13-1780040-t001.jpg Table 1. Main characteristics and data of each study. https://www.frontiersin.org/files/Articles/1780040/fmed-13-1780040-HTML/image_m/fmed-13-1780040-g002.jpg Figure 2. Association between UPFs consumption and IBS risk. https://www.frontiersin.org/files/Articles/1780040/fmed-13-1780040-HTML/image_m/fmed-13-1780040-g003.jpg Figure 3. Subgroup analysis of the association between UPFs consumption and IBS risk by type of proc https://www.frontiersin.org/files/Articles/1780040/fmed-13-1780040-HTML/image_m/fmed-13-1780040-g004.jpg Figure 4. Subgroup analysis of the association between UPFs consumption and IBS risk according to th https://www.frontiersin.org/journals/insect-science/articles/10.3389/finsc.2026.1806523/full https://www.frontiersin.org/files/Articles/1806523/finsc-06-1806523-HTML-r1/image_m/finsc-06-1806523-g001.jpg Figure 1. Map of central Veracruz, Mexico, showing the study region and sampling locations. The purp https://www.frontiersin.org/files/Articles/1806523/finsc-06-1806523-HTML-r1/image_m/finsc-06-1806523-t001.jpg Table 1. Study sites along an elevational gradient in Veracruz, Mexico, where Sideroxylon celastrinu https://www.frontiersin.org/files/Articles/1806523/finsc-06-1806523-HTML-r1/image_m/finsc-06-1806523-t002.jpg Table 2. Frequency counts of emerged adults of Anastrepha dentata and A. pallens across sampling sit https://www.frontiersin.org/files/Articles/1806523/finsc-06-1806523-HTML-r1/image_m/finsc-06-1806523-g002.jpg Figure 2. Canonical discriminant analysis of Sideroxylon celastrinum fruits classified as Anastrepha https://www.frontiersin.org/files/Articles/1806523/finsc-06-1806523-HTML-r1/image_m/finsc-06-1806523-g003.jpg Figure 3. Components of the study system. (A) Male (top) and female (bottom) Anastrepha dentata on t https://www.frontiersin.org/files/Articles/1806523/finsc-06-1806523-HTML-r1/image_m/finsc-06-1806523-g004.jpg Figure 4. Mean percentage of adult emergence (± SE) from the years 2021–2023 of Anastrepha dentata a https://www.frontiersin.org/files/Articles/1806523/finsc-06-1806523-HTML-r1/image_m/finsc-06-1806523-t003.jpg Table 3. Infestation levels and biological parameters of Anastrepha dentata and Anastrepha pallens a https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1808482/full https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-t001.jpg Table 1. Ingredient and nutrient composition of the basal diet (as-fed basis). https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-t002.jpg Table 2. Analyzed mycotoxin content of experimental diets. https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g001.jpg Figure 1. Schematic illustration of the experimental design. A total of 360 Cobb 500 broiler chicks https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g002.jpg Figure 2. Effects of synbiotic supplementation on bile and serum FUM-specific IgA, IgY, and DON-spec https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g003.jpg Figure 3. Effects of synbiotic supplementation on short-chain fatty acid concentrations of cecal con https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g004.jpg Figure 4. Correlation between major cecal microbial families and short-chain fatty acid (SCFA) conce https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g005.jpg Figure 5. Effect of synbiotic supplementation on the relative abundance of cecal microbial compositi https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g006.jpg Figure 6. Effect of synbiotic supplementation on the relative abundance of cecal microbial compositi https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g007.jpg Figure 7. Effect of synbiotic supplementation on the relative abundance of cecal microbial compositi https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g008.jpg Figure 8. Effect of synbiotic supplementation on alpha diversity indices of broiler chickens fed myc https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g009.jpg Figure 9. Effect of synbiotic supplementation on the relative abundance of cecal microbial compositi https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g010.jpg Figure 10. Effect of synbiotic supplementation on relative abundance of cecal microbial composition https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g011.jpg Figure 11. Effect of synbiotic supplementation on the relative abundance of cecal microbial composit https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g012.jpg Figure 12. Effect of synbiotic supplementation on the relative abundance of cecal microbial composit https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g013.jpg Figure 13. Effect of synbiotic supplementation on alpha diversity indices of broiler chickens fed my https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g014.jpg Figure 14. Effect of synbiotic supplementation on beta-diversity based on Jaccard distances of cecal https://www.frontiersin.org/files/Articles/1808482/fphys-17-1808482-HTML-r1/image_m/fphys-17-1808482-g015.jpg Figure 15. Predicted microbial metabolic pathways involved in amino acid biosynthesis, carbohydrate https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1740012/full https://www.frontiersin.org/files/Articles/1740012/fpsyg-17-1740012-HTML/image_m/fpsyg-17-1740012-g001.jpg Figure 1. Conceptual framework illustrating the mediating role of mentalizing in consumer behavior. https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2026.1751580/full https://www.frontiersin.org/files/Articles/1751580/frhs-06-1751580-HTML/image_m/frhs-06-1751580-g001.jpg Figure 1. Core components guide mapped against the model for improvement (8). https://www.frontiersin.org/files/Articles/1751580/frhs-06-1751580-HTML/image_m/frhs-06-1751580-t001.jpg Table 1. The 6 core components for designing quality improvement initiatives. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1719796/full https://www.frontiersin.org/files/Articles/1719796/fpubh-14-1719796-HTML/image_m/fpubh-14-1719796-t001.jpg Table 1. Analytical framework for comparative policy analysis. https://www.frontiersin.org/files/Articles/1719796/fpubh-14-1719796-HTML/image_m/fpubh-14-1719796-t002.jpg Table 2. Psychiatric workforce density in Poland and selected EU member states (25). https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1790531/full https://www.frontiersin.org/files/Articles/1790531/fimmu-17-1790531-HTML/image_m/fimmu-17-1790531-g001.jpg Figure 1. Psoriasis-like cytokines expression profile of in vitro polarized CD4+ and CD8+ T cell sub https://www.frontiersin.org/files/Articles/1790531/fimmu-17-1790531-HTML/image_m/fimmu-17-1790531-g002.jpg Figure 2. Integration of psoriasis-associated T cells into human full thickness skin equivalents. (A https://www.frontiersin.org/files/Articles/1790531/fimmu-17-1790531-HTML/image_m/fimmu-17-1790531-g003.jpg Figure 3. Polarized Th1, Th17, Tc1 and Tc17 cells induce inflammation and histopathological features https://www.frontiersin.org/files/Articles/1790531/fimmu-17-1790531-HTML/image_m/fimmu-17-1790531-g004.jpg Figure 4. PDE4 inhibitor alters inflammatory cytokine levels in T cell-incorporated human full thick https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1800237/full https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-t001.jpg Table 1. Classification performance across CHANGE-seq and GUIDE-seq datasets. Results refer to model https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-t002.jpg Table 2. Regression performance across CHANGE-seq and GUIDE-seq datasets. Results refer to models in https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g001.jpg Figure 1. Performance distribution of XGBoost models trained with One-Hot encoding. The boxplots com https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-t003.jpg Table 3. Performance comparison of XGBoost models on the Hanna dataset (Base Editing) across classif https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g002.jpg Figure 2. XGBoost (One-Hot) regression performance across targets. The bar chart shows the Pearson’s https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g003.jpg Figure 3. XGBoost (One-Hot) classification performance across targets. The classification model (AUP https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-t004.jpg Table 4. Regression performance across different targets. https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-t005.jpg Table 5. Classification performance across different targets. https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g004.jpg Figure 4. Analysis of Prediction Errors on the Hanna Dataset. The scatter plot (left) compares predi https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g005.jpg Figure 5. SHAP summary plots for One-Hot encoding on the CHANGE-seq dataset. Consistent with the bio https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g006.jpg Figure 6. SHAP summary plots for Bulges encoding on the CHANGE-seq dataset. While ‘Distance’ remains https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g007.jpg Figure 7. SHAP summary plots for K-mer encoding on the CHANGE-seq dataset. The K-mer models prioriti https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g008.jpg Figure 8. Accumulated Local Effects (ALE) analysis of the “Distance” feature. The plot quantifies th https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g009.jpg Figure 9. SHAP summary plots for One-Hot encoding on the Hanna dataset. Both the Classification (A) https://www.frontiersin.org/files/Articles/1800237/fbinf-06-1800237-HTML-r1/image_m/fbinf-06-1800237-g010.jpg Figure 10. SHAP summary plots for K-mer encoding on the Hanna dataset. The K-mer models for Classifi https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1595089/full https://www.frontiersin.org/files/Articles/1595089/fpubh-13-1595089-HTML-r1/image_m/fpubh-13-1595089-g001.jpg Figure 1. Flowchart of study design. https://www.frontiersin.org/files/Articles/1595089/fpubh-13-1595089-HTML-r1/image_m/fpubh-13-1595089-t001.jpg Table 1. Characteristics of the study participants. https://www.frontiersin.org/files/Articles/1595089/fpubh-13-1595089-HTML-r1/image_m/fpubh-13-1595089-t002.jpg Table 2. The mean (±SD) fatigue scores of children in 8 schools. https://www.frontiersin.org/files/Articles/1595089/fpubh-13-1595089-HTML-r1/image_m/fpubh-13-1595089-t003.jpg Table 3. Air quality indicators measured in schools. https://www.frontiersin.org/files/Articles/1595089/fpubh-13-1595089-HTML-r1/image_m/fpubh-13-1595089-t004.jpg Table 4. Correlations between air pollutants and PedsQL Fatigue scores. https://www.frontiersin.org/files/Articles/1595089/fpubh-13-1595089-HTML-r1/image_m/fpubh-13-1595089-t005.jpg Table 5. Linear regression models describing factors independently associated with PedsQL fatigue sc https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1815432/full https://www.frontiersin.org/files/Articles/1815432/fped-14-1815432-HTML/image_m/fped-14-1815432-t001.jpg Table 1. Demographic and clinical characteristics of children with H. pylori infection (n = 128). https://www.frontiersin.org/files/Articles/1815432/fped-14-1815432-HTML/image_m/fped-14-1815432-t002.jpg Table 2. H. pylori eradication rates among children treated with amoxicillin- and metronidazole-base https://www.frontiersin.org/files/Articles/1815432/fped-14-1815432-HTML/image_m/fped-14-1815432-g001.jpg Figure 1. Flow diagram of children included in the study of H. pylori infection and treatment outcom https://www.frontiersin.org/files/Articles/1815432/fped-14-1815432-HTML/image_m/fped-14-1815432-g002.jpg Figure 2. Comparison of H. pylori eradication rates between amoxicillin- and metronidazole-based tre https://www.frontiersin.org/files/Articles/1815432/fped-14-1815432-HTML/image_m/fped-14-1815432-t003.jpg Table 3. Recurrence rates of H. pylori infection during follow-up among children after successful er https://www.frontiersin.org/files/Articles/1815432/fped-14-1815432-HTML/image_m/fped-14-1815432-g003.jpg Figure 3. Recurrence pattern of H. pylori infection during follow-up in children after eradication t https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1804149/full https://www.frontiersin.org/files/Articles/1804149/fpsyt-17-1804149-HTML-r1/image_m/fpsyt-17-1804149-t001.jpg Table 1. Demograpfic and clinical characteristics of the total sample. https://www.frontiersin.org/files/Articles/1804149/fpsyt-17-1804149-HTML-r1/image_m/fpsyt-17-1804149-t002.jpg Table 2. Relationship between sleep and cognitive functioning. https://www.frontiersin.org/files/Articles/1804149/fpsyt-17-1804149-HTML-r1/image_m/fpsyt-17-1804149-t003.jpg Table 3. Logistic regression analysis for cognitive impairment. https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2025.1694963/full https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g001.jpg Figure 1. Study area map of selected locations of Mirzapur. https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g002.jpg Figure 2. Land use land cover map of Mirzapur district, Uttar Pradesh. https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g003.jpg Figure 3. Spatial distribution pattern of the physicochemical parameters in Mirzapur district. https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g004.jpg Figure 4. Spatial distribution pattern of the analysed heavy metals in Mirzapur district. https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-t001.jpg Table 1. Seasonal variation in the concentration of various heavy metals in groundwater of Mirzapur https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g005.jpg Figure 5. Correlation analysis among physicochemical parameters and heavy metals in Mirzapur. https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-t002.jpg Table 2. One-way ANOVA analysis showing significant seasonal variation in physicochemical parameters https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g006.jpg Figure 6. Cluster analysis of physicochemical parameters and heavy metals in Mirzapur. https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g007.jpg Figure 7. Spatial distribution pattern of the heavy metal pollution index (HPI) in Mirzapur district https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g008.jpg Figure 8. Spatial distribution pattern of the heavy metal evaluation index (HEI) in Mirzapur distric https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g009.jpg Figure 9. Spatial distribution pattern of the nemerow index (NI) in Mirzapur district. https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g010.jpg Figure 10. Spatial distribution pattern of the ecological risk index (ERI) in Mirzapur district. https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-t003.jpg Table 3. CDI, RfD, and HQ values as obtained in Mirzapur district. https://www.frontiersin.org/files/Articles/1694963/frwa-07-1694963-HTML/image_m/frwa-07-1694963-g011.jpg Figure 11. Graph showing cancer risk (CR) for adults and children in Mirzapur district. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1641311/full https://www.frontiersin.org/files/Articles/1641311/fpubh-13-1641311-HTML/image_m/fpubh-13-1641311-g001.jpg Figure 1. Distribution of the sample by educational level. https://www.frontiersin.org/files/Articles/1641311/fpubh-13-1641311-HTML/image_m/fpubh-13-1641311-g002.jpg Figure 2. Smoking habits in the sample. https://www.frontiersin.org/files/Articles/1641311/fpubh-13-1641311-HTML/image_m/fpubh-13-1641311-t001.jpg Table 1. Reason for last dental visit. https://www.frontiersin.org/files/Articles/1641311/fpubh-13-1641311-HTML/image_m/fpubh-13-1641311-g003.jpg Figure 3. Distribution of the sample by age and place of origin. https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2026.1741389/full https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-g001.jpg Figure 1. Modified PRISMA flow diagram applied for the selection of the game (Page et al., 2021). https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-g002.jpg Figure 2. Location map of the study area. https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-t001.jpg Table 1. Selected sample size from both grades/classes. https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-t002.jpg Table 2. Distribution of experimental and control group respondents. https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-t003.jpg Table 3. Univariate analysis of the understanding and knowledge retention in both groups before and https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-t004.jpg Table 4. Results of Levene’s test of equality of error variances for resilience scores across interv https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-t005.jpg Table 5. ANOVA results for variations among group mean resilience score. https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-t006.jpg Table 6. Comparison of mean resilience scores of experimental vs. control groups. https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-g003.jpg Figure 3. Comparison of mean resilience scores of experimental vs. control groups. https://www.frontiersin.org/files/Articles/1741389/fclim-08-1741389-HTML/image_m/fclim-08-1741389-t007.jpg Table 7. LSD test result for differences in resilience scores between several pairs of groups. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1549141/full https://www.frontiersin.org/files/Articles/1549141/fonc-15-1549141-HTML/image_m/fonc-15-1549141-t001.jpg Table 1. Clinical timeline and outcomes of two patients with tislelizumab-induced myocarditis. https://www.frontiersin.org/files/Articles/1549141/fonc-15-1549141-HTML/image_m/fonc-15-1549141-t002.jpg Table 2. More details of the clinical presentation are shown in thetable below: https://www.frontiersin.org/files/Articles/1549141/fonc-15-1549141-HTML/image_m/fonc-15-1549141-t003.jpg Table 3. Evolution of myocardial enzymes in Patient 1. https://www.frontiersin.org/files/Articles/1549141/fonc-15-1549141-HTML/image_m/fonc-15-1549141-t004.jpg Table 4. Evolution of myocardial enzymes in Patient 2. https://www.frontiersin.org/files/Articles/1549141/fonc-15-1549141-HTML/image_m/fonc-15-1549141-t005.jpg Table 5. Immunological and antinuclear antibody parameters. https://www.frontiersin.org/files/Articles/1549141/fonc-15-1549141-HTML/image_m/fonc-15-1549141-t006.jpg Table 6. Additional laboratory parameters. https://www.frontiersin.org/files/Articles/1549141/fonc-15-1549141-HTML/image_m/fonc-15-1549141-g001.jpg Figure 1. Patient 1 (The arrow indicates the left lower lobectomy site on the CT image.), Patient 2. https://www.frontiersin.org/files/Articles/1549141/fonc-15-1549141-HTML/image_m/fonc-15-1549141-t007.jpg Table 7. Cardiac enzyme profiles during four-cycle TP chemotherapy. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1800699/full https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-t001.jpg Table 1. Worldwide production of major legume crops classified as oilseed, dry grain, and vegetable https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-g001.jpg Figure 1. PRISMA flow diagram for study selection on stay-green research in legumes. https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-t002.jpg Table 2. Descriptive statistics of the bibliometric analysis of selected studies of stay-green in le https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-g002.jpg Figure 2. Bibliometric analysis of stay-green research in legumes including global trends, research https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-t003.jpg Table 3. List of top 15 most cited articles related to the stay-green research in legumes, based on https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-t004.jpg Table 4. List of top 15 journals based on the number of publications related to stay-green research https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-g003.jpg Figure 3. Co-authorship network analysis. The analysis was performed at three levels: (a) authors, ( https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-g004.jpg Figure 4. (a) Keyword co-occurrence network analysis; (b) Keyword co-occurrence overlay visualizatio https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-t005.jpg Table 5. Summary of relevant publications on stay-green research and associated molecular mechanisms https://www.frontiersin.org/files/Articles/1800699/fgene-17-1800699-HTML/image_m/fgene-17-1800699-g005.jpg Figure 5. (a) Three field plot analysis (Sankey diagram) showing the relationships among keywords, j https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1653086/full https://www.frontiersin.org/files/Articles/1653086/fnut-13-1653086-HTML-r1/image_m/fnut-13-1653086-g001.jpg Figure 1. Flowchart of participant selection. https://www.frontiersin.org/files/Articles/1653086/fnut-13-1653086-HTML-r1/image_m/fnut-13-1653086-t001.jpg Table 1. Characteristics of the study population with MCI. https://www.frontiersin.org/files/Articles/1653086/fnut-13-1653086-HTML-r1/image_m/fnut-13-1653086-t002.jpg Table 2. Associations of arthritis with MCI. https://www.frontiersin.org/files/Articles/1653086/fnut-13-1653086-HTML-r1/image_m/fnut-13-1653086-t003.jpg Table 3. Stratified analysis of associations between arthritis and MCI in MHO people. https://www.frontiersin.org/files/Articles/1653086/fnut-13-1653086-HTML-r1/image_m/fnut-13-1653086-g002.jpg Figure 2. Mediating role of depressive symptoms in metabolically unhealthy overweight/obesity people https://www.frontiersin.org/files/Articles/1653086/fnut-13-1653086-HTML-r1/image_m/fnut-13-1653086-g003.jpg Figure 3. Sensitivity analysis. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1689787/full https://www.frontiersin.org/files/Articles/1689787/fendo-16-1689787-HTML-r1/image_m/fendo-16-1689787-g001.jpg Figure 1. Vicious circle of pathophysiological events aggravating aging with focus on the role of va https://www.frontiersin.org/files/Articles/1689787/fendo-16-1689787-HTML-r1/image_m/fendo-16-1689787-g002.jpg Figure 2. Putative effects of hypothalamic microinflammation on the neuroendocrine hypothalamic-pitu https://www.frontiersin.org/files/Articles/1689787/fendo-16-1689787-HTML-r1/image_m/fendo-16-1689787-g003.jpg Figure 3. Putative molecular mechanisms mediating osmosensitivity in vasopressin-producing magnocell https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1656942/full https://www.frontiersin.org/files/Articles/1656942/fendo-16-1656942-HTML/image_m/fendo-16-1656942-g001.jpg Figure 1. Flowchart of study population. https://www.frontiersin.org/files/Articles/1656942/fendo-16-1656942-HTML/image_m/fendo-16-1656942-t001.jpg Table 1. Socio-demographic, anthropometric and clinical features of the study sample, overall and by https://www.frontiersin.org/files/Articles/1656942/fendo-16-1656942-HTML/image_m/fendo-16-1656942-t002.jpg Table 2. Descriptives of the levels of bile acids groups and FGF-19, overall and by diabetes status. https://www.frontiersin.org/files/Articles/1656942/fendo-16-1656942-HTML/image_m/fendo-16-1656942-t003.jpg Table 3. Results of the nested linear regression models investigating the independent and mutually-a https://www.frontiersin.org/files/Articles/1656942/fendo-16-1656942-HTML/image_m/fendo-16-1656942-g002.jpg Figure 2. Associations (standardized regression coefficient β values and 95% confidence intervals) b https://www.frontiersin.org/files/Articles/1656942/fendo-16-1656942-HTML/image_m/fendo-16-1656942-g003.jpg Figure 3. ROC curves for logistic regression models predicting type 2 diabetes. The figure shows the https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1652178/full https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-t001.jpg Table 1. Detailed information on the two datasets. https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g001.jpg Figure 1. Research design flowchart. https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g002.jpg Figure 2. Identification of DEGs. (A) Heatmap of DEGs in the PCOS dataset (GSE54248). (B) Volcano pl https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g003.jpg Figure 3. Batch effect removal in the PCOS_GC_DATASET and T2DM_PBMC_DATASET. (A) Boxplot depicting t https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g004.jpg Figure 4. GO Enrichment and KEGG pathway analysis of co-expressed genes between PCOS and T2DM. (A) G https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g005.jpg Figure 5. PPI Network interaction relationships. (A) Venn diagram showing the intersection of genes https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-t002.jpg Table 2. Gene information of the top 10 by degree. https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g006.jpg Figure 6. Expression levels of hub genes in both diseases. (A) Expression levels of hub genes in GSE https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g007.jpg Figure 7. Expression levels of hub genes in the PCOS_GC and T2DM_PBMC datasets. (A) Expression level https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g008.jpg Figure 8. ROC curves of hub genes in both diseases. (A) ROC curves of hub genes in GSE54248. (B) ROC https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g009.jpg Figure 9. ROC curves of hub genes in the integrated PCOS_GC and T2DM_PBMC datasets. (A) ROC curves o https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g010.jpg Figure 10. Enrichment analysis results of four key genes. (A) Enrichment analysis of RTN1. (B) Enric https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g011.jpg Figure 11. Heatmap of the correlation between key genes and immune cells. Red and green indicate pos https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g012.jpg Figure 12. Single-cell expression profiling of core genes in T2DM peripheral blood. (A) 18 subpopula https://www.frontiersin.org/files/Articles/1652178/fendo-16-1652178-HTML/image_m/fendo-16-1652178-g013.jpg Figure 13. qRT–PCR validation results. (A) JK represents the healthy control group, T2DM represents https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1798145/full https://www.frontiersin.org/files/Articles/1798145/fonc-16-1798145-HTML-r1/image_m/fonc-16-1798145-t001.jpg Table 1. Basic characteristics of the subjects before surgery. https://www.frontiersin.org/files/Articles/1798145/fonc-16-1798145-HTML-r1/image_m/fonc-16-1798145-t002.jpg Table 2. Gastric cancer-related characteristics and RGVs. https://www.frontiersin.org/files/Articles/1798145/fonc-16-1798145-HTML-r1/image_m/fonc-16-1798145-t003.jpg Table 3. Complications and corresponding treatments from 6 months to 12 months after surgery. https://www.frontiersin.org/files/Articles/1798145/fonc-16-1798145-HTML-r1/image_m/fonc-16-1798145-t004.jpg Table 4. Dietary patterns, physical activities, and nutritional status from 6 months to 12 months af https://www.frontiersin.org/files/Articles/1798145/fonc-16-1798145-HTML-r1/image_m/fonc-16-1798145-t005.jpg Table 5. Sleep quality, depression, anxiety, and quality of life from 6 months to 12 months after su https://www.frontiersin.org/files/Articles/1798145/fonc-16-1798145-HTML-r1/image_m/fonc-16-1798145-g001.jpg Figure 1. Association of postoperative RGV with sleep quality, depression, anxiety, and quality of l https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1784250/full https://www.frontiersin.org/files/Articles/1784250/fonc-16-1784250-HTML/image_m/fonc-16-1784250-g001.jpg Figure 1. MRI revealed a large mass lesion in the right thalamus and basal ganglia. (A) T2 image; (B https://www.frontiersin.org/files/Articles/1784250/fonc-16-1784250-HTML/image_m/fonc-16-1784250-g002.jpg Figure 2. A timeline with relevant data from the episode of care. https://www.frontiersin.org/files/Articles/1784250/fonc-16-1784250-HTML/image_m/fonc-16-1784250-g003.jpg Figure 3. Histopathological characteristics of the tumor. Hematoxylin and eosin (H&E) staining of th https://www.frontiersin.org/files/Articles/1784250/fonc-16-1784250-HTML/image_m/fonc-16-1784250-g004.jpg Figure 4. Immunohistochemistry results(original magnification, x200; Scale bar:100μm). (A) H3K27M st https://www.frontiersin.org/files/Articles/1784250/fonc-16-1784250-HTML/image_m/fonc-16-1784250-g005.jpg Figure 5. Brain MRI (T2 FLAIR Images). (A1-A3)Postoperative MRI following subtotal tumor resection. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1694030/full https://www.frontiersin.org/files/Articles/1694030/fneur-16-1694030-HTML/image_m/fneur-16-1694030-g001.jpg Figure 1. Study flowchart of the study design. https://www.frontiersin.org/files/Articles/1694030/fneur-16-1694030-HTML/image_m/fneur-16-1694030-t001.jpg Table 1. Baseline characteristics of the participants. https://www.frontiersin.org/files/Articles/1694030/fneur-16-1694030-HTML/image_m/fneur-16-1694030-g002.jpg Figure 2. Using the LASSO regression and REF method to identify the optimal variables. (A) Variation https://www.frontiersin.org/files/Articles/1694030/fneur-16-1694030-HTML/image_m/fneur-16-1694030-g003.jpg Figure 3. ROC curves for the machine learning models. https://www.frontiersin.org/files/Articles/1694030/fneur-16-1694030-HTML/image_m/fneur-16-1694030-g004.jpg Figure 4. Calibration capability and clinical benefit of the model. (A) Calibration curve of the Ran https://www.frontiersin.org/files/Articles/1694030/fneur-16-1694030-HTML/image_m/fneur-16-1694030-g005.jpg Figure 5. ROC curves for the internal validation and external validation. https://www.frontiersin.org/files/Articles/1694030/fneur-16-1694030-HTML/image_m/fneur-16-1694030-g006.jpg Figure 6. Visually interpret machine learning models using SHAP. (A) SHAP summary bar plot. (B) The https://www.frontiersin.org/journals/human-dynamics/articles/10.3389/fhumd.2026.1765197/full https://www.frontiersin.org/files/Articles/1765197/fhumd-08-1765197-HTML/image_m/fhumd-08-1765197-t001.jpg Table 1. Sentiment proportions, polarity scores, and engagement by category and language. https://www.frontiersin.org/files/Articles/1765197/fhumd-08-1765197-HTML/image_m/fhumd-08-1765197-g001.jpg Figure 1. Temporal sentiment trends for human mental health providers and AI mental health systems f https://www.frontiersin.org/files/Articles/1765197/fhumd-08-1765197-HTML/image_m/fhumd-08-1765197-t002.jpg Table 2. Monthly polarity scores (January–September 2025). https://www.frontiersin.org/files/Articles/1765197/fhumd-08-1765197-HTML/image_m/fhumd-08-1765197-t003.jpg Table 3. Joint display of integrated quantitative and qualitative findings. https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2026.1643403/full https://www.frontiersin.org/files/Articles/1643403/fcomm-11-1643403-HTML/image_m/fcomm-11-1643403-t001.jpg Table 1. National English language policies in Latin America: goals and implementation challenges. https://www.frontiersin.org/files/Articles/1643403/fcomm-11-1643403-HTML/image_m/fcomm-11-1643403-t002.jpg Table 2. English promotion in Latin America: examples by sector and country. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1771887/full https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g001.jpg Figure 1. Generation of plasmid DNA vaccine: DNA-HBVac. (A) Schematic representation of DNA-HBVac, w https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g002.jpg Figure 2. HBV-specific humoral and S-specific CD4+ T-cell responses induced by DNA prime – MVA boost https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g003.jpg Figure 3. HBV-specific CD8+ T-cell responses induced by DNA prime – MVA boost immunization in HBV-na https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g004.jpg Figure 4. HBV-specific humoral and S-specific CD4+ T-cell responses induced by DNA prime – MVA boost https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g005.jpg Figure 5. HBV-specific CD8+ T-cell responses induced by DNA prime – MVA boost immunization in HBV ca https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g006.jpg Figure 6. Antiviral and histopathological effects induced by DNA prime – MVA boost immunization in t https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g007.jpg Figure 7. HBV-specific humoral and S-specific CD4+ T-cell responses induced by simultaneous DNA/HBsA https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g008.jpg Figure 8. HBV-specific CD8+ T-cell responses induced by simultaneous DNA/HBsAg prime – MVA boost reg https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g009.jpg Figure 9. HBV-specific humoral and S-specific CD4+ T-cell responses induced by sequential DNA and HB https://www.frontiersin.org/files/Articles/1771887/fimmu-17-1771887-HTML/image_m/fimmu-17-1771887-g010.jpg Figure 10. HBV-specific CD8+ T-cell responses induced by sequential DNA and HBsAg prime – MVA boost https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1796516/full https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t001.jpg Table 1. Characteristics of the four options in the Iowa Gambling Task (IGT). https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t002.jpg Table 2. Characteristics of the four choice combinations in the Game of Dice Task (GDT). https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-g001.jpg Figure 1. Iowa gambling task (IGT) performance. (A) Mean probability of option selection across 20-t https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t003.jpg Table 3. Effect of predictors on option selection in block 1 (trials 1–20) of the IGT. https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t004.jpg Table 4. Effect of predictors on option selection in block 2 (trials 21–40) of the IGT. https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t005.jpg Table 5. Effect of predictors on option selection in block 3 (trials 41–60) of the IGT. https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t006.jpg Table 6. Effect of predictors on option selection in block 4 (trials 61–80) of the IGT. https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t007.jpg Table 7. Effect of predictors on option selection in block 5 (trials 81–100) of the IGT. https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t008.jpg Table 8. Effect of predictors on option selection across all 100 trials of the IGT. https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-g002.jpg Figure 2. Effect of non-nutritive sweeteners (NNS; dark gray) and free sugars (light gray) on rigidi https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t009.jpg Table 9. Effect of predictors on mean response time across all 100 trials. https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-g003.jpg Figure 3. Game of Dice Task (GDT) performance. The mean probabilities of selecting each combination https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t010.jpg Table 10. Effect of predictors on combination selection across all 18 trials of the GDT. https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-g004.jpg Figure 4. Binary dictator game performance. Altruistic choices were more frequently selected in the https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t011.jpg Table 11. Effect of predictors on the probability of selecting the altruistic option, in the “equal/ https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-g005.jpg Figure 5. Prisoner’s dilemma performance. Cooperative choices were significantly less frequent in Tr https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t012.jpg Table 12. Predictor’s effect on the cognitive reappraisal scores. https://www.frontiersin.org/files/Articles/1796516/fnut-13-1796516-HTML-r3/image_m/fnut-13-1796516-t013.jpg Table 13. Predictor’s effect on the expressive suppression scores. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1756847/full https://www.frontiersin.org/files/Articles/1756847/fpubh-14-1756847-HTML-r1/image_m/fpubh-14-1756847-t001.jpg Table 1. Demographic characteristics of respondents by study group (N = 106). https://www.frontiersin.org/files/Articles/1756847/fpubh-14-1756847-HTML-r1/image_m/fpubh-14-1756847-t002.jpg Table 2. Information about antibiotic use among respondents by study group (N = 106). https://www.frontiersin.org/files/Articles/1756847/fpubh-14-1756847-HTML-r1/image_m/fpubh-14-1756847-t003.jpg Table 3. Pre- and post-test comparison of school teachers’ knowledge and awareness regarding antibio https://www.frontiersin.org/files/Articles/1756847/fpubh-14-1756847-HTML-r1/image_m/fpubh-14-1756847-t004.jpg Table 4. Pre- and post-test comparison of schoolteachers’ attitudes toward antibiotics and antibioti https://www.frontiersin.org/files/Articles/1756847/fpubh-14-1756847-HTML-r1/image_m/fpubh-14-1756847-t005.jpg Table 5. Hierarchical multiple linear regression analysis predicting post-intervention knowledge sco https://www.frontiersin.org/files/Articles/1756847/fpubh-14-1756847-HTML-r1/image_m/fpubh-14-1756847-t006.jpg Table 6. Multiple logistic regression analysis of factors associated with positive attitude change t https://www.frontiersin.org/files/Articles/1756847/fpubh-14-1756847-HTML-r1/image_m/fpubh-14-1756847-t007.jpg Table 7. Exploratory post hoc performance classification and training recommendations based on post- https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1800385/full https://www.frontiersin.org/files/Articles/1800385/fpubh-14-1800385-HTML/image_m/fpubh-14-1800385-g001.jpg Figure 1. Map of sewersheds monitored by the Wisconsin Wastewater Monitoring Program and the Chicago https://www.frontiersin.org/files/Articles/1800385/fpubh-14-1800385-HTML/image_m/fpubh-14-1800385-t001.jpg Table 1. Summary of laboratory and analytic methods implemented at each site. https://www.frontiersin.org/files/Articles/1800385/fpubh-14-1800385-HTML/image_m/fpubh-14-1800385-t002.jpg Table 2. Quintile levels of wastewater concentration data (3-day running average) reported to the su https://www.frontiersin.org/files/Articles/1800385/fpubh-14-1800385-HTML/image_m/fpubh-14-1800385-g002.jpg Figure 2. Flow and population normalized wastewater concentration data for various pathogens monitor https://www.frontiersin.org/files/Articles/1800385/fpubh-14-1800385-HTML/image_m/fpubh-14-1800385-g003.jpg Figure 3. Flow and population normalized wastewater concentration data trends for various pathogens https://www.frontiersin.org/files/Articles/1800385/fpubh-14-1800385-HTML/image_m/fpubh-14-1800385-g004.jpg Figure 4. Recommended response framework for wastewater-based epidemiology at large-scale events. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1742990/full https://www.frontiersin.org/files/Articles/1742990/fonc-16-1742990-HTML/image_m/fonc-16-1742990-g001.jpg Figure 1. Imaging and surgical specimens of case 1. (A, B) cervical mass. (C) the gross specimen aft https://www.frontiersin.org/files/Articles/1742990/fonc-16-1742990-HTML/image_m/fonc-16-1742990-g002.jpg Figure 2. The pathology and imaging of case 2. (A) the biopsy pathological specimen (10×). (B) cervi https://www.frontiersin.org/files/Articles/1742990/fonc-16-1742990-HTML/image_m/fonc-16-1742990-t001.jpg Table 1. The summary of BRCA gene mutations in cervical cancer in the existing literature. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1771548/full https://www.frontiersin.org/files/Articles/1771548/fimmu-17-1771548-HTML/image_m/fimmu-17-1771548-t001.jpg Table 1. Safety profile of PD-1/CTLA-4-targeted therapies in endometrial carcinoma. https://www.frontiersin.org/files/Articles/1771548/fimmu-17-1771548-HTML/image_m/fimmu-17-1771548-t002.jpg Table 2. Selected clinical trials of PD-1 and CTLA-4 targeting in advanced or recurrent endometrial https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1769372/full https://www.frontiersin.org/files/Articles/1769372/fcell-14-1769372-HTML/image_m/fcell-14-1769372-g001.jpg Figure 1. Genetics and Epigenetics of Neuroblastoma Progenitor Development (A). Signaling ligands an https://www.frontiersin.org/files/Articles/1769372/fcell-14-1769372-HTML/image_m/fcell-14-1769372-g002.jpg Figure 2. Epigenetic Impact on Intrinsic NB Properties (A). Nucleosome remodelers are commonly mutat https://www.frontiersin.org/files/Articles/1769372/fcell-14-1769372-HTML/image_m/fcell-14-1769372-g003.jpg Figure 3. Epigenetic Impact on Extrinsic NB Properties (A). Epigenetic modifications are dependent o https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1767200/full https://www.frontiersin.org/files/Articles/1767200/fpsyt-17-1767200-HTML/image_m/fpsyt-17-1767200-t001.jpg Table 1. Demographics by analytic groupings. https://www.frontiersin.org/files/Articles/1767200/fpsyt-17-1767200-HTML/image_m/fpsyt-17-1767200-t002.jpg Table 2. Autism barriers and facilitators. https://www.frontiersin.org/files/Articles/1767200/fpsyt-17-1767200-HTML/image_m/fpsyt-17-1767200-t003.jpg Table 3. Caregiver group responses by language and indigenous identity. https://www.frontiersin.org/files/Articles/1767200/fpsyt-17-1767200-HTML/image_m/fpsyt-17-1767200-t004.jpg Table 4. Interpretation of patterns in medical diagnosis before IEP. https://www.frontiersin.org/journals/gastroenterology/articles/10.3389/fgstr.2026.1720586/full https://www.frontiersin.org/files/Articles/1720586/fgstr-05-1720586-HTML/image_m/fgstr-05-1720586-g001.jpg Figure 1. MRCP imaging showing focal nodular hyperplasia (green arrow). MRCP image demonstrating foc https://www.frontiersin.org/files/Articles/1720586/fgstr-05-1720586-HTML/image_m/fgstr-05-1720586-g002.jpg Figure 2. Non-blanching erythematous papules on the patient’s bilateral forearms and right thigh. Cl https://www.frontiersin.org/files/Articles/1720586/fgstr-05-1720586-HTML/image_m/fgstr-05-1720586-t001.jpg Table 1. Timeline of clinical presentation, diagnostic evaluation, therapeutic interventions, and ou https://www.frontiersin.org/journals/developmental-psychology/articles/10.3389/fdpys.2025.1686250/full https://www.frontiersin.org/files/Articles/1686250/fdpys-03-1686250-HTML/image_m/fdpys-03-1686250-t001.jpg Table 1. Sample characteristics. https://www.frontiersin.org/files/Articles/1686250/fdpys-03-1686250-HTML/image_m/fdpys-03-1686250-t002.jpg Table 2. Correlations Between Study Variables and Descriptive Statistics. https://www.frontiersin.org/files/Articles/1686250/fdpys-03-1686250-HTML/image_m/fdpys-03-1686250-t003.jpg Table 3. Standardized estimates for regression models predicting observed maternal contributions to https://www.frontiersin.org/files/Articles/1686250/fdpys-03-1686250-HTML/image_m/fdpys-03-1686250-t004.jpg Table 4. Standardized estimates for regression models predicting observed infant contributions to th https://www.frontiersin.org/files/Articles/1686250/fdpys-03-1686250-HTML/image_m/fdpys-03-1686250-t005.jpg Table 5. Standardized estimates for regression models predicting observed dyadic contingency and ove https://www.frontiersin.org/files/Articles/1686250/fdpys-03-1686250-HTML/image_m/fdpys-03-1686250-t006.jpg Table 6. Effects of smartphone-related empowerment on observed outcomes. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1809567/full https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-t001.jpg Table 1. Genetic instruments for MLXIPL expression derived from eQTLGen. https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g001.jpg Figure 1. Mendelian randomization scatter plots illustrating the association between genetically pre https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g002.jpg Figure 2. Forest plots showing the causal estimates of genetically predicted MLXIPL expression on Ty https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-t002.jpg Table 2. Two-sample Mendelian randomization estimates for genetically predicted MLXIPL expression on https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g003.jpg Figure 3. Mendelian randomization analysis of genetically predicted MLXIPL expression and urinary al https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g004.jpg Figure 4. Overview of the integrative transcriptomic and bioinformatics analytical workflow. https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g005.jpg Figure 5. Transcriptomic analysis and GSEA of MLXIPL-associated pathways in diabetic kidney diesase. https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g006.jpg Figure 6. GSVA analysis of signaling pathways associated with MLXIPL expression. (A) Heatmap visuali https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g007.jpg Figure 7. Functional enrichment analysis of genes associated with MLXIPL expression. (A–B) Heatmaps https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g008.jpg Figure 8. Protein–protein interaction and functional association networks of MLXIPL. (A) High-confid https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g009.jpg Figure 9. Upstream regulatory and chemical interaction landscapes of MLXIPL. (A) Transcription facto https://www.frontiersin.org/files/Articles/1809567/fendo-17-1809567-HTML/image_m/fendo-17-1809567-g010.jpg Figure 10. Experimental validation of MLXIPL upregulation and metabolic network consistency in diabe https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1735804/full https://www.frontiersin.org/files/Articles/1735804/fpubh-13-1735804-HTML/image_m/fpubh-13-1735804-t001.jpg Table 1. Demographic information of all gastric cancer patients. https://www.frontiersin.org/files/Articles/1735804/fpubh-13-1735804-HTML/image_m/fpubh-13-1735804-t002.jpg Table 2. Model fit indices for the scales. https://www.frontiersin.org/files/Articles/1735804/fpubh-13-1735804-HTML/image_m/fpubh-13-1735804-t003.jpg Table 3. Descriptive statistics of core variables. https://www.frontiersin.org/files/Articles/1735804/fpubh-13-1735804-HTML/image_m/fpubh-13-1735804-t004.jpg Table 4. Correlation analysis of core variables. https://www.frontiersin.org/files/Articles/1735804/fpubh-13-1735804-HTML/image_m/fpubh-13-1735804-t005.jpg Table 5. Regression equation model analysis for psychological adaptation and cognitive reappraisal. https://www.frontiersin.org/files/Articles/1735804/fpubh-13-1735804-HTML/image_m/fpubh-13-1735804-t006.jpg Table 6. Decomposition of chain mediation effects. https://www.frontiersin.org/files/Articles/1735804/fpubh-13-1735804-HTML/image_m/fpubh-13-1735804-g001.jpg Figure 1. Chain mediation of psychological adaptation and cognitive reappraisal, ***p < 0.001. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1805637/full https://www.frontiersin.org/files/Articles/1805637/fpubh-14-1805637-HTML/image_m/fpubh-14-1805637-t001.jpg Table 1. Sociodemographic and clinical characteristics of the sample. https://www.frontiersin.org/files/Articles/1805637/fpubh-14-1805637-HTML/image_m/fpubh-14-1805637-t002.jpg Table 2. Descriptive statistics and correlations among key variables. https://www.frontiersin.org/files/Articles/1805637/fpubh-14-1805637-HTML/image_m/fpubh-14-1805637-t003.jpg Table 3. Fit indices for LPA models of uncertainty in illness and eating self-efficacy. https://www.frontiersin.org/files/Articles/1805637/fpubh-14-1805637-HTML/image_m/fpubh-14-1805637-g001.jpg Figure 1. Contour plots of the potential profiles of illness uncertainty and dietary self-efficacy. https://www.frontiersin.org/files/Articles/1805637/fpubh-14-1805637-HTML/image_m/fpubh-14-1805637-t004.jpg Table 4. Univariable comparisons of sociodemographic, clinical, lifestyle, and psychological variabl https://www.frontiersin.org/files/Articles/1805637/fpubh-14-1805637-HTML/image_m/fpubh-14-1805637-t005.jpg Table 5. Multivariable logistic regression predicting latent profile membership (Reference Class 2). https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1744139/full https://www.frontiersin.org/files/Articles/1744139/fmicb-17-1744139-HTML/image_m/fmicb-17-1744139-t001.jpg Table 1. Antimicrobial susceptibility profiles of he2023 against different antimicrobials. https://www.frontiersin.org/files/Articles/1744139/fmicb-17-1744139-HTML/image_m/fmicb-17-1744139-t002.jpg Table 2. Whole genome information for he2023. https://www.frontiersin.org/files/Articles/1744139/fmicb-17-1744139-HTML/image_m/fmicb-17-1744139-g001.jpg Figure 1. (A) Shows a circular map of plasmid pNDM-IMP, highlighting gene regions, repA, bla IMP, bl https://www.frontiersin.org/files/Articles/1744139/fmicb-17-1744139-HTML/image_m/fmicb-17-1744139-g002.jpg Figure 2. Two synteny diagrams comparing genetic structures. (A) Shows a comparison between pNDM-IMP https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1665643/full https://www.frontiersin.org/files/Articles/1665643/fbioe-13-1665643-HTML/image_m/fbioe-13-1665643-g003.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1665643/fbioe-13-1665643-HTML/image_m/fbioe-13-1665643-g001.jpg Figure 1. Three key factors (VEGF-A, bFGF, Wnt) initiate signaling through membrane receptors, activ https://www.frontiersin.org/files/Articles/1665643/fbioe-13-1665643-HTML/image_m/fbioe-13-1665643-g002.jpg Figure 2. Schematic of angiogenesis optimization in subcutaneous islet transplantation for diabetes, https://www.frontiersin.org/files/Articles/1665643/fbioe-13-1665643-HTML/image_m/fbioe-13-1665643-t001.jpg Table 1. Recent advances in biomaterials for promoting angiogenesis in subcutaneous transplantation. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1744719/full https://www.frontiersin.org/files/Articles/1744719/fimmu-16-1744719-HTML/image_m/fimmu-16-1744719-g001.jpg Figure 1. Overall workflow of the study. https://www.frontiersin.org/files/Articles/1744719/fimmu-16-1744719-HTML/image_m/fimmu-16-1744719-g002.jpg Figure 2. The identification of the best performance signature. (A) C-indices of 83 combinations of https://www.frontiersin.org/files/Articles/1744719/fimmu-16-1744719-HTML/image_m/fimmu-16-1744719-g003.jpg Figure 3. Evaluation of MALISS model performance. (A) Time-dependent ROC analysis for predicting 1-, https://www.frontiersin.org/files/Articles/1744719/fimmu-16-1744719-HTML/image_m/fimmu-16-1744719-g004.jpg Figure 4. NR1D2 promotes the CRC migration. (A) The importance of 30 immunosenescence-related genes https://www.frontiersin.org/files/Articles/1744719/fimmu-16-1744719-HTML/image_m/fimmu-16-1744719-g005.jpg Figure 5. The Gene Mutational Landscape Among MALISS risk subgroups. (A) Mutation profile of the MAL https://www.frontiersin.org/files/Articles/1744719/fimmu-16-1744719-HTML/image_m/fimmu-16-1744719-g006.jpg Figure 6. The tumor microenvironment of MALISS. (A) Correlation between MALISS score and immune infi https://www.frontiersin.org/files/Articles/1744719/fimmu-16-1744719-HTML/image_m/fimmu-16-1744719-g007.jpg Figure 7. The biological pathway of MALISS. (A) Differential HALLMARK and KEGG Pathway Enrichment be https://www.frontiersin.org/files/Articles/1744719/fimmu-16-1744719-HTML/image_m/fimmu-16-1744719-g008.jpg Figure 8. The Prognostic Nomogram Model of MALISS with molecular features. (A) A prognostic nomogram https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1720729/full https://www.frontiersin.org/files/Articles/1720729/fmicb-16-1720729-HTML/image_m/fmicb-16-1720729-t001.jpg Table 1. Effect of grazing season on nutritional components of pasture grass (%). https://www.frontiersin.org/files/Articles/1720729/fmicb-16-1720729-HTML/image_m/fmicb-16-1720729-g001.jpg Figure 1. The effect of different seasons on average daily gain of grazing sheep. ADG, average daily https://www.frontiersin.org/files/Articles/1720729/fmicb-16-1720729-HTML/image_m/fmicb-16-1720729-t002.jpg Table 2. The effect of grazing season on nutrient apparent digestibility in sheep. https://www.frontiersin.org/files/Articles/1720729/fmicb-16-1720729-HTML/image_m/fmicb-16-1720729-t003.jpg Table 3. The effect of different seasons on blood biochemical parameters of grazing sheep. https://www.frontiersin.org/files/Articles/1720729/fmicb-16-1720729-HTML/image_m/fmicb-16-1720729-g002.jpg Figure 2. The effect of grazing season on the antioxidant capacity of Altay sheep. (A) Malondialdehy https://www.frontiersin.org/files/Articles/1720729/fmicb-16-1720729-HTML/image_m/fmicb-16-1720729-g003.jpg Figure 3. The effect of grazing season on inflammatory factors and immune performance in Altay sheep https://www.frontiersin.org/files/Articles/1720729/fmicb-16-1720729-HTML/image_m/fmicb-16-1720729-t004.jpg Table 4. The effect of different seasons on rumen fermentation parameters in grazing sheep. https://www.frontiersin.org/files/Articles/1720729/fmicb-16-1720729-HTML/image_m/fmicb-16-1720729-g004.jpg Figure 4. Diversity and richness indicators and OTUs of ruminal bacteria and classification of the b https://www.frontiersin.org/files/Articles/1720729/fmicb-16-1720729-HTML/image_m/fmicb-16-1720729-g005.jpg Figure 5. Bacterial biomarkers in the rumen content across different seasons (LDA > 2.5). https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1754585/full https://www.frontiersin.org/files/Articles/1754585/fpubh-14-1754585-HTML/image_m/fpubh-14-1754585-g001.jpg Figure 1. Machine learning prediction workflow. https://www.frontiersin.org/files/Articles/1754585/fpubh-14-1754585-HTML/image_m/fpubh-14-1754585-t001.jpg Table 1. Performance comparison of machine learning models for 30-day readmission prediction. https://www.frontiersin.org/files/Articles/1754585/fpubh-14-1754585-HTML/image_m/fpubh-14-1754585-g002.jpg Figure 2. Beeswarm plot of SHAP values for the top predictors of 30-day hospital readmission. https://www.frontiersin.org/files/Articles/1754585/fpubh-14-1754585-HTML/image_m/fpubh-14-1754585-t002.jpg Table 2. Anchor-based interpretable rules for patient risk stratification. https://www.frontiersin.org/files/Articles/1754585/fpubh-14-1754585-HTML/image_m/fpubh-14-1754585-g003.jpg Figure 3. Distribution of predicted readmission risk across patient cohort. https://www.frontiersin.org/files/Articles/1754585/fpubh-14-1754585-HTML/image_m/fpubh-14-1754585-g004.jpg Figure 4. Enhanced predictive performance through SDOH integration. (A) Improvement in model perform https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1681559/full https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g001.jpg Figure 1. Flow diagram of literature screening. https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g002.jpg Figure 2. Risk of bias graph (A) and summary (B) of the included RCTs. https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g003.jpg Figure 3. Effect size estimation (A), sensitivity (B), Leave-one-out size estimation (C), and subgro https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g004.jpg Figure 4. Effect size estimation of PANSS Negative (A), PANSS Positive (B), BPRS (C). https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g005.jpg Figure 5. Effect size estimation (A), sensitivity (B), Leave-one-out size estimation (C), and subgro https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g006.jpg Figure 6. Effect size estimation (A), sensitivity (B), and Leave-one-out size estimation (C) analysi https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g007.jpg Figure 7. Effect size estimation of TG. https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g008.jpg Figure 8. Effect size estimation of TC. https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g009.jpg Figure 9. Effect size estimation (A), sensitivity (B), and Leave-one-out size estimation (C) analysi https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g010.jpg Figure 10. Effect size estimation of LDL-cholesterol. https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g011.jpg Figure 11. Effect size estimation of HOMA-IR. https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g012.jpg Figure 12. Effect size estimation of QUICKI. https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g013.jpg Figure 13. Effect size estimation of BW. https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g014.jpg Figure 14. Effect size estimation of BMI. https://www.frontiersin.org/files/Articles/1681559/fmicb-16-1681559-HTML-r2/image_m/fmicb-16-1681559-g015.jpg Figure 15. Effect size estimation of the probiotic group and the non-probiotic group in FBS. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1816064/full https://www.frontiersin.org/files/Articles/1816064/fnut-13-1816064-HTML/image_m/fnut-13-1816064-g001.jpg Figure 1. Distribution of ANSKQ-HU results based on knowledge categories ANSKQ-HU: Abriged Nutrition https://www.frontiersin.org/files/Articles/1816064/fnut-13-1816064-HTML/image_m/fnut-13-1816064-t001.jpg Table 1. Kendall's tau correlation coefficients between ANSKQ-HU subscale scores and total score and https://www.frontiersin.org/files/Articles/1816064/fnut-13-1816064-HTML/image_m/fnut-13-1816064-t002.jpg Table 2. Multivariate analysis of covariance (MANCOVA) examining the effects of age, weekly training https://www.frontiersin.org/files/Articles/1816064/fnut-13-1816064-HTML/image_m/fnut-13-1816064-t003.jpg Table 3. Results of univariate ANCOVA follow-up tests examining differences in ANSKQ-HU scores by hi https://www.frontiersin.org/files/Articles/1816064/fnut-13-1816064-HTML/image_m/fnut-13-1816064-t004.jpg Table 4. Bonferroni post-hoc tests results between the 3 different educational level groups (n = 133 https://www.frontiersin.org/files/Articles/1816064/fnut-13-1816064-HTML/image_m/fnut-13-1816064-t005.jpg Table 5. Kruskal-Wallis H-test result by perceived importance of healthy eating (PIHE) groups (n = 1 https://www.frontiersin.org/files/Articles/1816064/fnut-13-1816064-HTML/image_m/fnut-13-1816064-t006.jpg Table 6. Kruskal-Wallis H-test result by perceived need for nutrition education and dietitian access https://www.frontiersin.org/files/Articles/1816064/fnut-13-1816064-HTML/image_m/fnut-13-1816064-t007.jpg Table 7. Kruskal-Wallis H-test result by dietetical groups (n = 1335). https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1648118/full https://www.frontiersin.org/files/Articles/1648118/fmicb-16-1648118-HTML-r1/image_m/fmicb-16-1648118-t001.jpg Table 1. Characteristics of scales and questionnaires applied across studies. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1824178/full https://www.frontiersin.org/files/Articles/1824178/fnins-20-1824178-HTML/image_m/fnins-20-1824178-t001.jpg Table 1. Mechanisms of mitochondrial dysfunction in sepsis-associated encephalopathy. https://www.frontiersin.org/files/Articles/1824178/fnins-20-1824178-HTML/image_m/fnins-20-1824178-t002.jpg Table 2. Neuroinflammation and microglial activation in SAE. https://www.frontiersin.org/files/Articles/1824178/fnins-20-1824178-HTML/image_m/fnins-20-1824178-t003.jpg Table 3. Mechanisms of blood–brain barrier disruption in SAE. https://www.frontiersin.org/files/Articles/1824178/fnins-20-1824178-HTML/image_m/fnins-20-1824178-t004.jpg Table 4. The role of the gut-brain axis and cerebral metabolomics in SAE. https://www.frontiersin.org/files/Articles/1824178/fnins-20-1824178-HTML/image_m/fnins-20-1824178-g001.jpg Figure 1. Therapeutic strategies targeting the mitochondrial-neuroinflammatory axis in sepsis-associ https://www.frontiersin.org/files/Articles/1824178/fnins-20-1824178-HTML/image_m/fnins-20-1824178-t005.jpg Table 5. Representative therapeutic strategies targeting the mitochondrial-neuroinflammatory axis in https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1618412/full https://www.frontiersin.org/files/Articles/1618412/fendo-16-1618412-HTML/image_m/fendo-16-1618412-g001.jpg Figure 1. AI in the diagnosis, classification, therapy and complication of PitNETs. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1692113/full https://www.frontiersin.org/files/Articles/1692113/fpsyg-16-1692113-HTML/image_m/fpsyg-16-1692113-g001.jpg Figure 1. SAEs framework in layers. https://www.frontiersin.org/files/Articles/1692113/fpsyg-16-1692113-HTML/image_m/fpsyg-16-1692113-g002.jpg Figure 2. Research process. https://www.frontiersin.org/files/Articles/1692113/fpsyg-16-1692113-HTML/image_m/fpsyg-16-1692113-t001.jpg Table 1. Participants. https://www.frontiersin.org/files/Articles/1692113/fpsyg-16-1692113-HTML/image_m/fpsyg-16-1692113-g003.jpg Figure 3. Brief results of the qualitative phase. https://www.frontiersin.org/files/Articles/1692113/fpsyg-16-1692113-HTML/image_m/fpsyg-16-1692113-t002.jpg Table 2. Results of theoretical coding. https://www.frontiersin.org/files/Articles/1692113/fpsyg-16-1692113-HTML/image_m/fpsyg-16-1692113-t003.jpg Table 3. Mean, SD, and correlation matrix. https://www.frontiersin.org/files/Articles/1692113/fpsyg-16-1692113-HTML/image_m/fpsyg-16-1692113-g004.jpg Figure 4. CFA model fit. https://www.frontiersin.org/files/Articles/1692113/fpsyg-16-1692113-HTML/image_m/fpsyg-16-1692113-t004.jpg Table 4. Item quality. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1620464/full https://www.frontiersin.org/files/Articles/1620464/fpsyg-16-1620464-HTML/image_m/fpsyg-16-1620464-t001.jpg Table 1. Detection of behavioral problems in children of different genders [n (%)]. https://www.frontiersin.org/files/Articles/1620464/fpsyg-16-1620464-HTML/image_m/fpsyg-16-1620464-t002.jpg Table 2. Analysis of variance in the prevalence of behavioral problems across demographic variables https://www.frontiersin.org/files/Articles/1620464/fpsyg-16-1620464-HTML/image_m/fpsyg-16-1620464-t003.jpg Table 3. Correlation analysis between temperament and parenting styles. https://www.frontiersin.org/files/Articles/1620464/fpsyg-16-1620464-HTML/image_m/fpsyg-16-1620464-t004.jpg Table 4. Summary of potential profile analysis fitting information (N = 5,138). https://www.frontiersin.org/files/Articles/1620464/fpsyg-16-1620464-HTML/image_m/fpsyg-16-1620464-g001.jpg Figure 1. The means of the indicator variables for each class. https://www.frontiersin.org/files/Articles/1620464/fpsyg-16-1620464-HTML/image_m/fpsyg-16-1620464-t005.jpg Table 5. Comparison of the detection rates of children’s behavioral problems in each latent profile. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1663784/full https://www.frontiersin.org/files/Articles/1663784/fped-13-1663784-HTML/image_m/fped-13-1663784-g001.jpg Figure 1. Contrast-enhanced cerebro-orbital scans demonstrating tissue expansion affecting both nasa https://www.frontiersin.org/files/Articles/1663784/fped-13-1663784-HTML/image_m/fped-13-1663784-g002.jpg Figure 2. Orbital and cerebro-cervical MRI in T2-weighted sequences in the coronal (A and C) and axi https://www.frontiersin.org/files/Articles/1663784/fped-13-1663784-HTML/image_m/fped-13-1663784-g003.jpg Figure 3. Anatomopathological study showing (HEx4) lymph node parenchyma altered by a specific infla https://www.frontiersin.org/files/Articles/1663784/fped-13-1663784-HTML/image_m/fped-13-1663784-t001.jpg Table 1. Immunological profile of the patient and the flow cytometry analysis results. https://www.frontiersin.org/files/Articles/1663784/fped-13-1663784-HTML/image_m/fped-13-1663784-g004.jpg Figure 4. MRI (after 6 months of treatment) showing regression of the previously described lesions. https://www.frontiersin.org/files/Articles/1663784/fped-13-1663784-HTML/image_m/fped-13-1663784-t002.jpg Table 2. Reported pediatric cavernous sinus tuberculoma cases: clinical features and outcomes. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1786052/full https://www.frontiersin.org/files/Articles/1786052/fpubh-14-1786052-HTML/image_m/fpubh-14-1786052-t001.jpg Table 1. Characteristics of participants. https://www.frontiersin.org/files/Articles/1786052/fpubh-14-1786052-HTML/image_m/fpubh-14-1786052-t002.jpg Table 2. Sensitization rates of different allergen species [n (%)]. https://www.frontiersin.org/files/Articles/1786052/fpubh-14-1786052-HTML/image_m/fpubh-14-1786052-t003.jpg Table 3. Sensitization rates of allergen species in year [n(%)]. https://www.frontiersin.org/files/Articles/1786052/fpubh-14-1786052-HTML/image_m/fpubh-14-1786052-t004.jpg Table 4. Sensitization rates of allergen species in sex [n(%)]. https://www.frontiersin.org/files/Articles/1786052/fpubh-14-1786052-HTML/image_m/fpubh-14-1786052-g001.jpg Figure 1. Prevalence of sensitization to allergen species in sex. https://www.frontiersin.org/files/Articles/1786052/fpubh-14-1786052-HTML/image_m/fpubh-14-1786052-g002.jpg Figure 2. Prevalence of sensitization to allergen species in age. https://www.frontiersin.org/files/Articles/1786052/fpubh-14-1786052-HTML/image_m/fpubh-14-1786052-t005.jpg Table 5. Sensitization rates of allergen species in age [n(%)]. https://www.frontiersin.org/files/Articles/1786052/fpubh-14-1786052-HTML/image_m/fpubh-14-1786052-t006.jpg Table 6. Sensitization rates of allergen species in clinical disease category [n(%)]. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1670824/full https://www.frontiersin.org/files/Articles/1670824/fmed-12-1670824-HTML/image_m/fmed-12-1670824-g001.jpg Figure 1. Flowchart of the study selection process. https://www.frontiersin.org/files/Articles/1670824/fmed-12-1670824-HTML/image_m/fmed-12-1670824-t001.jpg Table 1. Summary of applications and features of current LLMs in KOA. https://www.frontiersin.org/files/Articles/1670824/fmed-12-1670824-HTML/image_m/fmed-12-1670824-t002.jpg Table 2. Summary of methodological quality and risk of bias in included studies. https://www.frontiersin.org/files/Articles/1670824/fmed-12-1670824-HTML/image_m/fmed-12-1670824-g002.jpg Figure 2. Risk assessment of CLEAR-LLM. R1–16, research 1–16; A, clarity of research objectives; B, https://www.frontiersin.org/files/Articles/1670824/fmed-12-1670824-HTML/image_m/fmed-12-1670824-g003.jpg Figure 3. Performance heatmap of LLMs in KOA in current research. Higher values indicate better perf https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1695579/full https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-t001.jpg Table 1. Summary of the laboratory tests in the patient. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-t002.jpg Table 2. Follow-up records of blood tests in patients. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-t003.jpg Table 3. Primers and sequences for Abortus, Melitensis, Ovis, and Suis-PCR (AMOS-PCR). https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-t004.jpg Table 4. Reaction conditions for AMOS-PCR. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-g001.jpg Figure 1. Patient’s blood culture positive result chart. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-g002.jpg Figure 2. Depicts a bone marrow smear, exhibiting characteristics of hyperplastic bone marrow. Histi https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-t005.jpg Table 5. The patient was diagnosed according to the HLH-04 standard. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-g003.jpg Figure 3. The onset, progression, diagnosis, and treatment of this patient. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-g004.jpg Figure 4. Graphical representation of the associated trends in white blood cell count, platelet coun https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-g005.jpg Figure 5. The trend of the patient’s liver function-related indicators. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-g006.jpg Figure 6. The trend of the patient’s renal function-related indicators. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-g007.jpg Figure 7. Results of the identification of this Brucella by AMOS-PCR. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-g008.jpg Figure 8. Phylogenetic tree of the IS711 tandem sequence of Brucella melitensis (•) isolated from th https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-t006.jpg Table 6. Literature review. https://www.frontiersin.org/files/Articles/1695579/fimmu-16-1695579-HTML/image_m/fimmu-16-1695579-t007.jpg Table 7. Disease of the blood system caused by brucellosis. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1771620/full https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-g001.jpg Figure 1. Flowchart of the study. ICU, Intensive care unit; AIDS, Acquired immunodeficiency syndrome https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-t001.jpg Table 1. Baseline characteristics of the study participants. https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-t002.jpg Table 2. Baseline characteristics according to GLR quartiles. https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-g002.jpg Figure 2. Kaplan-Meier survival curves for 28-day ICU mortality stratified by GLR quartiles. The sha https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-g003.jpg Figure 3. Lasso regression and Boruta algorithm identified predictive variables associated with 28-d https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-t003.jpg Table 4. Prognostic accuracy of the SAPS II, APS III, and GLR. https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-g004.jpg Figure 4. Restricted cubic spline analysis of the association between GLR and the risk of 28-day ICU https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-g005.jpg Figure 5. Forest plot of subgroup analyses for the association between GLR and 28-day ICU mortality. https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-t004.jpg Table 3. Unadjusted and multivariate Cox regression analyses were employed to assess 28-day ICU mort https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-g006.jpg Figure 6. Internal data GLR ROC curve. https://www.frontiersin.org/files/Articles/1771620/fcimb-16-1771620-HTML/image_m/fcimb-16-1771620-g007.jpg Figure 7. ROC and DCA curve analysis of the incremental effect of GLR on 28-day- all-cause mortality https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1685609/full https://www.frontiersin.org/files/Articles/1685609/fcell-13-1685609-HTML/image_m/fcell-13-1685609-g001.jpg Figure 1. Schematic of the tooth. PDL, periodontal ligament; AB, alveolar bone; AC, acellular cement https://www.frontiersin.org/files/Articles/1685609/fcell-13-1685609-HTML/image_m/fcell-13-1685609-t001.jpg Table 1. The characteristics of acellular cementum and cellular cementum. https://www.frontiersin.org/files/Articles/1685609/fcell-13-1685609-HTML/image_m/fcell-13-1685609-g002.jpg Figure 2. Development of cementum. During tooth root formation, Hertwig’s epithelial root sheath (HE https://www.frontiersin.org/files/Articles/1685609/fcell-13-1685609-HTML/image_m/fcell-13-1685609-t002.jpg Table 2. The regulation of cementum matrix deposition by different molecules. https://www.frontiersin.org/files/Articles/1685609/fcell-13-1685609-HTML/image_m/fcell-13-1685609-g003.jpg Figure 3. Schematic of the periodontal microenvironment. Host cells, microorganisms, metabolites, an https://www.frontiersin.org/files/Articles/1685609/fcell-13-1685609-HTML/image_m/fcell-13-1685609-t003.jpg Table 3. Mechanisms of cementum regeneration. https://www.frontiersin.org/files/Articles/1685609/fcell-13-1685609-HTML/image_m/fcell-13-1685609-g004.jpg Figure 4. Cementum regeneration strategies. Traditional methods for periodontal regeneration, such a https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1667712/full https://www.frontiersin.org/files/Articles/1667712/fpsyg-16-1667712-HTML/image_m/fpsyg-16-1667712-g001.jpg Figure 1. PRISMA flowchart for HRQoL tools validated for African population. https://www.frontiersin.org/files/Articles/1667712/fpsyg-16-1667712-HTML/image_m/fpsyg-16-1667712-t001.jpg Table 1. Descriptive characteristics of the included studies in the review. https://www.frontiersin.org/files/Articles/1667712/fpsyg-16-1667712-HTML/image_m/fpsyg-16-1667712-t002.jpg Table 2. Descriptive characteristics of the participants in the included studies. https://www.frontiersin.org/files/Articles/1667712/fpsyg-16-1667712-HTML/image_m/fpsyg-16-1667712-t003.jpg Table 3. Characteristics of the health-related quality of life tools. https://www.frontiersin.org/files/Articles/1667712/fpsyg-16-1667712-HTML/image_m/fpsyg-16-1667712-t004.jpg Table 4. Strengths and limitations of the health-related quality of life tools. https://www.frontiersin.org/files/Articles/1667712/fpsyg-16-1667712-HTML/image_m/fpsyg-16-1667712-t005.jpg Table 5. Overview of the findings, strengths, and limitations of the included studies in the review. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1769089/full https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-g000.jpg Graphical Abstract. Nutritional biomarkers in osteoarthritis (OA): framework, mechanisms, evidence, https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-g001.jpg Figure 1. Integrated model of OA cartilage degeneration. Pathological physical stressors (excess mec https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-t001.jpg Table 1. Immune-cell-centered mapping of representative nutritional biomarkers to key signaling hubs https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-g002.jpg Figure 2. Overview of micronutrient intake, absorption, and systemic functions relevant to joint hea https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-g003.jpg Figure 3. Metabolic-stress circuitry linking nutritional biomarkers to OA pathogenesis. This schemat https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-g004.jpg Figure 4. Iron overload-driven ferroptosis promotes inflammatory catabolism and extracellular matrix https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-g005.jpg Figure 5. Gut microbiota–derived metabolites shape systemic immune tone relevant to OA. https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-g006.jpg Figure 6. Immune-cell–driven synovitis and joint tissue destruction in inflammatory arthritis (illus https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-t002.jpg Table 2. Clinically promising nutritional biomarkers in osteoarthritis: direction of change, immunom https://www.frontiersin.org/files/Articles/1769089/fimmu-17-1769089-HTML/image_m/fimmu-17-1769089-t003.jpg Table 3. Representative human studies evaluating candidate nutritional biomarkers in OA with multiva https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1729728/full https://www.frontiersin.org/files/Articles/1729728/fpubh-13-1729728-HTML-r1/image_m/fpubh-13-1729728-g001.jpg Figure 1. Flowchart of study identification, screening, and inclusion following PRISMA guidelines. https://www.frontiersin.org/files/Articles/1729728/fpubh-13-1729728-HTML-r1/image_m/fpubh-13-1729728-t001.jpg Table 1. Data extraction for scoping review. https://www.frontiersin.org/files/Articles/1729728/fpubh-13-1729728-HTML-r1/image_m/fpubh-13-1729728-g002.jpg Figure 2. Geographic distribution of initiatives. https://www.frontiersin.org/files/Articles/1729728/fpubh-13-1729728-HTML-r1/image_m/fpubh-13-1729728-t002.jpg Table 2. Target audiences of training initiatives. https://www.frontiersin.org/files/Articles/1729728/fpubh-13-1729728-HTML-r1/image_m/fpubh-13-1729728-t003.jpg Table 3. Competencies and skill domains in training initiatives. https://www.frontiersin.org/files/Articles/1729728/fpubh-13-1729728-HTML-r1/image_m/fpubh-13-1729728-t004.jpg Table 4. Gaps and key insights in health diplomacy training. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1714357/full https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-g001.jpg Figure 1. Framework for evaluating policy impacts on MG output and population dynamics. https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-t001.jpg Table 1. The impacts of policy on village land use patterns. https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-g002.jpg Figure 2. Difference in MGL-to-cropland ratio at village and national levels (2020–2002, %). (A) Sca https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-t002.jpg Table 2. The impacts of policy on MGL and yields. https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-g003.jpg Figure 3. Parallel Trend Test. (A) Parallel trend test on impervious surface; (B) Parallel trend tes https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-g004.jpg Figure 4. Placebo Test. (A) Placebo test on impervious surface; (B) Placebo test on cropland area; ( https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-t003.jpg Table 3. Policy effects estimated by the PSM-DID model. https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-t004.jpg Table 4. Excluding the impact of other policies. https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-t005.jpg Table 5. The moderating effect of technological improvements. https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-g005.jpg Figure 5. Spatial heterogeneity in the impact of the policy on administrative regions in China. (A) https://www.frontiersin.org/files/Articles/1714357/fsufs-09-1714357-HTML/image_m/fsufs-09-1714357-g006.jpg Figure 6. Trends in village MG production and demand across zones, 2002–2020 (Million kcal). (A) MG https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1687148/full https://www.frontiersin.org/files/Articles/1687148/fendo-16-1687148-HTML/image_m/fendo-16-1687148-t001.jpg Table 1. Characteristics of the cohort of patients with idiopathic adolescent-onset POI (n=63). https://www.frontiersin.org/files/Articles/1687148/fendo-16-1687148-HTML/image_m/fendo-16-1687148-g001.jpg Figure 1. Characteristics of the cohort of patients with idiopathic adolescent-onset POI (n=63). https://www.frontiersin.org/files/Articles/1687148/fendo-16-1687148-HTML/image_m/fendo-16-1687148-g002.jpg Figure 2. Pedigrees of familial POI cases. Proband is indicated by an arrow. Details of family histo https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1654694/full https://www.frontiersin.org/files/Articles/1654694/fmed-12-1654694-HTML/image_m/fmed-12-1654694-g001.jpg Figure 1. Flow chart of our study. https://www.frontiersin.org/files/Articles/1654694/fmed-12-1654694-HTML/image_m/fmed-12-1654694-t001.jpg Table 1. Baseline features of the individuals included in this study. https://www.frontiersin.org/files/Articles/1654694/fmed-12-1654694-HTML/image_m/fmed-12-1654694-g002.jpg Figure 2. CatD and CatK levels of individuals with different sarcopenia traits. (A) Comparison of Ca https://www.frontiersin.org/files/Articles/1654694/fmed-12-1654694-HTML/image_m/fmed-12-1654694-t002.jpg Table 2. Univariate and multivariate linear models of CatD and CatK for HGS and ASM. https://www.frontiersin.org/files/Articles/1654694/fmed-12-1654694-HTML/image_m/fmed-12-1654694-g003.jpg Figure 3. ROC curves of CatD and CatK levels for low HGS, low ASM, and sarcopenia. (A) for HGS; (B) https://www.frontiersin.org/files/Articles/1654694/fmed-12-1654694-HTML/image_m/fmed-12-1654694-t003.jpg Table 3. Sarcopenia traits of individuals grouped by CatD and CatK. https://www.frontiersin.org/files/Articles/1654694/fmed-12-1654694-HTML/image_m/fmed-12-1654694-t004.jpg Table 4. Univariate and multivariate logistic models of CatD and CatK for sarcopenia traits. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1804441/full https://www.frontiersin.org/files/Articles/1804441/fmed-13-1804441-HTML/image_m/fmed-13-1804441-t001.jpg Table 1. Baseline characteristics of populations included in our study. https://www.frontiersin.org/files/Articles/1804441/fmed-13-1804441-HTML/image_m/fmed-13-1804441-t002.jpg Table 2. Sarcopenia metrics between normal and AC groups. https://www.frontiersin.org/files/Articles/1804441/fmed-13-1804441-HTML/image_m/fmed-13-1804441-g001.jpg Figure 1. Receiver operating characteristic (ROC) curves for individual sarcopenia components in pre https://www.frontiersin.org/files/Articles/1804441/fmed-13-1804441-HTML/image_m/fmed-13-1804441-g002.jpg Figure 2. Kaplan-Meier curves of acute cholecystitis-free survival stratified by sarcopenia status. https://www.frontiersin.org/files/Articles/1804441/fmed-13-1804441-HTML/image_m/fmed-13-1804441-t003.jpg Table 3. Logistic regression analysis of sarcopenia metrics for acute cholecystitis. https://www.frontiersin.org/files/Articles/1804441/fmed-13-1804441-HTML/image_m/fmed-13-1804441-t004.jpg Table 4. Cox analysis of sarcopenia metrics and time to acute cholecystitis. https://www.frontiersin.org/journals/dementia/articles/10.3389/frdem.2026.1745504/full https://www.frontiersin.org/files/Articles/1745504/frdem-05-1745504-HTML-r1/image_m/frdem-05-1745504-t001.jpg Table 1. Summary table comparing diagnostic and therapeutic approaches used in neurodegenerative dis https://www.frontiersin.org/files/Articles/1745504/frdem-05-1745504-HTML-r1/image_m/frdem-05-1745504-t002.jpg Table 2. Genomic DATA https://www.frontiersin.org/files/Articles/1745504/frdem-05-1745504-HTML-r1/image_m/frdem-05-1745504-t003.jpg Table 3. Functional polygenic risk score. https://www.frontiersin.org/files/Articles/1745504/frdem-05-1745504-HTML-r1/image_m/frdem-05-1745504-t004.jpg Table 4. Biochemical markers. https://www.frontiersin.org/files/Articles/1745504/frdem-05-1745504-HTML-r1/image_m/frdem-05-1745504-t005.jpg Table 5. Personalized functional plan. https://www.frontiersin.org/files/Articles/1745504/frdem-05-1745504-HTML-r1/image_m/frdem-05-1745504-g001.jpg Figure 1. Integrative neurogenomics workflow. Schematic representation of the proposed neurogenomics https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1784283/full https://www.frontiersin.org/files/Articles/1784283/fgene-17-1784283-HTML/image_m/fgene-17-1784283-t001.jpg Table 1. Selected obesity- and metabolism-related SNVs. https://www.frontiersin.org/files/Articles/1784283/fgene-17-1784283-HTML/image_m/fgene-17-1784283-t002.jpg Table 2. Genotypic frequencies of six SNVs associated with obesity. https://www.frontiersin.org/files/Articles/1784283/fgene-17-1784283-HTML/image_m/fgene-17-1784283-t003.jpg Table 3. Allelic frequencies of six SNVs associated with obesity. https://www.frontiersin.org/files/Articles/1784283/fgene-17-1784283-HTML/image_m/fgene-17-1784283-t004.jpg Table 4. Genotypic frequencies of six SNVs associated with obesity by sex. https://www.frontiersin.org/files/Articles/1784283/fgene-17-1784283-HTML/image_m/fgene-17-1784283-t005.jpg Table 5. Allelic frequencies of six SNVs associated with obesity by sex. https://www.frontiersin.org/files/Articles/1784283/fgene-17-1784283-HTML/image_m/fgene-17-1784283-t006.jpg Table 6. Comparative allele frequency landscape across global populations. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1681170/full https://www.frontiersin.org/files/Articles/1681170/fcvm-12-1681170-HTML-r1/image_m/fcvm-12-1681170-i001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1681170/fcvm-12-1681170-HTML-r1/image_m/fcvm-12-1681170-g001.jpg Figure 1. Study flowchart. https://www.frontiersin.org/files/Articles/1681170/fcvm-12-1681170-HTML-r1/image_m/fcvm-12-1681170-t001.jpg Table 1. Baseline clinical and procedural characteristics by AIP tertiles. https://www.frontiersin.org/files/Articles/1681170/fcvm-12-1681170-HTML-r1/image_m/fcvm-12-1681170-g002.jpg Figure 2. Kaplan–meier curves for outcomes by atherogenic index of plasma (AIP) tertile. (A) Major a https://www.frontiersin.org/files/Articles/1681170/fcvm-12-1681170-HTML-r1/image_m/fcvm-12-1681170-g003.jpg Figure 3. Restricted cubic spline curves for the outcomes according to the AIP level. The solid blue https://www.frontiersin.org/files/Articles/1681170/fcvm-12-1681170-HTML-r1/image_m/fcvm-12-1681170-t002.jpg Table 2. Cox regression analysis of the association AIP tertiles with adverse outcomes. https://www.frontiersin.org/files/Articles/1681170/fcvm-12-1681170-HTML-r1/image_m/fcvm-12-1681170-g004.jpg Figure 4. Subgroup analysis. ACS, acute coronary syndrome; BMI, body mass index; LDL-C, low-density https://www.frontiersin.org/files/Articles/1681170/fcvm-12-1681170-HTML-r1/image_m/fcvm-12-1681170-g005.jpg Figure 5. Feature selection results using boruta, XGBoost, and recursive feature elimination (RFE) a https://www.frontiersin.org/files/Articles/1681170/fcvm-12-1681170-HTML-r1/image_m/fcvm-12-1681170-g006.jpg Figure 6. A 48-month performance of the machine-learning models: (A) receiver-operating-characterist https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1662343/full https://www.frontiersin.org/files/Articles/1662343/fmolb-12-1662343-HTML/image_m/fmolb-12-1662343-t001.jpg Table 1. Comparison of baseline characteristics and clinical indicators between NEC and non-NEC grou https://www.frontiersin.org/files/Articles/1662343/fmolb-12-1662343-HTML/image_m/fmolb-12-1662343-t002.jpg Table 2. Comparison of early-onset versus late-onset sepsis in NEC and Non-NEC groups. https://www.frontiersin.org/files/Articles/1662343/fmolb-12-1662343-HTML/image_m/fmolb-12-1662343-g001.jpg Figure 1. Study design flowchart and NEC risk factor forest map. (A) Univariate logistic regression https://www.frontiersin.org/files/Articles/1662343/fmolb-12-1662343-HTML/image_m/fmolb-12-1662343-g002.jpg Figure 2. Identification and functional annotation of shared differentially expressed genes (DEGs) i https://www.frontiersin.org/files/Articles/1662343/fmolb-12-1662343-HTML/image_m/fmolb-12-1662343-g003.jpg Figure 3. Protein-protein interaction (PPI) network and functional enrichment of shared hub genes in https://www.frontiersin.org/files/Articles/1662343/fmolb-12-1662343-HTML/image_m/fmolb-12-1662343-g004.jpg Figure 4. Validation of hub gene expression and diagnostic efficacy in NEC and neonatal sepsis. (A) https://www.frontiersin.org/files/Articles/1662343/fmolb-12-1662343-HTML/image_m/fmolb-12-1662343-g005.jpg Figure 5. Transcriptional regulatory network and validation in NEC and neonatal sepsis. (A) Hub gene https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1689969/full https://www.frontiersin.org/files/Articles/1689969/fcimb-15-1689969-HTML/image_m/fcimb-15-1689969-g001.jpg Figure 1. GEO data of intestinal tissue from NEC patients (GSE64801) were analyzed. Differential gen https://www.frontiersin.org/files/Articles/1689969/fcimb-15-1689969-HTML/image_m/fcimb-15-1689969-g002.jpg Figure 2. The study enrollment flow diagram. GA, gestational age; BW, birth weight; NEC, Necrotizing https://www.frontiersin.org/files/Articles/1689969/fcimb-15-1689969-HTML/image_m/fcimb-15-1689969-t001.jpg Table 1. Clinical characteristics, underlying diseases, and main treatments of the study cohort. https://www.frontiersin.org/files/Articles/1689969/fcimb-15-1689969-HTML/image_m/fcimb-15-1689969-t002.jpg Table 2. Laboratory tests in the study cohort. https://www.frontiersin.org/files/Articles/1689969/fcimb-15-1689969-HTML/image_m/fcimb-15-1689969-g003.jpg Figure 3. Serum DUOX2 levels and their correlation with other laboratory parameters. The serum DUOX2 https://www.frontiersin.org/files/Articles/1689969/fcimb-15-1689969-HTML/image_m/fcimb-15-1689969-t003.jpg Table 3. Analysis of risk factors for NEC in preterm infants with GA <32 weeks. https://www.frontiersin.org/files/Articles/1689969/fcimb-15-1689969-HTML/image_m/fcimb-15-1689969-g004.jpg Figure 4. ROC curves for DUOX2 on the presence of NEC. The AUC value of serum DUOX2 level for NEC in https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1754831/full https://www.frontiersin.org/files/Articles/1754831/fpls-17-1754831-HTML/image_m/fpls-17-1754831-t001.jpg Table 1. Information on the studied sample plots. https://www.frontiersin.org/files/Articles/1754831/fpls-17-1754831-HTML/image_m/fpls-17-1754831-g001.jpg Figure 1. Effects of slope aspect and year of abandonment on the quantitative characteristics of the https://www.frontiersin.org/files/Articles/1754831/fpls-17-1754831-HTML/image_m/fpls-17-1754831-g002.jpg Figure 2. Effects of slope aspect and year of abandonment on the species number, biomass and density https://www.frontiersin.org/files/Articles/1754831/fpls-17-1754831-HTML/image_m/fpls-17-1754831-g003.jpg Figure 3. Effects of slope aspect and year of abandonment on the species number, biomass and density https://www.frontiersin.org/files/Articles/1754831/fpls-17-1754831-HTML/image_m/fpls-17-1754831-g004.jpg Figure 4. Effects of slope aspect and year of abandonment on the species number, biomass and density https://www.frontiersin.org/files/Articles/1754831/fpls-17-1754831-HTML/image_m/fpls-17-1754831-g005.jpg Figure 5. Effects of slope aspect and year of abandonment on the species number, biomass and density https://www.frontiersin.org/files/Articles/1754831/fpls-17-1754831-HTML/image_m/fpls-17-1754831-g006.jpg Figure 6. Effects of slope aspect and year of abandonment on the α diversity of the grassland plant https://www.frontiersin.org/files/Articles/1754831/fpls-17-1754831-HTML/image_m/fpls-17-1754831-g007.jpg Figure 7. Effects of slope aspect and year of abandonment on the functional diversity of the grassla https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1689253/full https://www.frontiersin.org/files/Articles/1689253/fmicb-16-1689253-HTML/image_m/fmicb-16-1689253-g001.jpg Figure 1. Flowchart for enrolment. https://www.frontiersin.org/files/Articles/1689253/fmicb-16-1689253-HTML/image_m/fmicb-16-1689253-t001.jpg Table 1. Clinical characteristics of patients with suspected CNS infections. https://www.frontiersin.org/files/Articles/1689253/fmicb-16-1689253-HTML/image_m/fmicb-16-1689253-g002.jpg Figure 2. Comparison of diagnostic performance of mNGS, culture, conventional testing, and combined https://www.frontiersin.org/files/Articles/1689253/fmicb-16-1689253-HTML/image_m/fmicb-16-1689253-t002.jpg Table 2. The comparison of diagnostic performance of different detection methods. https://www.frontiersin.org/files/Articles/1689253/fmicb-16-1689253-HTML/image_m/fmicb-16-1689253-t003.jpg Table 3. The detection rate of pathogens by various methods in 129 cases of CNS infection. https://www.frontiersin.org/files/Articles/1689253/fmicb-16-1689253-HTML/image_m/fmicb-16-1689253-t004.jpg Table 4. Inconsistency between mNGS and culture detection results. https://www.frontiersin.org/files/Articles/1689253/fmicb-16-1689253-HTML/image_m/fmicb-16-1689253-g003.jpg Figure 3. Assessment of the impact of mNGS results on clinical treatment. (A) Assessment of the impa https://www.frontiersin.org/files/Articles/1689253/fmicb-16-1689253-HTML/image_m/fmicb-16-1689253-g004.jpg Figure 4. Risk factor analysis for mNGS false-negative results. (A) Risk factor analysis for overall https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1597616/full https://www.frontiersin.org/files/Articles/1597616/fneur-16-1597616-HTML/image_m/fneur-16-1597616-g001.jpg Figure 1. Flowchart for the selection of research subjects. https://www.frontiersin.org/files/Articles/1597616/fneur-16-1597616-HTML/image_m/fneur-16-1597616-t001.jpg Table 1. The baseline characteristics of participants. https://www.frontiersin.org/files/Articles/1597616/fneur-16-1597616-HTML/image_m/fneur-16-1597616-t002.jpg Table 2. The association between AIP and incidence of stroke across various models. https://www.frontiersin.org/files/Articles/1597616/fneur-16-1597616-HTML/image_m/fneur-16-1597616-g002.jpg Figure 2. The relationship between AIP and stroke risk. Models were tuned for age, sex, residence, m https://www.frontiersin.org/files/Articles/1597616/fneur-16-1597616-HTML/image_m/fneur-16-1597616-g003.jpg Figure 3. Sex-stratified association between AIP and stroke incidence rates (A: female; B: male). https://www.frontiersin.org/files/Articles/1597616/fneur-16-1597616-HTML/image_m/fneur-16-1597616-g004.jpg Figure 4. Age-stratified association between AIP and the incidence of stroke (A: 45–59, B: 60–69, C: https://www.frontiersin.org/files/Articles/1597616/fneur-16-1597616-HTML/image_m/fneur-16-1597616-g005.jpg Figure 5. Forest plot: subgroup analysis of AIP and stroke risk elements. In subgroup analyses diffe https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1735531/full https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-g001.jpg Figure 1. Flow of study selection. https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-t001.jpg Table 1. Included trial characteristics. https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-t002.jpg Table 2. Characteristics of included trials. https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-g002.jpg Figure 2. (A) risk of bias ratings. (B) Risk of bias graph. https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-g003.jpg Figure 3. Forest plot of taekwondo intervention on depressive symptoms. https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-g004.jpg Figure 4. Forest plot of taekwondo intervention on cognitive function. https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-t003.jpg Table 3. Regression analysis of taekwondo intervention for depression. https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-t004.jpg Table 4. Regression analysis of taekwondo intervention on cognitive function. https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-t005.jpg Table 5. Subgroup analysis of taekwondo intervention for depression,. https://www.frontiersin.org/files/Articles/1735531/fspor-07-1735531-HTML/image_m/fspor-07-1735531-t006.jpg Table 6. Subgroup analysis of taekwondo intervention on cognitive function. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1676356/full https://www.frontiersin.org/files/Articles/1676356/fpls-16-1676356-HTML/image_m/fpls-16-1676356-g001.jpg Figure 1. The location of study site. https://www.frontiersin.org/files/Articles/1676356/fpls-16-1676356-HTML/image_m/fpls-16-1676356-t001.jpg Table 1. Descriptive statistics of plant functional traits measured from sampled 109 species. https://www.frontiersin.org/files/Articles/1676356/fpls-16-1676356-HTML/image_m/fpls-16-1676356-t002.jpg Table 2. The phylogenetic signal of functional traits in karst plant community. https://www.frontiersin.org/files/Articles/1676356/fpls-16-1676356-HTML/image_m/fpls-16-1676356-g002.jpg Figure 2. Phylogenetic community structure index and functional trait community structure index of k https://www.frontiersin.org/files/Articles/1676356/fpls-16-1676356-HTML/image_m/fpls-16-1676356-g003.jpg Figure 3. Functional trait community structure index of different traits in karst plant community. https://www.frontiersin.org/files/Articles/1676356/fpls-16-1676356-HTML/image_m/fpls-16-1676356-g004.jpg Figure 4. The individual explanatory rates of phylogenetic development, topographic and soil factors https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1764618/full https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-g001.jpg Figure 1. Theoretical analysis framework. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-g002.jpg Figure 2. The mechanism diagram of the impact of non-herding employment on herding pressure. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t001.jpg Table 1. Distribution of the survey sample. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t002.jpg Table 2. Conversion coefficients between different forage materials and standard hay. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-g003.jpg Figure 3. The grazing conditions of sample herding households. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t003.jpg Table 3. Variable meaning and descriptive statistics. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t004.jpg Table 4. The influence of non-pastoral employment on the grazing pressure of herding households. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t005.jpg Table 5. Considers the regression results of endogeneity. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-g004.jpg Figure 4. Probability distribution of propensity score values for the treatment and control groups b https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t006.jpg Table 6. The results of balance test. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t007.jpg Table 7. The results of matching scores for different tendencies. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t008.jpg Table 8. Replacement of explanatory variables. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t009.jpg Table 9. Replacement of explanatory variables. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t010.jpg Table 10. Mechanisms of the role of non-pastoral employment on the grazing pressure. https://www.frontiersin.org/files/Articles/1764618/fsufs-10-1764618-HTML/image_m/fsufs-10-1764618-t011.jpg Table 11. Heterogeneity analysis: pastoral and agro-pastoral ecotone. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1760571/full https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g001.jpg Figure 1. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) study flow dia https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-t001.jpg Table 1. Characteristics of the included studies. https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g002.jpg Figure 2. Risk of bias summary: review of the authors judgments about each risk of bias item for eac https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g003.jpg Figure 3. Risk of bias graph: review authors' judgments about each risk of bias item, presented as p https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g004.jpg Figure 4. Forest plot of daily steps. https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g005.jpg Figure 5. Forest plot of body mass index (BMI). https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g006.jpg Figure 6. Exploratory subgroup analysis based on intervention duration. https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g007.jpg Figure 7. Subgroup analysis based on intervention type. https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g008.jpg Figure 8. Forest plot of body weight. https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g009.jpg Figure 9. Forest plot of the epworth sleepiness scale (ESS). https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-g010.jpg Figure 10. Forest plot of the apnea-hypopnea index (AHI). https://www.frontiersin.org/files/Articles/1760571/fpubh-14-1760571-HTML/image_m/fpubh-14-1760571-t002.jpg Table 2. GRADE evidence quality evaluation. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1679768/full https://www.frontiersin.org/files/Articles/1679768/fphys-16-1679768-HTML/image_m/fphys-16-1679768-g001.jpg Figure 1. The tissue-specific expression level (A) and silencing efficiency of CYP303A1 (B) in femal https://www.frontiersin.org/files/Articles/1679768/fphys-16-1679768-HTML/image_m/fphys-16-1679768-g002.jpg Figure 2. Effects of dsCYP303A1 on ovarian area (A), number of mature eggs (B), and ovary developmen https://www.frontiersin.org/files/Articles/1679768/fphys-16-1679768-HTML/image_m/fphys-16-1679768-t001.jpg Table 1. Effects of silencing CYP303A1 on the reproductive and population parameters of Nilaparvata https://www.frontiersin.org/files/Articles/1679768/fphys-16-1679768-HTML/image_m/fphys-16-1679768-g003.jpg Figure 3. Effects of silencing CYP303A1 on embryonic development in N. lugens. Note: (A): Eggs laid https://www.frontiersin.org/files/Articles/1679768/fphys-16-1679768-HTML/image_m/fphys-16-1679768-g004.jpg Figure 4. Effects of silencing CYP303A1 on the expression levels of genes involved in embryonic deve https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1615781/full https://www.frontiersin.org/files/Articles/1615781/fpubh-13-1615781-HTML/image_m/fpubh-13-1615781-g001.jpg Figure 1. Flow-chart of recruitment and inclusion of healthcare workers. https://www.frontiersin.org/files/Articles/1615781/fpubh-13-1615781-HTML/image_m/fpubh-13-1615781-t001.jpg Table 1. Baseline characteristics of the study participants (n = 37). https://www.frontiersin.org/files/Articles/1615781/fpubh-13-1615781-HTML/image_m/fpubh-13-1615781-t002.jpg Table 2. Themes and sub-themes constituting the research findings, Liangshan, China. 2024. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1704215/full https://www.frontiersin.org/files/Articles/1704215/fpubh-13-1704215-HTML-r1/image_m/fpubh-13-1704215-t001.jpg Table 1. Descriptive characteristics of participating healthcare workers (N = 492). https://www.frontiersin.org/files/Articles/1704215/fpubh-13-1704215-HTML-r1/image_m/fpubh-13-1704215-t002.jpg Table 2. Healthcare workers’ observation and evaluation of adherence among HIV–TB Co-infected patien https://www.frontiersin.org/files/Articles/1704215/fpubh-13-1704215-HTML-r1/image_m/fpubh-13-1704215-t003.jpg Table 3. Ordered multinomial logistic regression analysis of factors associated with healthcare work https://www.frontiersin.org/files/Articles/1704215/fpubh-13-1704215-HTML-r1/image_m/fpubh-13-1704215-t004.jpg Table 4. Healthcare workers’ attributions for poor treatment adherence among HIV-TB co-infected pati https://www.frontiersin.org/files/Articles/1704215/fpubh-13-1704215-HTML-r1/image_m/fpubh-13-1704215-t005.jpg Table 5. Healthcare workers’ perceptions and evaluation of the integrated prevention and control of https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1769773/full https://www.frontiersin.org/files/Articles/1769773/fendo-17-1769773-HTML/image_m/fendo-17-1769773-g001.jpg Figure 1. CONSORT chart. https://www.frontiersin.org/files/Articles/1769773/fendo-17-1769773-HTML/image_m/fendo-17-1769773-t001.jpg Table 1. Demography and baseline profile in ITT dataset (n=70). https://www.frontiersin.org/files/Articles/1769773/fendo-17-1769773-HTML/image_m/fendo-17-1769773-t002.jpg Table 2. BMI, PSS score, and ovarian ultrasound findings in PP dataset (n=66). https://www.frontiersin.org/files/Articles/1769773/fendo-17-1769773-HTML/image_m/fendo-17-1769773-g002.jpg Figure 2. Comparative effects of therapy on ovarian ultrasound values and PSS total score in the PP https://www.frontiersin.org/files/Articles/1769773/fendo-17-1769773-HTML/image_m/fendo-17-1769773-t003.jpg Table 3. Serum hormones in PP dataset (n=66). https://www.frontiersin.org/files/Articles/1769773/fendo-17-1769773-HTML/image_m/fendo-17-1769773-t004.jpg Table 4. Laboratory parameters in PP dataset (n=66). https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1703976/full https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-g001.jpg Figure 1. Patient selection flowchart showing inclusion and exclusion criteria. Note: Multiple TEIC https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-g002.jpg Figure 2. Machine learning workflow depicting the analysis process. Abbreviations: TDM, therapeutic https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-t001.jpg Table 1. Demographic and characteristic statistical description. https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-t002.jpg Table 2. Significance analysis of TEIC TDM and individual variables. https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-g003.jpg Figure 3. Feature selection workflow based on feature importance rankings from four machine learning https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-t003.jpg Table 3. The ten-fold cross-validation results of the model on the training set (mean ± std). https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-g004.jpg Figure 4. Model performance comparison on the test set. Note: The figure displays the RMSE (mean ± s https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-t004.jpg Table 4. The comparison of prediction accuracy results of the models on the test set. https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-t005.jpg Table 5. Variable importance scores based on the LightGBM model. https://www.frontiersin.org/files/Articles/1703976/fphar-16-1703976-HTML/image_m/fphar-16-1703976-g005.jpg Figure 5. SHAP plot based on the LightGBM model. Each point represents a patient sample, with color https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1740896/full https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-g001.jpg Figure 1. Technical workflow for radiomics model construction and evaluation. https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-t001.jpg Table 1. Demographics and clinical characteristics of the patients in training and validation set. https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-t002.jpg Table 2. Comparison of clinical characteristics between patients with well and poor responses to che https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-g002.jpg Figure 2. Screening of radiomics features and construction of Rad-score using LASSO regression. (A, https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-g003.jpg Figure 3. Diagnostic efficacy evaluation of Rad-score. The Rad-score demonstrated diagnostic perform https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-g004.jpg Figure 4. Transcriptomic analysis reveals the biological mechanisms underlying the Rad-score. (A) Vo https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-t003.jpg Table 3. Logistic regression analyses reveal independent risk predictors associated with chemotherap https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-g005.jpg Figure 5. Multi-cohort performance evaluation of machine learning predictive models. (A, D, G) Recei https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-t004.jpg Table 4. Comparison of clinical characteristics between different risk groups defined by the radiomi https://www.frontiersin.org/files/Articles/1740896/fonc-16-1740896-HTML/image_m/fonc-16-1740896-g006.jpg Figure 6. SHAP analysis for interpreting the optimal RandomForest model. (A) SHAP summary bar plot r https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1615748/full https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g001.jpg Figure 1. Schematic overview of the study design and workflow for identifying immune cell and metabo https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g002.jpg Figure 2. Identification of differentially expressed genes (DEGs) in ARDS. (A) Volcano plot of DEGs https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g003.jpg Figure 3. Immune infiltration analysis and weighted gene co-expression network analysis (WGCNA). (A) https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g004.jpg Figure 4. Identification and validation of potential biomarkers in ARDS. (A) Venn diagram of overlap https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g005.jpg Figure 5. Identification and validation of biomarkers in ARDS. (A-C) Expression analysis of candidat https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g006.jpg Figure 6. Construction and evaluation of the ANN model and functional enrichment analysis of biomark https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g007.jpg Figure 7. Regulatory networks, drug prediction and molecular docking analysis for biomarkers. (A, B) https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g008.jpg Figure 8. Single-cell RNA sequencing (scRNA-seq) analysis of ARDS samples. (A, B) Number of cells an https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g009.jpg Figure 9. Cell-cell communication and key cell identification in ARDS. (A) The cell communication ne https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g010.jpg Figure 10. The expression of RPL14 (H) and SMARCD3 in cells. (A) UMAP plot showing the expression of https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g011.jpg Figure 11. Pseudotime analysis of key cells in ARDS. (A) UMAP plot showing 16 subtypes of macrophage https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g012.jpg Figure 12. The expression levels of hub ARDS-ARDEGs (RPL14, SMARCD3, TCN1), inflammatory cytokines a https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g013.jpg Figure 13. Functional validation of hub ARDS-ARDEGs (RPL14, SMARCD3, TCN1) in THP-1-derived macropha https://www.frontiersin.org/files/Articles/1615748/fimmu-16-1615748-HTML/image_m/fimmu-16-1615748-g014.jpg Figure 14. CsA reversed LPS-induced mitochondrial damage, inflammation, and oxidative stress in macr https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1778717/full https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-t001.jpg Table 1. Timeline of the clinical course. https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g001.jpg Figure 1. Initial chest computed tomography (CT) performed on July 13, 2023 showed multiple bilatera https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g002.jpg Figure 2. Chest CT obtained after 1 week of treatment showed partial radiological improvement of pul https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g003.jpg Figure 3. Metagenomic next-generation sequencing of bronchoalveolar lavage fluid identified pneumocy https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g004.jpg Figure 4. Contrast-enhanced chest CT performed on July 29, 2023 demonstrated multiple pulmonary lesi https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g005.jpg Figure 5. Genetic analysis revealed a heterozygous IKZF1 mutation associated with underlying primary https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g006.jpg Figure 6. Chest CT prior to first hospital discharge on August 12, 2023 showed partial resolution of https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g007.jpg Figure 7. (A–C) Axial CT views demonstrating airway obstruction. (D–F) Coronal and sagittal reconstr https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g008.jpg Figure 8. https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g012.jpg . Bronchoscopic findings during four therapeutic interventions. Initial bronchoscopy showed necrotiz https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g009.jpg Figure 9. Serial chest CT images showed progressive improvement in airway obstruction and pulmonary https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g010.jpg Figure 10. Bronchoscopy performed on February 19, 2024 confirmed resolution of endobronchial stenosi https://www.frontiersin.org/files/Articles/1778717/fped-14-1778717-HTML/image_m/fped-14-1778717-g011.jpg Figure 11. Chest CT obtained on January 17, 2025 showed complete lung re-expansion with residual cal https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2026.1795258/full https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g001.jpg Figure 1. (a) Geological tectonic map of the study area (b) Geological sketch map of the study area https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g002.jpg Figure 2. Comprehensive stratigraphic column of cenozoic strata within the study area (Fang and Yan, https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-t001.jpg Table 1. The major elements of clay in the study area. https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-t002.jpg Table 2. Trace elements of clay in the study area. https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-t003.jpg Table 3. Clay mineral composition and lithium isotope values of the study area. https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g003.jpg Figure 3. Diagram of the Correlation between Lithium Elements and Minerals. (Red circles denote a po https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-t004.jpg Table 4. Statistical table of common environmental redox condition indicators (Hatch and Leventhal, https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g004.jpg Figure 4. Diagram showing the relationship between Li content and V/(V + Ni), V/Cr, Ni/Co, δU, Ce/Ce https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-t005.jpg Table 5. Statistical table of common environmental paleoclimate indicators (Tian and Zhang, 2016; Ya https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g005.jpg Figure 5. Diagram showing the relationship between Li content and Ga,Sr/Ba,Rb/K. https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g006.jpg Figure 6. Diagram showing the relationship between Li content and C.I., Sr/Cu. https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g007.jpg Figure 7. SiO2/Al2O3–K2O/Na2O discrimination diagram and Sc/Cr–La/Y tectono-provenance diagram (Bhat https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g008.jpg Figure 8. Tectonic-setting discrimination diagram (after Verma and Armstrong-Altrin, 2013). https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g009.jpg Figure 9. Provenance discrimination diagram based on major-element functions for the Guide area (Ros https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g010.jpg Figure 10. TiO2–Al2O3 discrimination diagram (Schieber, 1992). https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g011.jpg Figure 11. Th/Sc–Zr/Sc provenance diagram for the Guide area (McLennan et al., 1993). https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g012.jpg Figure 12. Rb–K2O discrimination plot (Floyd and Leveridge, 1987). https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g013.jpg Figure 13. Ni–TiO2 discrimination plot (Floyd et al., 1989). https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g014.jpg Figure 14. Cr–Ni discrimination plot (Taylor and McLennan, 1985). https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g015.jpg Figure 15. ΣREE–La/Yb source-rock discrimination diagram (Allegre et al., 1973). https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g016.jpg Figure 16. A–CN–K weathering discrimination diagram for the Guide area (Nesbitt and Young, 1984). https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g017.jpg Figure 17. A–CNK–FM weathering discrimination diagram for the Guide area (Nesbitt and Wilson, 1992) https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g018.jpg Figure 18. CIA–ICV diagram for claystones in the Guide area (Cox et al., 1995). https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g019.jpg Figure 19. The δ7Li distribution of clay samples and other geological bodies in the study area. (The https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g020.jpg Figure 20. The relationship between Li and δ7Li in the clay of the study area. https://www.frontiersin.org/files/Articles/1795258/feart-14-1795258-HTML-r1/image_m/feart-14-1795258-g021.jpg Figure 21. The genetic model diagram of the study area. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1742938/full https://www.frontiersin.org/files/Articles/1742938/fendo-16-1742938-HTML-r1/image_m/fendo-16-1742938-g000.jpg Graphical Abstract. Conceptual overview of the association between systemic inflammation and left ve https://www.frontiersin.org/files/Articles/1742938/fendo-16-1742938-HTML-r1/image_m/fendo-16-1742938-t001.jpg Table 1. Baseline characteristics of participants (n = 3, 632). https://www.frontiersin.org/files/Articles/1742938/fendo-16-1742938-HTML-r1/image_m/fendo-16-1742938-t002.jpg Table 2. Odds ratios (95%CI) of LVH and SII-SIRI pattern/SII/SIRI levels in the patients (n = 3, 632 https://www.frontiersin.org/files/Articles/1742938/fendo-16-1742938-HTML-r1/image_m/fendo-16-1742938-g001.jpg Figure 1. Restricted cubic spline (RCS) and threshold effect analyses for the associations of lnSII https://www.frontiersin.org/files/Articles/1742938/fendo-16-1742938-HTML-r1/image_m/fendo-16-1742938-g002.jpg Figure 2. Subgroup analyses of the associations between lnSII and lnSIRI with left ventricular hyper https://www.frontiersin.org/files/Articles/1742938/fendo-16-1742938-HTML-r1/image_m/fendo-16-1742938-g003.jpg Figure 3. Mediation analysis of the association between serum urate, systemic inflammation, and left https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1794633/full https://www.frontiersin.org/files/Articles/1794633/fimmu-17-1794633-HTML/image_m/fimmu-17-1794633-g001.jpg Figure 1. Flow chart of patient screening and inclusion. * One female patient with a UBA1 gene mutat https://www.frontiersin.org/files/Articles/1794633/fimmu-17-1794633-HTML/image_m/fimmu-17-1794633-t001.jpg Table 1. Pathogenic somatic UBA1 mutations and co-occurring genetic variants in 16 patients with VEX https://www.frontiersin.org/files/Articles/1794633/fimmu-17-1794633-HTML/image_m/fimmu-17-1794633-t002.jpg Table 2. Demographic, clinical and laboratory characteristics of 16 male patients with VEXAS syndrom https://www.frontiersin.org/files/Articles/1794633/fimmu-17-1794633-HTML/image_m/fimmu-17-1794633-g002.jpg Figure 2. Representative cutaneous manifestations of VEXAS syndrome. One 58-year-old male patient wi https://www.frontiersin.org/files/Articles/1794633/fimmu-17-1794633-HTML/image_m/fimmu-17-1794633-g003.jpg Figure 3. Representative CT manifestations of VEXAS syndrome patients with pulmonary involvement in https://www.frontiersin.org/files/Articles/1794633/fimmu-17-1794633-HTML/image_m/fimmu-17-1794633-g004.jpg Figure 4. Representative morphological abnormalities found in bone marrow of VEXAS syndrome patients https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1807267/full https://www.frontiersin.org/files/Articles/1807267/fimmu-17-1807267-HTML/image_m/fimmu-17-1807267-t001.jpg Table 1. Characteristics of the included studies. https://www.frontiersin.org/files/Articles/1807267/fimmu-17-1807267-HTML/image_m/fimmu-17-1807267-g001.jpg Figure 1. Forest plot of the meta-analysis evaluating the association between COVID-19 vaccination a https://www.frontiersin.org/files/Articles/1807267/fimmu-17-1807267-HTML/image_m/fimmu-17-1807267-g002.jpg Figure 2. Forest plot of the meta-analysis evaluating the association between COVID-19 vaccination a https://www.frontiersin.org/files/Articles/1807267/fimmu-17-1807267-HTML/image_m/fimmu-17-1807267-g003.jpg Figure 3. Forest plot of subgroup analysis for progression-free survival (PFS), stratified by COVID- https://www.frontiersin.org/files/Articles/1807267/fimmu-17-1807267-HTML/image_m/fimmu-17-1807267-g004.jpg Figure 4. Forest plot of subgroup analyses for overall survival (OS), stratified by (A) COVID-19 vac https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2025.1648121/full https://www.frontiersin.org/files/Articles/1648121/fevo-13-1648121-HTML/image_m/fevo-13-1648121-g001.jpg Figure 1. A map (A) of study sites sampled across Southern California. Average temperatures were rec https://www.frontiersin.org/files/Articles/1648121/fevo-13-1648121-HTML/image_m/fevo-13-1648121-g002.jpg Figure 2. The relative effects of increasing shrub density across arid sites within Southern Califor https://www.frontiersin.org/files/Articles/1648121/fevo-13-1648121-HTML/image_m/fevo-13-1648121-t001.jpg Table 1. Analysis of microsite-level vertebrate observations, richness, and evenness from general li https://www.frontiersin.org/files/Articles/1648121/fevo-13-1648121-HTML/image_m/fevo-13-1648121-g003.jpg Figure 3. The relative effects of near-surface air temperature across arid study sites within Southe https://www.frontiersin.org/files/Articles/1648121/fevo-13-1648121-HTML/image_m/fevo-13-1648121-g004.jpg Figure 4. The relative percent proportion of vertebrate species observations across shrub and open m https://www.frontiersin.org/files/Articles/1648121/fevo-13-1648121-HTML/image_m/fevo-13-1648121-g005.jpg Figure 5. PCOA figure displaying the relative similarity in community composition across a shrub den https://www.frontiersin.org/files/Articles/1648121/fevo-13-1648121-HTML/image_m/fevo-13-1648121-t002.jpg Table 2. Analysis of site-level vertebrate observations, richness, and evenness from general linear https://www.frontiersin.org/files/Articles/1648121/fevo-13-1648121-HTML/image_m/fevo-13-1648121-g006.jpg Figure 6. The relative effects of aridity across various arid sites within Southern California. Data https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1781646/full https://www.frontiersin.org/files/Articles/1781646/fmed-13-1781646-HTML/image_m/fmed-13-1781646-g006.jpg Graphical Abstract. Integrated multi-omics and experimental validation workflow for identifying shar https://www.frontiersin.org/files/Articles/1781646/fmed-13-1781646-HTML/image_m/fmed-13-1781646-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1781646/fmed-13-1781646-HTML/image_m/fmed-13-1781646-t002.jpg Table 2. Primer sequences used for qRT-PCR. https://www.frontiersin.org/files/Articles/1781646/fmed-13-1781646-HTML/image_m/fmed-13-1781646-g001.jpg Figure 1. Differential expression and overlap analysis in HIV and hypertension datasets. (A) DEG hea https://www.frontiersin.org/files/Articles/1781646/fmed-13-1781646-HTML/image_m/fmed-13-1781646-g002.jpg Figure 2. Integrated bioinformatics analysis of HIV and HTN. (A) KEGG enrichment of 109 DEGs (Cluste https://www.frontiersin.org/files/Articles/1781646/fmed-13-1781646-HTML/image_m/fmed-13-1781646-g003.jpg Figure 3. Immune cell infiltration analysis in HIV and HTN. (A,B) CIBERSORT-based immune infiltratio https://www.frontiersin.org/files/Articles/1781646/fmed-13-1781646-HTML/image_m/fmed-13-1781646-g004.jpg Figure 4. Validation of key diagnostic gene expression in HIV and HTN. (A,B) Differential expression https://www.frontiersin.org/files/Articles/1781646/fmed-13-1781646-HTML/image_m/fmed-13-1781646-g005.jpg Figure 5. Construction of the lncRNA–miRNA–mRNA regulatory network associated with HIV and HTN. (A) https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1734551/full https://www.frontiersin.org/files/Articles/1734551/fmicb-17-1734551-HTML/image_m/fmicb-17-1734551-g001.jpg Figure 1. Gender distribution. Distribution of urinary tract infection cases by sex. The bar chart i https://www.frontiersin.org/files/Articles/1734551/fmicb-17-1734551-HTML/image_m/fmicb-17-1734551-g002.jpg Figure 2. Relationship between age and infection duration. Scatter plot illustrates the relationship https://www.frontiersin.org/files/Articles/1734551/fmicb-17-1734551-HTML/image_m/fmicb-17-1734551-g003.jpg Figure 3. Monthly incidence of infections and general trend. Monthly incidence of urinary tract infe https://www.frontiersin.org/files/Articles/1734551/fmicb-17-1734551-HTML/image_m/fmicb-17-1734551-g004.jpg Figure 4. HAUTIs by medical service. Horizontal bar chart showing the distribution of patients by me https://www.frontiersin.org/files/Articles/1734551/fmicb-17-1734551-HTML/image_m/fmicb-17-1734551-t001.jpg Table 1. Etiologic agents. https://www.frontiersin.org/files/Articles/1734551/fmicb-17-1734551-HTML/image_m/fmicb-17-1734551-g005.jpg Figure 5. Antimicrobial resistance heatmap. Heatmap illustrating antimicrobial susceptibility profil https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1643517/full https://www.frontiersin.org/files/Articles/1643517/fneur-16-1643517-HTML/image_m/fneur-16-1643517-g001.jpg Figure 1. A flowchart of the patients. https://www.frontiersin.org/files/Articles/1643517/fneur-16-1643517-HTML/image_m/fneur-16-1643517-t001.jpg Table 1. Baseline characteristics of the patients aged ≥65 years versus <65 years, stratified accord https://www.frontiersin.org/files/Articles/1643517/fneur-16-1643517-HTML/image_m/fneur-16-1643517-g002.jpg Figure 2. Probability curves for excellent functional outcomes at 90 days, stratified by treatment g https://www.frontiersin.org/files/Articles/1643517/fneur-16-1643517-HTML/image_m/fneur-16-1643517-t002.jpg Table 2. Association of clopidogrel plus aspirin versus aspirin alone with clinical outcomes in the https://www.frontiersin.org/files/Articles/1643517/fneur-16-1643517-HTML/image_m/fneur-16-1643517-g003.jpg Figure 3. Distribution (in percentage) of modified Rankin Scale (mRS) scores at 90 days in the clopi https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1769889/full https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g001.jpg Figure 1. Biological characteristics of L. rhamnosus CIQ249. (A) Flow cytometry analysis showing the https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g002.jpg Figure 2. Antimicrobial activity of the CIQ249-free culture supernatant in vitro. (A) Inhibitory zon https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g003.jpg Figure 3. Protective efficacy of L. rhamnosus CIQ249 against pathogens infection in mice. (A) Surviv https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g004.jpg Figure 4. L. rhamnosus CIQ249 preserves tight junction integrity in porcine intestinal epithelial ce https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g005.jpg Figure 5. L. rhamnosus CIQ249 regulates the expression of tight junction proteins. mRNA expression l https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g006.jpg Figure 6. L. rhamnosus CIQ249 modulates systemic and mucosal cytokine production. Levels of IFN-γ, I https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g007.jpg Figure 7. L. rhamnosus CIQ249 enhances mucosal humoral immunity and immune cell activation. (A) Seru https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g008.jpg Figure 8. L. rhamnosus CIQ249 promotes B cell differentiation and IgA secretion. (A) Flow cytometry https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g009.jpg Figure 9. Transcriptomic analysis of L. rhamnosus CIQ249-treated PIEC. (A) Venn diagram illustrating https://www.frontiersin.org/files/Articles/1769889/fcimb-16-1769889-HTML/image_m/fcimb-16-1769889-g010.jpg Figure 10. Schematic diagram of the mechanism by which L. rhamnosus CIQ249 improves intestinal mucos https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1793884/full https://www.frontiersin.org/files/Articles/1793884/fsurg-13-1793884-HTML/image_m/fsurg-13-1793884-g001.jpg Figure 1. Preoperative design and marking. (A) Frontal view of the abdomen illustrating the planned https://www.frontiersin.org/files/Articles/1793884/fsurg-13-1793884-HTML/image_m/fsurg-13-1793884-g002.jpg Figure 2. Intraoperative view of extensive liposuction until the subcutaneous adipose layer was sign https://www.frontiersin.org/files/Articles/1793884/fsurg-13-1793884-HTML/image_m/fsurg-13-1793884-g003.jpg Figure 3. Intraoperative procedures of abdominoplasty. (A) Oval umbilical design under skin tension, https://www.frontiersin.org/files/Articles/1793884/fsurg-13-1793884-HTML/image_m/fsurg-13-1793884-g004.jpg Figure 4. A 31-year-old female with two pregnancies underwent abdominoplasty with liposuction. (A–C) https://www.frontiersin.org/files/Articles/1793884/fsurg-13-1793884-HTML/image_m/fsurg-13-1793884-g005.jpg Figure 5. A 40-year-old female with one pregnancy for abdominal wall reconstruction. (A–C) preoperat https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1657274/full https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g001.jpg Figure 1. Flow diagram. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-t001.jpg Table 1. Basic study information. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g002.jpg Figure 2. Risk of bias graph. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g003.jpg Figure 3. Risk of bias summary. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g004.jpg Figure 4. Forest plot of efficacy rate. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g005.jpg Figure 5. Forest plot of adverse reactions. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g006.jpg Figure 6. Forest plot of SUA. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g007.jpg Figure 7. Forest plot of UACR. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g008.jpg Figure 8. Forest plot of Scr. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g009.jpg Figure 9. Funnel plot of eGFR. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g010.jpg Figure 10. Funnel plot of BUN. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g011.jpg Figure 11. Funnel plot of efficacy rate. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g012.jpg Figure 12. Forest plot of SUA. https://www.frontiersin.org/files/Articles/1657274/fmed-12-1657274-HTML/image_m/fmed-12-1657274-g013.jpg Figure 13. Funnel plot of Scr. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1748343/full https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g001.jpg Figure 1. Experimental design schematic. (A) Inclusion criteria: 60 male Wistar rats (90 days, ∼330  https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g002.jpg Figure 2. Micro-CT images (transaxial/coronal views) of 8 mm calvarial defects at 14 days post-surge https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g003.jpg Figure 3. Micro-CT images (transaxial/coronal views) of 8 mm calvarial defects at 42 days post-surge https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g004.jpg Figure 4. Hematoxylin-eosin (H&E) staining of 8 mm calvarial defects. 14 and 42 days post-surgery (4 https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g005.jpg Figure 5. Masson’s trichrome (MT) and picrosirius red (PRS) staining under polarized light (20×) of https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g006.jpg Figure 6. Immunohistochemistry for VEGF (top) and OCN (bottom) in 8 mm calvarial defects at 14/42 da https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g007.jpg Figure 7. Immunohistochemistry for TRAP, BMP2, and BMP4 in 8 mm calvarial defects at 14/42 days (20× https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-t001.jpg Table 1. Percentage (%) of new bone tissue formed in the 14-day experimental period. https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g008.jpg Figure 8. Means and standard deviations of the percentage of newly formed bone tissue after a 14-day https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-t002.jpg Table 2. Means and standard deviations of the percentage (%) of new bone tissue formed in the experi https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g009.jpg Figure 9. Means and standard deviations of the percentage of newly formed bone tissue after a 42-day https://www.frontiersin.org/files/Articles/1748343/fbioe-13-1748343-HTML/image_m/fbioe-13-1748343-g010.jpg Figure 10. Temporal evolution of new bone formation (mean ± SD) across groups at 14 vs. 42 days. Dif https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1797260/full https://www.frontiersin.org/files/Articles/1797260/fcvm-13-1797260-HTML/image_m/fcvm-13-1797260-g001.jpg Figure 1. Flowchart of surgical modality selection. Preoperative assessment: anatomical assessment ( https://www.frontiersin.org/files/Articles/1797260/fcvm-13-1797260-HTML/image_m/fcvm-13-1797260-t001.jpg Table 1. Baseline data and modality distribution of patients. https://www.frontiersin.org/files/Articles/1797260/fcvm-13-1797260-HTML/image_m/fcvm-13-1797260-t002.jpg Table 2. Surgical and early postoperative indicators of different modalities. https://www.frontiersin.org/files/Articles/1797260/fcvm-13-1797260-HTML/image_m/fcvm-13-1797260-t003.jpg Table 3. Short-to-medium-term follow-up results of the three groups. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1797104/full https://www.frontiersin.org/files/Articles/1797104/fcimb-16-1797104-HTML/image_m/fcimb-16-1797104-t001.jpg Table 1. │ Major barriers to antifungal innovation by category. https://www.frontiersin.org/files/Articles/1797104/fcimb-16-1797104-HTML/image_m/fcimb-16-1797104-g001.jpg Figure 1. Conceptual framework illustrating the drivers and consequences of the antifungal innovatio https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1780669/full https://www.frontiersin.org/files/Articles/1780669/fmed-13-1780669-HTML/image_m/fmed-13-1780669-g001.jpg Figure 1. HIF-2α iron metabolism regulation diagram This schematic diagram summarizes the central ro https://www.frontiersin.org/files/Articles/1780669/fmed-13-1780669-HTML/image_m/fmed-13-1780669-g002.jpg Figure 2. Mechanism of action of EPO in erythropoiesis Schematic diagram: Classical and non-classica https://www.frontiersin.org/files/Articles/1780669/fmed-13-1780669-HTML/image_m/fmed-13-1780669-t001.jpg Table 1. Major branches of the EPO signaling pathway and their roles in erythropoiesis. https://www.frontiersin.org/files/Articles/1780669/fmed-13-1780669-HTML/image_m/fmed-13-1780669-t002.jpg Table 2. Core timeline of HIF subtype dynamics during hypoxia. https://www.frontiersin.org/files/Articles/1780669/fmed-13-1780669-HTML/image_m/fmed-13-1780669-t003.jpg Table 3. Comparative expression profiles of HIF subtypes in acute and chronic hypoxia. https://www.frontiersin.org/files/Articles/1780669/fmed-13-1780669-HTML/image_m/fmed-13-1780669-t004.jpg Table 4. Specific dysregulation characteristics of the HIF-2α-EPO–Hb axis. https://www.frontiersin.org/files/Articles/1780669/fmed-13-1780669-HTML/image_m/fmed-13-1780669-g003.jpg Figure 3. Mechanism Diagram of HIF Pathway Regulators. Summarizes two regulatory strategies targetin https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1426485/full https://www.frontiersin.org/files/Articles/1426485/fnut-12-1426485-HTML-r1/image_m/fnut-12-1426485-g001.jpg Figure 1. Flow chart of the inclusion and exclusion process. https://www.frontiersin.org/files/Articles/1426485/fnut-12-1426485-HTML-r1/image_m/fnut-12-1426485-t001.jpg Table 1. Characteristics of participated family by child EBPs levels (n = 1,126). https://www.frontiersin.org/files/Articles/1426485/fnut-12-1426485-HTML-r1/image_m/fnut-12-1426485-g002.jpg Figure 2. Proportion of children with healthy dietary habits. https://www.frontiersin.org/files/Articles/1426485/fnut-12-1426485-HTML-r1/image_m/fnut-12-1426485-g003.jpg Figure 3. Multilevel logistic regression analysis of dietary habits associated with self-concept (A) https://www.frontiersin.org/files/Articles/1426485/fnut-12-1426485-HTML-r1/image_m/fnut-12-1426485-t002.jpg Table 2. Bivariate correlations among overall healthy dietary habits, self-concept and EBPs. https://www.frontiersin.org/files/Articles/1426485/fnut-12-1426485-HTML-r1/image_m/fnut-12-1426485-g004.jpg Figure 4. Mediation analysis of the role of self-concept in the relationship between dietary habits https://www.frontiersin.org/files/Articles/1426485/fnut-12-1426485-HTML-r1/image_m/fnut-12-1426485-t003.jpg Table 3. Mediation analysis of relationship between healthy dietary habits, self-concept and EBPs. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1682636/full https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-g001.jpg Figure 1. The inclusion and exclusion of the criterion. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-t001.jpg Table 1. Baseline demographic characteristics of the participants. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-t002.jpg Table 2. Association between TyG and ABSI, and ASCVD. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-g002.jpg Figure 2. The dose-response association between TyG (A) and ABSI (B) and ASCVD. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-t003.jpg Table 3. Association of the combined effect of TyG and ABSI with ASCVD risk. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-g003.jpg Figure 3. Association (A) and proportion (B) between the combined effect of TyG and ABSI and ASCVD. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-t004.jpg Table 4. Association between TyG-ABSI and ASCVD. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-g004.jpg Figure 4. The linear dose-response association between TyG-ABSI and ASCVD. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-t005.jpg Table 5. Association between TyG-ABSI and ASCVD in various subgroups. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-g005.jpg Figure 5. The diagnostic performance of related TyG indices for the ASCVD. (A) The ROC curve for the https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-t006.jpg Table 6. Discrimination performance of the ASCVD diagnosis. https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-g006.jpg Figure 6. Mediation analysis was used to show the mediation effects of CRP, SIRI, NLR, MLR, GGT, and https://www.frontiersin.org/files/Articles/1682636/fnut-13-1682636-HTML/image_m/fnut-13-1682636-t007.jpg Table 7. Association of TyG-ABSI with ASCVD in various levels of four dietary patterns https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1767637/full https://www.frontiersin.org/files/Articles/1767637/fpubh-14-1767637-HTML/image_m/fpubh-14-1767637-g001.jpg Figure 1. Flowchart of the study. https://www.frontiersin.org/files/Articles/1767637/fpubh-14-1767637-HTML/image_m/fpubh-14-1767637-t001.jpg Table 1. Assessment tools of YouTube videos for Nutrition and Pediatric Cancers. https://www.frontiersin.org/files/Articles/1767637/fpubh-14-1767637-HTML/image_m/fpubh-14-1767637-t002.jpg Table 2. Characteristics and usefulness of YouTube videos on nutrition in pediatric cancer. https://www.frontiersin.org/files/Articles/1767637/fpubh-14-1767637-HTML/image_m/fpubh-14-1767637-t003.jpg Table 3. Quality, reliability, and content-related characteristics of YouTube videos addressing nutr https://www.frontiersin.org/files/Articles/1767637/fpubh-14-1767637-HTML/image_m/fpubh-14-1767637-g002.jpg Figure 2. Video counts of each year (A); distribution of videos based on original countries (B). https://www.frontiersin.org/files/Articles/1767637/fpubh-14-1767637-HTML/image_m/fpubh-14-1767637-t004.jpg Table 4. Comparative characteristics of useful and unuseful YouTube videos on nutrition and pediatri https://www.frontiersin.org/files/Articles/1767637/fpubh-14-1767637-HTML/image_m/fpubh-14-1767637-t005.jpg Table 5. Correlation analysis among video characteristics and quality scores of YouTube videos on nu https://www.frontiersin.org/files/Articles/1767637/fpubh-14-1767637-HTML/image_m/fpubh-14-1767637-t006.jpg Table 6. Receiver operating characteristics (ROC) curve analyses for evaluation scores to discrimina https://www.frontiersin.org/files/Articles/1767637/fpubh-14-1767637-HTML/image_m/fpubh-14-1767637-g003.jpg Figure 3. Receiver operating characteristic (ROC) curve for evaluation scores to discriminate highqu https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1724041/full https://www.frontiersin.org/files/Articles/1724041/fnut-12-1724041-HTML/image_m/fnut-12-1724041-g001.jpg Figure 1. Flowchart of participant selection. https://www.frontiersin.org/files/Articles/1724041/fnut-12-1724041-HTML/image_m/fnut-12-1724041-t001.jpg Table 1. Demographic and lifestyle characteristics of 276,209 participants by coffee and tea intake https://www.frontiersin.org/files/Articles/1724041/fnut-12-1724041-HTML/image_m/fnut-12-1724041-t002.jpg Table 2. HRs and 95% CIs for lung cancer by coffee and tea intake among the 276209 persons in the UK https://www.frontiersin.org/files/Articles/1724041/fnut-12-1724041-HTML/image_m/fnut-12-1724041-g002.jpg Figure 2. Concentration response between tea and coffee intake and the risk of lung cancer. https://www.frontiersin.org/files/Articles/1724041/fnut-12-1724041-HTML/image_m/fnut-12-1724041-t003.jpg Table 3. Stratified analysis HRs and 95% CIs for lung cancer by Coffee and Tea intake among 498,043 https://www.frontiersin.org/files/Articles/1724041/fnut-12-1724041-HTML/image_m/fnut-12-1724041-t004.jpg Table 4. Sensitivity analysis HRs and 95% CIs for lung cancer by coffee and tea intake among the 276 https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1773531/full https://www.frontiersin.org/files/Articles/1773531/fnut-13-1773531-HTML/image_m/fnut-13-1773531-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1773531/fnut-13-1773531-HTML/image_m/fnut-13-1773531-g001.jpg Figure 1. Flowchart of participants in this study. https://www.frontiersin.org/files/Articles/1773531/fnut-13-1773531-HTML/image_m/fnut-13-1773531-t001.jpg Table 1. Characteristics of participants by sleep pattern in the baseline. https://www.frontiersin.org/files/Articles/1773531/fnut-13-1773531-HTML/image_m/fnut-13-1773531-t002.jpg Table 2. Logistic regression between table salt use and unhealthy sleep patterns. https://www.frontiersin.org/files/Articles/1773531/fnut-13-1773531-HTML/image_m/fnut-13-1773531-t003.jpg Table 3. Logistic regression between table salt use and depressive symptoms. https://www.frontiersin.org/files/Articles/1773531/fnut-13-1773531-HTML/image_m/fnut-13-1773531-g002.jpg Figure 2. Mediation analysis of depressive symptoms in the association between the frequency of tabl https://www.frontiersin.org/files/Articles/1773531/fnut-13-1773531-HTML/image_m/fnut-13-1773531-g003.jpg Figure 3. Subgroup analyses of the association between the frequency of table salt use and unhealthy https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1709232/full https://www.frontiersin.org/files/Articles/1709232/fmed-13-1709232-HTML-r1/image_m/fmed-13-1709232-g001.jpg Figure 1. Flowchart of study design. https://www.frontiersin.org/files/Articles/1709232/fmed-13-1709232-HTML-r1/image_m/fmed-13-1709232-t001.jpg Table 1. Baseline characteristics of participants (n = 90). https://www.frontiersin.org/files/Articles/1709232/fmed-13-1709232-HTML-r1/image_m/fmed-13-1709232-t002.jpg Table 2. Primary outcome by group in mechanical neck pain. https://www.frontiersin.org/files/Articles/1709232/fmed-13-1709232-HTML-r1/image_m/fmed-13-1709232-t003.jpg Table 3. Secondary outcomes by group in mechanical neck pain. https://www.frontiersin.org/files/Articles/1709232/fmed-13-1709232-HTML-r1/image_m/fmed-13-1709232-g002.jpg Figure 2. Changes in VAS and NDI scores. (A, B) depict the longitudinal changes in visual analog sca https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1767310/full https://www.frontiersin.org/files/Articles/1767310/fmed-13-1767310-HTML/image_m/fmed-13-1767310-g001.jpg Figure 1. Study flow chart. https://www.frontiersin.org/files/Articles/1767310/fmed-13-1767310-HTML/image_m/fmed-13-1767310-t001.jpg Table 1. Comparison of baseline characteristics between the DR and NDR groups. https://www.frontiersin.org/files/Articles/1767310/fmed-13-1767310-HTML/image_m/fmed-13-1767310-g002.jpg Figure 2. LASSO regression analysis was used to screen the influencing factors of DR in T2DM patient https://www.frontiersin.org/files/Articles/1767310/fmed-13-1767310-HTML/image_m/fmed-13-1767310-t002.jpg Table 2. Use variance inflation factors (VIFs) to assess the multicollinearity among significant var https://www.frontiersin.org/files/Articles/1767310/fmed-13-1767310-HTML/image_m/fmed-13-1767310-t003.jpg Table 3. Identification of risk factors for DR in patients with T2DM using logistic regression analy https://www.frontiersin.org/files/Articles/1767310/fmed-13-1767310-HTML/image_m/fmed-13-1767310-g003.jpg Figure 3. A nomogram for assessing DR risk in T2DM. https://www.frontiersin.org/files/Articles/1767310/fmed-13-1767310-HTML/image_m/fmed-13-1767310-g004.jpg Figure 4. Validation of the nomogram model: (A) Calibration plot, (B) ROC curve, (C) DCA curve. https://www.frontiersin.org/files/Articles/1767310/fmed-13-1767310-HTML/image_m/fmed-13-1767310-t004.jpg Table 4. Comparison of baseline characteristics between the NPDR and PDR groups. https://www.frontiersin.org/files/Articles/1767310/fmed-13-1767310-HTML/image_m/fmed-13-1767310-t005.jpg Table 5. Univariate and Firth’s penalized logistic regression analysis of risk factors for PDR in T2 https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1629436/full https://www.frontiersin.org/files/Articles/1629436/fmed-12-1629436-HTML/image_m/fmed-12-1629436-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/files/Articles/1629436/fmed-12-1629436-HTML/image_m/fmed-12-1629436-t002.jpg Table 2. Grouped CIM therapies by category and frequency. https://www.frontiersin.org/files/Articles/1629436/fmed-12-1629436-HTML/image_m/fmed-12-1629436-t003.jpg Table 3. Reported experiences with selected CIM therapies (n = 5–9 per method). https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1642749/full https://www.frontiersin.org/files/Articles/1642749/fnut-12-1642749-HTML/image_m/fnut-12-1642749-t001.jpg Table 1. Baseline characteristics of participants according to LE8 score (n = 242,278)a. https://www.frontiersin.org/files/Articles/1642749/fnut-12-1642749-HTML/image_m/fnut-12-1642749-g001.jpg Figure 1. Associations of the risk of (A) hip and/or knee osteoarthritis, (B) hip osteoarthritis, an https://www.frontiersin.org/files/Articles/1642749/fnut-12-1642749-HTML/image_m/fnut-12-1642749-t002.jpg Table 2. Associations between LE8 score and the risk of OA (n = 242,278)a. https://www.frontiersin.org/files/Articles/1642749/fnut-12-1642749-HTML/image_m/fnut-12-1642749-t003.jpg Table 3. Associations between LE8 score and the risks of OA of hip and knee according to GRS (n = 24 https://www.frontiersin.org/files/Articles/1642749/fnut-12-1642749-HTML/image_m/fnut-12-1642749-g002.jpg Figure 2. Joint associations of Life's Essential 8 score with the risks of (A) hip and/or knee osteo https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1638127/full https://www.frontiersin.org/files/Articles/1638127/fmed-12-1638127-HTML/image_m/fmed-12-1638127-t001.jpg Table 1. Quantity of blood drained and the duration of bleeding on the 1st day and 15th day. https://www.frontiersin.org/files/Articles/1638127/fmed-12-1638127-HTML/image_m/fmed-12-1638127-t002.jpg Table 2. Assessment of the objective and subjective parameters before and after treatment in the rec https://www.frontiersin.org/files/Articles/1638127/fmed-12-1638127-HTML/image_m/fmed-12-1638127-t003.jpg Table 3. Paired sample statistics for IL-6. https://www.frontiersin.org/files/Articles/1638127/fmed-12-1638127-HTML/image_m/fmed-12-1638127-t004.jpg Table 4. Effect of treatment on the objective parameter IL-6. https://www.frontiersin.org/files/Articles/1638127/fmed-12-1638127-HTML/image_m/fmed-12-1638127-t005.jpg Table 5. Wilcoxon signed-rank test for the subjective parameters. https://www.frontiersin.org/files/Articles/1638127/fmed-12-1638127-HTML/image_m/fmed-12-1638127-t006.jpg Table 6. Effect of treatment on the subjective parameters. https://www.frontiersin.org/files/Articles/1638127/fmed-12-1638127-HTML/image_m/fmed-12-1638127-g001.jpg Figure 1. (a) During the procedure in a patient, and (b) blood measured by collecting in a glass bea https://www.frontiersin.org/files/Articles/1638127/fmed-12-1638127-HTML/image_m/fmed-12-1638127-g002.jpg Figure 2. (a) During the first sitting (day 1) of the procedure and (b) during the second sitting of https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1674464/full https://www.frontiersin.org/files/Articles/1674464/fmed-12-1674464-HTML/image_m/fmed-12-1674464-g001.jpg Figure 1. Demonstration diagram of Chuzhen therapy. (a) Chuzhen therapy involves the use of four blu https://www.frontiersin.org/files/Articles/1674464/fmed-12-1674464-HTML/image_m/fmed-12-1674464-g002.jpg Figure 2. Lumbar spine CT scan at the initial visit (2021-08-29). (a) On the cross-section view, the https://www.frontiersin.org/files/Articles/1674464/fmed-12-1674464-HTML/image_m/fmed-12-1674464-g003.jpg Figure 3. The timeline of the patient's treatment. https://www.frontiersin.org/files/Articles/1674464/fmed-12-1674464-HTML/image_m/fmed-12-1674464-g004.jpg Figure 4. Lumbar spine CT and MRI scans performed at the last visit (2024-07-09). (a) In the CT cros https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1733925/full https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-g001.jpg Figure 1. Flow chart of the study population. https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-g002.jpg Figure 2. Schematic of left atrial and left ventricular strain and volume analysis. (A,B) Left atria https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-g003.jpg Figure 3. Measurement of mitral annular plane systolic excursion (MAPSE) and papillary muscle signal https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-t002.jpg Table 2. CMR parameters. https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-g004.jpg Figure 4. Diagnostic performance of MAPSE (A), left ventricular strain (B), and left atrial strain ( https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-g005.jpg Figure 5. Prognostic value of MAPSE. Optimal lateral MAPSE (A) and optimal septal MAPSE (B) cutoffs https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-t003.jpg Table 3. Univariate and multivariate Cox regression analyses used to predict MACE. https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-g006.jpg Figure 6. Predictive value of different Cox regression models for adverse cardiovascular events in p https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-t004.jpg Table 4. Correlation between MRI parameters and MAPSE. https://www.frontiersin.org/files/Articles/1733925/fcvm-13-1733925-HTML/image_m/fcvm-13-1733925-t005.jpg Table 5. Intra- and inter-observer variability of MRI parameters. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1806770/full https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-g001.jpg Figure 1. Integrated theoretical framework guiding the study hypotheses. https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-t001.jpg Table 1. Demographic characteristics of the sample. https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-t002.jpg Table 2. Reliability and validity results. https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-t003.jpg Table 3. Demographic profile of the sample. https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-t004.jpg Table 4. Exploratory comparisons of PSQI scores across demographic and behavioral subgroups. https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-g002.jpg Figure 2. Comparison of sleep quality across demographic categories including gender, academic grade https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-g003.jpg Figure 3. Pearson correlation matrix illustrating the associations among sleep quality components, s https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-g004.jpg Figure 4. Network structure of sleep quality, short video addiction and FoxMO. https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-t005.jpg Table 5. Standardized direct, indirect, and total effects of the four dimensions of short-video addi https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-g005.jpg Figure 5. Standardized path coefficients for the mediation models illustrating the relationships bet https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-g006.jpg Figure 6. Strength centrality and bridge strength of nodes within the symptom network comprising sho https://www.frontiersin.org/files/Articles/1806770/fpubh-14-1806770-HTML/image_m/fpubh-14-1806770-g007.jpg Figure 7. Stability of Expected Influence and bridge strength within the short video addiction, fear https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1749018/full https://www.frontiersin.org/files/Articles/1749018/fpubh-14-1749018-HTML/image_m/fpubh-14-1749018-t001.jpg Table 1. Neuropsychiatric family history, substance use, pregnancy violence, and treatment at admiss https://www.frontiersin.org/files/Articles/1749018/fpubh-14-1749018-HTML/image_m/fpubh-14-1749018-g001.jpg Figure 1. This figure illustrates the hypothesized mediation model, where children's post-traumatic https://www.frontiersin.org/files/Articles/1749018/fpubh-14-1749018-HTML/image_m/fpubh-14-1749018-t002.jpg Table 2. Direct effects between variables in the hypothesized mediation model. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1696983/full https://www.frontiersin.org/files/Articles/1696983/fpsyt-16-1696983-HTML/image_m/fpsyt-16-1696983-t001.jpg Table 1. Demographic and clinical characteristics of participants with ASD. https://www.frontiersin.org/files/Articles/1696983/fpsyt-16-1696983-HTML/image_m/fpsyt-16-1696983-t002.jpg Table 2. Pearson correlations among sensory processing, executive function, and social responsivenes https://www.frontiersin.org/files/Articles/1696983/fpsyt-16-1696983-HTML/image_m/fpsyt-16-1696983-g001.jpg Figure 1. Mediation models illustrating the relationships among sensory processing, executive functi https://www.frontiersin.org/files/Articles/1696983/fpsyt-16-1696983-HTML/image_m/fpsyt-16-1696983-t003.jpg Table 3. Mediation analysis results for executive function mediating sensory-processing–social-respo https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1605380/full https://www.frontiersin.org/files/Articles/1605380/fimmu-16-1605380-HTML/image_m/fimmu-16-1605380-t001.jpg Table 1. Comparison of clinical characteristics between recompensated and persistently decompensated https://www.frontiersin.org/files/Articles/1605380/fimmu-16-1605380-HTML/image_m/fimmu-16-1605380-t002.jpg Table 2. Factors associated with a higher probability of recompensation. https://www.frontiersin.org/files/Articles/1605380/fimmu-16-1605380-HTML/image_m/fimmu-16-1605380-t003.jpg Table 3. Subgroup analysis of PNI as a factor influencing recompensation in patients with decompensa https://www.frontiersin.org/files/Articles/1605380/fimmu-16-1605380-HTML/image_m/fimmu-16-1605380-t004.jpg Table 4. Subgroup analysis of PNI as a factor influencing recompensation in patients with decompensa https://www.frontiersin.org/files/Articles/1605380/fimmu-16-1605380-HTML/image_m/fimmu-16-1605380-t005.jpg Table 5. Sensitivity analysis of PNI as a factor influencing recompensation in patients with decompe https://www.frontiersin.org/files/Articles/1605380/fimmu-16-1605380-HTML/image_m/fimmu-16-1605380-t006.jpg Table 6. Relationship between PNI and recompensation. https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2025.1675003/full https://www.frontiersin.org/files/Articles/1675003/fncel-19-1675003-HTML/image_m/fncel-19-1675003-g001.jpg Figure 1. Effect of LPS and BDNF on hippocampal Bdnf, Sst, Cort, and Npy mRNA and protein levels. (A https://www.frontiersin.org/files/Articles/1675003/fncel-19-1675003-HTML/image_m/fncel-19-1675003-g002.jpg Figure 2. Effect of LPS and BDNF on hippocampal IL-1beta protein, Gfap and Iba1 mRNA levels. (A) LPS https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1694826/full https://www.frontiersin.org/files/Articles/1694826/fneur-16-1694826-HTML/image_m/fneur-16-1694826-t001.jpg Table 1. Demographic and clinical characteristics. https://www.frontiersin.org/files/Articles/1694826/fneur-16-1694826-HTML/image_m/fneur-16-1694826-t002.jpg Table 2. Regional activations for the second-level contrast for AUD only and SUD only subgroups – re https://www.frontiersin.org/files/Articles/1694826/fneur-16-1694826-HTML/image_m/fneur-16-1694826-t003.jpg Table 3. Regional activations for the second-level contrast for AUD and SUD combined – group compari https://www.frontiersin.org/files/Articles/1694826/fneur-16-1694826-HTML/image_m/fneur-16-1694826-t004.jpg Table 4. Regional activations for the second-level contrast for AUD only and SUD only subgroups – gr https://www.frontiersin.org/files/Articles/1694826/fneur-16-1694826-HTML/image_m/fneur-16-1694826-g001.jpg Figure 1. Brain activation in association with impaired illness awareness compared to intact illness https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1797074/full https://www.frontiersin.org/files/Articles/1797074/fcell-14-1797074-HTML/image_m/fcell-14-1797074-g001.jpg Figure 1. Physiological and behavioral parameters in CFS mice treated with 1DOE and 3DOE. (A) Body w https://www.frontiersin.org/files/Articles/1797074/fcell-14-1797074-HTML/image_m/fcell-14-1797074-g002.jpg Figure 2. Plasma biochemical parameters and histopathological changes. (A–C) Plasma ALT, AST, LDH, a https://www.frontiersin.org/files/Articles/1797074/fcell-14-1797074-HTML/image_m/fcell-14-1797074-g003.jpg Figure 3. Plasma metabolomics analysis of CFS mice. (A, B) PCA and PLS-DA score plots. (C) Heatmap o https://www.frontiersin.org/files/Articles/1797074/fcell-14-1797074-HTML/image_m/fcell-14-1797074-g004.jpg Figure 4. Comparative metabolite profiling of 1-year and 3-year Dendrobium officinale. (A, B) PCA an https://www.frontiersin.org/files/Articles/1797074/fcell-14-1797074-HTML/image_m/fcell-14-1797074-g005.jpg Figure 5. Analysis of Characteristic Components in Dendrobium officinale for Treating CFS. (A) Corre https://www.frontiersin.org/files/Articles/1797074/fcell-14-1797074-HTML/image_m/fcell-14-1797074-g006.jpg Figure 6. Proposed pharmacological mechanism of Dendrobium officinale against CFS. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1770539/full https://www.frontiersin.org/files/Articles/1770539/fmed-13-1770539-HTML-r1/image_m/fmed-13-1770539-g001.jpg Figure 1. High-frequency ultrasonographic images of inverted Meckel’s diverticulum. (A) White arrow https://www.frontiersin.org/files/Articles/1770539/fmed-13-1770539-HTML-r1/image_m/fmed-13-1770539-g002.jpg Figure 2. Intraoperative images of inverted Meckel’s diverticulum. (A) Meckel’s diverticulum (MD) in https://www.frontiersin.org/files/Articles/1770539/fmed-13-1770539-HTML-r1/image_m/fmed-13-1770539-g003.jpg Figure 3. Histopathological sections of Meckel’s diverticulum. (A) Heterotopic gastric mucosa tissue https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1806275/full https://www.frontiersin.org/files/Articles/1806275/fmed-13-1806275-HTML/image_m/fmed-13-1806275-t001.jpg Table 1. Comparison of baseline characteristics between patients with normal and abnormal ALT levels https://www.frontiersin.org/files/Articles/1806275/fmed-13-1806275-HTML/image_m/fmed-13-1806275-t002.jpg Table 2. Comparison of laboratory parameters between patients with normal and abnormal ALT levels. https://www.frontiersin.org/files/Articles/1806275/fmed-13-1806275-HTML/image_m/fmed-13-1806275-t003.jpg Table 3. Comparative analysis of demographic and clinical features across NAFLD severity subgroups. https://www.frontiersin.org/files/Articles/1806275/fmed-13-1806275-HTML/image_m/fmed-13-1806275-t004.jpg Table 4. Univariate and multivariate analyses of hepatic inflammation severity, liver fibrosis stage https://www.frontiersin.org/files/Articles/1806275/fmed-13-1806275-HTML/image_m/fmed-13-1806275-g001.jpg Figure 1. ROC curves of the predictive models for significant inflammation (A), fibrosis (B), and st https://www.frontiersin.org/files/Articles/1806275/fmed-13-1806275-HTML/image_m/fmed-13-1806275-t005.jpg Table 5. Comparison between non-significant and significant MAFLD groups. https://www.frontiersin.org/files/Articles/1806275/fmed-13-1806275-HTML/image_m/fmed-13-1806275-t006.jpg Table 6. Univariate and multivariate logistic regression analysis for the significant MAFLD group. https://www.frontiersin.org/files/Articles/1806275/fmed-13-1806275-HTML/image_m/fmed-13-1806275-g002.jpg Figure 2. ROC curve of the predictive model for significant MAFLD. https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2026.1745197/full https://www.frontiersin.org/files/Articles/1745197/frobt-13-1745197-HTML-r2/image_m/frobt-13-1745197-g001.jpg Figure 1. Illustration of ROS4HC framework components. With ROS4HC, we connect under the same umbrel https://www.frontiersin.org/files/Articles/1745197/frobt-13-1745197-HTML-r2/image_m/frobt-13-1745197-g002.jpg Figure 2. Conceptual diagram of our system, multiple sensors can be brought into the ROS2 network us https://www.frontiersin.org/files/Articles/1745197/frobt-13-1745197-HTML-r2/image_m/frobt-13-1745197-t001.jpg Table 1. Comparison of the proposed framework and the currently available solutions. https://www.frontiersin.org/files/Articles/1745197/frobt-13-1745197-HTML-r2/image_m/frobt-13-1745197-t002.jpg Table 2. Sensor overview and integration in ROS4HC. https://www.frontiersin.org/files/Articles/1745197/frobt-13-1745197-HTML-r2/image_m/frobt-13-1745197-g003.jpg Figure 3. Heart rate–controlled wheelchair traversal. (a) Experimental setup. A wearable HR sensor a https://www.frontiersin.org/files/Articles/1745197/frobt-13-1745197-HTML-r2/image_m/frobt-13-1745197-g004.jpg Figure 4. AI-enabled personal trainer. (a) Experimental setup. of the AI-enabled personal trainer. T https://www.frontiersin.org/files/Articles/1745197/frobt-13-1745197-HTML-r2/image_m/frobt-13-1745197-g005.jpg Figure 5. Nocturnal monitoring and intervention. (a) Experimental setup of the Somnomat Care robotic https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1639704/full https://www.frontiersin.org/files/Articles/1639704/fmed-12-1639704-HTML-r1/image_m/fmed-12-1639704-g001.jpg Figure 1. Flow diagram describing selection of trials for meta-analysis. https://www.frontiersin.org/files/Articles/1639704/fmed-12-1639704-HTML-r1/image_m/fmed-12-1639704-t001.jpg Table 1. Quality assessment for cohort studies. https://www.frontiersin.org/files/Articles/1639704/fmed-12-1639704-HTML-r1/image_m/fmed-12-1639704-g002.jpg Figure 2. Pooled risk ratios (95% CI) for diabetic retinopathy comparing GLP-1 receptor agonists wit https://www.frontiersin.org/files/Articles/1639704/fmed-12-1639704-HTML-r1/image_m/fmed-12-1639704-g003.jpg Figure 3. Subgroup analysis comparing randomized controlled trials and observational studies. https://www.frontiersin.org/files/Articles/1639704/fmed-12-1639704-HTML-r1/image_m/fmed-12-1639704-g004.jpg Figure 4. Subgroup analysis comparing comparator groups (placebo vs. other antidiabetic drugs). https://www.frontiersin.org/files/Articles/1639704/fmed-12-1639704-HTML-r1/image_m/fmed-12-1639704-g005.jpg Figure 5. Funnel plot of the studies assessing GLP-1 use compared to other treatments. https://www.frontiersin.org/files/Articles/1639704/fmed-12-1639704-HTML-r1/image_m/fmed-12-1639704-t002.jpg Table 2. Quality assessment for clinical trials. https://www.frontiersin.org/files/Articles/1639704/fmed-12-1639704-HTML-r1/image_m/fmed-12-1639704-g006.jpg Figure 6. Risk of bias graph, presented as % across all included studies. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1768597/full https://www.frontiersin.org/files/Articles/1768597/feduc-11-1768597-HTML/image_m/feduc-11-1768597-g001.jpg Figure 1. Theoretical framework for analyzing the policy agenda of China's “double-qualified” teache https://www.frontiersin.org/files/Articles/1768597/feduc-11-1768597-HTML/image_m/feduc-11-1768597-g002.jpg Figure 2. The evolution of China's “double-qualified” teachers. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1725782/full https://www.frontiersin.org/files/Articles/1725782/fphar-16-1725782-HTML/image_m/fphar-16-1725782-t001.jpg Table 1. Clinical trials overview of the effects of MRAs in patients with HFmrEF/HFpEF or patients w https://www.frontiersin.org/files/Articles/1725782/fphar-16-1725782-HTML/image_m/fphar-16-1725782-t002.jpg Table 2. Planned and ongoing clinical trials with MRAs in HF. https://www.frontiersin.org/files/Articles/1725782/fphar-16-1725782-HTML/image_m/fphar-16-1725782-g001.jpg Figure 1. Mechanisms of MR overactivation leading to cardiac and renal injury and the specific effec https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1658890/full https://www.frontiersin.org/files/Articles/1658890/fmed-12-1658890-HTML/image_m/fmed-12-1658890-g001.jpg Figure 1. mOCT setup with imaging optics. (A) Schematics of the OCT device: FC: 50/50 fiber coupler; https://www.frontiersin.org/files/Articles/1658890/fmed-12-1658890-HTML/image_m/fmed-12-1658890-g002.jpg Figure 2. Dynamic contrast increases the information in mOCT images. FFPE sections stained with H&E https://www.frontiersin.org/files/Articles/1658890/fmed-12-1658890-HTML/image_m/fmed-12-1658890-g003.jpg Figure 3. Inflammation and epithelial alterations in airways. FFPE-section after staining with H&E ( https://www.frontiersin.org/files/Articles/1658890/fmed-12-1658890-HTML/image_m/fmed-12-1658890-g004.jpg Figure 4. Growth pattern of different lung tumors imaged by dmOCT. H&E-stained FFPE-sections (A, D, https://www.frontiersin.org/files/Articles/1658890/fmed-12-1658890-HTML/image_m/fmed-12-1658890-g005.jpg Figure 5. Reduction of acquisition time for dmOCT. (A–D) Human airway, (E–H) lung tumor. Images in A https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1720146/full https://www.frontiersin.org/files/Articles/1720146/fpsyg-17-1720146-HTML/image_m/fpsyg-17-1720146-t001.jpg Table 1. Basic demographics of the study sample. https://www.frontiersin.org/files/Articles/1720146/fpsyg-17-1720146-HTML/image_m/fpsyg-17-1720146-t002.jpg Table 2. The correlation matrix between variables. https://www.frontiersin.org/files/Articles/1720146/fpsyg-17-1720146-HTML/image_m/fpsyg-17-1720146-t003.jpg Table 3. Model fit statistics for the 1- to 5-profile solutions. https://www.frontiersin.org/files/Articles/1720146/fpsyg-17-1720146-HTML/image_m/fpsyg-17-1720146-g001.jpg Figure 1. The four-profile model based on the performance of HPA function. HPA: Hypothalamic–Pituita https://www.frontiersin.org/files/Articles/1720146/fpsyg-17-1720146-HTML/image_m/fpsyg-17-1720146-g002.jpg Figure 2. Differences in Cortisol, CD values, and CC values among different groups. DHEA: Dehydroepi https://www.frontiersin.org/files/Articles/1720146/fpsyg-17-1720146-HTML/image_m/fpsyg-17-1720146-g003.jpg Figure 3. HPA axis adaptation categories across sexes and disadvantaged conditions. HPA: Hypothalami https://www.frontiersin.org/files/Articles/1720146/fpsyg-17-1720146-HTML/image_m/fpsyg-17-1720146-g004.jpg Figure 4. Differences in perceived stress and CDI scores among different groups. CDI: Children’s Dep https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1648010/full https://www.frontiersin.org/files/Articles/1648010/fcvm-12-1648010-HTML-r1/image_m/fcvm-12-1648010-g001.jpg Figure 1. IEMs classification. https://www.frontiersin.org/files/Articles/1648010/fcvm-12-1648010-HTML-r1/image_m/fcvm-12-1648010-g002.jpg Figure 2. Flow diagram of the literature search process. https://www.frontiersin.org/files/Articles/1648010/fcvm-12-1648010-HTML-r1/image_m/fcvm-12-1648010-g003.jpg Figure 3. Cardiac damage pathophysiological mechanisms. PMDs, primary mitochondrial diseases; FAODs, https://www.frontiersin.org/files/Articles/1648010/fcvm-12-1648010-HTML-r1/image_m/fcvm-12-1648010-g004.jpg Figure 4. Cardiovascular diseases in IEMs. GSDs, glicogen storage diseases; MPS, mucopolisaccaridosi https://www.frontiersin.org/files/Articles/1648010/fcvm-12-1648010-HTML-r1/image_m/fcvm-12-1648010-g005.jpg Figure 5. Approach to differential diagnosis. ACAD9, ACAD9D, Acyl-CoA dehydrogenase 9; AVB, atrio-ve https://www.frontiersin.org/files/Articles/1648010/fcvm-12-1648010-HTML-r1/image_m/fcvm-12-1648010-t001.jpg Table 1. Dietary treatments Inborn Errors of Metabolism. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1681381/full https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-g001.jpg Figure 1. Estimation strategy. https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-t001.jpg Table 1. Descriptive statistics. https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-t002.jpg Table 2. Cross sectional dependency. https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-t003.jpg Table 3. Panel unit root test. https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-t004.jpg Table 4. Kao residual cointegration test. https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-t005.jpg Table 5. Johansen Fisher panel cointegration test. https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-t006.jpg Table 6. CS – ARDL estimates. https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-t007.jpg Table 7. Robustness analysis. https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-t008.jpg Table A1. PCA output for intuitional quality index. https://www.frontiersin.org/files/Articles/1681381/fpubh-14-1681381-HTML/image_m/fpubh-14-1681381-t009.jpg Table A2. PCA output for ICT index. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1810357/full https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-g001.jpg Figure 1. PRISMA flow diagram of study selection. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-t001.jpg Table 1. The basic characteristics table of the included literature. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-g002.jpg Figure 2. Meta-analysis forest plot of BMI. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-g003.jpg Figure 3. Meta-analysis forest plot of HDL. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-g004.jpg Figure 4. Meta-analysis forest plot of LDL. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-g005.jpg Figure 5. Meta-analysis forest plot of TC. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-g006.jpg Figure 6. Meta-analysis forest plot of TG. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-g007.jpg Figure 7. Meta-analysis forest plot of IL-6. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-g008.jpg Figure 8. Meta-analysis forest plot of SBP. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-g009.jpg Figure 9. Meta-analysis forest plot of DBP. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-t002.jpg Table 2. subgroup analysis results. https://www.frontiersin.org/files/Articles/1810357/fphys-17-1810357-HTML/image_m/fphys-17-1810357-t003.jpg Table 3. Meta-regression analysis of potential sources of heterogeneity across outcomes. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1668776/full https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-g001.jpg Figure 1. Description of the study design (quasi-experimental design). https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-t001.jpg Table 1. Digital tools and outcome mapping. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-t002.jpg Table 2. Telehealth intervention timeline and rollout phases. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-g002.jpg Figure 2. Telehealth engagement journey. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-t003.jpg Table 3. Registered clients and adoption rate by community unit. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-t004.jpg Table 4. Frequency of use by community unity. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-g003.jpg Figure 3. Motivation of telehealth Use. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-g004.jpg Figure 4. Inhibitors of telehealth Use. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-g005.jpg Figure 5. Telehealth services utilization. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-t005.jpg Table 5. Clinical consultations risk analysis. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-g006.jpg Figure 6. Percentage of newborns completing referrals. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-g007.jpg Figure 7. Scalability considerations. https://www.frontiersin.org/files/Articles/1668776/fdgth-07-1668776-HTML-r1/image_m/fdgth-07-1668776-t006.jpg Table 6. Determinants of on time PNC visits. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1807414/full https://www.frontiersin.org/files/Articles/1807414/fnut-13-1807414-HTML-r2/image_m/fnut-13-1807414-g001.jpg Figure 1. Between-country differences in MEDLIFE block scores (mean ± SD). a: Significantly differen https://www.frontiersin.org/files/Articles/1807414/fnut-13-1807414-HTML-r2/image_m/fnut-13-1807414-g002.jpg Figure 2. Medlife Index group levels (%) by country. https://www.frontiersin.org/files/Articles/1807414/fnut-13-1807414-HTML-r2/image_m/fnut-13-1807414-g003.jpg Figure 3. Between-country differences in DASS-21 and life satisfaction scores (mean ± SD). †Signific https://www.frontiersin.org/files/Articles/1807414/fnut-13-1807414-HTML-r2/image_m/fnut-13-1807414-g004.jpg Figure 4. Between-country differences in social participation score, IPAQ score, and sitting time (m https://www.frontiersin.org/files/Articles/1807414/fnut-13-1807414-HTML-r2/image_m/fnut-13-1807414-g005.jpg Figure 5. Between-country differences in sleep parameters, insomnia, and technology use behaviours ( https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1659880/full https://www.frontiersin.org/files/Articles/1659880/fcell-13-1659880-HTML/image_m/fcell-13-1659880-g007.jpg GRAPHICAL ABSTRACT | Spatial multi-omics reveal placental heterogeneity in late-onset preeclampsia. https://www.frontiersin.org/files/Articles/1659880/fcell-13-1659880-HTML/image_m/fcell-13-1659880-g001.jpg Figure 1. SM landscape of placental tissue from the preeclampsia and NC groups. (A) H&E staining ima https://www.frontiersin.org/files/Articles/1659880/fcell-13-1659880-HTML/image_m/fcell-13-1659880-g002.jpg Figure 2. ST landscape of placental tissue from patients with preeclampsia and NC individuals. (A) H https://www.frontiersin.org/files/Articles/1659880/fcell-13-1659880-HTML/image_m/fcell-13-1659880-g003.jpg Figure 3. Mapping of cell types to spatial locations in two placental samples. (A–C) Mapping of cell https://www.frontiersin.org/files/Articles/1659880/fcell-13-1659880-HTML/image_m/fcell-13-1659880-g004.jpg Figure 4. Differentially abundant metabolite ex pression in SCTs between patients with preeclampsia https://www.frontiersin.org/files/Articles/1659880/fcell-13-1659880-HTML/image_m/fcell-13-1659880-g005.jpg Figure 5. Pseudotime analysis revealing the dynamic metabolite and gene change patterns during troph https://www.frontiersin.org/files/Articles/1659880/fcell-13-1659880-HTML/image_m/fcell-13-1659880-g006.jpg Figure 6. Metabolic reprogramming of glycerophospholipid metabolism in the placenta of patients with https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1729623/full https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-g001.jpg Figure 1. Map of study site. https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-g002.jpg Figure 2. Research flow chart. https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-t001.jpg Table 1. Frequency distribution of subjects by age and of anaemia status in control and intervention https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-t002.jpg Table 2. The difference between the level of knowledge about anaemia and IFA supplementation between https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-g003.jpg Figure 3. Distribution of correct answers for each knowledge question Among female students in the i https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-g004.jpg Figure 4. Distribution of correct answers for each knowledge question Among female students in the c https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-t003.jpg Table 3. The difference between adherence to weekly IFA supplementation between subjects in control https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-t004.jpg Table 4. Comparations the levels of knowledge and adherence between the intervention and control gro https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-t005.jpg Table 5. Results of multivariable binary logistic regression to assess factors associated with high https://www.frontiersin.org/files/Articles/1729623/fdgth-07-1729623-HTML/image_m/fdgth-07-1729623-g005.jpg Figure 5. Percentage of taking IFA each week after intervention (intervention school). https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1753906/full https://www.frontiersin.org/files/Articles/1753906/fdgth-08-1753906-HTML/image_m/fdgth-08-1753906-t001.jpg Table 1. Summary of references included. https://www.frontiersin.org/files/Articles/1753906/fdgth-08-1753906-HTML/image_m/fdgth-08-1753906-g001.jpg Figure 1. Flowchart. https://www.frontiersin.org/files/Articles/1753906/fdgth-08-1753906-HTML/image_m/fdgth-08-1753906-g002.jpg Figure 2. Hierarchical structure of data layers integrated into multi-scale digital twins. https://www.frontiersin.org/files/Articles/1753906/fdgth-08-1753906-HTML/image_m/fdgth-08-1753906-g003.jpg Figure 3. Conceptual architecture of a multi-scale digital twin for personalized medicine. https://www.frontiersin.org/files/Articles/1753906/fdgth-08-1753906-HTML/image_m/fdgth-08-1753906-t002.jpg Table 2. Examples of clinically relevant digital twins reported in literature. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1782951/full https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-g001.jpg Figure 1. Flowchart of participant selection and analytical workflow. https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-t001.jpg Table 1. Comparison of variables between two groups. https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-g002.jpg Figure 2. Pairwise correlation matrix of the all features, visualized as a bubble plot. https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-t002.jpg Table 2. Performance of machine learning models in predicting incident hemodialysis among 400 CKD pa https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-g003.jpg Figure 3. Ten-fold cross-validated ROC curves for predictive models. https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-g004.jpg Figure 4. Density plot of predicted probabilities (ANN model, internal validation). https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-t003.jpg Table 3. Performance of the final model on temporal validation test set. https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-t004.jpg Table 4. Subgroup analysis of the final random forest model. https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-g005.jpg Figure 5. Calibration curve of the ANN model. https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-g006.jpg Figure 6. Decision curve analysis (DCA) of the ANN model. https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-g007.jpg Figure 7. Global feature importance based on SHAP analysis of the Random Forest model. https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-g008.jpg Figure 8. SHAP beeswarm and dependence plots analyzing the effects and interactions of key features https://www.frontiersin.org/files/Articles/1782951/fpubh-14-1782951-HTML/image_m/fpubh-14-1782951-g009.jpg Figure 9. SHAP decision plots for representative individual predictions. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1648671/full https://www.frontiersin.org/files/Articles/1648671/fdgth-07-1648671-HTML-r1/image_m/fdgth-07-1648671-g001.jpg Figure 1. Prevalence and reposting trends of health-related X-posts. The collected data were categor https://www.frontiersin.org/files/Articles/1648671/fdgth-07-1648671-HTML-r1/image_m/fdgth-07-1648671-g002.jpg Figure 2. Perception of current events differs based on sentiment. (A) Individuals with positive sen https://www.frontiersin.org/files/Articles/1648671/fdgth-07-1648671-HTML-r1/image_m/fdgth-07-1648671-g003.jpg Figure 3. Key themes in negative sentiment X-posts. (A) Negative sentiment X-posts frequently convey https://www.frontiersin.org/files/Articles/1648671/fdgth-07-1648671-HTML-r1/image_m/fdgth-07-1648671-t001.jpg Table 1. Cognitive distortions and complaining in health-related X-posts by Sentiment. https://www.frontiersin.org/files/Articles/1648671/fdgth-07-1648671-HTML-r1/image_m/fdgth-07-1648671-g004.jpg Figure 4. Cognitive load in X-posts: readability, sentence count, word count, and average sentence l https://www.frontiersin.org/files/Articles/1648671/fdgth-07-1648671-HTML-r1/image_m/fdgth-07-1648671-g005.jpg Figure 5. Therapeutic support and cognitive behavioral techniques in X-posts by sentiment. (A) Negat https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1721363/full https://www.frontiersin.org/files/Articles/1721363/fdgth-07-1721363-HTML-r1/image_m/fdgth-07-1721363-g001.jpg Figure 1. mHealth devices and digital phenotyping. This figure illustrates various data inputs that https://www.frontiersin.org/files/Articles/1721363/fdgth-07-1721363-HTML-r1/image_m/fdgth-07-1721363-t001.jpg Table 1. Administrative information and summary of the three mHealth oncology studies discussed in t https://www.frontiersin.org/files/Articles/1721363/fdgth-07-1721363-HTML-r1/image_m/fdgth-07-1721363-t002.jpg Table 2. Digital endpoints collected in the three oncology mHealth trials. https://www.frontiersin.org/files/Articles/1721363/fdgth-07-1721363-HTML-r1/image_m/fdgth-07-1721363-g002.jpg Figure 2. General mHealth study design of the eBladder, CHOPIN and LAPSTAR studies. *ICD, informed c https://www.frontiersin.org/files/Articles/1721363/fdgth-07-1721363-HTML-r1/image_m/fdgth-07-1721363-t003.jpg Table 3. Overview of the challenges of mHealth trials in oncology. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1698019/full https://www.frontiersin.org/files/Articles/1698019/fdgth-08-1698019-HTML/image_m/fdgth-08-1698019-g001.jpg Figure 1. Conditions and tools used to develop the search strategy. https://www.frontiersin.org/files/Articles/1698019/fdgth-08-1698019-HTML/image_m/fdgth-08-1698019-t001.jpg Table 1. Eligibility criteria and rationale for study inclusion and exclusion. https://www.frontiersin.org/files/Articles/1698019/fdgth-08-1698019-HTML/image_m/fdgth-08-1698019-g002.jpg Figure 2. PRISMA flowchart of included studies. https://www.frontiersin.org/files/Articles/1698019/fdgth-08-1698019-HTML/image_m/fdgth-08-1698019-t002.jpg Table 2. Summary of studies on digital endpoints in HBPR for COPD. https://www.frontiersin.org/files/Articles/1698019/fdgth-08-1698019-HTML/image_m/fdgth-08-1698019-g003.jpg Figure 3. Frequency of digital endpoints used. https://www.frontiersin.org/files/Articles/1698019/fdgth-08-1698019-HTML/image_m/fdgth-08-1698019-g004.jpg Figure 4. Frequency of monitoring devices used. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1666888/full https://www.frontiersin.org/files/Articles/1666888/fmed-12-1666888-HTML/image_m/fmed-12-1666888-g001.jpg Figure 1. Trial participant’s flowchart. https://www.frontiersin.org/files/Articles/1666888/fmed-12-1666888-HTML/image_m/fmed-12-1666888-t001.jpg Table 1. Participant timeline. https://www.frontiersin.org/files/Articles/1666888/fmed-12-1666888-HTML/image_m/fmed-12-1666888-t002.jpg Table 2. Treatment protocol. https://www.frontiersin.org/files/Articles/1666888/fmed-12-1666888-HTML/image_m/fmed-12-1666888-t003.jpg Table 3. Expected adverse events and management. https://www.frontiersin.org/files/Articles/1666888/fmed-12-1666888-HTML/image_m/fmed-12-1666888-g002.jpg Figure 2. The overall workflow for (a) proteomics (b) metabolomics and (c) transcriptomics analysis. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1686880/full https://www.frontiersin.org/files/Articles/1686880/fimmu-16-1686880-HTML/image_m/fimmu-16-1686880-g001.jpg Figure 1. Bacterial modulation of host SUMOylation in epithelial and macrophage cells: (A) In epithe https://www.frontiersin.org/files/Articles/1686880/fimmu-16-1686880-HTML/image_m/fimmu-16-1686880-t001.jpg Table 1. Bacterial modulation of host SUMOylation: effectors, targets, and mechanisms. https://www.frontiersin.org/files/Articles/1686880/fimmu-16-1686880-HTML/image_m/fimmu-16-1686880-g002.jpg Figure 2. Viral hijacking of the host SUMO pathway: RNA and DNA viruses exploit host SUMOylation to https://www.frontiersin.org/files/Articles/1686880/fimmu-16-1686880-HTML/image_m/fimmu-16-1686880-t002.jpg Table 2. Viral hijacking of host SUMOylation machinery: strategies and consequences. https://www.frontiersin.org/files/Articles/1686880/fimmu-16-1686880-HTML/image_m/fimmu-16-1686880-g003.jpg Figure 3. SUMOylation systems in fungal pathogens: Fungal species such as Candida albicans, Cryptoco https://www.frontiersin.org/files/Articles/1686880/fimmu-16-1686880-HTML/image_m/fimmu-16-1686880-t003.jpg Table 3. Fungal SUMOylation and host interaction: molecular components and pathogenic outcomes. https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2026.1770272/full https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g001.jpg Figure 1. Phylogenetic tree of Fusarium sp. GI-FS1 (query MN598647) gene sequences by maximum likeli https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g002.jpg Figure 2. HPLC chromatogram of plumbagin. (A) Standard plumbagin. (B) Plumbagin isolated from Plumba https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g003.jpg Figure 3. (A) MIC determination of plumbagin against GI-FS1. Plumbagin inhibited fungal growth in a https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g004.jpg Figure 4. Radial growth assay showing the antifungal effect of Plumbagin on GI-FS1 conidial germinat https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g005.jpg Figure 5. Radial growth assay demonstrating the inhibitory effect of Plumbagin on GI-FS1 mycelial gr https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g006.jpg Figure 6. Inhibition of conidial germination of GI-FS1 by plumbagin in liquid media. Plumbagin signi https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g007.jpg Figure 7. Effect of plumbagin on GI-FS1 mycelial growth at 2 h and 24 h post-inoculation (hpi). Fung https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g008.jpg Figure 8. SEM images illustrating the concentration-dependent effect of plumbagin on the morphology https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g009.jpg Figure 9. Light microscopy images (40X) showing GI-FS1 penetration in onion epidermis at 24 hpi. Sca https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g010.jpg Figure 10. (A) FDA/PI staining of GI-FS1 spores after Plumbagin treatment (5-20 µg/mL). Scale bars = https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g011.jpg Figure 11. Effect of Plumbagin on electrolyte leakage in GI-FS1. Conductivity (µS/cm) increased with https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g012.jpg Figure 12. (A) Representative bright-field (BF) and corresponding DCFH-DA fluorescence images of GI- https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g013.jpg Figure 13. DNA laddering assay showing the effect of plumbagin on genomic DNA integrity in GI-FS1. L https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g014.jpg Figure 14. Plumbagin induces apoptosis-like changes in GI-FS1. (A) AO/EB staining shows predominantl https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g015.jpg Figure 15. Molecular docking interaction of plumbagin with Lanosterol 14α-demethylase (CYP51) of Fus https://www.frontiersin.org/files/Articles/1770272/fagro-08-1770272-HTML/image_m/fagro-08-1770272-g016.jpg Figure 16. Effect of plumbagin on chlorophyll content in ginger leaf discs.Leaf discs excised from h https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1784311/full https://www.frontiersin.org/files/Articles/1784311/fpls-17-1784311-HTML/image_m/fpls-17-1784311-g001.jpg Figure 1. Pine cone scale bending is driven by a tri-layer tissue system, in which the sclereid cell https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1698673/full https://www.frontiersin.org/files/Articles/1698673/fchem-13-1698673-HTML/image_m/fchem-13-1698673-g001.jpg Figure 1. Schematic illustration of the manufacturing strategy for the hydroxyapatite (Pd-HAP) parti https://www.frontiersin.org/files/Articles/1698673/fchem-13-1698673-HTML/image_m/fchem-13-1698673-g002.jpg Figure 2. Structural characterization of the as-synthesized HAP particles. (a–c) Scanning electron m https://www.frontiersin.org/files/Articles/1698673/fchem-13-1698673-HTML/image_m/fchem-13-1698673-g003.jpg Figure 3. Antibacterial activity of the manufactured HAP particles. (a–c) Antibacterial activity aga https://www.frontiersin.org/files/Articles/1698673/fchem-13-1698673-HTML/image_m/fchem-13-1698673-g004.jpg Figure 4. Structural characterization of HAP particles partially substituted with Palladium (Pd). (a https://www.frontiersin.org/files/Articles/1698673/fchem-13-1698673-HTML/image_m/fchem-13-1698673-g005.jpg Figure 5. Antibacterial activity for the Pd-HAP particles. (a) Photographs of colony-forming cell as https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1635084/full https://www.frontiersin.org/files/Articles/1635084/fchem-13-1635084-HTML-r2/image_m/fchem-13-1635084-g001.jpg Figure 1. Linear sweep voltammograms for Co, Fe, and CoFe electrodepositions in choline chloride–ure https://www.frontiersin.org/files/Articles/1635084/fchem-13-1635084-HTML-r2/image_m/fchem-13-1635084-g002.jpg Figure 2. Chronoamperograms of FexCo1-x electrodeposition at different applied potentials: (I) −0.7 https://www.frontiersin.org/files/Articles/1635084/fchem-13-1635084-HTML-r2/image_m/fchem-13-1635084-g003.jpg Figure 3. SEM images of FexCo1-x electrodeposited at different applied potentials: (A) −0.7 V; (B) − https://www.frontiersin.org/files/Articles/1635084/fchem-13-1635084-HTML-r2/image_m/fchem-13-1635084-g004.jpg Figure 4. SEM images of FexCo1-x electrodeposited at different temperatures: (A) 70 °C; (B) 100 °C; https://www.frontiersin.org/files/Articles/1635084/fchem-13-1635084-HTML-r2/image_m/fchem-13-1635084-g005.jpg Figure 5. Deposited Fe content (A) and current efficiency (CE) (B) of FexCo1-x thin films electrodep https://www.frontiersin.org/files/Articles/1635084/fchem-13-1635084-HTML-r2/image_m/fchem-13-1635084-g006.jpg Figure 6. Chronoamperograms of FexCo1-x electrodeposition at different temperatures: (I) 70 °C; (II) https://www.frontiersin.org/files/Articles/1635084/fchem-13-1635084-HTML-r2/image_m/fchem-13-1635084-g007.jpg Figure 7. X-ray diffraction patterns of electrodeposited FexCo1-x at different operating temperature https://www.frontiersin.org/files/Articles/1635084/fchem-13-1635084-HTML-r2/image_m/fchem-13-1635084-g008.jpg Figure 8. Parallel magnetic hysteresis loops of FexCo1-x thin films electrodeposited at different te https://www.frontiersin.org/files/Articles/1635084/fchem-13-1635084-HTML-r2/image_m/fchem-13-1635084-g009.jpg Figure 9. Magnetic saturation (A), remanence (B), and coercivity (C) of electrodeposited FexCo1-x at https://www.frontiersin.org/journals/food-science-and-technology/articles/10.3389/frfst.2026.1815905/full https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g001.jpg Figure 1. Dried adult TM insect by-products. https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g002.jpg Figure 2. Schematic overview of the present study. https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-t001.jpg Table 1. Experimental matrix and experimental plan (in brackets), and DoE results: chitin yield (%) https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-t002.jpg Table 2. Proximate analysis of TM side streams. https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g003.jpg Figure 3. Coefficients plot. https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g004.jpg Figure 4. Response surfaces (pH vs. extraction time) on yield are shown for three enzyme concentrati https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-t003.jpg Table 3. Characterization of isolated chitin via EAE. https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g005.jpg Figure 5. FTIR spectra of chitin isolated via chemical extraction (green) and enzymatic extraction u https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g006.jpg Figure 6. SEM images of adult TM at 270x and 540x. https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g007.jpg Figure 7. SEM images of chitin: (a) chitin isolated via chemical extraction; (b) chitin isolated via https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g008.jpg Figure 8. Spectra of extracted and commercial chitosan. https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g009.jpg Figure 9. SEM images of chitosan: (a) commercial chitosan; (b) extracted chitosan. https://www.frontiersin.org/files/Articles/1815905/frfst-06-1815905-HTML-r1/image_m/frfst-06-1815905-g010.jpg Figure 10. TGA of extracted chitosan. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1744260/full https://www.frontiersin.org/files/Articles/1744260/fphys-17-1744260-HTML/image_m/fphys-17-1744260-g001.jpg Figure 1. Anatomical network of the SMA and its remodeling after stroke. https://www.frontiersin.org/files/Articles/1744260/fphys-17-1744260-HTML/image_m/fphys-17-1744260-g002.jpg Figure 2. Summary of multimodal neuroimaging evidence supporting SMA functional remodeling. https://www.frontiersin.org/files/Articles/1744260/fphys-17-1744260-HTML/image_m/fphys-17-1744260-t001.jpg Table 1. Exemplary studies on SMA-related neuroimaging changes and clinical functional improvements https://www.frontiersin.org/files/Articles/1744260/fphys-17-1744260-HTML/image_m/fphys-17-1744260-g003.jpg Figure 3. Schematic diagram of the potential mechanisms and clinical correlations of non-invasive ne https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1667800/full https://www.frontiersin.org/files/Articles/1667800/fmed-12-1667800-HTML/image_m/fmed-12-1667800-t001.jpg Table 1. Information on the drugs used in the management of hemotoxic snakebite in the study area. https://www.frontiersin.org/files/Articles/1667800/fmed-12-1667800-HTML/image_m/fmed-12-1667800-t002.jpg Table 2. Sociodemographics, snake species, bite location, clinical presentation, and hospital stay o https://www.frontiersin.org/files/Articles/1667800/fmed-12-1667800-HTML/image_m/fmed-12-1667800-t003.jpg Table 3. The differences in coagulation indicators at admission between patients treated with antive https://www.frontiersin.org/files/Articles/1667800/fmed-12-1667800-HTML/image_m/fmed-12-1667800-t004.jpg Table 4. The differences in coagulation indicators at discharge between patients treated with antive https://www.frontiersin.org/files/Articles/1667800/fmed-12-1667800-HTML/image_m/fmed-12-1667800-g001.jpg Figure 1. The differences in coagulation indicators at discharge between patients treated with antiv https://www.frontiersin.org/files/Articles/1667800/fmed-12-1667800-HTML/image_m/fmed-12-1667800-g002.jpg Figure 2. Comparison of coagulation indicators at admission and discharge in patients treated with a https://www.frontiersin.org/journals/tropical-diseases/articles/10.3389/fitd.2026.1745845/full https://www.frontiersin.org/files/Articles/1745845/fitd-07-1745845-HTML/image_m/fitd-07-1745845-t001.jpg Table 1. The two sample selection scenarios by health district for the LQAS Niger. https://www.frontiersin.org/files/Articles/1745845/fitd-07-1745845-HTML/image_m/fitd-07-1745845-t002.jpg Table 2. Performance of Dosso districts according to the evidence. https://www.frontiersin.org/files/Articles/1745845/fitd-07-1745845-HTML/image_m/fitd-07-1745845-t003.jpg Table 3. Performance of Tahoua districts according to the evidence. https://www.frontiersin.org/files/Articles/1745845/fitd-07-1745845-HTML/image_m/fitd-07-1745845-g001.jpg Figure 1. Map of monitored districts in Dosso and Tahoua regions, Niger. https://www.frontiersin.org/journals/tropical-diseases/articles/10.3389/fitd.2026.1725224/full https://www.frontiersin.org/files/Articles/1725224/fitd-07-1725224-HTML/image_m/fitd-07-1725224-g001.jpg Figure 1. PRISMA flow diagram of the study. https://www.frontiersin.org/files/Articles/1725224/fitd-07-1725224-HTML/image_m/fitd-07-1725224-t001.jpg Table 1. Transferability framework dimensions. https://www.frontiersin.org/files/Articles/1725224/fitd-07-1725224-HTML/image_m/fitd-07-1725224-g002.jpg Figure 2. Malaria vaccine framework. https://www.frontiersin.org/files/Articles/1725224/fitd-07-1725224-HTML/image_m/fitd-07-1725224-t002.jpg Table 2. Framework evaluation metrics. https://www.frontiersin.org/journals/tropical-diseases/articles/10.3389/fitd.2025.1691239/full https://www.frontiersin.org/files/Articles/1691239/fitd-06-1691239-HTML/image_m/fitd-06-1691239-t001.jpg Table 1. Status of malaria vaccine recommended by WHO. https://www.frontiersin.org/files/Articles/1691239/fitd-06-1691239-HTML/image_m/fitd-06-1691239-g001.jpg Figure 1. Three C model for trust and acceptance of malaria vaccine. https://www.frontiersin.org/files/Articles/1691239/fitd-06-1691239-HTML/image_m/fitd-06-1691239-g002.jpg Figure 2. Community ownership framework. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1790208/full https://www.frontiersin.org/files/Articles/1790208/fvets-13-1790208-HTML-r1/image_m/fvets-13-1790208-gr0001.jpg Graphical Abstract. A graphical summary of the study. This illustration synthesizes key findings fro https://www.frontiersin.org/files/Articles/1790208/fvets-13-1790208-HTML-r1/image_m/fvets-13-1790208-g001.jpg Figure 1. Species-specific patterns of uterine torsion in camels, buffaloes, and cattle. (a) Influen https://www.frontiersin.org/files/Articles/1790208/fvets-13-1790208-HTML-r1/image_m/fvets-13-1790208-t001.jpg Table 1. Association between pregnant horn and torsion direction. https://www.frontiersin.org/files/Articles/1790208/fvets-13-1790208-HTML-r1/image_m/fvets-13-1790208-t002.jpg Table 2. Species differences in torsion severity. https://www.frontiersin.org/files/Articles/1790208/fvets-13-1790208-HTML-r1/image_m/fvets-13-1790208-t003.jpg Table 3. Logistic regression analysis of factors affecting fetal mortality in uterine torsion cases. https://www.frontiersin.org/files/Articles/1790208/fvets-13-1790208-HTML-r1/image_m/fvets-13-1790208-t004.jpg Table 4. Logistic regression analysis of risk factors for maternal mortality. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1733695/full https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-t001.jpg Table 1. Demographic characteristics of the patients recruited for the study. https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-t002.jpg Table 2. Summary of haematological characteristics of hospitalised COVID-19 patients. https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-t003.jpg Table 3. Summary of lymphocyte profiles of hospitalised COVID-19 patients. https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-g001.jpg Figure 1. Column scatter plots of COVID-19 patients’ cytokine expression. On every graph, the cytoki https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-t004.jpg Table 4. Distribution of C-reactive protein with respect to cytokine expression. https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-g002.jpg Figure 2. Column scatter plots of cell counts in COVID-19 patients stratified by TNF-α expression. E https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-g003.jpg Figure 3. Column scatter plots of cell counts in COVID-19 patients stratified by IFN-γ expression. E https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-g004.jpg Figure 4. Column scatter plots of cell counts in COVID-19 patients stratified by IL-10 expression. E https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-g005.jpg Figure 5. Column scatter plots of cell counts in COVID-19 patients stratified by IL-17A expression. https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-t005.jpg Table 5. Raw p-values and Benjamini–Hochberg false discovery rate–adjusted q-values for immune cell https://www.frontiersin.org/files/Articles/1733695/fimmu-17-1733695-HTML/image_m/fimmu-17-1733695-t006.jpg Table 6. Spearman correlation analysis between IL-6 levels and hematologic parameters. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1772116/full https://www.frontiersin.org/files/Articles/1772116/fped-14-1772116-HTML/image_m/fped-14-1772116-t001.jpg Table 1. Analysis of the general situation of different blood glucose levels in neonates at admissio https://www.frontiersin.org/files/Articles/1772116/fped-14-1772116-HTML/image_m/fped-14-1772116-t002.jpg Table 2. Analysis of the occurrence of severe NRDS and respiratory-metabolic indicators with differe https://www.frontiersin.org/files/Articles/1772116/fped-14-1772116-HTML/image_m/fped-14-1772116-t003.jpg Table 3. Analysis of the NRDS complications and short-term clinical outcomes with different admissio https://www.frontiersin.org/files/Articles/1772116/fped-14-1772116-HTML/image_m/fped-14-1772116-t004.jpg Table 4. Multivariate logistic regression analysis of indicators associated with admission hyperglyc https://www.frontiersin.org/files/Articles/1772116/fped-14-1772116-HTML/image_m/fped-14-1772116-t005.jpg Table 5. Multivariate logistic regression analysis of indicators associated with admission hypoglyce https://www.frontiersin.org/files/Articles/1772116/fped-14-1772116-HTML/image_m/fped-14-1772116-g001.jpg Figure 1. Study flowchart and conceptual model evaluating the impact of admission dysglycemia on NRD https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1785903/full https://www.frontiersin.org/files/Articles/1785903/fonc-16-1785903-HTML/image_m/fonc-16-1785903-g000.jpg Graphical Abstract. Schematic representation of metformin-mediated stabilization of mTORC2/RUNX2 axi https://www.frontiersin.org/files/Articles/1785903/fonc-16-1785903-HTML/image_m/fonc-16-1785903-g001.jpg Figure 1. RUNX2 is a substrate of AMPK in breast cancer cells. MDA-MB-231 cells were treated with ei https://www.frontiersin.org/files/Articles/1785903/fonc-16-1785903-HTML/image_m/fonc-16-1785903-g002.jpg Figure 2. AMPK mediated phosphorylation of RUNX2 results in increased nuclear localization and trans https://www.frontiersin.org/files/Articles/1785903/fonc-16-1785903-HTML/image_m/fonc-16-1785903-g003.jpg Figure 3. mTORC2 is crucial for AMPK/RUNX2 axis. MDA-MB-231 cells were transfected with siRNA’s agai https://www.frontiersin.org/files/Articles/1785903/fonc-16-1785903-HTML/image_m/fonc-16-1785903-g004.jpg Figure 4. Metformin promotes EMT and induces osteoblast like phenotype to breast cancer cells throug https://www.frontiersin.org/files/Articles/1785903/fonc-16-1785903-HTML/image_m/fonc-16-1785903-g005.jpg Figure 5. Metformin promotes chemotaxis/metastasis of transformed breast cancer cells. (A) MDA-MB-23 https://www.frontiersin.org/files/Articles/1785903/fonc-16-1785903-HTML/image_m/fonc-16-1785903-g006.jpg Figure 6. RUNX2-Dependent Bone Metastatic Outgrowth of MCF-7 Cells in NOD-SCID Mice. This figure ill https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1681985/full https://www.frontiersin.org/files/Articles/1681985/fmed-12-1681985-HTML/image_m/fmed-12-1681985-g001.jpg Figure 1. Flowchart depicting the process of analyzing adverse events (AEs) related to Symdeko using https://www.frontiersin.org/files/Articles/1681985/fmed-12-1681985-HTML/image_m/fmed-12-1681985-t001.jpg Table 1. Clinical characteristics of adverse event (AE) reports for Symdeko from the Food and Drug A https://www.frontiersin.org/files/Articles/1681985/fmed-12-1681985-HTML/image_m/fmed-12-1681985-t002.jpg Table 2. Signal strength of Symdeko-associated adverse events (AEs) across system organ classes (SOC https://www.frontiersin.org/files/Articles/1681985/fmed-12-1681985-HTML/image_m/fmed-12-1681985-g002.jpg Figure 2. Adverse event (AE) distribution across system organ classes for Symdeko. https://www.frontiersin.org/files/Articles/1681985/fmed-12-1681985-HTML/image_m/fmed-12-1681985-t003.jpg Table 3. Top 50 positive adverse events (AEs) of Symdeko at the PT level. https://www.frontiersin.org/files/Articles/1681985/fmed-12-1681985-HTML/image_m/fmed-12-1681985-g003.jpg Figure 3. Time to onset of adverse events (AEs) associated with Symdeko. https://www.frontiersin.org/files/Articles/1681985/fmed-12-1681985-HTML/image_m/fmed-12-1681985-t004.jpg Table 4. Time to onset of Symdeko-associated adverse events (AEs) and weibull distribution analysis. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1765635/full https://www.frontiersin.org/files/Articles/1765635/fpsyt-17-1765635-HTML-r1/image_m/fpsyt-17-1765635-t001.jpg Table 1. Basic characteristics of participants (n = 17). https://www.frontiersin.org/files/Articles/1765635/fpsyt-17-1765635-HTML-r1/image_m/fpsyt-17-1765635-t002.jpg Table 2. Themes and subthemes. https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2025.1736865/full https://www.frontiersin.org/files/Articles/1736865/fncel-19-1736865-HTML/image_m/fncel-19-1736865-g001.jpg Figure 1. Molecular pathways regulating NSCs fate. NSCs gradually lose stemness and acquire speciali https://www.frontiersin.org/files/Articles/1736865/fncel-19-1736865-HTML/image_m/fncel-19-1736865-g002.jpg Figure 2. Microglia and reactive astrocytes in the CNS, such as microglia and astrocytes, secrete pr https://www.frontiersin.org/files/Articles/1736865/fncel-19-1736865-HTML/image_m/fncel-19-1736865-g003.jpg Figure 3. Reactive oxygen species (ROS) act as key players of NSCs behavior by influencing the balan https://www.frontiersin.org/files/Articles/1736865/fncel-19-1736865-HTML/image_m/fncel-19-1736865-t001.jpg Table 1. Neurodegenerative diseases feature imbalanced pro- and anti-inflammatory signaling that aff https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1770453/full https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-g001.jpg Figure 1. Study area in the China’s east coast and the sampling sites. Symbol fill indicates the pre https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-t001.jpg Table 1. Summary of monitoring sites and net setups. https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-g002.jpg Figure 2. Rotating (A) and fixed (B) fishing nets, protection devices (C) for glass eel along the ea https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-g003.jpg Figure 3. Temporal changes in daily mean glass eel damage rate during the recruitment season. Tempor https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-t002.jpg Table 2. Summary of glass eel damage rate at site level (median, IQR, and 95% CI). https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-g004.jpg Figure 4. Spatial distribution of median glass eel damage rate at the ten monitoring stations along https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-g005.jpg Figure 5. Individual effects of environmental and seasonal variables from GAM. Illustrates the parti https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-g006.jpg Figure 6. Fixed-effects forest plot of odds ratios and 95% confidence intervals on a logarithmic sca https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-g007.jpg Figure 7. Interaction effects with fishing effort from GAM. Smooth curves illustrate the model-estim https://www.frontiersin.org/files/Articles/1770453/fmars-13-1770453-HTML/image_m/fmars-13-1770453-t003.jpg Table 3. Predicted management thresholds, compliance rates, and mitigation efficacy. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1767826/full https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g001.jpg Figure 1. Clustering structure in underwater wireless sensor network. https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-t001.jpg Table 1. Summary of existing underwater wireless sensor network (UWSN) routing and data collection t https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g002.jpg Figure 2. Architecture of underwater wireless sensor network. https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g003.jpg Figure 3. Proposed three-phase secure and energy-aware framework for UWSNs. https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-t002.jpg Table 2. Simulation parameters. https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g004.jpg Figure 4. Alive nodes vs. number of rounds. https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g005.jpg Figure 5. Network lifetime comparison (FND, HND, LND). https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g006.jpg Figure 6. End-to-end delay vs. number of rounds. https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g007.jpg Figure 7. Residual energy comparison (joules). https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g008.jpg Figure 8. Successful packet transmissions per round. https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g009.jpg Figure 9. Packet delivery ratio (PDR) vs. rounds. https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-g010.jpg Figure 10. Routing overhead (%) vs. rounds. https://www.frontiersin.org/files/Articles/1767826/fmars-13-1767826-HTML-r1/image_m/fmars-13-1767826-t003.jpg Table 3. Average performance comparison of routing protocols. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1702294/full https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g001.jpg Figure 1. NOMA structure. https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g002.jpg Figure 2. Proposed ESS. https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g003.jpg Figure 3. Proposed DMF. https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g004.jpg Figure 4. Pd vs SNR under Rayleigh channel (pfa<0.5). https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g005.jpg Figure 5. Pd vs SNR under Rician channel (pfa<0.5). https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g006.jpg Figure 6. Pd vs SNR under Rayleigh channel (pfa≥0.5). https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g007.jpg Figure 7. Pd vs SNR under Rayleigh channel (pfa≥0.5). https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g008.jpg Figure 8. Pfa Vs Pd under Rayleigh Channel. https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g009.jpg Figure 9. Pfa Vs Pd under Rician Channel. https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g010.jpg Figure 10. SNR Vs BER in Rayleigh Channel. https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-g011.jpg Figure 11. SNR Vs BER in Rician Channel. https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-t001.jpg Table 1. Performance comparison of proposed and conventional spectrum sensing techniques under rayle https://www.frontiersin.org/files/Articles/1702294/fmars-12-1702294-HTML/image_m/fmars-12-1702294-t002.jpg Table 2. Integration of the proposed DMF–ESS framework with existing marine monitoring architectures https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1687877/full https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-g001.jpg Figure 1. The figure representing noise reduction through transformer model. https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-g002.jpg Figure 2. The proposed architecture is working of transformer using attention-enhanced convolutional https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-g003.jpg Figure 3. Comparison of loss curves training epochs for all model variants. https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-g004.jpg Figure 4. Progressive enhancement of underwater images through each transformer stage. https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-t001.jpg Table 1. Comparison of PSNR and SSIM values for different underwater image enhancement methods on th https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-t002.jpg Table 2. Statistical comparison between DM-AECB and DM-Trans on LSUI and UIEB datasets. https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-g005.jpg Figure 5. A visual comparison of underwater images and their corresponding enhanced results is prese https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-t003.jpg Table 3. Performance improvements from diffusion, AECB, and skip-sampling modules measured by final https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-g006.jpg Figure 6. Comparison of PSNR values over training epochs for all model variants. https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-g007.jpg Figure 7. Visual comparison of underwater images and enhanced outputs produced by different model va https://www.frontiersin.org/files/Articles/1687877/fmars-12-1687877-HTML-r1/image_m/fmars-12-1687877-g008.jpg Figure 8. GPU utilization during training process. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1671853/full https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g001.jpg Figure 1. Proposed system model architecture. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-t001.jpg Table 1. Underwater acoustic channel parameter ranges. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-t002.jpg Table 2. Model training options for proposed CRNet model. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-t003.jpg Table 3. Simulation parameters for UWA-OFDM system. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-t004.jpg Table 4. Bellhop input parameters & test channel characteristics. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g002.jpg Figure 2. Demonstration of Bellhop ray model in shallow water. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g003.jpg Figure 3. Demonstration of Bellhop ray model in continental shelf water. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g004.jpg Figure 4. Impact of QPSK modulation using (a) shallow coastal and (b) continental shelf channels. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g005.jpg Figure 5. Comparison of 16-QAM BER vs SNR for (a) shallow coastal and (b) continental shelf channels https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g006.jpg Figure 6. Comparison of 32-QAM BER vs SNR for (a) shallow coastal and (b) continental shelf channels https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g007.jpg Figure 7. Comparison of 64-QAM BER vs SNR for (a) shallow coastal and (b)continental shelf channels. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g008.jpg Figure 8. Amplitude error and phase error in estimation of Shallow coastal. (a) Amplitude error. (b) https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g009.jpg Figure 9. Amplitude error and phase error in estimation of continental shelf. (a) Amplitude error. ( https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g010.jpg Figure 10. MSE loss of CRNet along with traditional models in shallow coastal channel. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g011.jpg Figure 11. MSE loss of CRNet along with traditional models in continental shelf channel. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g012.jpg Figure 12. Dynamic signal decomposition techniques (a) Local mean decomposition (b) Empirical model https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g013.jpg Figure 13. Comparison of the received and denoised signal using the LMD technique. The denoised sign https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-t005.jpg Table 5. Comparison of MSE between LMD and EMD denoising techniques at different thresholds. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g014.jpg Figure 14. Impact on pilot number. https://www.frontiersin.org/files/Articles/1671853/fmars-12-1671853-HTML-r1/image_m/fmars-12-1671853-g015.jpg Figure 15. CRNet BER performance for (a) Shallow Coastal and (b) Continental Shelf Channels on train https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1671492/full https://www.frontiersin.org/files/Articles/1671492/fmars-12-1671492-HTML/image_m/fmars-12-1671492-g001.jpg Figure 1. The twelve sampling stations in the Dongsha Atoll. https://www.frontiersin.org/files/Articles/1671492/fmars-12-1671492-HTML/image_m/fmars-12-1671492-t001.jpg Table 1. Sampling date, location and depth of each sample. https://www.frontiersin.org/files/Articles/1671492/fmars-12-1671492-HTML/image_m/fmars-12-1671492-t002.jpg Table 2. The eDNA samples amplified by MiFish primers and sequencing data. https://www.frontiersin.org/files/Articles/1671492/fmars-12-1671492-HTML/image_m/fmars-12-1671492-t003.jpg Table 3. Summary of MiSeq reads. https://www.frontiersin.org/files/Articles/1671492/fmars-12-1671492-HTML/image_m/fmars-12-1671492-g002.jpg Figure 2. The comparison of the family recorded by conventional survey and eDNA metabarcoding in the https://www.frontiersin.org/files/Articles/1671492/fmars-12-1671492-HTML/image_m/fmars-12-1671492-g003.jpg Figure 3. The species richness of each family detected by conventional survey (Liao et al., 2018) an https://www.frontiersin.org/files/Articles/1671492/fmars-12-1671492-HTML/image_m/fmars-12-1671492-g004.jpg Figure 4. The estimation of species number curve. Order q presents the coefficient of species estima https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1781656/full https://www.frontiersin.org/files/Articles/1781656/fnins-20-1781656-HTML/image_m/fnins-20-1781656-t001.jpg Table 1. Inclusion and exclusion criteria for study selection. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1759897/full https://www.frontiersin.org/files/Articles/1759897/fpls-17-1759897-HTML-r2/image_m/fpls-17-1759897-g001.jpg Figure 1. Pairwise Pearson’s correlations were conducted using the adjusted means of yield component https://www.frontiersin.org/files/Articles/1759897/fpls-17-1759897-HTML-r2/image_m/fpls-17-1759897-g002.jpg Figure 2. Genotype plus Genotype x Environment (G+GE) biplot obtained from sites regression (SREG) a https://www.frontiersin.org/files/Articles/1759897/fpls-17-1759897-HTML-r2/image_m/fpls-17-1759897-g003.jpg Figure 3. Single-trait-single-environment (SE), multi-trait-single-environment (MT), single-trait-mu https://www.frontiersin.org/files/Articles/1759897/fpls-17-1759897-HTML-r2/image_m/fpls-17-1759897-g004.jpg Figure 4. Single-trait-single-environment (SE), multi-trait-single-environment (MT), single-trait-mu https://www.frontiersin.org/files/Articles/1759897/fpls-17-1759897-HTML-r2/image_m/fpls-17-1759897-g005.jpg Figure 5. Single-trait-single-environment (SE), multi-trait-single-environment (MT), single-trait-mu https://www.frontiersin.org/files/Articles/1759897/fpls-17-1759897-HTML-r2/image_m/fpls-17-1759897-g006.jpg Figure 6. Single-trait-single-environment (SE), multi-trait-single-environment (MT), single-trait-mu https://www.frontiersin.org/files/Articles/1759897/fpls-17-1759897-HTML-r2/image_m/fpls-17-1759897-g007.jpg Figure 7. Single-trait-single-environment (SE), multi-trait-single-environment (MT), single-trait-mu https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2025.1690519/full https://www.frontiersin.org/files/Articles/1690519/frhs-05-1690519-HTML-r1/image_m/frhs-05-1690519-t001.jpg Table 1. Included intravenous iron formulations and their administration characteristics. https://www.frontiersin.org/files/Articles/1690519/frhs-05-1690519-HTML-r1/image_m/frhs-05-1690519-t002.jpg Table 2. Inclusion and exclusion criteria for reviewed studies. https://www.frontiersin.org/files/Articles/1690519/frhs-05-1690519-HTML-r1/image_m/frhs-05-1690519-g001.jpg Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart. https://www.frontiersin.org/files/Articles/1690519/frhs-05-1690519-HTML-r1/image_m/frhs-05-1690519-t003.jpg Table 3. Main characteristics of economic evaluations. https://www.frontiersin.org/files/Articles/1690519/frhs-05-1690519-HTML-r1/image_m/frhs-05-1690519-t004.jpg Table 4. The results of the economic evaluations. https://www.frontiersin.org/files/Articles/1690519/frhs-05-1690519-HTML-r1/image_m/frhs-05-1690519-g002.jpg Figure 2. The results of the quality assessment of the studies using the CHEERS 2022 checklist. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1741007/full https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-g001.jpg Figure 1. Distribution of buffalo brucellosis outbreaks in the province of Caserta between 01.01.201 https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-g002.jpg Figure 2. Distribution of farms with (orange dots) or without reinfection (grey dots) in the provinc https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-t001.jpg Table 1. Characteristics of brucellosis outbreaks (N = 222) stratified by reinfection status. https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-g003.jpg Figure 3. Cumulative incidence of reinfection stratified by eradication strategy. Kaplan–Meier survi https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-t002.jpg Table 2. Univariate Cox regression analysis of factors associated with time to reinfection. https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-g004.jpg Figure 4. Restricted cubic spline analysis of the association between buffalo density at the municip https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-t003.jpg Table 3. Final Cox regression model (N = 222, events = 65). https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-t004.jpg Table 4. Municipalities housing >200 buffalo/km2 in 2021 and brucellosis cases recorded since 2016. https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-t005.jpg Table 5. Epidemiological pattern in the period time 2016–2024 (VETINFO). https://www.frontiersin.org/files/Articles/1741007/fmicb-17-1741007-HTML/image_m/fmicb-17-1741007-t006.jpg Table 6. Human brucellosis: number of reported cases in Italy and food-borne outbreaks in EU/EAA (EF https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2026.1767786/full https://www.frontiersin.org/files/Articles/1767786/fresc-07-1767786-HTML/image_m/fresc-07-1767786-g001.jpg Figure 1. Clinical timeline of the case. The figure illustrates the clinical course from stroke onse https://www.frontiersin.org/files/Articles/1767786/fresc-07-1767786-HTML/image_m/fresc-07-1767786-g002.jpg Figure 2. IoT-based rehabilitation system architecture and data flow. The left panel shows the physi https://www.frontiersin.org/files/Articles/1767786/fresc-07-1767786-HTML/image_m/fresc-07-1767786-g003.jpg Figure 3. Task content of the IoT-based rehabilitation application. Five task-based exercises were i https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2025.1622510/full https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-g001.jpg Figure 1. CONSORT flowchart of the study. https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-t001.jpg Table 1. Demographic characteristics in experimental and control groups. https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-t002.jpg Table 2. Comparative analysis of TNSS at baseline and post-treatment four weeks in experimental and https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-t003.jpg Table 3. Comparative analysis of TNSS at baseline and post-treatment two weeks in experimental and c https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-g002.jpg Figure 2. Lsmean of subscores in the TNSS after two and four weeks of treatment. LSmean: The change https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-g003.jpg Figure 3. Rhinoscopy scores after four weeks of treatment. LSmean: The change in rhinoscopy scores a https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-g004.jpg Figure 4. Trends in VAS total scores within two weeks of treatment. LSmean, the change in VAS total https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-g005.jpg Figure 5. Changes in RQLQ total scores and subscores after treatment. RQLQ, rhinoconjunctivitis qual https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-t004.jpg Table 4. TNSS variations from baseline to post-treatment four weeks across three AR subgroups. https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-g006.jpg Figure 6. Subgroup analysis between the experimental group (A) and the control group (B). https://www.frontiersin.org/files/Articles/1622510/falgy-06-1622510-HTML/image_m/falgy-06-1622510-t005.jpg Table 5. Incidence of TEAEs and TRAEs in the experimental and control groups. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1720974/full https://www.frontiersin.org/files/Articles/1720974/fmed-13-1720974-HTML-r1/image_m/fmed-13-1720974-t001.jpg Table 1. Background characteristics of the respondents. https://www.frontiersin.org/files/Articles/1720974/fmed-13-1720974-HTML-r1/image_m/fmed-13-1720974-t002.jpg Table 2. Knowledge of Health Management descriptive statistics. https://www.frontiersin.org/files/Articles/1720974/fmed-13-1720974-HTML-r1/image_m/fmed-13-1720974-t003.jpg Table 3. Multivariate logistic regression between knowledge of health management and respondents’ ch https://www.frontiersin.org/files/Articles/1720974/fmed-13-1720974-HTML-r1/image_m/fmed-13-1720974-t004.jpg Table 4. Attitude toward health management education. https://www.frontiersin.org/files/Articles/1720974/fmed-13-1720974-HTML-r1/image_m/fmed-13-1720974-t005.jpg Table 5. Attitude (positivity) logistic regression. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1649311/full https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-g001.jpg Figure 1. Bathymetric map of the South Aegean Sea (data from EMODnet Bathymetry Consortium, 2016). T https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-g002.jpg Figure 2. CTD cast of March 17th, 2023. Cast position: latitude 35° 45.20 N, longitude 026° 13.71 E https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-t001.jpg Table 1. Terminology. https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-t002.jpg Table 2. Formulation of the SF_ThS Flux models of Radko and Smith 2012, (RS12), Large et al., 1994 ( https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-t003.jpg Table 3. Characteristics of the observed staircase in the Cretan Sea: layer sequence number (L#), pr https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-g003.jpg Figure 3. SeaExplorer glider cast of March 19th, 2023. Cast position: latitude 35° 46.47 N, longitud https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-t004.jpg Table 4. Vertical salt and heat diffusivities KS, KΘ, over interfaces 1 to 4 and over the whole stai https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-g004.jpg Figure 4. Panels (a–d) show for each of the four models (Large et al, 1994 (L94), Zhang et al., 1998 https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-t005.jpg Table 5. Downward fluxes of potential temperature, salt, buoyancy, and heat (Fθ FS,FB,FH) in the int https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-g005.jpg Figure 5. Interfacial diffusivity profile predictions (small symbols) of the four models (RS12, L14, https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-g006.jpg Figure 6. Normalized distribution of the Turner angle in the 250-dbar to end-of-cast layer from the https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-g007.jpg Figure 7. (a) Sampling sites of the CRELEV, PERLE 2, PERLE 4, and glider cruises in the Cretan Sea. https://www.frontiersin.org/files/Articles/1649311/fmars-12-1649311-HTML/image_m/fmars-12-1649311-g008.jpg Figure 8. Normalized distribution of the Turner angle from 250 dbar to the deep salinity minimum cor https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1712654/full https://www.frontiersin.org/files/Articles/1712654/feduc-11-1712654-HTML/image_m/feduc-11-1712654-t001.jpg Table 1. THS senior class ability and attitude toward science and intention for a health science car https://www.frontiersin.org/files/Articles/1712654/feduc-11-1712654-HTML/image_m/feduc-11-1712654-g001.jpg Figure 1. Seniors expressed interest in health careers and professions. https://www.frontiersin.org/files/Articles/1712654/feduc-11-1712654-HTML/image_m/feduc-11-1712654-t002.jpg Table 2. Foundational knowledge of five health topics (n = 32). https://www.frontiersin.org/files/Articles/1712654/feduc-11-1712654-HTML/image_m/feduc-11-1712654-t003.jpg Table 3. Sociodemographic descriptors senior class—The High School (n = 35). https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1673867/full https://www.frontiersin.org/files/Articles/1673867/fnut-12-1673867-HTML/image_m/fnut-12-1673867-t001.jpg Table 1. Sources of FODMAPs in the diet (70). https://www.frontiersin.org/files/Articles/1673867/fnut-12-1673867-HTML/image_m/fnut-12-1673867-t002.jpg Table 2. Summary of randomized controlled trials on the low-FODMAP diet in IBD. https://www.frontiersin.org/files/Articles/1673867/fnut-12-1673867-HTML/image_m/fnut-12-1673867-t003.jpg Table 3. Future directions and clinical implications for low-FODMAP diet use in the course of IBD. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1809070/full https://www.frontiersin.org/files/Articles/1809070/fmicb-17-1809070-HTML/image_m/fmicb-17-1809070-g001.jpg Figure 1. Ecological framework for integrated food safety under the One Health approach. This framew https://www.frontiersin.org/files/Articles/1809070/fmicb-17-1809070-HTML/image_m/fmicb-17-1809070-g002.jpg Figure 2. Transmission pathways of bacterial hazards along the food chain. This schematic outlines t https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1805015/full https://www.frontiersin.org/files/Articles/1805015/fendo-17-1805015-HTML/image_m/fendo-17-1805015-t001.jpg Table 1. Landmark GLP-1 receptor agonist trials and renal eligibility. https://www.frontiersin.org/files/Articles/1805015/fendo-17-1805015-HTML/image_m/fendo-17-1805015-g001.jpg Figure 1. Flow diagram of patient screening, exclusions, and final analytic cohort. https://www.frontiersin.org/files/Articles/1805015/fendo-17-1805015-HTML/image_m/fendo-17-1805015-t002.jpg Table 2. Baseline characteristics of the study population (n=17). https://www.frontiersin.org/files/Articles/1805015/fendo-17-1805015-HTML/image_m/fendo-17-1805015-t003.jpg Table 3. Semaglutide exposure characteristics among the study cohort. https://www.frontiersin.org/files/Articles/1805015/fendo-17-1805015-HTML/image_m/fendo-17-1805015-t004.jpg Table 4. Efficacy and safety outcomes following GLP-1 receptor agonist initiation (N = 17). https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1725633/full https://www.frontiersin.org/files/Articles/1725633/fpubh-14-1725633-HTML-r2/image_m/fpubh-14-1725633-g001.jpg Figure 1. SPIRIT figure for schedule of enrollment, interventions, and assessments. Note: ‘×' indica https://www.frontiersin.org/files/Articles/1725633/fpubh-14-1725633-HTML-r2/image_m/fpubh-14-1725633-g002.jpg Figure 2. Task initiation delay paradigm (TIDP) process diagram. It depicts the process of the task https://www.frontiersin.org/files/Articles/1725633/fpubh-14-1725633-HTML-r2/image_m/fpubh-14-1725633-g003.jpg Figure 3. Schematic of the trial protocol. The primary outcome measure will be the GPS and secondary https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1745837/full https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g001.jpg Figure 1. Flowchart of PRISMA (preferred reporting items for systematic evaluation and meta-analysis https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-t001.jpg Table 1. Included study characteristics. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g002.jpg Figure 2. Risk of bias summary and chart. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g003.jpg Figure 3. Forest plot of iCBT on depression. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g004.jpg Figure 4. Forest plot for depression after study exclusion. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g005.jpg Figure 5. Sensitivity analysis plot for depression. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g006.jpg Figure 6. Forest plot of iCBT on anxiety. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g007.jpg Figure 7. Sensitivity analysis plot for anxiety outcomes. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g008.jpg Figure 8. Forest plot of iCBT on stress. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g009.jpg Figure 9. Sensitivity analysis plot for stress. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g010.jpg Figure 10. Meta-analysis and forest plot of depression at follow-up. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g011.jpg Figure 11. Meta-analysis and forest plot of anxiety at follow-up. https://www.frontiersin.org/files/Articles/1745837/fpsyg-17-1745837-HTML-r1/image_m/fpsyg-17-1745837-g012.jpg Figure 12. Meta-analysis and forest plot of stress at follow-up. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1714028/full https://www.frontiersin.org/files/Articles/1714028/fmolb-13-1714028-HTML-r1/image_m/fmolb-13-1714028-t001.jpg Table 1. Strains and plasmids. https://www.frontiersin.org/files/Articles/1714028/fmolb-13-1714028-HTML-r1/image_m/fmolb-13-1714028-t002.jpg Table 2. Primers used to construct +/−OSC strains. https://www.frontiersin.org/files/Articles/1714028/fmolb-13-1714028-HTML-r1/image_m/fmolb-13-1714028-t003.jpg Table 3. Sequencing primers. https://www.frontiersin.org/files/Articles/1714028/fmolb-13-1714028-HTML-r1/image_m/fmolb-13-1714028-g001.jpg Figure 1. Summarized results from a protein sequence alignment of 100 Otc proteins of diverse origin https://www.frontiersin.org/files/Articles/1714028/fmolb-13-1714028-HTML-r1/image_m/fmolb-13-1714028-g002.jpg Figure 2. Taxonomic analysis of Otc loop length. The loop length was determined for 100 sequences, s https://www.frontiersin.org/files/Articles/1714028/fmolb-13-1714028-HTML-r1/image_m/fmolb-13-1714028-g003.jpg Figure 3. (A) Schematic of the Bacillus subtilis Otc structure and loop region K36–K47 taken from Un https://www.frontiersin.org/files/Articles/1714028/fmolb-13-1714028-HTML-r1/image_m/fmolb-13-1714028-g004.jpg Figure 4. Loss of OSC increases unique indels in Bacillus subtilis argFprotein loop region during st https://www.frontiersin.org/files/Articles/1714028/fmolb-13-1714028-HTML-r1/image_m/fmolb-13-1714028-g005.jpg Figure 5. Impact of indel events on the amino acid sequence and length of the Otc protein loop regio https://www.frontiersin.org/files/Articles/1714028/fmolb-13-1714028-HTML-r1/image_m/fmolb-13-1714028-g006.jpg Figure 6. Results from the stationary-phase assay comparing the +OSC and −OSC strains +/− 0.1 mM IPT https://www.frontiersin.org/journals/cognition/articles/10.3389/fcogn.2026.1664983/full https://www.frontiersin.org/files/Articles/1664983/fcogn-05-1664983-HTML/image_m/fcogn-05-1664983-g001.jpg Figure 1. PRISMA flow diagram illustrating the identification and selection of studies on tactical k https://www.frontiersin.org/files/Articles/1664983/fcogn-05-1664983-HTML/image_m/fcogn-05-1664983-t001.jpg Table 1. Mapping of the 40 validated tactical actions to the ten principles of the FUT-SAT model (Co https://www.frontiersin.org/files/Articles/1664983/fcogn-05-1664983-HTML/image_m/fcogn-05-1664983-t002.jpg Table 2. Training and experience characteristics by age category. https://www.frontiersin.org/files/Articles/1664983/fcogn-05-1664983-HTML/image_m/fcogn-05-1664983-g002.jpg Figure 2. Example of a tactical action animation presented twice in identical form. Solid lines (___ https://www.frontiersin.org/files/Articles/1664983/fcogn-05-1664983-HTML/image_m/fcogn-05-1664983-g003.jpg Figure 3. Illustration of the repetition intervals used in DP condition. Each circle represents an a https://www.frontiersin.org/files/Articles/1664983/fcogn-05-1664983-HTML/image_m/fcogn-05-1664983-t003.jpg Table 3. Distribution of tactical actions by score under SP and DP conditions across age categories https://www.frontiersin.org/files/Articles/1664983/fcogn-05-1664983-HTML/image_m/fcogn-05-1664983-g004.jpg Figure 4. Mean declarative recognition scores (%) in the SP and DP conditions across age categories https://www.frontiersin.org/files/Articles/1664983/fcogn-05-1664983-HTML/image_m/fcogn-05-1664983-t004.jpg Table 4. Proportions of tactical actions perceived as covered during training, according to integrat https://www.frontiersin.org/files/Articles/1664983/fcogn-05-1664983-HTML/image_m/fcogn-05-1664983-g005.jpg Figure 5. Heatmap of recognition rates for tactical actions across age categories (U-13, U-15, U-18) https://www.frontiersin.org/journals/transplantation/articles/10.3389/frtra.2026.1758576/full https://www.frontiersin.org/files/Articles/1758576/frtra-05-1758576-HTML/image_m/frtra-05-1758576-t001.jpg Table 1. Demographics, clinical characteristics, and outcomes of patients who had undergone liver tr https://www.frontiersin.org/files/Articles/1758576/frtra-05-1758576-HTML/image_m/frtra-05-1758576-g001.jpg Figure 1. An OncoPrint of (top down) altered gene signaling pathways, pathological and clinical feat https://www.frontiersin.org/files/Articles/1758576/frtra-05-1758576-HTML/image_m/frtra-05-1758576-t002.jpg Table 2. Uni- and multivariable Cox proportional hazards models for recurrence-free and overall surv https://www.frontiersin.org/files/Articles/1758576/frtra-05-1758576-HTML/image_m/frtra-05-1758576-g002.jpg Figure 2. Post-liver transplant outcomes for 91 patients with hepatocellular carcinoma. Overall (A,B https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1750297/full https://www.frontiersin.org/files/Articles/1750297/fonc-15-1750297-HTML/image_m/fonc-15-1750297-t001.jpg Table 1. The list of primers used in RT-qPCR for mRNA expression. https://www.frontiersin.org/files/Articles/1750297/fonc-15-1750297-HTML/image_m/fonc-15-1750297-g001.jpg Figure 1. Clinical relevance of OLFML2A in TNBC. (A) OLFML2A expression in multiple cancers (Human P https://www.frontiersin.org/files/Articles/1750297/fonc-15-1750297-HTML/image_m/fonc-15-1750297-g002.jpg Figure 2. OLFML2A promotes proliferation and suppresses apoptosis in MDA-MB-231 cells. (A) Cell cycl https://www.frontiersin.org/files/Articles/1750297/fonc-15-1750297-HTML/image_m/fonc-15-1750297-g003.jpg Figure 3. OLFML2A induces G1 phase arrest in MDA-MB-231 cells. (A) Representative western blots show https://www.frontiersin.org/files/Articles/1750297/fonc-15-1750297-HTML/image_m/fonc-15-1750297-g004.jpg Figure 4. Immunofluorescence analysis of G1 phase-associated biomarkers in MDA-MB-231 cells. (A) Qua https://www.frontiersin.org/files/Articles/1750297/fonc-15-1750297-HTML/image_m/fonc-15-1750297-g005.jpg Figure 5. OLFML2A induces G1 phase arrest in a TNBC xenograft mouse model. (A) Schematic diagram of https://www.frontiersin.org/files/Articles/1750297/fonc-15-1750297-HTML/image_m/fonc-15-1750297-g006.jpg Figure 6. OLFML2A interacts with EZH2 to regulate biological processes in MDA-MB-231 cells. (A) Prot https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1779004/full https://www.frontiersin.org/files/Articles/1779004/fpsyt-17-1779004-HTML/image_m/fpsyt-17-1779004-g001.jpg Figure 1. CONSORT Trial flow diagram. https://www.frontiersin.org/files/Articles/1779004/fpsyt-17-1779004-HTML/image_m/fpsyt-17-1779004-t001.jpg Table 1. Demographic characteristics of the participants. https://www.frontiersin.org/files/Articles/1779004/fpsyt-17-1779004-HTML/image_m/fpsyt-17-1779004-t002.jpg Table 2. Changes in primary and secondary outcomes between AC group and Acu group. https://www.frontiersin.org/files/Articles/1779004/fpsyt-17-1779004-HTML/image_m/fpsyt-17-1779004-t003.jpg Table 3. Changes in hematological indicators between AC group and Acu group. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1781600/full https://www.frontiersin.org/files/Articles/1781600/fpubh-14-1781600-HTML-r2/image_m/fpubh-14-1781600-g001.jpg Figure 1. Flowchart of the scoping review based on the PRISMA 2020 model. Source: Adapted from Page https://www.frontiersin.org/files/Articles/1781600/fpubh-14-1781600-HTML-r2/image_m/fpubh-14-1781600-t001.jpg Table 1. Characteristics of included studies on technological interventions in mental health and wel https://www.frontiersin.org/files/Articles/1781600/fpubh-14-1781600-HTML-r2/image_m/fpubh-14-1781600-g002.jpg Figure 2. Global map of studies on Digital EduHealth interventions aimed at the wellbeing of minorit https://www.frontiersin.org/files/Articles/1781600/fpubh-14-1781600-HTML-r2/image_m/fpubh-14-1781600-g003.jpg Figure 3. Reported effects of Digital EduHealth interventions aimed at the wellbeing of university s https://www.frontiersin.org/files/Articles/1781600/fpubh-14-1781600-HTML-r2/image_m/fpubh-14-1781600-g004.jpg Figure 4. Factors associated with the implementation of Digital EduHealth interventions to promote t https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1809342/full https://www.frontiersin.org/files/Articles/1809342/fimmu-17-1809342-HTML/image_m/fimmu-17-1809342-g001.jpg Figure 1. ADCP assay for detecting phagocytosis of antibody-opsonized HIV-1 virions. (A) Schematic o https://www.frontiersin.org/files/Articles/1809342/fimmu-17-1809342-HTML/image_m/fimmu-17-1809342-g002.jpg Figure 2. ADCP assay for detection of phagocytosis of human antibody opsonized virions. Sucrose pell https://www.frontiersin.org/files/Articles/1809342/fimmu-17-1809342-HTML/image_m/fimmu-17-1809342-g003.jpg Figure 3. Broad applicability of the ADCP assay across HIV-1 strains and antigen formats. Sucrose pe https://www.frontiersin.org/files/Articles/1809342/fimmu-17-1809342-HTML/image_m/fimmu-17-1809342-g004.jpg Figure 4. ADCP assay to measure phagocytosis of HIV-1 virions opsonized with polyclonal antibodies f https://www.frontiersin.org/files/Articles/1809342/fimmu-17-1809342-HTML/image_m/fimmu-17-1809342-g005.jpg Figure 5. ADCP activity of murine monoclonal antibodies against HIV-1 virion-coupled beads. Sucrose https://www.frontiersin.org/files/Articles/1809342/fimmu-17-1809342-HTML/image_m/fimmu-17-1809342-g006.jpg Figure 6. Antibody-dependent cellular phagocytosis mediated by sera from immunized mice against REJO https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1642039/full https://www.frontiersin.org/files/Articles/1642039/fbinf-05-1642039-HTML/image_m/fbinf-05-1642039-g001.jpg Figure 1. Flow diagram of the presented methodology. https://www.frontiersin.org/files/Articles/1642039/fbinf-05-1642039-HTML/image_m/fbinf-05-1642039-t001.jpg Table 1. Performance metrics of the different modeling strategies, including ensemble approaches. https://www.frontiersin.org/files/Articles/1642039/fbinf-05-1642039-HTML/image_m/fbinf-05-1642039-g002.jpg Figure 2. Left: Variation of the BEDROC values across different α-values for various model combinati https://www.frontiersin.org/files/Articles/1642039/fbinf-05-1642039-HTML/image_m/fbinf-05-1642039-g003.jpg Figure 3. Molecules M1–M3 represent the top-ranked compounds in the C1 model (score >0.65). Molecule https://www.frontiersin.org/files/Articles/1642039/fbinf-05-1642039-HTML/image_m/fbinf-05-1642039-g004.jpg Figure 4. Analysis of molecular classes and subclasses. (A) Distribution of molecules by chemical cl https://www.frontiersin.org/files/Articles/1642039/fbinf-05-1642039-HTML/image_m/fbinf-05-1642039-g005.jpg Figure 5. (A) Species with five or more active compounds, sorted by the total number of active compo https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2026.1805953/full https://www.frontiersin.org/files/Articles/1805953/fnbeh-20-1805953-HTML/image_m/fnbeh-20-1805953-g001.jpg Figure 1. Localization and mechanistic pathways underlying the metabolic and appetite-regulating act https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1733641/full https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-g001.jpg Figure 1. Analytical framework on the intersection of gender, ethnicity, and insurance. This figure https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-t001.jpg Table 1. Demographic characteristics for NSDUH sample, 2023 (n = 56,705) (weighted). https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-t002.jpg Table 2. Logistic regression models for AMI/SUD and SUD behaviors for NSDUH sample, 2023. https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-g002.jpg Figure 2. Income disparities by gender. NSDUH sample, 2023 (n = 56,705). Y-axis represents the perce https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-g003.jpg Figure 3. Income and gender disparities by Hispanic identification. NSDUH sample, 2023 (n = 56,705). https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-t003.jpg Table 3. Logistic regression models for seeking care or NSDUH sample, 2023. https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-t004.jpg Table 4. Logistic regression models for forgone care in substance use for NSDUH sample, 2023. https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-t005.jpg Table 5. Logistic regression models for forgone care in mental health treatment for NSDUH sample, 20 https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-g004.jpg Figure 4. Skip-pattern structure for behavioral health utilization and unmet need. Sample sizes are https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-t006.jpg Table 6. Effect modification of Hispanic identification on past-year AMI/SUD treatment by AMI/SUD cr https://www.frontiersin.org/files/Articles/1733641/fpubh-14-1733641-HTML/image_m/fpubh-14-1733641-g005.jpg Figure 5. Predicted probability of receiving AMI/SUD treatment by ethnicity and AMI/SUD criteria sta https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1683009/full https://www.frontiersin.org/files/Articles/1683009/fonc-16-1683009-HTML/image_m/fonc-16-1683009-t001.jpg Table 1. Baseline patient and tumor characteristics. https://www.frontiersin.org/files/Articles/1683009/fonc-16-1683009-HTML/image_m/fonc-16-1683009-t002.jpg Table 2. Safety and efficacy outcomes. https://www.frontiersin.org/files/Articles/1683009/fonc-16-1683009-HTML/image_m/fonc-16-1683009-t003.jpg Table 3. Analysis of risk factors for disease recurrence. https://www.frontiersin.org/files/Articles/1683009/fonc-16-1683009-HTML/image_m/fonc-16-1683009-g001.jpg Figure 1. Recurrence rates at three months by patient age. https://www.frontiersin.org/files/Articles/1683009/fonc-16-1683009-HTML/image_m/fonc-16-1683009-g002.jpg Figure 2. Recurrence rates at three months stratified by gender. https://www.frontiersin.org/files/Articles/1683009/fonc-16-1683009-HTML/image_m/fonc-16-1683009-t004.jpg Table 4. Characteristics of tumor recurrence. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1721223/full https://www.frontiersin.org/files/Articles/1721223/fnut-12-1721223-HTML/image_m/fnut-12-1721223-t001.jpg Table 1. Demographic data and anthropometric measurements of the participants. https://www.frontiersin.org/files/Articles/1721223/fnut-12-1721223-HTML/image_m/fnut-12-1721223-t002.jpg Table 2. Convergent validity based on the BEVQ-15 questionnaire and the 3-day food record. https://www.frontiersin.org/files/Articles/1721223/fnut-12-1721223-HTML/image_m/fnut-12-1721223-t003.jpg Table 3. Test–retest reliability and consistency of beverage intake estimates across repeated measur https://www.frontiersin.org/files/Articles/1721223/fnut-12-1721223-HTML/image_m/fnut-12-1721223-g001.jpg Figure 1. BEVQ-recall correlations and 95% confidence intervals over time. https://www.frontiersin.org/files/Articles/1721223/fnut-12-1721223-HTML/image_m/fnut-12-1721223-g002.jpg Figure 2. Bland–Altman plot for all beverage categories (average of 3 time points). https://www.frontiersin.org/files/Articles/1721223/fnut-12-1721223-HTML/image_m/fnut-12-1721223-t004.jpg Table 4. Bland–Altman analysis results for agreement between the BEVQ-15 and 3-day dietary records a https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1761348/full https://www.frontiersin.org/files/Articles/1761348/fnut-13-1761348-HTML-r2/image_m/fnut-13-1761348-g001.jpg Figure 1. Conceptual model of psychosocial, normative, and ethical determinants of vegan lifestyle a https://www.frontiersin.org/files/Articles/1761348/fnut-13-1761348-HTML-r2/image_m/fnut-13-1761348-t001.jpg Table 1. Latent variables and items. https://www.frontiersin.org/files/Articles/1761348/fnut-13-1761348-HTML-r2/image_m/fnut-13-1761348-t002.jpg Table 2. Sociodemographic characteristics of participants. https://www.frontiersin.org/files/Articles/1761348/fnut-13-1761348-HTML-r2/image_m/fnut-13-1761348-t003.jpg Table 3. Construct validity and reliability of latent variables. https://www.frontiersin.org/files/Articles/1761348/fnut-13-1761348-HTML-r2/image_m/fnut-13-1761348-g002.jpg Figure 2. Correlations among latent variables. https://www.frontiersin.org/files/Articles/1761348/fnut-13-1761348-HTML-r2/image_m/fnut-13-1761348-t004.jpg Table 4. Model path coefficients estimated with natural spline, PCA, and elastic net correction for https://www.frontiersin.org/files/Articles/1761348/fnut-13-1761348-HTML-r2/image_m/fnut-13-1761348-t005.jpg Table 5. Model performance: R-squared values with bootstrap confidence intervals. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1693459/full https://www.frontiersin.org/files/Articles/1693459/fpubh-13-1693459-HTML-r1/image_m/fpubh-13-1693459-g001.jpg Figure 1. Researcher priorities and community-inclusive approaches to enhance trust. https://www.frontiersin.org/files/Articles/1693459/fpubh-13-1693459-HTML-r1/image_m/fpubh-13-1693459-t001.jpg Table 1. Culturally-centered community-based participatory research initiatives for and with margina https://www.frontiersin.org/files/Articles/1693459/fpubh-13-1693459-HTML-r1/image_m/fpubh-13-1693459-t002.jpg Table 2. Community-Based Participatory Research Blueprint to Realize the Key Messages of the World H https://www.frontiersin.org/files/Articles/1693459/fpubh-13-1693459-HTML-r1/image_m/fpubh-13-1693459-g002.jpg Figure 2. Additional recommendations for diverse actors to promote community-based participatory res https://www.frontiersin.org/files/Articles/1693459/fpubh-13-1693459-HTML-r1/image_m/fpubh-13-1693459-g003.jpg Figure 3. Recommendations for investigators to incorporate participatory values and methods across t https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1671681/full https://www.frontiersin.org/files/Articles/1671681/fvets-12-1671681-HTML/image_m/fvets-12-1671681-t001.jpg Table 1. Prevalence of SARS-CoV-2 RNA, anti-nucleocapsid antibodies and neutralizing antibodies in d https://www.frontiersin.org/files/Articles/1671681/fvets-12-1671681-HTML/image_m/fvets-12-1671681-t002.jpg Table 2. Univariate analyses for factors associates with the neutralizing antibodies positivity. https://www.frontiersin.org/journals/toxicology/articles/10.3389/ftox.2025.1701021/full https://www.frontiersin.org/files/Articles/1701021/ftox-07-1701021-HTML/image_m/ftox-07-1701021-g008.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1701021/ftox-07-1701021-HTML/image_m/ftox-07-1701021-g001.jpg Figure 1. The Effect of Berberine Nanoliposomes (BBR-Lip) on Imidacloprid (IMI)-induced cardiotoxici https://www.frontiersin.org/files/Articles/1701021/ftox-07-1701021-HTML/image_m/ftox-07-1701021-g002.jpg Figure 2. Representative set of electron micrographs of sections from left ventricular walls of diff https://www.frontiersin.org/files/Articles/1701021/ftox-07-1701021-HTML/image_m/ftox-07-1701021-g003.jpg Figure 3. Ontology and pathway enrichment analysis of potential imidacloprid and cardiotoxicity targ https://www.frontiersin.org/files/Articles/1701021/ftox-07-1701021-HTML/image_m/ftox-07-1701021-g004.jpg Figure 4. Effect of BBR-Lip administration on the levels of oxidative stress markers in IMI-exposed https://www.frontiersin.org/files/Articles/1701021/ftox-07-1701021-HTML/image_m/ftox-07-1701021-g005.jpg Figure 5. Effect of BBR-Lip administration on the TLR4/NLRP3 Inflammasome pathway and inflammatory m https://www.frontiersin.org/files/Articles/1701021/ftox-07-1701021-HTML/image_m/ftox-07-1701021-g006.jpg Figure 6. Effect of BBR-Lip administration on the inflammatory markers expression in the myocardium https://www.frontiersin.org/files/Articles/1701021/ftox-07-1701021-HTML/image_m/ftox-07-1701021-g007.jpg Figure 7. Effect of BBR-Lip administration on the phosphorylation of JAK/STAT and the expression of https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1796102/full https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g001.jpg Figure 1. Radiometric thermal imaging workflow. (Upper) Calibrated FLIR Cx-series thermal camera use https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g002.jpg Figure 2. Acoustic recording systems deployed in experimental rooms. (a) Zoom H4n Pro recorder with https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g003.jpg Figure 3. Video data acquisition hardware. (Upper) Overhead-mounted GoPro Hero 13 camera positioned https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g004.jpg Figure 4. Environmental monitoring apparatus. Portable multi-sensor array used to record temperature https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-t001.jpg Table 1. Summary of multimodal data collection, temporal coverage, spatial coverage, sampling freque https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g005.jpg Figure 5. Ambient environmental conditions during rearing. Daily morning (08:00) and afternoon (16:0 https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-t002.jpg Table 2. One-way ANOVA results and significant Tukey post-hoc pairwise comparisons for weekly head a https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g006.jpg Figure 6. Thermoregulatory development across early life. Weekly mean head and foot surface temperat https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-t003.jpg Table 3. One-way ANOVA results for age-related differences in acoustic spectral features across deve https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g007.jpg Figure 7. Acoustic feature trajectories across development. Z-score-normalized weekly trends in spec https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-t004.jpg Table 4. Statistical summary of optical-flow-based flock movement responses to routine caretaker ent https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g008.jpg Figure 8. Behavioral response to caretaker entry. Weekly mean optical flow magnitude before, during, https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g009.jpg Figure 9. Integrated multimodal developmental trajectories. Z-score-normalized weekly features illus https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-t005.jpg Table 5. Significant Pearson correlations between thermal, acoustic, behavioral, and environmental f https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g010.jpg Figure 10. Cross-modal correlation structure. Heatmap of Pearson correlation coefficients between th https://www.frontiersin.org/files/Articles/1796102/fvets-13-1796102-HTML-r3/image_m/fvets-13-1796102-g011.jpg Figure 11. Conceptual synthesis of multimodal development in laying hens. Schematic representation o https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1656611/full https://www.frontiersin.org/files/Articles/1656611/fendo-16-1656611-HTML-r1/image_m/fendo-16-1656611-g001.jpg Figure 1. Overview of BMI-based treatment recommendations for obesity management and landmark anti-o https://www.frontiersin.org/files/Articles/1656611/fendo-16-1656611-HTML-r1/image_m/fendo-16-1656611-g002.jpg Figure 2. Weight loss response per anti-obesity drug (19-21, 23, 25, 34, 35). This figure indicates https://www.frontiersin.org/files/Articles/1656611/fendo-16-1656611-HTML-r1/image_m/fendo-16-1656611-g003.jpg Figure 3. Stepwise algorithm for personalized obesity pharmacotherapy A proposed treatment algorithm https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1669896/full https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-g001.jpg Figure 1. Overview of the study’s methodology: (a) fusion model: automatic speech-recognition transc https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-t001.jpg Table 1. Baseline demographic, cognitive, and speech characteristics of participants across training https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-g002.jpg Figure 2. Prompt-engineering workflow for synthetic transcript generation and classification. Fine-t https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-g003.jpg Figure 3. Performance of general-purpose and clinical-domain transformer models across fine-tuning s https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-t002.jpg Table 2. Performance comparison of BERT, linguistic feature-based, and fusion models on validation a https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-g004.jpg Figure 4. Evaluation of synthetic speech generated by LLMs for data augmentation on the ADReSSo benc https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-g005.jpg Figure 5. Effect of synthetic data volume on embedding structure and model performance. t-SNE plots https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-g006.jpg Figure 6. Impact of MedAlpaca-7B synthetic data on screening model performance and prediction confid https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-t003.jpg Table 3. Zero-shot versus fine-tuned F1 performance of unimodal (text-only) and multimodal LLM class https://www.frontiersin.org/files/Articles/1669896/frai-08-1669896-HTML/image_m/frai-08-1669896-t004.jpg Table 4. Zero-shot versus fine-tuned F1 performance of unimodal (text-only) and multimodal LLM class https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1687798/full https://www.frontiersin.org/files/Articles/1687798/fpubh-13-1687798-HTML/image_m/fpubh-13-1687798-t001.jpg Table 1. The contribution of changes in mortality rates across different age groups on the increase https://www.frontiersin.org/files/Articles/1687798/fpubh-13-1687798-HTML/image_m/fpubh-13-1687798-g001.jpg Figure 1. (A) Age group decomposition, (B) Cause decomposition, and (C) Age-specific cause decomposi https://www.frontiersin.org/files/Articles/1687798/fpubh-13-1687798-HTML/image_m/fpubh-13-1687798-t002.jpg Table 2. The contribution of changes in mortality rates by sex and age group on the increase in life https://www.frontiersin.org/files/Articles/1687798/fpubh-13-1687798-HTML/image_m/fpubh-13-1687798-t003.jpg Table 3. Changes of death spectrum by diseases in Quzhou, 2015–2023. https://www.frontiersin.org/files/Articles/1687798/fpubh-13-1687798-HTML/image_m/fpubh-13-1687798-t004.jpg Table 4. Changes of cause-eliminated life expectancy by diseases in Quzhou, 2015–2023. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1625247/full https://www.frontiersin.org/files/Articles/1625247/fped-13-1625247-HTML/image_m/fped-13-1625247-t001.jpg Table 1. Patient's serum lipid levels. https://www.frontiersin.org/files/Articles/1625247/fped-13-1625247-HTML/image_m/fped-13-1625247-g001.jpg Figure 1. Coronary angiography of the 14-year-old patient. Coronary angiography (27 July 2023) demon https://www.frontiersin.org/files/Articles/1625247/fped-13-1625247-HTML/image_m/fped-13-1625247-t002.jpg Table 2. Timeline of the patient's treatment course. https://www.frontiersin.org/files/Articles/1625247/fped-13-1625247-HTML/image_m/fped-13-1625247-g002.jpg Figure 2. LDL-C trajectory. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1714816/full https://www.frontiersin.org/files/Articles/1714816/fphar-16-1714816-HTML-r1/image_m/fphar-16-1714816-t001.jpg Table 1. Total phenolic and flavonoid contents of CVE. https://www.frontiersin.org/files/Articles/1714816/fphar-16-1714816-HTML-r1/image_m/fphar-16-1714816-g001.jpg Figure 1. CVE exhibits antioxidant effects and protects HaCaT cells against oxidative stress. (A) DP https://www.frontiersin.org/files/Articles/1714816/fphar-16-1714816-HTML-r1/image_m/fphar-16-1714816-g002.jpg Figure 2. CVE inhibits NF-κB activation and pro-inflammatory protein expression through ALDH2 activa https://www.frontiersin.org/files/Articles/1714816/fphar-16-1714816-HTML-r1/image_m/fphar-16-1714816-g003.jpg Figure 3. Effects of CVE on DNCB-Induced AD in SKH-1 mouse (A) Scheme of experiment designs (B) effe https://www.frontiersin.org/files/Articles/1714816/fphar-16-1714816-HTML-r1/image_m/fphar-16-1714816-g004.jpg Figure 4. Effect of CVE on epidermis thickness change in dorsal skin tissue of DNCB-induced AD in SK https://www.frontiersin.org/files/Articles/1714816/fphar-16-1714816-HTML-r1/image_m/fphar-16-1714816-g005.jpg Figure 5. Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (LC-QTOF-MS-MS) analysis https://www.frontiersin.org/files/Articles/1714816/fphar-16-1714816-HTML-r1/image_m/fphar-16-1714816-t002.jpg Table 2. Tentative identification of the chemical components of the CVE obtained from the UPLC-TOF-M https://www.frontiersin.org/files/Articles/1714816/fphar-16-1714816-HTML-r1/image_m/fphar-16-1714816-g006.jpg Figure 6. Schematic illustration of the CVE was evaluated using in vitro keratinocytes, in vivo SKH- https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1745395/full https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g001.jpg Figure 1. Analysis process for spatiotemporal monitoring of grasshopper habitats using ensemble mach https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g002.jpg Figure 2. (A) Location map of the study area. (B) Distribution of grasslands in the study area. (C–E https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-t001.jpg Table 1. Environmental factors. Altogether, there are 28 factors. https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g003.jpg Figure 3. Duration of life stages of the grasshopper by region. https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g004.jpg Figure 4. Graphical representation of multicollinearity among the selected habitat variables. (a) Sp https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g005.jpg Figure 5. Spatial patterns of O. d. asiaticus and hot spot areas (GHAs). https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g006.jpg Figure 6. Spatial patterns of D. barbipes and hot spot areas (GHAs). https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g007.jpg Figure 7. ROC curve and AUC value in ensemble machine learning. https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g008.jpg Figure 8. Habitat suitability of Oedaleus decorus asiaticus by ensemble model. https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g009.jpg Figure 9. Percentage of habitat suitability area by region for Oedaleus decorus asiaticus. https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g010.jpg Figure 10. Habitat suitability of Dasyhippus barbipes by ensemble model. https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g011.jpg Figure 11. Percentage of habitat suitability area by region for Dasyhippus barbipes. https://www.frontiersin.org/files/Articles/1745395/fenvs-14-1745395-HTML/image_m/fenvs-14-1745395-g012.jpg Figure 12. Contribution of factors across regions and years. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1724665/full https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t001.jpg Table 1. Platform economy development level indicator system. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t002.jpg Table 2. Descriptive statistics of variables. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-g001.jpg Figure 1. Theoretical mechanism of platform economy narrowing urban–rural income gap by improving la https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-g002.jpg Figure 2. Comparative analysis of spatial characteristics of digital economy level between 2015 and https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-g003.jpg Figure 3. Comparative analysis of spatial characteristics of urban–rural income gap between 2015 and https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-g004.jpg Figure 4. Comparative analysis of the spatial characteristics of the level of narrowing the income g https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t003.jpg Table 3. Global Moran’s index values of variables from 2015 to 2024. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-g005.jpg Figure 5. Morland scatter plot of Theil index. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-g006.jpg Figure 6. Moran scatter plot of platform economy development level. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t004.jpg Table 4. Results of spatial econometric model verification. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t005.jpg Table 5. Benchmark regression results. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t006.jpg Table 6. Spatial econometric regression results. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t007.jpg Table 7. Results of spatial spillover effect. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t008.jpg Table 8. Results of U-shaped relationship test. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-g007.jpg Figure 7. U-shaped relationship between pfed and urban-rural income gap. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t009.jpg Table 9. Results of robustness test. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t010.jpg Table 10. Analysis of intermediary effects based on land use efficiency. https://www.frontiersin.org/files/Articles/1724665/fsufs-09-1724665-HTML/image_m/fsufs-09-1724665-t011.jpg Table 11. Heterogeneity test results. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1729244/full https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-g001.jpg Figure 1. The impact of the digital economy on LCC efficiency, both directly and indirectly. https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-g002.jpg Figure 2. The parameters used in LCCE for input and outcome. https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-t001.jpg Table 1. Measures of the digital economy were developed for this research. https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-t002.jpg Table 2. Methods for collecting data and where to find indicators. https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-t003.jpg Table 3. Findings from studying how the rise of the digital economy has affected emission reductions https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-t004.jpg Table 4. The outcomes of the resilience analysis. https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-t005.jpg Table 5. Mechanism evaluation. https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-t006.jpg Table 6. Heterogeneous impacts rely on place of origin. https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-t007.jpg Table 7. Heterogeneous impacts from different-sized cities. https://www.frontiersin.org/files/Articles/1729244/fsufs-09-1729244-HTML/image_m/fsufs-09-1729244-t008.jpg Table 8. Heterogeneous outcomes depending on available resources. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1783944/full https://www.frontiersin.org/files/Articles/1783944/fphar-17-1783944-HTML/image_m/fphar-17-1783944-g001.jpg Figure 1. Risk factors for intracerebral hemorrhage and underlying mechanisms. https://www.frontiersin.org/files/Articles/1783944/fphar-17-1783944-HTML/image_m/fphar-17-1783944-g002.jpg Figure 2. Flowchart of study selection. https://www.frontiersin.org/files/Articles/1783944/fphar-17-1783944-HTML/image_m/fphar-17-1783944-t001.jpg Table 1. Structured summary of preclinical studies on exercise-based rehabilitation after intracereb https://www.frontiersin.org/files/Articles/1783944/fphar-17-1783944-HTML/image_m/fphar-17-1783944-g003.jpg Figure 3. Proposed mechanistic framework of exercise after intracerebral hemorrhage. The mechanistic https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1763131/full https://www.frontiersin.org/files/Articles/1763131/fneur-17-1763131-HTML-r1/image_m/fneur-17-1763131-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1763131/fneur-17-1763131-HTML-r1/image_m/fneur-17-1763131-g001.jpg Figure 1. Schematic diagram. (A) CH and Its Surgical Interventions. (B) Lymphatic pathways of CSF. C https://www.frontiersin.org/files/Articles/1763131/fneur-17-1763131-HTML-r1/image_m/fneur-17-1763131-g002.jpg Figure 2. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1763131/fneur-17-1763131-HTML-r1/image_m/fneur-17-1763131-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1763131/fneur-17-1763131-HTML-r1/image_m/fneur-17-1763131-g003.jpg Figure 3. NMA of eligible comparisons for efficacy (A) and safety (B). The node size is proportional https://www.frontiersin.org/files/Articles/1763131/fneur-17-1763131-HTML-r1/image_m/fneur-17-1763131-g004.jpg Figure 4. Relative effects of surgical interventions for primary outcomes. (A) League tables of NMA https://www.frontiersin.org/files/Articles/1763131/fneur-17-1763131-HTML-r1/image_m/fneur-17-1763131-g005.jpg Figure 5. Two-dimensional graphs about efficacy and safety in all studies (A) and head-to-head studi https://www.frontiersin.org/files/Articles/1763131/fneur-17-1763131-HTML-r1/image_m/fneur-17-1763131-g006.jpg Figure 6. Forest plot of lumbar approach versus cranial approach. The forest plot shows the outcomes https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1703923/full https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-t001.jpg Table 1. Suitability and limitations of SML, interviews and questionnaires in order to gather patien https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-g001.jpg Figure 1. Schematic representation of main categories of research questions addressed in 63 scientif https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-t002.jpg Table 2. Common research questions in publications using SML for patient experience data. https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-g002.jpg Figure 2. Schematic overview of the conceptual patient experience model (CPEM) to define the relevan https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-t003.jpg Table 3. Key concepts of the conceptual patient experience model (CPEM). https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-t004.jpg Table 4. Examples for lay language descriptions of medical terms. https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-t005.jpg Table 5. NLP-based methods, advantages, and limitations. https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-g003.jpg Figure 3. Example of a heat map display of descriptive statistics from an unpublished SML study. Sym https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-g004.jpg Figure 4. Pie chart of the author-reported gender distribution in the T2DM example case study. https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-g005.jpg Figure 5. Bar chart of the experienced comorbidities by severity in the T2DM example case study. Thi https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-g006.jpg Figure 6. Example prompt for summarizing online patient experience reports in a tabular format. https://www.frontiersin.org/files/Articles/1703923/fmed-12-1703923-HTML/image_m/fmed-12-1703923-t006.jpg Table 6. LLM generated summary highlighting various areas of impact and example quotes of collected https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1712214/full https://www.frontiersin.org/files/Articles/1712214/fpls-16-1712214-HTML/image_m/fpls-16-1712214-g001.jpg Figure 1. Chromosomal location of the identified MsCOI1 genes on alfalfa chromosomes. The chromosoma https://www.frontiersin.org/files/Articles/1712214/fpls-16-1712214-HTML/image_m/fpls-16-1712214-g002.jpg Figure 2. Phylogenetic tree of MsCOI1 proteins from alfalfa, maize, rice, and Arabidopsis. The phylo https://www.frontiersin.org/files/Articles/1712214/fpls-16-1712214-HTML/image_m/fpls-16-1712214-g003.jpg Figure 3. Analysis of conserved motifs and gene structure in the MsCOI1 genes. (A) The phylogenetic https://www.frontiersin.org/files/Articles/1712214/fpls-16-1712214-HTML/image_m/fpls-16-1712214-g004.jpg Figure 4. Prediction of cis-acting elements in the MsCOI1s promoter regions. https://www.frontiersin.org/files/Articles/1712214/fpls-16-1712214-HTML/image_m/fpls-16-1712214-g005.jpg Figure 5. Expression levels of MsCOI1s in different tissues. Transcriptome data were used to analyze https://www.frontiersin.org/files/Articles/1712214/fpls-16-1712214-HTML/image_m/fpls-16-1712214-g006.jpg Figure 6. The expression profiles of 32 MsCOI1 genes under 100 mM exogenous methyl jasmonate (MeJA) https://www.frontiersin.org/files/Articles/1712214/fpls-16-1712214-HTML/image_m/fpls-16-1712214-g007.jpg Figure 7. Expression analysis of eight MsCOI1s after AMV treatment. RT-qPCR analysis of the mRNA exp https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2025.1602963/full https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g001.jpg Figure 1. Workflow illustrating the main steps in the proposed model for urban seismic performance a https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g002.jpg Figure 2. A flowchart presenting the authors’ framework proposal for urban seismic performance asses https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g003.jpg Figure 3. An example for a set of two harmonised fragility curves for two limit states (yielding and https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g004.jpg Figure 4. Impact radius of a damaged building and its influence on the nearby road: an isolated buil https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g005.jpg Figure 5. Phases of urban system functionality represented as the resilience curve in relation to th https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-t001.jpg Table 1. Different time phases considered in the study. https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-t002.jpg Table 2. Weights describing the priorities of needs (n–for nomenclature see Section 2.2) in differen https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-t003.jpg Table 3. Weights describing the significance of urban functions (f) in fulfilling a specific need (n https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-t004.jpg Table 4. The share that buildings and OSs contribute to the operation of a specific urban function ( https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g006.jpg Figure 6. Graph of the computational model of the town of Brežice. https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g007.jpg Figure 7. The damage to the investigated town after the severe earthquake (0.30 g_C) and its distrib https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g008.jpg Figure 8. Overall evaluation of OSs (35 in total) based on five defined parameters (left) and select https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-t005.jpg Table 5. Estimated restoration time for the physical components of the urban system for different ea https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g009.jpg Figure 9. Proportion of residents with low, medium, and high level of accessibility to urban functio https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g010.jpg Figure 10. Distribution of local accessibility to all urban functions (functional buildings) BEFORE https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g011.jpg Figure 11. Global accessibility to urban functions (f1-7) in different time phases for various earth https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g012.jpg Figure 12. Functionality of the investigated urban system for all analysed earthquake scenarios. https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g013.jpg Figure 13. Short-term (1 month), medium-term (half a year to 1 year) and long-term (5 years) resilie https://www.frontiersin.org/files/Articles/1602963/fbuil-11-1602963-HTML-r1/image_m/fbuil-11-1602963-g014.jpg Figure 14. The town’s performance in the case of the 0.30 g_R scenario: normalised functionality lev https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1669287/full https://www.frontiersin.org/files/Articles/1669287/fpsyt-16-1669287-HTML/image_m/fpsyt-16-1669287-t001.jpg Table 1. The estimated marginal (predicted) GAD-7 scores over all six sessions, for pre- and post-in https://www.frontiersin.org/files/Articles/1669287/fpsyt-16-1669287-HTML/image_m/fpsyt-16-1669287-t002.jpg Table 2. Analysis of variance (ANOVA) comparing pre-intervention anxiety scores across six VR sessio https://www.frontiersin.org/files/Articles/1669287/fpsyt-16-1669287-HTML/image_m/fpsyt-16-1669287-t003.jpg Table 3. Mean GAD-7 score change per session. https://www.frontiersin.org/files/Articles/1669287/fpsyt-16-1669287-HTML/image_m/fpsyt-16-1669287-t004.jpg Table 4. Fixed effects from linear mixed-effects model predicting GAD-7 scores across time (pre vs. https://www.frontiersin.org/files/Articles/1669287/fpsyt-16-1669287-HTML/image_m/fpsyt-16-1669287-g001.jpg Figure 1. Adjusted predictions of GAD-7 by session and time. https://www.frontiersin.org/files/Articles/1669287/fpsyt-16-1669287-HTML/image_m/fpsyt-16-1669287-t005.jpg Table 5. Model fit indices. https://www.frontiersin.org/files/Articles/1669287/fpsyt-16-1669287-HTML/image_m/fpsyt-16-1669287-t006.jpg Table 6. Thematic summary of participant experiences with the SpiritVR Journey mindfulness programme https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1718795/full https://www.frontiersin.org/files/Articles/1718795/fpubh-13-1718795-HTML-r1/image_m/fpubh-13-1718795-t001.jpg Table 1. Descriptive statistics for key variables across four time periods. https://www.frontiersin.org/files/Articles/1718795/fpubh-13-1718795-HTML-r1/image_m/fpubh-13-1718795-t002.jpg Table 2. Distribution of hospital admissions by clinical department before and after trainee doctor https://www.frontiersin.org/files/Articles/1718795/fpubh-13-1718795-HTML-r1/image_m/fpubh-13-1718795-g001.jpg Figure 1. Graphic presentation of hospital admission, length of stay, healthcare expenditure, 30-day https://www.frontiersin.org/files/Articles/1718795/fpubh-13-1718795-HTML-r1/image_m/fpubh-13-1718795-t003.jpg Table 3. Associations between mass resignation and hospital admissions, length of stay, hospital exp https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1650230/full https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-t001.jpg Table 1. Average of one to three standard meteorological weeks of sowing of Sclerotinia rot incidenc https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-g001.jpg Figure 1. Trends in weather variables and Sclerotinia rot incidence. https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-t002.jpg Table 2. Percent petal infestation (PPI) from 2009–2010 to 2022–2023. https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-g002.jpg Figure 2. Condensed weekly petal infestation trajectories into a single measure of inoculum pressure https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-g003.jpg Figure 3. Important base weather features for Sclerotinia rot incidence and their interaction. https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-t003.jpg Table 3. Single multi-level model with sowing date as a categorical factor with different performanc https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-g004.jpg Figure 4. Performance across categories and sowing windows. https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-g005.jpg Figure 5. Partial dependence plots illustrating the marginal effects of key weather drivers on patho https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-g006.jpg Figure 6. SHAP (Shapley Additive Explanations) summaries illustrating the marginal effects of key we https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-g007.jpg Figure 7. Historical vs. predicted Sclerotinia rot incidence. https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-g008.jpg Figure 8. Future prediction of Sclerotinia rot incidence. https://www.frontiersin.org/files/Articles/1650230/fpls-16-1650230-HTML/image_m/fpls-16-1650230-g009.jpg Figure 9. Uncertainty bands via a multivariate first-order auto-regression weather ensemble (N = 500 https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1773281/full https://www.frontiersin.org/files/Articles/1773281/fped-14-1773281-HTML-r1/image_m/fped-14-1773281-g001.jpg Figure 1. Flow of study participants. https://www.frontiersin.org/files/Articles/1773281/fped-14-1773281-HTML-r1/image_m/fped-14-1773281-g002.jpg Figure 2. Distribution of Ureaplasma spp. colonization by gestational age and birth weight. (A) Perc https://www.frontiersin.org/files/Articles/1773281/fped-14-1773281-HTML-r1/image_m/fped-14-1773281-t001.jpg Table 1. Comparison of perinatal and laboratory characteristics between preterm neonates with and wi https://www.frontiersin.org/files/Articles/1773281/fped-14-1773281-HTML-r1/image_m/fped-14-1773281-t002.jpg Table 2. Outcomes of preterm infants with and without Ureaplasma spp. colonization. https://www.frontiersin.org/files/Articles/1773281/fped-14-1773281-HTML-r1/image_m/fped-14-1773281-t003.jpg Table 3. Multivariable logistic regression of Ureaplasma spp. colonization and clinical outcomes in https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1813564/full https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-g001.jpg Figure 1. PRISMA flow diagram of the study selection process. https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-t001.jpg Table 1. Characteristics of the included cohort studies. https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-t002.jpg Table 2. Study quality evaluation via the Newcastle-Ottawa scale. https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-g002.jpg Figure 2. Forest plots showing the meta-analysis of the association between preeclampsia and the ris https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-t003.jpg Table 3. Summary of findings and certainty of evidence (GRADE). https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-g003.jpg Figure 3. Forest plots for subgroup analyses of the association between preeclampsia and the risk of https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-g004.jpg Figure 4. Forest plots for subgroup analyses of the association between preeclampsia and the risk of https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-g005.jpg Figure 5. Forest plots for subgroup analyses of the association between preeclampsia and the risk of https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-t004.jpg Table 4. Results of univariate meta-regression analysis. https://www.frontiersin.org/files/Articles/1813564/fped-14-1813564-HTML-r1/image_m/fped-14-1813564-g006.jpg Figure 6. Funnel plots evaluating potential publication bias in the meta-analysis of the association https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1815128/full https://www.frontiersin.org/files/Articles/1815128/fped-14-1815128-HTML-r1/image_m/fped-14-1815128-g001.jpg Figure 1. PRISMA flow diagram of the literature search. https://www.frontiersin.org/files/Articles/1815128/fped-14-1815128-HTML-r1/image_m/fped-14-1815128-t001.jpg Table 1. Summary characteristics of 43 studies. https://www.frontiersin.org/files/Articles/1815128/fped-14-1815128-HTML-r1/image_m/fped-14-1815128-g002.jpg Figure 2. Forest plot of the incidence of unclassified sepsis (A), EOS (B), LOS (C). https://www.frontiersin.org/files/Articles/1815128/fped-14-1815128-HTML-r1/image_m/fped-14-1815128-g003.jpg Figure 3. Funnel plot of incidence of unclassified sepsis (A), EOS (B) and LOS (C). https://www.frontiersin.org/files/Articles/1815128/fped-14-1815128-HTML-r1/image_m/fped-14-1815128-t002.jpg Table 2. Subgroup summary incidence of sepsis among very preterm infants in China. https://www.frontiersin.org/files/Articles/1815128/fped-14-1815128-HTML-r1/image_m/fped-14-1815128-g004.jpg Figure 4. Sensitivity analysis of unclassified sepsis (A), EOS (B) and LOS (C). https://www.frontiersin.org/files/Articles/1815128/fped-14-1815128-HTML-r1/image_m/fped-14-1815128-g005.jpg Figure 5. Factors associated with EOS among VPIs in China: lower gestational age (A), lower birth we https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1604049/full https://www.frontiersin.org/files/Articles/1604049/fpubh-13-1604049-HTML/image_m/fpubh-13-1604049-t001.jpg Table 1. Demographic characteristics of survey participants. https://www.frontiersin.org/files/Articles/1604049/fpubh-13-1604049-HTML/image_m/fpubh-13-1604049-t002.jpg Table 2. HEV-related knowledge of survey participants. https://www.frontiersin.org/files/Articles/1604049/fpubh-13-1604049-HTML/image_m/fpubh-13-1604049-t003.jpg Table 3. The multivariate logistic analysis of HEV-related knowledge. https://www.frontiersin.org/files/Articles/1604049/fpubh-13-1604049-HTML/image_m/fpubh-13-1604049-t004.jpg Table 4. The multivariate logistic analysis of HEV-related attitude. https://www.frontiersin.org/files/Articles/1604049/fpubh-13-1604049-HTML/image_m/fpubh-13-1604049-t005.jpg Table 5. The multivariate logistic analysis of HEV-related practice. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1745808/full https://www.frontiersin.org/files/Articles/1745808/fmicb-17-1745808-HTML/image_m/fmicb-17-1745808-g001.jpg Figure 1. Schematic overview of the RSV life cycle and therapeutic intervention points. https://www.frontiersin.org/files/Articles/1745808/fmicb-17-1745808-HTML/image_m/fmicb-17-1745808-t001.jpg Table 1. Approved and late-stage monoclonal antibodies targeting RSV. https://www.frontiersin.org/files/Articles/1745808/fmicb-17-1745808-HTML/image_m/fmicb-17-1745808-t002.jpg Table 2. Representative small-molecule antivirals targeting RSV and key translational lessons. https://www.frontiersin.org/files/Articles/1745808/fmicb-17-1745808-HTML/image_m/fmicb-17-1745808-t003.jpg Table 3. Comparative regulatory focus for RSV vaccine and therapeutic development. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1725242/full https://www.frontiersin.org/files/Articles/1725242/fmed-13-1725242-HTML/image_m/fmed-13-1725242-g001.jpg Figure 1. Analysis of lactulose/mannitol ratio in a patient with long COVID in relation to clinical https://www.frontiersin.org/files/Articles/1725242/fmed-13-1725242-HTML/image_m/fmed-13-1725242-t001.jpg Table 1. Recovery rates of carbohydrates in intestinal permeability testing. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1751843/full https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g001.jpg Figure 1. PRISMA flow chart of study selection. https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-t001.jpg Table 1. Baseline characteristics of the included studies. https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-t002.jpg Table 2. GRADE Assessment table of the included studies. https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g002.jpg Figure 2. (A) Risk of bias (RoB) table of the included studies, D1 – Other, D2 – Selective reporting https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g003.jpg Figure 3. Forest plot of studies about nutritional interventions in pregnancy and offspring outcomes https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g004.jpg Figure 4. Forest plot of studies about viral prophylaxis and vaccination in infants and pregnant wom https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g005.jpg Figure 5. Forest plot of studies about early probiotics, microbiota, and childhood allergic outcomes https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g006.jpg Figure 6. Forest plot of studies about early-life respiratory viral and infection exposures. https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g007.jpg Figure 7. Forest plot of studies about early-life environmental exposures and childhood respiratory https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g008.jpg Figure 8. Forest plot of studies about early-life allergen and atopy exposures. https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g009.jpg Figure 9. Forest plot of studies about infant and early-life clinical factors. https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g010.jpg Figure 10. Forest plot of studies about early-life cohort and long-term observational studies. https://www.frontiersin.org/files/Articles/1751843/fmed-13-1751843-HTML/image_m/fmed-13-1751843-g011.jpg Figure 11. Funnel plot of studies. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1783529/full https://www.frontiersin.org/files/Articles/1783529/fmed-13-1783529-HTML/image_m/fmed-13-1783529-g001.jpg Figure 1. The dynamic distribution of AMD in rats following a dose of 1/2 LD50. Cardiac blood and Ti https://www.frontiersin.org/files/Articles/1783529/fmed-13-1783529-HTML/image_m/fmed-13-1783529-g002.jpg Figure 2. The PMR of AMD in rats following three dose levels low (42 mg/kg), medium (LD50), and high https://www.frontiersin.org/files/Articles/1783529/fmed-13-1783529-HTML/image_m/fmed-13-1783529-t001.jpg Table 1. Pharmacokinetic parameters of AMD in tissues of male rats. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1813059/full https://www.frontiersin.org/files/Articles/1813059/fpls-17-1813059-HTML/image_m/fpls-17-1813059-t001.jpg Table 1. Summary of key constraints limiting pasture root system research. https://www.frontiersin.org/files/Articles/1813059/fpls-17-1813059-HTML/image_m/fpls-17-1813059-g001.jpg Figure 1. Conceptual framework positioning root phenology as a dynamic functional trait linking plan https://www.frontiersin.org/files/Articles/1813059/fpls-17-1813059-HTML/image_m/fpls-17-1813059-t002.jpg Table 2. Comparative overview and synthesis of root characterisation methods. https://www.frontiersin.org/files/Articles/1813059/fpls-17-1813059-HTML/image_m/fpls-17-1813059-g002.jpg Figure 2. Strategic pathway for advancing root research in New Zealand. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1769099/full https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g001.jpg Figure 1. Patient with patellofemoral instability. (A) Weight-bearing CT in full extension demonstra https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g002.jpg Figure 2. Patient with left knee patellofemoral instability. WBCT images acquired in extension allow https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g003.jpg Figure 3. Patellar height assessment on WBCT at 30° of knee flexion. A/B: Insall–Salvati index. C/D: https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g004.jpg Figure 4. WBCT of a patient with medial compartment osteoarthritis of the right knee, demonstrating https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g005.jpg Figure 5. Weight-bearing CT in a patient with severe knee osteoarthritis. (A) Sagittal WBCT image sh https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g006.jpg Figure 6. WBCT in a patient with a unilateral ACL tear of the right knee (A–C) and an intact ACL in https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g007.jpg Figure 7. WBCT acquisition protocol for patients with anterior cruciate ligament injury (curveBeam L https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g008.jpg Figure 8. Weight-bearing CT 12 months after anatomic ACL reconstruction using interference screws fo https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g009.jpg Figure 9. Projectional distortion related to femoral flexion and external rotation, causing an artif https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g010.jpg Figure 10. WBCT images with the knee in full extension. a: posterior femoral condylar axis; b: trans https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-g011.jpg Figure 11. WBCT image with the knee in 30 degrees of flexion. a: posterior femoral condylar axis; b: https://www.frontiersin.org/files/Articles/1769099/fsurg-13-1769099-HTML/image_m/fsurg-13-1769099-t001.jpg Table 1. Summary of weight-bearing CT (WBCT) evidence for the assessment of knee pathologies and the https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1764864/full https://www.frontiersin.org/files/Articles/1764864/fbioe-14-1764864-HTML/image_m/fbioe-14-1764864-g001.jpg Figure 1. Expression and membrane reconstitution of AtaApore. (A) Schematic representation of the C- https://www.frontiersin.org/files/Articles/1764864/fbioe-14-1764864-HTML/image_m/fbioe-14-1764864-g002.jpg Figure 2. Electrophysiological measurements of AtaApore. (A) Schematic illustration of proteoliposom https://www.frontiersin.org/files/Articles/1764864/fbioe-14-1764864-HTML/image_m/fbioe-14-1764864-g003.jpg Figure 3. MD simulations of ion transport through AtaApore. (A) Comparison of the predicted structur https://www.frontiersin.org/files/Articles/1764864/fbioe-14-1764864-HTML/image_m/fbioe-14-1764864-g004.jpg Figure 4. MD simulation of the R3597G/R3622G mutant. (A) Model structure of the R3597G/R3622G mutant https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1682172/full https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-g001.jpg Figure 1. Illustration of IMA measurement in the weight-bearing plain film; the centres of the proxi https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-g002.jpg Figure 2. Illustration of HVA measurement in the weight-bearing plain film; the centres of the proxi https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-g003.jpg Figure 3. Illustration of α angle measurement in the weight-bearing CT scan; the dashed lines repres https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t001.jpg Table 1. The inter- and intraobserver reliability of parameters. https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-g004.jpg Figure 4. Interobserver reliability of parameter measurements: Bland–Altman plots comparing the para https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t002.jpg Table 2. Comparison of clinical outcomes between the groups of feet with normal and abnormal α angle https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t003.jpg Table 3. Comparison of clinical outcomes between the groups of feet with normal and abnormal IMA. https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t004.jpg Table 4. Comparison of clinical outcomes between the groups of feet with normal and abnormal HVA. https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t005.jpg Table 5. Comparison of clinical outcomes between the normal and abnormal α angle following unilatera https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t006.jpg Table 6. Comparison of clinical outcomes between the normal and abnormal IMA following unilateral op https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t007.jpg Table 7. Comparison of clinical outcomes between the normal and abnormal HVA following unilateral op https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t008.jpg Table 8. Comparison of clinical outcomes between the normal and abnormal α angle following bilateral https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t009.jpg Table 9. Comparison of clinical outcomes between the normal and abnormal IMA following bilateral ope https://www.frontiersin.org/files/Articles/1682172/fsurg-12-1682172-HTML/image_m/fsurg-12-1682172-t010.jpg Table 10. Comparison of clinical outcomes between the normal and abnormal HVA following bilateral op https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2026.1731069/full https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-g001.jpg Figure 1. Location of the study area and its corresponding climate zones. The abbreviations used for https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-t001.jpg Table 1. Temperature extremes considered in this study. https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-g002.jpg Figure 2. Overall ranking of gridded temperature products for simulating Tmax (left) and Tmin (right https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-g003.jpg Figure 3. Performance of gridded temperature datasets in simulating daily Tmax (left) and Tmin (righ https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-g004.jpg Figure 4. Monthly performance of gridded temperature datasets for Tmax (left) and Tmin (right). The https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-g005.jpg Figure 5. Long-term monthly average Tmax (left) and Tmin (right) of GHCN (observed data) and other g https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-g006.jpg Figure 6. Performance of the temperature products in simulating annual Tmax (left) and Tmin (right). https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-g007.jpg Figure 7. Overall performance of gridded temperature products in simulating temperature extremes acr https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-t002.jpg Table 2. Performance of the gridded datasets in estimating TXx and TXn across the climate zones of t https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-t003.jpg Table 3. Performance metrics of gridded temperature datasets for estimating TNx and TNn. https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-t004.jpg Table 4. Performance metrics of the datasets for estimating TN10p and TN90p across different climate https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-t005.jpg Table 5. Statistical metrics indicating the performance of the studied datasets in estimating TX10p https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-t006.jpg Table 6. Performance of the datasets in estimating WSDI and CSDI across climate zones. https://www.frontiersin.org/files/Articles/1731069/fclim-08-1731069-HTML/image_m/fclim-08-1731069-t007.jpg Table 7. Performance of the datasets in estimating SU25 and FD0 across the climate zones. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1683264/full https://www.frontiersin.org/files/Articles/1683264/fnut-12-1683264-HTML-r1/image_m/fnut-12-1683264-t001.jpg Table 1. Sociodemographic characteristics of participants with and without diabetic foot ulcer. https://www.frontiersin.org/files/Articles/1683264/fnut-12-1683264-HTML-r1/image_m/fnut-12-1683264-t002.jpg Table 2. Clinical and laboratory characteristics of participants. https://www.frontiersin.org/files/Articles/1683264/fnut-12-1683264-HTML-r1/image_m/fnut-12-1683264-t003.jpg Table 3. Diabetic foot ulcer characteristics in DFU group only. https://www.frontiersin.org/files/Articles/1683264/fnut-12-1683264-HTML-r1/image_m/fnut-12-1683264-t004.jpg Table 4. Correlation between E-DII and ulcer characteristics among DFU patients. https://www.frontiersin.org/files/Articles/1683264/fnut-12-1683264-HTML-r1/image_m/fnut-12-1683264-t005.jpg Table 5. Crude and adjusted associations between E-DII Score and DFU among the study population. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1796203/full https://www.frontiersin.org/files/Articles/1796203/fpubh-14-1796203-HTML/image_m/fpubh-14-1796203-g001.jpg Figure 1. Flow chart of participant selection. https://www.frontiersin.org/files/Articles/1796203/fpubh-14-1796203-HTML/image_m/fpubh-14-1796203-t001.jpg Table 1. Baseline characteristics of the case and control groups before and after propensity score m https://www.frontiersin.org/files/Articles/1796203/fpubh-14-1796203-HTML/image_m/fpubh-14-1796203-g002.jpg Figure 2. Standardized mean differences between the case and control groups before and after propens https://www.frontiersin.org/files/Articles/1796203/fpubh-14-1796203-HTML/image_m/fpubh-14-1796203-g003.jpg Figure 3. Distribution of propensity scores in the case and control groups before and after matching https://www.frontiersin.org/files/Articles/1796203/fpubh-14-1796203-HTML/image_m/fpubh-14-1796203-t002.jpg Table 2. Comparison of itemized direct medical costs between the case and control groups (CNY). https://www.frontiersin.org/files/Articles/1796203/fpubh-14-1796203-HTML/image_m/fpubh-14-1796203-g004.jpg Figure 4. Comparison of the length of preoperative stay (LPPS), length of postoperative stay (LPOS), https://www.frontiersin.org/files/Articles/1796203/fpubh-14-1796203-HTML/image_m/fpubh-14-1796203-t003.jpg Table 3. Double-robust estimation of the incremental medical costs and length of stay attributable t https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1551298/full https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-g001.jpg Figure 1. Conditional probabilities of experiencing specific symptoms given a positive COVID-19 diag https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-g002.jpg Figure 2. The workflow of SympCoughNet. https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-g003.jpg Figure 3. Network structure details of the proposed SympCoughNet. The symbol “×” represents element- https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-t001.jpg Table 1. Network parameter details for symptom-fused attention block (SFA block). https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-t002.jpg Table 2. Performance of different methods in diagnosing COVID-19. https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-g004.jpg Figure 4. Confusion matrix. https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-t003.jpg Table 3. The performance of the ablated symptom attention model and the random input symptom informa https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-g005.jpg Figure 5. t-SNE visualizations of embeddings generated by (a) SympCoughNet and (b) SympCoughNet-abla https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-g006.jpg Figure 6. Symptom prediction accuracy. https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-g007.jpg Figure 7. The performance of the model in predicting COVID-19 after ablating individual symptoms. Th https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-t004.jpg Table 4. The table presents accuracy (ACC), area under the receiver operating characteristic curve ( https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-t005.jpg Table 5. Symptom pre-training extracts limited symptom information (CL Pre-training refers to contra https://www.frontiersin.org/files/Articles/1551298/fdgth-07-1551298-HTML/image_m/fdgth-07-1551298-t006.jpg Table 6. Model performance under controlled testing conditions. https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1710121/full https://www.frontiersin.org/files/Articles/1710121/fcomp-07-1710121-HTML/image_m/fcomp-07-1710121-g001.jpg Figure 1. The architecture of the enhanced RAPTOR framework. (A) Semantic chunking mechanism: illust https://www.frontiersin.org/files/Articles/1710121/fcomp-07-1710121-HTML/image_m/fcomp-07-1710121-g003.jpg https://www.frontiersin.org/files/Articles/1710121/fcomp-07-1710121-HTML/image_m/fcomp-07-1710121-g004.jpg https://www.frontiersin.org/files/Articles/1710121/fcomp-07-1710121-HTML/image_m/fcomp-07-1710121-g002.jpg Figure 2. Model accuracy on the QuALITY dataset as a function of the semantic segmentation threshold https://www.frontiersin.org/files/Articles/1710121/fcomp-07-1710121-HTML/image_m/fcomp-07-1710121-t001.jpg Table 1. Performance comparison across different semantic thresholds (τ). https://www.frontiersin.org/files/Articles/1710121/fcomp-07-1710121-HTML/image_m/fcomp-07-1710121-t002.jpg Table 2. Accuracy comparison of different clustering algorithms on the QuALITY dataset (fixed semant https://www.frontiersin.org/files/Articles/1710121/fcomp-07-1710121-HTML/image_m/fcomp-07-1710121-t003.jpg Table 3. Performance comparison on the Qasper dataset. https://www.frontiersin.org/files/Articles/1710121/fcomp-07-1710121-HTML/image_m/fcomp-07-1710121-t004.jpg Table 4. Computational cost comparison for a ~65 k token document at the optimal threshold (τ = 0.7) https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1790035/full https://www.frontiersin.org/files/Articles/1790035/fpsyg-17-1790035-HTML/image_m/fpsyg-17-1790035-t001.jpg Table 1. Factor loadings and reliability of the WB-Pro. https://www.frontiersin.org/files/Articles/1790035/fpsyg-17-1790035-HTML/image_m/fpsyg-17-1790035-t002.jpg Table 2. Correlations of WB-Pro dimensions with psychological school correlates of wellbeing. https://www.frontiersin.org/files/Articles/1790035/fpsyg-17-1790035-HTML/image_m/fpsyg-17-1790035-t003.jpg Table 3. Correlations of the Global dimension of wellbeing and the formative 5-item and 15-item shor https://www.frontiersin.org/files/Articles/1790035/fpsyg-17-1790035-HTML/image_m/fpsyg-17-1790035-t004.jpg Table 4. Synthesis of the characteristics of the learning path to enhance wellbeing in school. https://www.frontiersin.org/files/Articles/1790035/fpsyg-17-1790035-HTML/image_m/fpsyg-17-1790035-g001.jpg Figure 1. Flowchart of participants for the intervention study. https://www.frontiersin.org/files/Articles/1790035/fpsyg-17-1790035-HTML/image_m/fpsyg-17-1790035-t005.jpg Table 5. Sample after attrition. https://www.frontiersin.org/files/Articles/1790035/fpsyg-17-1790035-HTML/image_m/fpsyg-17-1790035-g002.jpg Figure 2. Means over time for the primary and secondary dimensions of the wellbeing training in the https://www.frontiersin.org/files/Articles/1790035/fpsyg-17-1790035-HTML/image_m/fpsyg-17-1790035-g003.jpg Figure 3. Wellbeing over time in the intervention group of the intervention study. T1 = pre-test; T2 https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1768824/full https://www.frontiersin.org/files/Articles/1768824/fpsyg-17-1768824-HTML/image_m/fpsyg-17-1768824-g001.jpg Figure 1. PRISMA flow diagram of the literature screening process. https://www.frontiersin.org/files/Articles/1768824/fpsyg-17-1768824-HTML/image_m/fpsyg-17-1768824-g002.jpg Figure 2. Forest plot of working memory (WM) outcomes. https://www.frontiersin.org/files/Articles/1768824/fpsyg-17-1768824-HTML/image_m/fpsyg-17-1768824-g003.jpg Figure 3. Network plot of working memory (WM) outcomes. https://www.frontiersin.org/files/Articles/1768824/fpsyg-17-1768824-HTML/image_m/fpsyg-17-1768824-g004.jpg Figure 4. Forest plot of inhibitory control (IC) outcomes. https://www.frontiersin.org/files/Articles/1768824/fpsyg-17-1768824-HTML/image_m/fpsyg-17-1768824-g005.jpg Figure 5. Network plot of inhibitory control (IC) outcomes. https://www.frontiersin.org/files/Articles/1768824/fpsyg-17-1768824-HTML/image_m/fpsyg-17-1768824-g006.jpg Figure 6. Forest plot of cognitive flexibility (CF) outcomes. https://www.frontiersin.org/files/Articles/1768824/fpsyg-17-1768824-HTML/image_m/fpsyg-17-1768824-g007.jpg Figure 7. Network plot of cognitive flexibility (CF) outcomes. https://www.frontiersin.org/files/Articles/1768824/fpsyg-17-1768824-HTML/image_m/fpsyg-17-1768824-g008.jpg Figure 8. Intervention effect sizes across executive function domains. https://www.frontiersin.org/files/Articles/1768824/fpsyg-17-1768824-HTML/image_m/fpsyg-17-1768824-g009.jpg Figure 9. Publication bias assessment using Egger’s test. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1767660/full https://www.frontiersin.org/files/Articles/1767660/fped-14-1767660-HTML/image_m/fped-14-1767660-t001.jpg Table 1. Characteristics of 405 Project Viva infants. https://www.frontiersin.org/files/Articles/1767660/fped-14-1767660-HTML/image_m/fped-14-1767660-g001.jpg Figure 1. Participant flow from the project Viva cohort. Biomarkers were measured in a subset of par https://www.frontiersin.org/files/Articles/1767660/fped-14-1767660-HTML/image_m/fped-14-1767660-g002.jpg Figure 2. Cord blood trans fatty acid levels differ between colic groups. Fatty acid levels were mea https://www.frontiersin.org/files/Articles/1767660/fped-14-1767660-HTML/image_m/fped-14-1767660-t002.jpg Table 2. Association of cord blood biomarkers with colic/crying groups compared to the unaffected gr https://www.frontiersin.org/files/Articles/1767660/fped-14-1767660-HTML/image_m/fped-14-1767660-g003.jpg Figure 3. Cord blood microbiome alpha diversity is associated with colic groups. 16S sequencing alph https://www.frontiersin.org/files/Articles/1767660/fped-14-1767660-HTML/image_m/fped-14-1767660-t003.jpg Table 3. Differentially abundant microbial taxa between colic/crying groups using the discrete false https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1805996/full https://www.frontiersin.org/files/Articles/1805996/fped-14-1805996-HTML/image_m/fped-14-1805996-t001.jpg Table 1. Clinical characteristics of the patients. https://www.frontiersin.org/files/Articles/1805996/fped-14-1805996-HTML/image_m/fped-14-1805996-g001.jpg Figure 1. (A) Pre-treatment coronal T2-weighted MRI image showing a large, well-defined hyperintense https://www.frontiersin.org/files/Articles/1805996/fped-14-1805996-HTML/image_m/fped-14-1805996-g002.jpg Figure 2. (A): Pre-treatment MRI sagittal fat-suppressed image shows a mixed-signal mass in the head https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1797648/full https://www.frontiersin.org/files/Articles/1797648/fpls-17-1797648-HTML/image_m/fpls-17-1797648-g001.jpg Figure 1. Sampling point information. https://www.frontiersin.org/files/Articles/1797648/fpls-17-1797648-HTML/image_m/fpls-17-1797648-g002.jpg Figure 2. Physicochemical component contents in tea leaves. (a) CA (caffeine); (b) GA (Gallic acid); https://www.frontiersin.org/files/Articles/1797648/fpls-17-1797648-HTML/image_m/fpls-17-1797648-g003.jpg Figure 3. Soil physicochemical property contents. (a) AK (available potassium); (b) NO2−-N (nitrite https://www.frontiersin.org/files/Articles/1797648/fpls-17-1797648-HTML/image_m/fpls-17-1797648-g004.jpg Figure 4. Soil metabolite analysis. (a) PCA of soil metabolites across different producing areas; (b https://www.frontiersin.org/files/Articles/1797648/fpls-17-1797648-HTML/image_m/fpls-17-1797648-g005.jpg Figure 5. Soil microbial community composition. (a) Relative abundance of dominant fungal phyla in S https://www.frontiersin.org/files/Articles/1797648/fpls-17-1797648-HTML/image_m/fpls-17-1797648-g006.jpg Figure 6. Soil microbial diversity analysis. (a) Bacterial Shannon index at the OTU level. (b) Krusk https://www.frontiersin.org/files/Articles/1797648/fpls-17-1797648-HTML/image_m/fpls-17-1797648-g007.jpg Figure 7. LEfSe analysis of soil microbial communities across different production regions. (A) LEfS https://www.frontiersin.org/files/Articles/1797648/fpls-17-1797648-HTML/image_m/fpls-17-1797648-g008.jpg Figure 8. Correlation analysis of soil microorganisms, soil metabolites, and tea physicochemical com https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1701771/full https://www.frontiersin.org/files/Articles/1701771/fpubh-14-1701771-HTML/image_m/fpubh-14-1701771-t001.jpg Table 1. Basic information of interview subjects. https://www.frontiersin.org/files/Articles/1701771/fpubh-14-1701771-HTML/image_m/fpubh-14-1701771-t002.jpg Table 2. Open coding and categorization. https://www.frontiersin.org/files/Articles/1701771/fpubh-14-1701771-HTML/image_m/fpubh-14-1701771-t003.jpg Table 3. Axial coding. https://www.frontiersin.org/files/Articles/1701771/fpubh-14-1701771-HTML/image_m/fpubh-14-1701771-g001.jpg Figure 1. Generation mechanism of illness coping behavior in urban empty-nest older adults. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1778131/full https://www.frontiersin.org/files/Articles/1778131/fpls-17-1778131-HTML-r1/image_m/fpls-17-1778131-g001.jpg Figure 1. Phenotypic comparison between normal and aborted fruits and associated differences in frui https://www.frontiersin.org/files/Articles/1778131/fpls-17-1778131-HTML-r1/image_m/fpls-17-1778131-g002.jpg Figure 2. Comparative mineral nutrient profiles in leaf, pericarp and seed/embryo tissues between No https://www.frontiersin.org/files/Articles/1778131/fpls-17-1778131-HTML-r1/image_m/fpls-17-1778131-g003.jpg Figure 3. Transcriptomic differences between aborted-fruit embryos and normal-fruit embryos and expr https://www.frontiersin.org/files/Articles/1778131/fpls-17-1778131-HTML-r1/image_m/fpls-17-1778131-g004.jpg Figure 4. Transcriptomic divergence between aborted fruit and normal fruit embryos and pathway-level https://www.frontiersin.org/files/Articles/1778131/fpls-17-1778131-HTML-r1/image_m/fpls-17-1778131-g005.jpg Figure 5. Integration of transcriptomic and proteomic fold changes and qRT–PCR validation of represe https://www.frontiersin.org/files/Articles/1778131/fpls-17-1778131-HTML-r1/image_m/fpls-17-1778131-g006.jpg Figure 6. Proposed model of longan embryo abortion. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1796047/full https://www.frontiersin.org/files/Articles/1796047/fpls-17-1796047-HTML/image_m/fpls-17-1796047-g001.jpg Figure 1. Population structure and genetic relationships among longan (Dimocarpus longan Lour.) acce https://www.frontiersin.org/files/Articles/1796047/fpls-17-1796047-HTML/image_m/fpls-17-1796047-g002.jpg Figure 2. Historical gene flow and genome-wide differentiation among four longan genetic groups. (A) https://www.frontiersin.org/files/Articles/1796047/fpls-17-1796047-HTML/image_m/fpls-17-1796047-g003.jpg Figure 3. Linkage disequilibrium decay and genome-wide scan for putative selective sweeps. (A) Genom https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1725218/full https://www.frontiersin.org/files/Articles/1725218/fimmu-16-1725218-HTML-r2/image_m/fimmu-16-1725218-t001.jpg Table 1. Selected genes and SNPs. https://www.frontiersin.org/files/Articles/1725218/fimmu-16-1725218-HTML-r2/image_m/fimmu-16-1725218-t002.jpg Table 2. Demographic and clinical characteristics of DLBCL patients and healthy controls. https://www.frontiersin.org/files/Articles/1725218/fimmu-16-1725218-HTML-r2/image_m/fimmu-16-1725218-t003.jpg Table 3. Association between SNPs and DLBCL susceptibility. https://www.frontiersin.org/files/Articles/1725218/fimmu-16-1725218-HTML-r2/image_m/fimmu-16-1725218-t004.jpg Table 4. Association between SNPs and the baseline data of DLBCL patients. https://www.frontiersin.org/files/Articles/1725218/fimmu-16-1725218-HTML-r2/image_m/fimmu-16-1725218-t005.jpg Table 5. TREX1 rs11797, IFNB1 rs1051922 were associated with DLBCL chemotherapy response. https://www.frontiersin.org/files/Articles/1725218/fimmu-16-1725218-HTML-r2/image_m/fimmu-16-1725218-g001.jpg Figure 1. (A) The overall survival of DLBCL patients with CG, GG and CC genotypes in PRMT1 rs975484 https://www.frontiersin.org/files/Articles/1725218/fimmu-16-1725218-HTML-r2/image_m/fimmu-16-1725218-g002.jpg Figure 2. The impact of PRMT1 rs975484 on the outcome of DLBCL patients. https://www.frontiersin.org/files/Articles/1725218/fimmu-16-1725218-HTML-r2/image_m/fimmu-16-1725218-g003.jpg Figure 3. mRNA expression of CXCL10,TREX1 and PRMT1 in DLBCL patients (TCGA) and healthy controls (G https://www.frontiersin.org/files/Articles/1725218/fimmu-16-1725218-HTML-r2/image_m/fimmu-16-1725218-g004.jpg Figure 4. Overview of the SNPs analysis and the translational pathway. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1622770/full https://www.frontiersin.org/files/Articles/1622770/fmed-12-1622770-HTML/image_m/fmed-12-1622770-t001.jpg Table 1. Major studies on HLH conducted on large cohorts of ICU patients. https://www.frontiersin.org/files/Articles/1622770/fmed-12-1622770-HTML/image_m/fmed-12-1622770-g001.jpg Figure 1. Review search algorithm. https://www.frontiersin.org/files/Articles/1622770/fmed-12-1622770-HTML/image_m/fmed-12-1622770-t002.jpg Table 2. Results from literature search. https://www.frontiersin.org/files/Articles/1622770/fmed-12-1622770-HTML/image_m/fmed-12-1622770-t003.jpg Table 3. Results from univariate analysis of ICU mortality. https://www.frontiersin.org/files/Articles/1622770/fmed-12-1622770-HTML/image_m/fmed-12-1622770-t004.jpg Table 4. Results of multivariable analysis of ICU mortality. https://www.frontiersin.org/files/Articles/1622770/fmed-12-1622770-HTML/image_m/fmed-12-1622770-g002.jpg Figure 2. Diagnostic approach and treatment strategies in critically ill patients with HLH. *Knaak e https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1688949/full https://www.frontiersin.org/files/Articles/1688949/fonc-16-1688949-HTML/image_m/fonc-16-1688949-g001.jpg Figure 1. Flow diagram of patient inclusion and analysis. Flow chart showing cohort assembly and ana https://www.frontiersin.org/files/Articles/1688949/fonc-16-1688949-HTML/image_m/fonc-16-1688949-t001.jpg Table 1. Baseline characteristics of DLBCL patients stratified by FGR expression. https://www.frontiersin.org/files/Articles/1688949/fonc-16-1688949-HTML/image_m/fonc-16-1688949-g002.jpg Figure 2. Immunohistochemical detection of FGR in DLBCL tumor samples. Representative immunohistoche https://www.frontiersin.org/files/Articles/1688949/fonc-16-1688949-HTML/image_m/fonc-16-1688949-g003.jpg Figure 3. Kaplan-Meier survival curves stratified by FGR expression. Kaplan-Meier survival analyses https://www.frontiersin.org/files/Articles/1688949/fonc-16-1688949-HTML/image_m/fonc-16-1688949-t002.jpg Table 2. Univariate and multivariate Cox regression analysis of prognostic factors for 5-year PFS. https://www.frontiersin.org/files/Articles/1688949/fonc-16-1688949-HTML/image_m/fonc-16-1688949-t003.jpg Table 3. Univariate and multivariate Cox regression analysis of prognostic factors for 5-year OS. https://www.frontiersin.org/files/Articles/1688949/fonc-16-1688949-HTML/image_m/fonc-16-1688949-g004.jpg Figure 4. ROC analysis comparing prognostic discrimination at 5 years.ROC curves comparing IPI and F https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1793140/full https://www.frontiersin.org/files/Articles/1793140/fcimb-16-1793140-HTML/image_m/fcimb-16-1793140-g001.jpg Figure 1. Validation of the ASvicR overexpression strain and its drug susceptibility compared to UA1 https://www.frontiersin.org/files/Articles/1793140/fcimb-16-1793140-HTML/image_m/fcimb-16-1793140-g002.jpg Figure 2. Combined effect of ASvicR overexpression and DMAHDM on the viability of S. mutans. (a) CFU https://www.frontiersin.org/files/Articles/1793140/fcimb-16-1793140-HTML/image_m/fcimb-16-1793140-g003.jpg Figure 3. ASvicR overexpression enhances the antibacterial effects of DMAHDM on cariogenic virulence https://www.frontiersin.org/files/Articles/1793140/fcimb-16-1793140-HTML/image_m/fcimb-16-1793140-g004.jpg Figure 4. Combined treatment with ASvicR and DMAHDM suppresses EPS production in biofilms. (a) WSG a https://www.frontiersin.org/files/Articles/1793140/fcimb-16-1793140-HTML/image_m/fcimb-16-1793140-g005.jpg Figure 5. Analysis of gene and protein expression related to glucose metabolism. (a) Gene expression https://www.frontiersin.org/files/Articles/1793140/fcimb-16-1793140-HTML/image_m/fcimb-16-1793140-g006.jpg Figure 6. ASvicR combined with DMAHDM regulates the expression of genes and proteins of VicRK TCS. G https://www.frontiersin.org/files/Articles/1793140/fcimb-16-1793140-HTML/image_m/fcimb-16-1793140-g007.jpg Figure 7. Combined treatment modulates the cariogenicity of S. mutans biofilms. (a) Representative s https://www.frontiersin.org/files/Articles/1793140/fcimb-16-1793140-HTML/image_m/fcimb-16-1793140-g008.jpg Figure 8. Toxicological evaluation of local drug administration. (a) Body weight steadily increased https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1833531/full https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-t001.jpg Table 1. The breed types (first column) and the main demographic variables, including the CCD scores https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-t002.jpg Table 2. The three components found with the Principal Component Analysis. https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-t003.jpg Table 3. Results of the one-way ANOVA on the connection between dogs’ age and some of the fixed fact https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-g001.jpg Figure 1. (A–D) The association between dogs’ age and (A) the end of their sports career; (B) the fr https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-t004.jpg Table 4. Results of the one-way ANOVA on the connection between dogs’ CCD score and the demographic https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-g002.jpg Figure 2. The association between dogs’ CCD score and their role according to the owner. Different l https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-t005.jpg Table 5. Results of the one-way ANOVA on the connection between dogs’ CCD score and the dogs’ breed https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-g003.jpg Figure 3. The association between dogs’ CCD score and their age. Age was used as continuous variable https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-t006.jpg Table 6. Results of the one-way ANOVA on the connection between dogs’ CCD score and the dogs’ breed https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-g004.jpg Figure 4. (A–C) The association between dogs’ CCD score and (A) the presence of other dog(s) in the https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-g005.jpg Figure 5. The trend-like effect of the interaction between breed type and frequency of walks on the https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-t007.jpg Table 7. Results of the one-way ANOVA on the connection between dogs’ CCD score and the dogs’ body c https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-g006.jpg Figure 6. The effect of the significant interaction between age and body condition on the dogs’ CCD https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-t008.jpg Table 8. Results of the one-way ANOVA on the connection of dogs’ CCD score and age with the three pr https://www.frontiersin.org/files/Articles/1833531/fvets-13-1833531-HTML-r1/image_m/fvets-13-1833531-g007.jpg Figure 7. (A,B) The association between dogs’ CCD score and (A) the ‘Ideal Dog’ principal component; https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1784314/full https://www.frontiersin.org/files/Articles/1784314/fendo-17-1784314-HTML/image_m/fendo-17-1784314-t001.jpg Table 1. Sociodemographic and clinical characteristics of the three study groups: HT, GD, and SAT. https://www.frontiersin.org/files/Articles/1784314/fendo-17-1784314-HTML/image_m/fendo-17-1784314-t002.jpg Table 2. Demographic characteristics of Clalit Health Services patients included in study cohort thr https://www.frontiersin.org/files/Articles/1784314/fendo-17-1784314-HTML/image_m/fendo-17-1784314-t003.jpg Table 3. Interrupted time series analysis and incidence rate ratios for HT, GD, and SAT. https://www.frontiersin.org/files/Articles/1784314/fendo-17-1784314-HTML/image_m/fendo-17-1784314-g001.jpg Figure 1. Locally Weighted Scatterplot Smoothing (LOWESS) of monthly disease-specific incidence per https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1735741/full https://www.frontiersin.org/files/Articles/1735741/fimmu-17-1735741-HTML/image_m/fimmu-17-1735741-g001.jpg Figure 1. Intrinsic tumour-cell mechanisms that impair antigenicity and promote tumour immune evasio https://www.frontiersin.org/files/Articles/1735741/fimmu-17-1735741-HTML/image_m/fimmu-17-1735741-t001.jpg Table 1. Summary of the main intrinsic resistance mechanisms to immune checkpoint inhibitors. https://www.frontiersin.org/files/Articles/1735741/fimmu-17-1735741-HTML/image_m/fimmu-17-1735741-g002.jpg Figure 2. Schematic overview of extrinsic mechanisms in the tumour microenvironment (TME) that promo https://www.frontiersin.org/files/Articles/1735741/fimmu-17-1735741-HTML/image_m/fimmu-17-1735741-t002.jpg Table 2. Summary of the main extrinsic resistance mechanisms to immune checkpoint inhibitors. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1717058/full https://www.frontiersin.org/files/Articles/1717058/fimmu-17-1717058-HTML/image_m/fimmu-17-1717058-g001.jpg Figure 1. Flow chart describing phenotype definition, study sample selection criteria, and biologica https://www.frontiersin.org/files/Articles/1717058/fimmu-17-1717058-HTML/image_m/fimmu-17-1717058-g002.jpg Figure 2. (A) Q-Q plot of observed and expected p-values. The genomic inflation factor λ is equal to https://www.frontiersin.org/files/Articles/1717058/fimmu-17-1717058-HTML/image_m/fimmu-17-1717058-g003.jpg Figure 3. Regional association plots of the chromosome 3 locus identified in the GWAS. Plots span a https://www.frontiersin.org/files/Articles/1717058/fimmu-17-1717058-HTML/image_m/fimmu-17-1717058-g004.jpg Figure 4. Regional association plots of the HLA region locus on chromosome 6 identified in the GWAS. https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2026.1732813/full https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g001.jpg Figure 1. Schematic representation of the generic basic PyPSA model for supplying demand with a comb https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g002.jpg Figure 2. Schematic representation of the model for an island grid with one type of ORE farm only. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g003.jpg Figure 3. Schematic representation of the model allowing optimization of three ORE farm capacities a https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t001.jpg Table 1. CapEx costs from the literature. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t002.jpg Table 2. OpEx costs for various components. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t003.jpg Table 3. Learning rates, growth rates, and annual cost reduction rates for components for which a co https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t004.jpg Table 4. Total costs for the 25-year project. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t005.jpg Table 5. Indications provided by van Gerwen et al. (2019) for hydrogen storage costs. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t006.jpg Table 6. Overview of hydrogen production platform costs (DNV-GL, 2018; North Sea, 2020). https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t007.jpg Table 7. Properties used for determining the cost of desalination. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t008.jpg Table 8. Cost for a floating wind farm in 2030, according to Spyroudi et al. (2020), used for Case A https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t009.jpg Table 9. Cost for a floating wind farm in 2050, according to Spyroudi et al. (2020), used for cases https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t010.jpg Table 10. Costs and properties of the wave and the tidal farm in 2030—cases B and C. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t011.jpg Table 11. CapEx determined for wave and tidal farms installed in 2050. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g004.jpg Figure 4. Prediction for wave energy installed globally before 2050. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g005.jpg Figure 5. Prediction for tidal energy installed globally before 2050. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t012.jpg Table 12. Annual growth rates for wave and tidal capacities based on predictions for global installe https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t013.jpg Table 13. Predictions for UK-installed capacities for wave and tidal energy used for Method 2. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t014.jpg Table 14. Determination of costs for infrastructure for a project starting in 2050 (used for cases D https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t015.jpg Table 15. Learning rates of individual components of SOC. In yellow: components that are part of the https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g006.jpg Figure 6. Growth curve for the installed capacity of electrolyzers. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g007.jpg Figure 7. Evolution of power and efficiency with current (electrolyzer mode when current is negative https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t016.jpg Table 16. Ratio of powers and efficiencies. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t017.jpg Table 17. Energy requirements for desalination. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g008.jpg Figure 8. Location of wind speed data extraction: 5.6° longitude, 51° latitude, at the center of sea https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g009.jpg Figure 9. Location for wave data in the Celtic Sea, near areas for future wind farms (longitude −6.1 https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g010.jpg Figure 10. ORE farm production profiles and demand. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t018.jpg Table 18. Summary table of cases run in the PyPSA model. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t019.jpg Table 19. Recommended installed capacities when the ORE farm and rSOC capacity are optimized in the https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g011.jpg Figure 11. Electricity excess or deficit (5 days cumulated) and hydrogen stored over the course of a https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t020.jpg Table 20. Minimum and maximum values for electricity excess or deficit over the course of 5 days. https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-g012.jpg Figure 12. Electricity excess or deficit (1 h) and hydrogen stored over the course of a year for thr https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t021.jpg Table 21. Minimum and maximum values for electricity excess or deficit in 1 hour https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t022.jpg Table 22. Recommended installed capacities when the ORE farm and the rSOC capacity are optimized in https://www.frontiersin.org/files/Articles/1732813/fenrg-14-1732813-HTML/image_m/fenrg-14-1732813-t023.jpg Table 23. Results for simulations where wave and tidal farms are made to have equal properties to a https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2026.1797690/full https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-g001.jpg Figure 1. Location of the study area. https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-g002.jpg Figure 2. Sampling map. https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-g003.jpg Figure 3. Methodological framework. https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-t001.jpg Table 1. Datasets used for carrying out different analyses in the Eastern Okavango Panhandle, Botswa https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-t002.jpg Table 2. Reclassification criteria, risk scores, weights, and rationale for fire risk index (FRI) va https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-g004.jpg Figure 4. Relative importance of fire risk drivers derived from the analytic hierarchy process (AHP) https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-g005.jpg Figure 5. Wildfire potential risk variables: (A) Slope, (B) aspect, (C) elevation, (D) LST, (E) dist https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-t003.jpg Table 3. Random forest classification confusion matrix for land cover in the Eastern Okavango Panhan https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-g006.jpg Figure 6. Wildfire potential risk map. https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-g007.jpg Figure 7. (A) 2024 fire points on FRI map and (B) 2024 kernel density map in the EOP. https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-g008.jpg Figure 8. ROC curve for the fire risk index model. https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-t004.jpg Table 4. A comparison between risk areas covered by KDE and risk map. https://www.frontiersin.org/files/Articles/1797690/ffgc-09-1797690-HTML-r1/image_m/ffgc-09-1797690-g009.jpg Figure 9. GIZScore maps of Getis-Ord Gi* hotspot analysis. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1729503/full https://www.frontiersin.org/files/Articles/1729503/fbioe-13-1729503-HTML/image_m/fbioe-13-1729503-g001.jpg Figure 1. Morphological characterization of exosome-like nanovesicles derived from cacao pulp by cry https://www.frontiersin.org/files/Articles/1729503/fbioe-13-1729503-HTML/image_m/fbioe-13-1729503-g002.jpg Figure 2. Morphological characterization of nanostructures derived from cocoa pod husk by cryogenic https://www.frontiersin.org/files/Articles/1729503/fbioe-13-1729503-HTML/image_m/fbioe-13-1729503-g003.jpg Figure 3. Dynamic light scattering (DLS) analysis of exosome-like nanovesicles (PENs) derived from c https://www.frontiersin.org/files/Articles/1729503/fbioe-13-1729503-HTML/image_m/fbioe-13-1729503-t001.jpg Table 1. Size distribution of cocoa-derived exosome-like nanovesicles (PENs) obtained through high-p https://www.frontiersin.org/files/Articles/1729503/fbioe-13-1729503-HTML/image_m/fbioe-13-1729503-g004.jpg Figure 4. Total protein concentration of exosome-like nanovesicles (PENs) isolated from cocoa pulp f https://www.frontiersin.org/files/Articles/1729503/fbioe-13-1729503-HTML/image_m/fbioe-13-1729503-g005.jpg Figure 5. Dynamic light scattering (DLS) analysis of exosome-like nanovesicles derived from cocoa pu https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1647275/full https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-t001.jpg Table 1. The primer sequences of biomarkers. https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-g001.jpg Figure 1. Identification and enrichment analysis of atrial fibrillation (AF)-related and mitochondri https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-g002.jpg Figure 2. Identification of MAM-related biomarkers for AF (A) Selection of characteristic genes base https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-t002.jpg Table 2. MR analysis. https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-t003.jpg Table 3. Heterogeneity and pleiotropy analyses. https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-g003.jpg Figure 3. Development and assessment of the nomogram. The four figures in the upper row represent th https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-g004.jpg Figure 4. Exploration of possible mechanisms by which biomarkers modulate AF (A–D) Functional enrich https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-g005.jpg Figure 5. Prediction of drugs with interacting with biomarkers (A) The drug-mRNA nework Blue represe https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-g006.jpg Figure 6. Single-cell analysis (A) UMAP distribution of 16 independent clusters (B) Bubble diagram s https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-g007.jpg Figure 7. Pseudotime analysis and cell communication analyses (A,B) Pseudotime analysis (A) Lymphoid https://www.frontiersin.org/files/Articles/1647275/fphys-16-1647275-HTML/image_m/fphys-16-1647275-g008.jpg Figure 8. AF model validation in canines (A,B) The electrophysiological results of the AF induction https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1740332/full https://www.frontiersin.org/files/Articles/1740332/fonc-16-1740332-HTML/image_m/fonc-16-1740332-g001.jpg Figure 1. The pathophysiological mechanisms of vasogenic and cytotoxic edema. (1) Vasogenic Edema: B https://www.frontiersin.org/files/Articles/1740332/fonc-16-1740332-HTML/image_m/fonc-16-1740332-g002.jpg Figure 2. Overview of factors affecting PTBE in meningioma patients. VEGF, Vascular Endothelial Grow https://www.frontiersin.org/files/Articles/1740332/fonc-16-1740332-HTML/image_m/fonc-16-1740332-g003.jpg Figure 3. Overview of the interaction mechanisms among various influencing factors under hypoxic con https://www.frontiersin.org/files/Articles/1740332/fonc-16-1740332-HTML/image_m/fonc-16-1740332-t001.jpg Table 1. Histological subtypes of meningioma, histological features, and edema frequency in each sub https://www.frontiersin.org/files/Articles/1740332/fonc-16-1740332-HTML/image_m/fonc-16-1740332-t002.jpg Table 2. List of drugs used to treat PTBE surrounding meningiomas. https://www.frontiersin.org/files/Articles/1740332/fonc-16-1740332-HTML/image_m/fonc-16-1740332-g004.jpg Figure 4. The mechanisms of action in drugs used to treat meningioma-related edema. Several drugs fo https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1807137/full https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-g001.jpg Figure 1. Course knowledge graph of International Trade Practice. https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-g002.jpg Figure 2. MOOC videos of International Trade Practice. https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-g003.jpg Figure 3. Textbook of International Trade Practice. https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-t001.jpg Table 1. The human–machine teacher task allocation matrix. https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-g004.jpg Figure 4. International import and export trade process on POCIB. https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-g005.jpg Figure 5. Rates of completion and mastery for the knowledge point of partial shipment and transshipm https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-g006.jpg Figure 6. The distribution of student scores of CG and EG. https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-t002.jpg Table 2. Result of normality test of CG and EG. https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-t003.jpg Table 3. Result of homogeneity of variance (Levene). https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-t004.jpg Table 4. Result of non-parametric test. https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-t005.jpg Table 5. Result of effect size (in-depth). https://www.frontiersin.org/files/Articles/1807137/fpsyg-17-1807137-HTML-r1/image_m/fpsyg-17-1807137-t006.jpg Table 6. Results of power analysis. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1763272/full https://www.frontiersin.org/files/Articles/1763272/fpubh-14-1763272-HTML-r1/image_m/fpubh-14-1763272-g001.jpg Figure 1. Procedure of studies selection. https://www.frontiersin.org/files/Articles/1763272/fpubh-14-1763272-HTML-r1/image_m/fpubh-14-1763272-t001.jpg Table 1. Characteristics of 14 studies measuring social capital and DM control. https://www.frontiersin.org/files/Articles/1763272/fpubh-14-1763272-HTML-r1/image_m/fpubh-14-1763272-t002.jpg Table 2. Heterogeneity, the pooled effect size and sensitivity analysis. https://www.frontiersin.org/files/Articles/1763272/fpubh-14-1763272-HTML-r1/image_m/fpubh-14-1763272-t003.jpg Table 3. Subgroup analyses of Research Type (SMD vs. Z-scores). https://www.frontiersin.org/files/Articles/1763272/fpubh-14-1763272-HTML-r1/image_m/fpubh-14-1763272-t004.jpg Table 4. Subgroup analyses of Measure Type of Social Capital (Cognitive vs. Structural). https://www.frontiersin.org/files/Articles/1763272/fpubh-14-1763272-HTML-r1/image_m/fpubh-14-1763272-t005.jpg Table 5. Subgroup analyses of Measure Type & Perspective of Diabetes Self Control. https://www.frontiersin.org/files/Articles/1763272/fpubh-14-1763272-HTML-r1/image_m/fpubh-14-1763272-t006.jpg Table 6. Results of meta-regression. https://www.frontiersin.org/files/Articles/1763272/fpubh-14-1763272-HTML-r1/image_m/fpubh-14-1763272-g002.jpg Figure 2. Funnel plot for diabetes self-control before and after trim-and-fill adjustment. https://www.frontiersin.org/files/Articles/1763272/fpubh-14-1763272-HTML-r1/image_m/fpubh-14-1763272-t007.jpg Table 7. Publication bias assessment. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1797026/full https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-g001.jpg Figure 1. The conceptual framework of the research. https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-t001.jpg Table 1. Model measurement list. https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-t002.jpg Table 2. Participants’ profile. https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-t003.jpg Table 3. Discriminant validity (Fornell-Larcker criterion, correlations, and HTMT). https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-g002.jpg Figure 2. Path coefficient of the structural model. https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-t004.jpg Table 4. The main fit indices of the model. https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-g003.jpg Figure 3. Multi-group analysis: comparison of path coefficients between male and female groups. https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-g004.jpg Figure 4. Forest plot of the specific indirect effects (with 95% CI) in the mediation model. https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-t005.jpg Table 5. The results hypotheses test. https://www.frontiersin.org/files/Articles/1797026/fpsyg-17-1797026-HTML/image_m/fpsyg-17-1797026-g005.jpg Figure 5. Simple slope plot of the moderating variable cultural identity (H4a). https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2026.1803118/full https://www.frontiersin.org/files/Articles/1803118/fncir-20-1803118-HTML/image_m/fncir-20-1803118-g001.jpg Figure 1. Neurogenesis in the subventricular zone of the lateral ventricle and the subgranular zone https://www.frontiersin.org/files/Articles/1803118/fncir-20-1803118-HTML/image_m/fncir-20-1803118-g002.jpg Figure 2. Oligodendrogenesis after ischemic stroke and inhibitors of white matter regeneration. (A,B https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1585693/full https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-g001.jpg Figure 1. The Yangtze river economic belt. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t001.jpg Table 1. Green finance. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-g002.jpg Figure 2. Green finance. Note: The values in the lower right corner of each picture are the GF value https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t002.jpg Table 2. Evaluation system of environmental regulation indicators. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-g003.jpg Figure 3. Environment regulation. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t003.jpg Table 3. Descriptive statistics. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t004.jpg Table 4. Correlation Test-UIS. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t005.jpg Table 5. Correlation Test-RIS. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t006.jpg Table 6. Panel regression results. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t007.jpg Table 7. Robustness test 1. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t008.jpg Table 8. Robustness test 2. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t009.jpg Table 9. Resource-based regression results. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t010.jpg Table 10. Non-resource-based regression Results. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t011.jpg Table 11. Upstream regression results. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t012.jpg Table 12. Midstream regression results. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t013.jpg Table 13. Downstream regression results. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t014.jpg Table 14. Threshold test. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t015.jpg Table 15. Threshold value. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-g004.jpg Figure 4. LR statistical figure of the threshold. Note: (a) Explained variable: UIS; ER threshold: − https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t016.jpg Table 16. Threshold return. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t017.jpg Table 17. Moran index test for industrial structure optimization. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-g005.jpg Figure 5. Moran scatter plot for industrial structure optimization. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t018.jpg Table 18. Test results of space applicability. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t019.jpg Table 19. The spatial effect of green finance on the upgrading of industrial structure. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t020.jpg Table 20. Spatial effects of ER on the upgrading of industrial structure. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t021.jpg Table 21. Spatial effects of GF and ER on the UIS. https://www.frontiersin.org/files/Articles/1585693/fenvs-13-1585693-HTML/image_m/fenvs-13-1585693-t022.jpg Table 22. Spatial effects of GF on industrial structure rationalization. https://www.frontiersin.org/journals/animal-science/articles/10.3389/fanim.2026.1745664/full https://www.frontiersin.org/files/Articles/1745664/fanim-07-1745664-HTML/image_m/fanim-07-1745664-g001.jpg Figure 1. Schematic representation of experiment schedule in treatment group. Progesterone implant ( https://www.frontiersin.org/files/Articles/1745664/fanim-07-1745664-HTML/image_m/fanim-07-1745664-g002.jpg Figure 2. Schematic representation of experiment schedule in the control group. https://www.frontiersin.org/files/Articles/1745664/fanim-07-1745664-HTML/image_m/fanim-07-1745664-t001.jpg Table 1. Induction of ovarian cyclicity (%) in treatment and control groups using anti-stress feed s https://www.frontiersin.org/files/Articles/1745664/fanim-07-1745664-HTML/image_m/fanim-07-1745664-t002.jpg Table 2. Estrus response (%) and its intensity following progesterone implant. https://www.frontiersin.org/files/Articles/1745664/fanim-07-1745664-HTML/image_m/fanim-07-1745664-t003.jpg Table 3. Conception rate at different AI attempts and overall conception rate (OCR) within 90 days o https://www.frontiersin.org/files/Articles/1745664/fanim-07-1745664-HTML/image_m/fanim-07-1745664-t004.jpg Table 4. Post-treatment conception rate at different time periods in treatment and control groups du https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1717432/full https://www.frontiersin.org/files/Articles/1717432/fonc-16-1717432-HTML/image_m/fonc-16-1717432-t001.jpg Table 1. A comparison of key NSCLC clinical trials by mutation, treatment, and efficacy outcomes. https://www.frontiersin.org/journals/genome-editing/articles/10.3389/fgeed.2025.1634193/full https://www.frontiersin.org/files/Articles/1634193/fgeed-07-1634193-HTML/image_m/fgeed-07-1634193-g001.jpg Figure 1. Chart summarizing patients that have been cured of HIV. Seven patients have been deemed “c https://www.frontiersin.org/files/Articles/1634193/fgeed-07-1634193-HTML/image_m/fgeed-07-1634193-t001.jpg Table 1. Studies for which a gene therapy modality has been used to neutralize, excise, or eliminate https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1784001/full https://www.frontiersin.org/files/Articles/1784001/fmicb-17-1784001-HTML/image_m/fmicb-17-1784001-g001.jpg Figure 1. (A) Horizontal bar chart depicting the most prevalent clinical bacterial isolates across h https://www.frontiersin.org/files/Articles/1784001/fmicb-17-1784001-HTML/image_m/fmicb-17-1784001-g002.jpg Figure 2. Percentage distribution of MDR Acinetobacter baumannii isolates by hospital ward. https://www.frontiersin.org/files/Articles/1784001/fmicb-17-1784001-HTML/image_m/fmicb-17-1784001-g003.jpg Figure 3. Resistance distribution of MDR Acinetobacter baumannii isolates to key antibiotics. https://www.frontiersin.org/files/Articles/1784001/fmicb-17-1784001-HTML/image_m/fmicb-17-1784001-g004.jpg Figure 4. Multidrug resistance network of Acinetobacter baumannii. Nodes represent antibiotics, with https://www.frontiersin.org/files/Articles/1784001/fmicb-17-1784001-HTML/image_m/fmicb-17-1784001-g005.jpg Figure 5. Antibiotic Resistance Patterns of Multidrug-Resistant Acinetobacter baumannii Stratified b https://www.frontiersin.org/files/Articles/1784001/fmicb-17-1784001-HTML/image_m/fmicb-17-1784001-g006.jpg Figure 6. Multidrug-resistant Acinetobacter baumannii antibiotic resistance by patient sex. This bar https://www.frontiersin.org/files/Articles/1784001/fmicb-17-1784001-HTML/image_m/fmicb-17-1784001-g007.jpg Figure 7. Receiver operating characteristic (ROC) curves for the efficacy of nine antibiotics agains https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1792505/full https://www.frontiersin.org/files/Articles/1792505/fimmu-17-1792505-HTML/image_m/fimmu-17-1792505-t001.jpg Table 1. Baseline and posttreatment clinical and laboratory characteristics of serofast and non-sero https://www.frontiersin.org/files/Articles/1792505/fimmu-17-1792505-HTML/image_m/fimmu-17-1792505-t002.jpg Table 2. Baseline and posttreatment immunological parameters in serofast and non-serofast patients. https://www.frontiersin.org/files/Articles/1792505/fimmu-17-1792505-HTML/image_m/fimmu-17-1792505-g001.jpg Figure 1. Baseline lymphocyte subsets in serofast and serologically cured patients.Box plots with in https://www.frontiersin.org/files/Articles/1792505/fimmu-17-1792505-HTML/image_m/fimmu-17-1792505-g002.jpg Figure 2. Waterfall plots demonstrating changes in anticardiolipin (aCL) antibody levels following t https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1683683/full https://www.frontiersin.org/files/Articles/1683683/fped-13-1683683-HTML/image_m/fped-13-1683683-t001.jpg Table 1. Baseline demographics and clinical characteristics. https://www.frontiersin.org/files/Articles/1683683/fped-13-1683683-HTML/image_m/fped-13-1683683-t002.jpg Table 2. Bias and precision for a priori predictions. https://www.frontiersin.org/files/Articles/1683683/fped-13-1683683-HTML/image_m/fped-13-1683683-g001.jpg Figure 1. Observed vs. predicted concentration for a priori predictions. Left is the full cohort, Mi https://www.frontiersin.org/files/Articles/1683683/fped-13-1683683-HTML/image_m/fped-13-1683683-t003.jpg Table 3. Bias and precision for posteriori predictions. https://www.frontiersin.org/files/Articles/1683683/fped-13-1683683-HTML/image_m/fped-13-1683683-g002.jpg Figure 2. Observed VS predicted concentration for a posterioiri predictions. Left is the full cohort https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1771709/full https://www.frontiersin.org/files/Articles/1771709/fmicb-17-1771709-HTML-r2/image_m/fmicb-17-1771709-g001.jpg Figure 1. Alpha and beta diversity plots. Box plots illustrate alpha diversity indexes Shannon (A) C https://www.frontiersin.org/files/Articles/1771709/fmicb-17-1771709-HTML-r2/image_m/fmicb-17-1771709-g002.jpg Figure 2. Relevant genera in the hyperammonemic group. (A) Genus associations with the HA rats. Colo https://www.frontiersin.org/files/Articles/1771709/fmicb-17-1771709-HTML-r2/image_m/fmicb-17-1771709-g003.jpg Figure 3. GMMs associations with hyperammonemic group. Color scale and size illustrate the effect si https://www.frontiersin.org/files/Articles/1771709/fmicb-17-1771709-HTML-r2/image_m/fmicb-17-1771709-g004.jpg Figure 4. Scheme showing the probable effect of ammonium (NH4+) depressing specific gut metabolic mo https://www.frontiersin.org/files/Articles/1771709/fmicb-17-1771709-HTML-r2/image_m/fmicb-17-1771709-g005.jpg Figure 5. Fecal metabolites of HA vs control rats. These graphs show mean±SD of the concentrations o https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1795308/full https://www.frontiersin.org/files/Articles/1795308/fpsyg-17-1795308-HTML/image_m/fpsyg-17-1795308-t001.jpg Table 1. Sociodemographic characteristics of the final sample (n = 210). https://www.frontiersin.org/files/Articles/1795308/fpsyg-17-1795308-HTML/image_m/fpsyg-17-1795308-g001.jpg Figure 1. Flowchart of the study. https://www.frontiersin.org/files/Articles/1795308/fpsyg-17-1795308-HTML/image_m/fpsyg-17-1795308-g002.jpg Figure 2. Trial procedure. https://www.frontiersin.org/files/Articles/1795308/fpsyg-17-1795308-HTML/image_m/fpsyg-17-1795308-t002.jpg Table 2. Linear mixed model of the variation in daily steps count: parameter estimates (fixed coeffi https://www.frontiersin.org/files/Articles/1795308/fpsyg-17-1795308-HTML/image_m/fpsyg-17-1795308-g003.jpg Figure 3. Marginal means of the variation in daily step counts compared to the starting point as a f https://www.frontiersin.org/files/Articles/1795308/fpsyg-17-1795308-HTML/image_m/fpsyg-17-1795308-g004.jpg Figure 4. Marginal means of the variation in daily step counts compared to the starting point as a f https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1730693/full https://www.frontiersin.org/files/Articles/1730693/fpsyg-17-1730693-HTML/image_m/fpsyg-17-1730693-t001.jpg Table 1. Demographic characteristics of the sample. https://www.frontiersin.org/files/Articles/1730693/fpsyg-17-1730693-HTML/image_m/fpsyg-17-1730693-t002.jpg Table 2. Descriptive Statistics of Main Variables (N = 309). https://www.frontiersin.org/files/Articles/1730693/fpsyg-17-1730693-HTML/image_m/fpsyg-17-1730693-t003.jpg Table 3. Correlations among critical thinking, sustainable behaviors, and emotional avoidance (N = 3 https://www.frontiersin.org/files/Articles/1730693/fpsyg-17-1730693-HTML/image_m/fpsyg-17-1730693-t004.jpg Table 4. Correlations among all subscales of critical thinking, sustainable behaviors, and emotional https://www.frontiersin.org/files/Articles/1730693/fpsyg-17-1730693-HTML/image_m/fpsyg-17-1730693-g001.jpg Figure 1. Schematic representation of the regression model predicting sustainable behaviors. Critica https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1776417/full https://www.frontiersin.org/files/Articles/1776417/fmed-13-1776417-HTML/image_m/fmed-13-1776417-t001.jpg Table 1. List of terms. https://www.frontiersin.org/files/Articles/1776417/fmed-13-1776417-HTML/image_m/fmed-13-1776417-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1776417/fmed-13-1776417-HTML/image_m/fmed-13-1776417-t002.jpg Table 2. Main characteristics of the included studies. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1807251/full https://www.frontiersin.org/files/Articles/1807251/fpls-17-1807251-HTML/image_m/fpls-17-1807251-t001.jpg Table 1. Soil properties of the sampling area in the 0–40 cm soil layer. https://www.frontiersin.org/files/Articles/1807251/fpls-17-1807251-HTML/image_m/fpls-17-1807251-g001.jpg Figure 1. Effect of humic acid (HA) application on wheat yield (a), and the correlations between whe https://www.frontiersin.org/files/Articles/1807251/fpls-17-1807251-HTML/image_m/fpls-17-1807251-t002.jpg Table 2. Effect of humic acid (HA) application on wheat yield components. HA treatments included CK https://www.frontiersin.org/files/Articles/1807251/fpls-17-1807251-HTML/image_m/fpls-17-1807251-g002.jpg Figure 2. Radar chart: relative responses of root traits, root and shoot N, P, and K uptake to diffe https://www.frontiersin.org/files/Articles/1807251/fpls-17-1807251-HTML/image_m/fpls-17-1807251-g003.jpg Figure 3. Radar charts showing the relative responses of soil fertility indicators to different humi https://www.frontiersin.org/files/Articles/1807251/fpls-17-1807251-HTML/image_m/fpls-17-1807251-g004.jpg Figure 4. Results of community composition analysis of rhizosphere soil bacteria (a) and fungi (b): https://www.frontiersin.org/files/Articles/1807251/fpls-17-1807251-HTML/image_m/fpls-17-1807251-g005.jpg Figure 5. Relationships between wheat yield and plant growth index (PGI) (a), soil quality index (SQ https://www.frontiersin.org/files/Articles/1807251/fpls-17-1807251-HTML/image_m/fpls-17-1807251-g006.jpg Figure 6. Key factors affecting wheat yield, plant growth index (PGI), and soil quality index (SQI) https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1777024/full https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-g001.jpg Figure 1. Basic outline of a brain computer interface. https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-t001.jpg Table 1. Characteristics of various brain-computer interface source signals. https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-g002.jpg Figure 2. Various brain computer interface (BCI) source signal devices. LiveAmp, ActiChamp, Enobio, https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-g003.jpg Figure 3. Commonly used electroencephalography features: ERD/ERS (Event-related desynchronisation/Ev https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-g004.jpg Figure 4. Clinical application domains of brain-computer interface (BCI, Brain Computer Interface). https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-t002.jpg Table 2. Summary table of studies evaluating non-invasive brain computer interface for post-stroke l https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-t003.jpg Table 3. Summary table of studies evaluating non-invasive brain computer interface for motor rehabil https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-t004.jpg Table 4. Summary table of studies evaluating non-invasive brain computer interface for motor rehabil https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-t005.jpg Table 5. Summary table of studies evaluating non-invasive brain computer interface for gait rehabili https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-t006.jpg Table 6. Summary table of studies evaluating non-invasive brain computer interface for cognitive reh https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-t007.jpg Table 7. Summary table of studies evaluating invasive brain computer interface for motor rehabilitat https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-g005.jpg Figure 5. Ideal candidates for a brain computer interface at the current state of the art (BCI, Brai https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-t008.jpg Table 8. Type of studies available for various clinical indications of the brain computer interface. https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-g006.jpg Figure 6. Characteristics of various paradigms in the context of spellers. SMR, sensorimotor rhythm; https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-g007.jpg Figure 7. Proposed flowchart for clinical decision-making in the Brain Computer Interface (BCI, Brai https://www.frontiersin.org/files/Articles/1777024/fnhum-20-1777024-HTML/image_m/fnhum-20-1777024-g008.jpg Figure 8. Challenges in the clinical integration of brain-computer interfaces (BCI, Brain Computer I https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1771638/full https://www.frontiersin.org/files/Articles/1771638/fcimb-16-1771638-HTML/image_m/fcimb-16-1771638-g001.jpg Figure 1. Four compounds were screened from the natural chemical compounds library derived from trad https://www.frontiersin.org/files/Articles/1771638/fcimb-16-1771638-HTML/image_m/fcimb-16-1771638-g002.jpg Figure 2. The four compounds showed significant inhibitory effects on wild type IAV. (A) Viral repli https://www.frontiersin.org/files/Articles/1771638/fcimb-16-1771638-HTML/image_m/fcimb-16-1771638-g003.jpg Figure 3. Cell Activity and Polymerase Activity of Four Compounds (A) Cell viability of compounds at https://www.frontiersin.org/files/Articles/1771638/fcimb-16-1771638-HTML/image_m/fcimb-16-1771638-g004.jpg Figure 4. Differential expression analysis of transcriptome and proteome of four compounds in influe https://www.frontiersin.org/files/Articles/1771638/fcimb-16-1771638-HTML/image_m/fcimb-16-1771638-g005.jpg Figure 5. The differential expression of the four compounds in the transcriptome and proteome of A54 https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1774934/full https://www.frontiersin.org/files/Articles/1774934/fmed-13-1774934-HTML/image_m/fmed-13-1774934-g001.jpg Figure 1. ROC curve assessing the diagnostic performance of LUS for the detection of ILD. https://www.frontiersin.org/files/Articles/1774934/fmed-13-1774934-HTML/image_m/fmed-13-1774934-t001.jpg Table 1. Demographic, clinical, and treatment characteristics of RA patients evaluated for the role https://www.frontiersin.org/files/Articles/1774934/fmed-13-1774934-HTML/image_m/fmed-13-1774934-g002.jpg Figure 2. The study population flowchart. https://www.frontiersin.org/files/Articles/1774934/fmed-13-1774934-HTML/image_m/fmed-13-1774934-t002.jpg Table 2. Association between LUS abnormalities and chest HRCT findings in RA patients. https://www.frontiersin.org/files/Articles/1774934/fmed-13-1774934-HTML/image_m/fmed-13-1774934-t003.jpg Table 3. Comparison of LUS and HRCT findings for the detection of ILD. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1790087/full https://www.frontiersin.org/files/Articles/1790087/fonc-16-1790087-HTML/image_m/fonc-16-1790087-t001.jpg Table 1. Summary of the white blood cell count, hemoglobin level, platelet count and absolute neutro https://www.frontiersin.org/files/Articles/1790087/fonc-16-1790087-HTML/image_m/fonc-16-1790087-g001.jpg Figure 1. Morphological analysis and staining with POX and α-NAE in bone marrow cells at the initial https://www.frontiersin.org/files/Articles/1790087/fonc-16-1790087-HTML/image_m/fonc-16-1790087-g002.jpg Figure 2. Histological examination of bone marrow via H&E staining. H&E staining of bone marrow. Ima https://www.frontiersin.org/files/Articles/1790087/fonc-16-1790087-HTML/image_m/fonc-16-1790087-g003.jpg Figure 3. Flow cytometry analysis of a bone marrow aspirate at the initial stage of diagnosis. Bone https://www.frontiersin.org/files/Articles/1790087/fonc-16-1790087-HTML/image_m/fonc-16-1790087-g004.jpg Figure 4. Flow cytometry analysis of bone marrow aspirate after VAH regimen (before ATRA addition). https://www.frontiersin.org/files/Articles/1790087/fonc-16-1790087-HTML/image_m/fonc-16-1790087-g005.jpg Figure 5. Flow cytometry analysis of bone marrow aspirate after addition of ATRA to the VAH regimen. https://www.frontiersin.org/files/Articles/1790087/fonc-16-1790087-HTML/image_m/fonc-16-1790087-t002.jpg Table 2. Summary of the marrow assessment and gene quantification at different stages. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1680907/full https://www.frontiersin.org/files/Articles/1680907/fpsyt-16-1680907-HTML/image_m/fpsyt-16-1680907-g001.jpg Figure 1. The two main non-protein metabolic pathways of the essential amino acid tryptophan in huma https://www.frontiersin.org/files/Articles/1680907/fpsyt-16-1680907-HTML/image_m/fpsyt-16-1680907-t001.jpg Table 1. Comparison of TRP, its major metabolites and ratios in the subjects’ groups under investiga https://www.frontiersin.org/files/Articles/1680907/fpsyt-16-1680907-HTML/image_m/fpsyt-16-1680907-g002.jpg Figure 2. Graphical representation of the results obtained for plasma TRP, 5-HT, KYN and QA presente https://www.frontiersin.org/files/Articles/1680907/fpsyt-16-1680907-HTML/image_m/fpsyt-16-1680907-g003.jpg Figure 3. Graphical representation of the results obtained for plasma 5-HT/TRP, KYN/TRP, QUIN/TRP an https://www.frontiersin.org/files/Articles/1680907/fpsyt-16-1680907-HTML/image_m/fpsyt-16-1680907-t002.jpg Table 2. Correlations (Spearman r) between biochemical parameters in the whole sample (n = 64). https://www.frontiersin.org/files/Articles/1680907/fpsyt-16-1680907-HTML/image_m/fpsyt-16-1680907-t003.jpg Table 3. Correlations between biochemical parameters, current HAMD, YMRS, IES and lifetime MOOD-SR r https://www.frontiersin.org/files/Articles/1680907/fpsyt-16-1680907-HTML/image_m/fpsyt-16-1680907-t004.jpg Table 4. Correlations between biochemical parameters and lifetime TALS-SR and functional WSAS scores https://www.frontiersin.org/files/Articles/1680907/fpsyt-16-1680907-HTML/image_m/fpsyt-16-1680907-g004.jpg Figure 4. Hypotheses based on results obtained in plasma of BD patients with major depressive episod https://www.frontiersin.org/files/Articles/1680907/fpsyt-16-1680907-HTML/image_m/fpsyt-16-1680907-g005.jpg Figure 5. Hypotheses based on results obtained in plasma of BD patients with PTSD symptoms when euty https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1747667/full https://www.frontiersin.org/files/Articles/1747667/fnhum-20-1747667-HTML/image_m/fnhum-20-1747667-t001.jpg Table 1. Baseline demographic, clinical, and radiological characteristics of the overall cohort (N = https://www.frontiersin.org/files/Articles/1747667/fnhum-20-1747667-HTML/image_m/fnhum-20-1747667-t002.jpg Table 2. LCMM model comparison for CVST mRS trajectories. https://www.frontiersin.org/files/Articles/1747667/fnhum-20-1747667-HTML/image_m/fnhum-20-1747667-g001.jpg Figure 1. Heterogeneity in long-term functional outcomes and identification of distinct recovery tra https://www.frontiersin.org/files/Articles/1747667/fnhum-20-1747667-HTML/image_m/fnhum-20-1747667-t003.jpg Table 3. Comparison of baseline characteristics between the two identified latent classes. https://www.frontiersin.org/files/Articles/1747667/fnhum-20-1747667-HTML/image_m/fnhum-20-1747667-t004.jpg Table 4. Multivariable logistic regression analysis of factors associated with “Poor-Recovery” class https://www.frontiersin.org/files/Articles/1747667/fnhum-20-1747667-HTML/image_m/fnhum-20-1747667-g002.jpg Figure 2. Receiver operating characteristic (ROC) curve for the multivariable prediction model. The https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1805960/full https://www.frontiersin.org/files/Articles/1805960/fped-14-1805960-HTML/image_m/fped-14-1805960-g001.jpg Figure 1. Initial contrast-enhanced cranial computed tomography showing a cerebral abscess. Axial co https://www.frontiersin.org/files/Articles/1805960/fped-14-1805960-HTML/image_m/fped-14-1805960-g002.jpg Figure 2. Postoperative contrast-enhanced cranial computed tomography (CT). An axial contrast-enhanc https://www.frontiersin.org/files/Articles/1805960/fped-14-1805960-HTML/image_m/fped-14-1805960-t001.jpg Table 1.. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1726422/full https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-g001.jpg Figure 1. Climate-centred DPSIR–GIS conceptual framework for diagnosing socio-ecological vulnerabili https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-g002.jpg Figure 2. Geographical setting and socio-ecological context of Lake Guidimouni within the Sahelian d https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-t001.jpg Table 1. Environmental analysis of the Lake using the DPSIR framework. https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-t002.jpg Table 2. Distribution of reference samples by land use class (N = 210). https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-t003.jpg Table 3. Confusion matrix of land cover classification results. https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-t004.jpg Table 4. Estimated Typha density by vulnerability level. https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-g003.jpg Figure 3. Operational DPSIR cycle linking climate drivers, ecological transitions and resilience res https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-g004.jpg Figure 4. Long-term land-use transitions and climate-driven spatial reconfiguration around Lake Guid https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-t005.jpg Table 5. Average annual spatial expansion rate of land use units around Lake Guidimouni from 2013 to https://www.frontiersin.org/files/Articles/1726422/fenvs-14-1726422-HTML/image_m/fenvs-14-1726422-g005.jpg Figure 5. Spatial vulnerability of Lake Guidimouni to hydrological stress and Typha domingensis inva https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1706890/full https://www.frontiersin.org/files/Articles/1706890/fpsyg-17-1706890-HTML/image_m/fpsyg-17-1706890-t001.jpg Table 1. Participant demographic characteristics by country. https://www.frontiersin.org/files/Articles/1706890/fpsyg-17-1706890-HTML/image_m/fpsyg-17-1706890-t002.jpg Table 2. Quantitative–qualitative triangulation matrix. https://www.frontiersin.org/files/Articles/1706890/fpsyg-17-1706890-HTML/image_m/fpsyg-17-1706890-t003.jpg Table 3. Sample characteristics and reliability by country. https://www.frontiersin.org/files/Articles/1706890/fpsyg-17-1706890-HTML/image_m/fpsyg-17-1706890-t004.jpg Table 4. Descriptive statistics and group comparisons. https://www.frontiersin.org/files/Articles/1706890/fpsyg-17-1706890-HTML/image_m/fpsyg-17-1706890-t005.jpg Table 5. Intercorrelations among study variables and discriminant validity. https://www.frontiersin.org/files/Articles/1706890/fpsyg-17-1706890-HTML/image_m/fpsyg-17-1706890-t006.jpg Table 6. Mediation analysis—full regression output. https://www.frontiersin.org/files/Articles/1706890/fpsyg-17-1706890-HTML/image_m/fpsyg-17-1706890-g001.jpg Figure 1. Moderation effects of country on resource–outcome and stress–outcome relationships: (A) Re https://www.frontiersin.org/files/Articles/1706890/fpsyg-17-1706890-HTML/image_m/fpsyg-17-1706890-t007.jpg Table 7. Cross-cultural moderation analysis—resources and stress effects on life satisfaction by cou https://www.frontiersin.org/files/Articles/1706890/fpsyg-17-1706890-HTML/image_m/fpsyg-17-1706890-g002.jpg Figure 2. Psychological resource pathways to life satisfaction: cross-national mediation and moderat https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1797638/full https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-g001.jpg Figure 1. (A) Scopus-indexed articles whose titles, keywords, or abstracts mention “CBL” or “PBL,” f https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-t001.jpg Table 1. Summary of seven global student design competitions evaluated as potential semester-long en https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-g002.jpg Figure 2. (A) Two steering-powertrain-chassis alternatives produced during the concept-screening spr https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-t002.jpg Table 2. Summary of the 15-week sprint roadmap—EiE phase, focus, and exit deliverables. https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-t003.jpg Table 3. Intended learning outcomes, operational indicators, evidence sources, and analysis approach https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-g003.jpg Figure 3. Design verification plan and report (DVP&R) for the rear-drive subsystem executed through https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-g004.jpg Figure 4. (A) Initial sketch of the transmission system. (B) First CAD design. (C) Final design with https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-g005.jpg Figure 5. Failure mode mapping and weld design screening for the 90° rectangular tube corner joint. https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-t004.jpg Table 4. Representative competition-based capstone cases used to benchmark the present study in term https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-g006.jpg Figure 6. (A) Semester-long Gantt schedule derived from weekly logbooks. (B) Steering subteam time s https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-g007.jpg Figure 7. (A) Chassis adaptations prompted by an imported steering assembly: a rollover-protection ( https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-g008.jpg Figure 8. (A) Treemap of individual component expenses; darker tiles denote higher absolute cost (en https://www.frontiersin.org/files/Articles/1797638/feduc-11-1797638-HTML/image_m/feduc-11-1797638-g009.jpg Figure 9. (A) Successive on-track test runs demonstrating acceleration, cornering, and stability und https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1588424/full https://www.frontiersin.org/files/Articles/1588424/fphar-16-1588424-HTML/image_m/fphar-16-1588424-g007.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1588424/fphar-16-1588424-HTML/image_m/fphar-16-1588424-g001.jpg Figure 1. Search strategy used for the study. https://www.frontiersin.org/files/Articles/1588424/fphar-16-1588424-HTML/image_m/fphar-16-1588424-g002.jpg Figure 2. Chronic kidney disease (a) Global prevalence (b) Prevalence in high-risk countries (c) Pre https://www.frontiersin.org/files/Articles/1588424/fphar-16-1588424-HTML/image_m/fphar-16-1588424-g003.jpg Figure 3. Mechanism showing different metabolites acting on various signals of chronic kidney diseas https://www.frontiersin.org/files/Articles/1588424/fphar-16-1588424-HTML/image_m/fphar-16-1588424-g004.jpg Figure 4. Glomerular filtration rate (a) Rate in mL/min/1.73 m2 and (b) GFR at different stages of c https://www.frontiersin.org/files/Articles/1588424/fphar-16-1588424-HTML/image_m/fphar-16-1588424-g005.jpg Figure 5. Various classes used in Indian Traditional Indian Medicine. https://www.frontiersin.org/files/Articles/1588424/fphar-16-1588424-HTML/image_m/fphar-16-1588424-t001.jpg Table 1. The table comprises of the preclinical studies conducted on medicinal plants mentioned in A https://www.frontiersin.org/files/Articles/1588424/fphar-16-1588424-HTML/image_m/fphar-16-1588424-g006.jpg Figure 6. Combining the knowledge of Traditional Indian Medicine with Modern Medicine to improve Kid https://www.frontiersin.org/journals/ophthalmology/articles/10.3389/fopht.2026.1812914/full https://www.frontiersin.org/files/Articles/1812914/fopht-06-1812914-HTML/image_m/fopht-06-1812914-t001.jpg Table 1. Descriptive data for the Manuka and control groups, along with the p-value representing the https://www.frontiersin.org/files/Articles/1812914/fopht-06-1812914-HTML/image_m/fopht-06-1812914-g001.jpg Figure 1. OSDI scores and conjunctival redness over time comparing the Manuka and control groups. Le https://www.frontiersin.org/files/Articles/1812914/fopht-06-1812914-HTML/image_m/fopht-06-1812914-t002.jpg Table 2. Comparison of OSDI scores between the two groups across four follow-up visits. https://www.frontiersin.org/files/Articles/1812914/fopht-06-1812914-HTML/image_m/fopht-06-1812914-t003.jpg Table 3. Assessment of conjunctival redness in the Manuka and control groups. https://www.frontiersin.org/files/Articles/1812914/fopht-06-1812914-HTML/image_m/fopht-06-1812914-t004.jpg Table 4. Non-invasive tear break-up time (NIBUT), including both first tear and mean tear time (meas https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1765501/full https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t001.jpg Table 1. Evaluation index system of GPQI. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t002.jpg Table 2. Variable definition table. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t003.jpg Table 3. Descriptive statistics. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t004.jpg Table 4. Benchmark regression results. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t005.jpg Table 5. Results of robustness test. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t006.jpg Table 6. Endogeneity test: multi-time DID test. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t007.jpg Table 7. Heterogeneity test of development level of different types of new energy. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-g001.jpg Figure 1. Parallel trend test results. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-g002.jpg Figure 2. Placebo test results. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t008.jpg Table 8. Spatial heterogeneity. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t009.jpg Table 9. Heterogeneity of policy support. https://www.frontiersin.org/files/Articles/1765501/fsufs-10-1765501-HTML-r1/image_m/fsufs-10-1765501-t010.jpg Table 10. Heterogeneity of economic and financial development levels. https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1573083/full https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-t001.jpg Table 1. Differences in ALFF, fALFF and PerAF between dysphagic stroke patients (all patients combin https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-g001.jpg Figure 1. Flowchart of the participant inclusion and exclusion process. https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-t002.jpg Table 2. Demographic and clinical characteristics of dysphagic stroke patients and healthy controls https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-g002.jpg Figure 2. Differences in ALFF between stroke patients (11 in the rTMS group and 10 in the sham rTMS https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-g003.jpg Figure 3. Differences in fALFF between stroke patients (11 in the rTMS group and 10 in the sham rTMS https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-g004.jpg Figure 4. Differences in PerAF between stroke patients (11 in the rTMS group and 10 in the sham rTMS https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-t003.jpg Table 3. ALFF before and after treatment in the rTMS and sham rTMS groups. https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-g005.jpg Figure 5. Effects of active (left) and sham rTMS (right) treatments on ALFF in brain areas that show https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-t004.jpg Table 4. PerAF before and after treatment in the rTMS and sham rTMS groups. https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-g006.jpg Figure 6. Effects of active (left) and sham rTMS (right) treatments on PerAF in brain areas that sho https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-t005.jpg Table 5. fALFF before and after treatment in the rTMS and sham rTMS groups. https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-g007.jpg Figure 7. Effects of active (left) and sham rTMS (right) treatments on fALFF in brain areas that sho https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-t006.jpg Table 6. Decrease in clustering coefficient of the left medial superior frontal gyrus after rTMS tre https://www.frontiersin.org/files/Articles/1573083/fnhum-19-1573083-HTML/image_m/fnhum-19-1573083-t007.jpg Table 7. Decrease in local efficiency of the left medial superior frontal gyrus after rTMS treatment https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1698518/full https://www.frontiersin.org/files/Articles/1698518/fnut-12-1698518-HTML/image_m/fnut-12-1698518-g001.jpg Figure 1. Effects of different dietary methionine levels on body weight, colon length, and intestina https://www.frontiersin.org/files/Articles/1698518/fnut-12-1698518-HTML/image_m/fnut-12-1698518-g002.jpg Figure 2. Effects of different methionine doses on the intestinal mechanical barrier. (A,B) Relative https://www.frontiersin.org/files/Articles/1698518/fnut-12-1698518-HTML/image_m/fnut-12-1698518-g003.jpg Figure 3. Methionate supplementation restored the disrupted gut microbiota of SAMP8 mice. (A) Venn d https://www.frontiersin.org/files/Articles/1698518/fnut-12-1698518-HTML/image_m/fnut-12-1698518-g004.jpg Figure 4. Impact of distinct methionine doses on the murine fecal microbiota. (A) Kruskal–Wallis H t https://www.frontiersin.org/files/Articles/1698518/fnut-12-1698518-HTML/image_m/fnut-12-1698518-g005.jpg Figure 5. Impact of distinct methionine doses on the fecal microbiota. (A) Wilcoxon rank-sum test ba https://www.frontiersin.org/files/Articles/1698518/fnut-12-1698518-HTML/image_m/fnut-12-1698518-g006.jpg Figure 6. Effects of graded dietary methionine on bacterial translocation, H2S concentration, and mu https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1612766/full https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-g001.jpg Figure 1. Schematic illustration of thumbtack needle therapy for improving GI function recovery thro https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-g002.jpg Figure 2. Patient enrollment flowchart. https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-t001.jpg Table 1. Baseline characteristics of participants. https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-g003.jpg Figure 3. Comparison of the postoperative time to bowel sound recovery and time to first flatus betw https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-t002.jpg Table 2. Primary and secondary outcomes. https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-g004.jpg Figure 4. Comparison of the postoperative time to first defecation and time to removal of nasogastri https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-g005.jpg Figure 5. Comparison of the pre- and postoperative pain scores between the treatment and control gro https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-g006.jpg Figure 6. Comparison of the pre- and postoperative nausea and vomiting scores between the treatment https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-g007.jpg Figure 7. Comparison of the pre- and postoperative abdominal distention scores between the treatment https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-g008.jpg Figure 8. Comparison of the postoperative hospital stay between the treatment and control groups. Da https://www.frontiersin.org/files/Articles/1612766/fsurg-12-1612766-HTML/image_m/fsurg-12-1612766-g009.jpg Figure 9. Comparison of the overall response rate between the treatment and control groups. The over https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1552495/full https://www.frontiersin.org/files/Articles/1552495/fonc-15-1552495-HTML/image_m/fonc-15-1552495-g001.jpg Figure 1. Representative imaging of case 1 in the perioperative period. It showed a heterogeneous le https://www.frontiersin.org/files/Articles/1552495/fonc-15-1552495-HTML/image_m/fonc-15-1552495-g002.jpg Figure 2. Representative magnetic resonance imaging of case 2 with gross total resection (GTR). It s https://www.frontiersin.org/files/Articles/1552495/fonc-15-1552495-HTML/image_m/fonc-15-1552495-g003.jpg Figure 3. Representative imaging of case 3 in the perioperative period. It showed a heterogeneous le https://www.frontiersin.org/files/Articles/1552495/fonc-15-1552495-HTML/image_m/fonc-15-1552495-g004.jpg Figure 4. Evidence of Cowden syndrome in case 3. (A–C) Multiple subcutaneous cysts in the backside ( https://www.frontiersin.org/files/Articles/1552495/fonc-15-1552495-HTML/image_m/fonc-15-1552495-t001.jpg Table 1. Key features distinguishing Lhermitte–Duclos disease and common posterior fossa tumors. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1770809/full https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-g001.jpg Figure 1. Photographic representation of the single-leg vertical drop jump (A) and single-leg latera https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-t001.jpg Table 1. Correlation between jump tests and 500 m skating time (n = 39). https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-t002.jpg Table 2. Correlation between the critical variables asymmetries and 500 m skating time (n = 39). https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-t003.jpg Table 3. Linear regression analysis of the effect of single-leg vertical drop jump height asymmetry https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-t004.jpg Table 4. Variable selected for predicting 100 m and 500 m skating time based on LASSO regression ana https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-g002.jpg Figure 2. CART model with single split to discriminate skating time in different phase. The root nod https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-g003.jpg Figure 3. CART model with two splits to discriminate skating time in different phase. The splitting https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-g004.jpg Figure 4. Differences in 20 m skating time between high and low asymmetry groups partitioned by diff https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-g005.jpg Figure 5. Differences in 100 m skating time between high and low asymmetry groups partitioned by dif https://www.frontiersin.org/files/Articles/1770809/fphys-17-1770809-HTML/image_m/fphys-17-1770809-g006.jpg Figure 6. Differences in 500 m skating time between high and low asymmetry groups partitioned by dif https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1786528/full https://www.frontiersin.org/files/Articles/1786528/fgene-17-1786528-HTML-r1/image_m/fgene-17-1786528-g001.jpg Figure 1. Establishing mESCs with functional loss of Lyar. (A) Schematic of Lyar gene targeting desi https://www.frontiersin.org/files/Articles/1786528/fgene-17-1786528-HTML-r1/image_m/fgene-17-1786528-g002.jpg Figure 2. Effects of Lyar knockout on the genomic stability. (A) Karyotype analysis of Lyar-KO mESCs https://www.frontiersin.org/files/Articles/1786528/fgene-17-1786528-HTML-r1/image_m/fgene-17-1786528-g003.jpg Figure 3. Lyar is required for efficient proliferation of mESCs. Immunofluorescence staining of plur https://www.frontiersin.org/files/Articles/1786528/fgene-17-1786528-HTML-r1/image_m/fgene-17-1786528-g004.jpg Figure 4. Loss of Lyar alters the expression of lineage-specific markers during embryoid body differ https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1779874/full https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g001.jpg Figure 1. Identification of differentially expressed genes (DEGs) and enrichment pathways associated https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g002.jpg Figure 2. Screening of key genes (A). PPI network of 34 candidate genes constructed using STRING dat https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g003.jpg Figure 3. Validation of candidate key genes and assessment of their diagnostic value. (A,B) Box plot https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g004.jpg Figure 4. Construction and validation of a diagnostic nomogram model for OA. (A) Nomogram developed https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g005.jpg Figure 5. Functional enrichment and interaction network analysis of key genes NUP98 and TOP1. (A,B) https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g006.jpg Figure 6. Immune infiltration landscape and correlation with key genes in OA. (A) Stacked bar chart https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g007.jpg Figure 7. Regulatory networks and potential therapeutic agents targeting key genes in OA. (A) Venn d https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g008.jpg Figure 8. Single-cell RNA sequencing analysis of chondrocyte heterogeneity in osteoarthritic samples https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g009.jpg Figure 9. Single-cell expression and intercellular communication of key genes in chondrocyte subtype https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g010.jpg Figure 10. Subclustering and differentiation trajectory of homeostatic chondrocytes (HomCs). (A) UMA https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g011.jpg Figure 11. Histopathological evaluation of joint damage and collagen fibrosis in a rat OA model. (A) https://www.frontiersin.org/files/Articles/1779874/fmed-13-1779874-HTML/image_m/fmed-13-1779874-g012.jpg Figure 12. Validation of key gene expression in OA articular tissues. (A,B) Immunohistochemistry (IH https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2026.1746674/full https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g001.jpg Figure 1. (a) Spatial analysis diagram. (b) System setup at the Now Arcade site. The defined analysi https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g002.jpg Figure 2. Content analysis diagrams of (a) Sequence A, (b) Sequence B, (c) Subsequence A, and (d) Su https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g003.jpg Figure 3. Visual content of Clip0, ClipA, ClipB1, ClipB2, and ClipB3. (a) Clip0 is consistent in bot https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g004.jpg Figure 4. Example behaviors observed in Now Arcade. (a) Sequence A: 1. Enter Now Arcade from the mai https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g005.jpg Figure 5. Data processing pipeline for behavior detection. https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-t001.jpg Table 1. Summary of viewer counts and behavior metrics for the two 60-min sequences (Sequence A = Ra https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g006.jpg Figure 6. Data visualization pipeline: 1. Establish coordinate plane; 2. Count the behavior instance https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-t002.jpg Table 2. Classification performance of GPT-4o under Prompt 1 (shooting vs. non-shooting) and Prompt https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-t003.jpg Table 3. Comparison of motion behavior classification performance using speed-only and speed + GPT-4 https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-t004.jpg Table 4. Behavior frequency comparison between Sequence A (Rainbow) and Sequence B (Space) scenes. https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-t005.jpg Table 5. Mann–Whitney U comparison of behavioral instance counts per viewer between scenes (Bonferro https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g007.jpg Figure 7. BiD heatmap of behavioral patterns across Sequences A and B for passing-by, lingering, and https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g008.jpg Figure 8. BiD heatmap of behavioral patterns across all subsequence for passing-by, lingering, and s https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g009.jpg Figure 9. BiD heatmap of behavioral patterns across clips in Sequence A and Sequence B for passing-b https://www.frontiersin.org/files/Articles/1746674/fcomp-08-1746674-HTML/image_m/fcomp-08-1746674-g010.jpg Figure 10. Instance trails and BiD heatmaps for selected viewers in Sequences A and B. (a) Sequences https://www.frontiersin.org/journals/organizational-psychology/articles/10.3389/forgp.2025.1622893/full https://www.frontiersin.org/files/Articles/1622893/forgp-03-1622893-HTML/image_m/forgp-03-1622893-g001.jpg Figure 1. Conceptual framework. https://www.frontiersin.org/files/Articles/1622893/forgp-03-1622893-HTML/image_m/forgp-03-1622893-t001.jpg Table 1. Characteristics of respondents (n = 6,444). https://www.frontiersin.org/files/Articles/1622893/forgp-03-1622893-HTML/image_m/forgp-03-1622893-t002.jpg Table 2. Reliability and validity test. https://www.frontiersin.org/files/Articles/1622893/forgp-03-1622893-HTML/image_m/forgp-03-1622893-t003.jpg Table 3. Zero-order correlations among antecedents, moderators, and consequences. https://www.frontiersin.org/files/Articles/1622893/forgp-03-1622893-HTML/image_m/forgp-03-1622893-t004.jpg Table 4. The hierarchical multivariate regression results of QOL. https://www.frontiersin.org/files/Articles/1622893/forgp-03-1622893-HTML/image_m/forgp-03-1622893-t005.jpg Table 5. The hierarchical multivariate regression results of public service satisfaction. https://www.frontiersin.org/files/Articles/1622893/forgp-03-1622893-HTML/image_m/forgp-03-1622893-t006.jpg Table 6. The hierarchical multivariate regression results of turnover intention. https://www.frontiersin.org/journals/systems-biology/articles/10.3389/fsysb.2025.1668595/full https://www.frontiersin.org/files/Articles/1668595/fsysb-05-1668595-HTML/image_m/fsysb-05-1668595-g001.jpg Figure 1. Two-plasmid system. (a) Values of RFP/OD600 comparing the two plasmids in Acinetobacter ba https://www.frontiersin.org/files/Articles/1668595/fsysb-05-1668595-HTML/image_m/fsysb-05-1668595-g002.jpg Figure 2. Constitutive library characterization. (a) Genetic circuit of the pSGAbi_J23XX_RFP set of https://www.frontiersin.org/files/Articles/1668595/fsysb-05-1668595-HTML/image_m/fsysb-05-1668595-g003.jpg Figure 3. Average RFP/OD600 values for the three different inducible systems tested. (a) General cir https://www.frontiersin.org/files/Articles/1668595/fsysb-05-1668595-HTML/image_m/fsysb-05-1668595-g004.jpg Figure 4. Characterization of CRISPRi on reporter gene target in A baumannii. (a) Schematics of the https://www.frontiersin.org/files/Articles/1668595/fsysb-05-1668595-HTML/image_m/fsysb-05-1668595-g005.jpg Figure 5. Characterization of CRISPRi on an endogenous gene in A. baumannii. (a) Schematics of the g https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1732609/full https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-t001.jpg Table 1. Comparative analysis of recent literature on MT/text evaluation. https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-t002.jpg Table 2. Analysis of variance (ANOVA) for Mean Dependency Distance across human benchmark and LLM-ge https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-g001.jpg Figure 1. Boxplot of sentence lengths for the source corpus (n = 644). https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-t003.jpg Table 3. Mean Dependency Distance (MDD) for individual Chinese-to-Uyghur translated sentences genera https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-t004.jpg Table 4. Overall Mean Dependency Distance (MDD) of texts from different sources. https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-t005.jpg Table 5. Semantic Comprehensibility (COMET) scores for individual Chinese-to-Uyghur translated sente https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-t006.jpg Table 6. Overall MDD and COMET scores for each LLM. https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-g002.jpg Figure 2. Correlation between mean Cognitive Divergence and COMET scores. https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-t007.jpg Table 7. Correlation analysis between “Cognitive Divergence” and semantic comprehensibility (COMET) https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-t008.jpg Table 8. Case analysis of high Cognitive Divergence associated with low comprehensibility. https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-g003.jpg Figure 3. Dependency tree of the text generated by the Kimi model. https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-g004.jpg Figure 4. Dependency tree of the human benchmark text. https://www.frontiersin.org/files/Articles/1732609/fpsyg-17-1732609-HTML/image_m/fpsyg-17-1732609-t009.jpg Table 9. Case analysis of comprehension failure despite low Cognitive Divergence. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1745259/full https://www.frontiersin.org/files/Articles/1745259/fpubh-14-1745259-HTML-r1/image_m/fpubh-14-1745259-t001.jpg Table 1. Sociodemographic characteristics, mean POSIT scores, and prevalence of psychosocial risk ac https://www.frontiersin.org/files/Articles/1745259/fpubh-14-1745259-HTML-r1/image_m/fpubh-14-1745259-g001.jpg Figure 1. Mean POSIT scores across academic disciplines. Displays mean scores of the POSIT general s https://www.frontiersin.org/files/Articles/1745259/fpubh-14-1745259-HTML-r1/image_m/fpubh-14-1745259-g002.jpg Figure 2. Odds ratios comparing men and women. Logistic regression models comparing risk classificat https://www.frontiersin.org/files/Articles/1745259/fpubh-14-1745259-HTML-r1/image_m/fpubh-14-1745259-g003.jpg Figure 3. Odds ratios for students in Arts and Humanities. Odds ratios comparing Arts and Humanities https://www.frontiersin.org/files/Articles/1745259/fpubh-14-1745259-HTML-r1/image_m/fpubh-14-1745259-g004.jpg Figure 4. Odds ratios for students in Health Sciences. Odds ratios comparing Health Sciences student https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1801915/full https://www.frontiersin.org/files/Articles/1801915/fpls-17-1801915-HTML/image_m/fpls-17-1801915-g001.jpg Figure 1. Hong Kong and China Red List status of Hong Kong orchids. https://www.frontiersin.org/files/Articles/1801915/fpls-17-1801915-HTML/image_m/fpls-17-1801915-t001.jpg Table 1. Interspecific and intraspecific genetic distances for 5.8S, matK, trnL-F and trnH-psbA data https://www.frontiersin.org/files/Articles/1801915/fpls-17-1801915-HTML/image_m/fpls-17-1801915-g002.jpg Figure 2. Interspecific and intraspecific genetic distance for 5.8S, matK, trnL-F and trnH-psbA mark https://www.frontiersin.org/files/Articles/1801915/fpls-17-1801915-HTML/image_m/fpls-17-1801915-g003.jpg Figure 3. A Time-calibrated phylogenetic tree of Hong Kong orchid species, indicating Hong Kong and https://www.frontiersin.org/files/Articles/1801915/fpls-17-1801915-HTML/image_m/fpls-17-1801915-g004.jpg Figure 4. Distribution of ED scores of Hong Kong orchids. The black line indicates the threshold for https://www.frontiersin.org/files/Articles/1801915/fpls-17-1801915-HTML/image_m/fpls-17-1801915-t002.jpg Table 2. Top-20 Hong Kong orchids with the highest-ranking ED scores exceeding 50 Myr. https://www.frontiersin.org/files/Articles/1801915/fpls-17-1801915-HTML/image_m/fpls-17-1801915-g005.jpg Figure 5. Distribution of EDGE2 scores of Hong Kong orchids. https://www.frontiersin.org/files/Articles/1801915/fpls-17-1801915-HTML/image_m/fpls-17-1801915-g006.jpg Figure 6. Top-50 taxa with highest EDGE2 scores based on Hong Kong regional (A) and China national ( https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1783342/full https://www.frontiersin.org/files/Articles/1783342/fonc-16-1783342-HTML/image_m/fonc-16-1783342-g001.jpg Figure 1. The PRISMA flowchart illustrates the systematic literature search and study selection proc https://www.frontiersin.org/files/Articles/1783342/fonc-16-1783342-HTML/image_m/fonc-16-1783342-t001.jpg Table 1. Basic characteristics of the included studies. https://www.frontiersin.org/files/Articles/1783342/fonc-16-1783342-HTML/image_m/fonc-16-1783342-g002.jpg Figure 2. Forest plot of high-grade infection occurrence. (A) Forest plot showing the incidence of h https://www.frontiersin.org/files/Articles/1783342/fonc-16-1783342-HTML/image_m/fonc-16-1783342-g003.jpg Figure 3. Risk ratio for high-grade infections. (A) Forest plot comparing the risk of high-grade inf https://www.frontiersin.org/files/Articles/1783342/fonc-16-1783342-HTML/image_m/fonc-16-1783342-g004.jpg Figure 4. Forest plot of febrile neutropenia occurrence. (A) Incidence of febrile neutropenia in the https://www.frontiersin.org/files/Articles/1783342/fonc-16-1783342-HTML/image_m/fonc-16-1783342-g005.jpg Figure 5. Risk ratio for febrile neutropenia. (A) Forest plot comparing febrile neutropenia risk bet https://www.frontiersin.org/files/Articles/1783342/fonc-16-1783342-HTML/image_m/fonc-16-1783342-g006.jpg Figure 6. Funnel plots of publication bias: (A) High-grade infections; (B) Febrile neutropenia; Asym https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1713822/full https://www.frontiersin.org/files/Articles/1713822/feduc-10-1713822-HTML/image_m/feduc-10-1713822-g001.jpg Figure 1. The Prosocial Classroom Model [adapted from Jennings and Greenberg (2009)]. The pathways e https://www.frontiersin.org/files/Articles/1713822/feduc-10-1713822-HTML/image_m/feduc-10-1713822-t001.jpg Table 1. Participants. https://www.frontiersin.org/files/Articles/1713822/feduc-10-1713822-HTML/image_m/feduc-10-1713822-t002.jpg Table 2. Descriptive statistics and correlations of the study variables. https://www.frontiersin.org/files/Articles/1713822/feduc-10-1713822-HTML/image_m/feduc-10-1713822-g002.jpg Figure 2. Mediation analysis results for self-perceived social integration. a, path from teachers’ e https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1831785/full https://www.frontiersin.org/files/Articles/1831785/fpsyg-17-1831785-HTML/image_m/fpsyg-17-1831785-g001.jpg Figure 1. Theoretical model. https://www.frontiersin.org/files/Articles/1831785/fpsyg-17-1831785-HTML/image_m/fpsyg-17-1831785-t001.jpg Table 1. Preliminary analyses. https://www.frontiersin.org/files/Articles/1831785/fpsyg-17-1831785-HTML/image_m/fpsyg-17-1831785-t002.jpg Table 2. Regression results for the moderated serial mediation model (PROCESS model 87). https://www.frontiersin.org/files/Articles/1831785/fpsyg-17-1831785-HTML/image_m/fpsyg-17-1831785-t003.jpg Table 3. Indirect and conditional indirect effects of threat-related AI anxiety on engagement. https://www.frontiersin.org/files/Articles/1831785/fpsyg-17-1831785-HTML/image_m/fpsyg-17-1831785-t004.jpg Table 4. Conditional effects of perceived continuous teacher support on engagement at different leve https://www.frontiersin.org/files/Articles/1831785/fpsyg-17-1831785-HTML/image_m/fpsyg-17-1831785-g002.jpg Figure 2. Simple slope. https://www.frontiersin.org/files/Articles/1831785/fpsyg-17-1831785-HTML/image_m/fpsyg-17-1831785-g003.jpg Figure 3. Pathway. Solid lines indicate significance; dashed lines indicate no significance. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1588108/full https://www.frontiersin.org/files/Articles/1588108/fgene-16-1588108-HTML/image_m/fgene-16-1588108-g001.jpg Figure 1. Chronology of notable events throughout the patient’s history. https://www.frontiersin.org/files/Articles/1588108/fgene-16-1588108-HTML/image_m/fgene-16-1588108-g002.jpg Figure 2. Clinical and genealogical data from the studied patient. (A) Pedigree showing the proband https://www.frontiersin.org/files/Articles/1588108/fgene-16-1588108-HTML/image_m/fgene-16-1588108-g003.jpg Figure 3. Gene expression analysis: (A) list of candidate gene interaction with MAP3K3 and AIRE dete https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2026.1688535/full https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g001.jpg Figure 1. Schematic of experimental setup (not to scale). https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g002.jpg Figure 2. Physical model of the plexiglass chamber (unit: mm): (a) front view; (b) back view. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g003.jpg Figure 3. Close-up view of the defective pipe slot: (a) front view; (b) top view. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-t001.jpg Table 1. Inflow rate control strategies. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g004.jpg Figure 4. Size distributions and microscope images of the particles (unit: millimeters). https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-t002.jpg Table 2. Properties of granular materials. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-t003.jpg Table 3. Experimental program. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g005.jpg Figure 5. Variation of the cavity height and pipe pressure with water flow rate in Run #3: (a) cavit https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g006.jpg Figure 6. Static bed in stage 1: (a) Q = 20 mL/s; (b) Q = 200 mL/s; (c) Q = 230 mL/s. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g007.jpg Figure 7. Internal fluidization in sand bed: (a) Q = 260 mL/s; (b) Q = 280 mL/s. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g008.jpg Figure 8. Surface fluidization stage: (a) surface fluidization (Q = 300 mL/s, the yellow arrows show https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g009.jpg Figure 9. Variation of phase proportion with particle size. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g010.jpg Figure 10. Variation of critical flow rate with sand bed height and slot width: (a) sand bed height; https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g011.jpg Figure 11. Variation of the localized cavity size before Qf with water flow rate under various slot https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g012.jpg Figure 12. Schematic for head loss calculation. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g013.jpg Figure 13. Variation of orifice loss with water flow rate under various conditions of (a) sand bed h https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g014.jpg Figure 14. Variation of vortex head loss and cavity height with water flow rate under various sand b https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g015.jpg Figure 15. Variation of seepage head loss with water flow rate under various sand bed heights and sl https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g016.jpg Figure 16. Variation of average intensity and sustained intensity with different sand bed heights an https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g017.jpg Figure 17. Variation of fluidized areas with water flow rate under various (a) sand bed heights; (b) https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g018.jpg Figure 18. Variation of expansion ratio with water flow rate under various (a) sand bed heights; (b) https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-t004.jpg Table 4. The specified values of shear stress (τ) and the impulsive force correction factor θ. https://www.frontiersin.org/files/Articles/1688535/fphy-14-1688535-HTML/image_m/fphy-14-1688535-g019.jpg Figure 19. Comparison between the theoretical and experimental Froude number for coarse sand A: (a) https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1755017/full https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g001.jpg Figure 1. Experimental design. HCjE and THP-1 cells were inoculated with live or HIA CtA or CtB, wit https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g002.jpg Figure 2. PCA and hierarchical clustering of gene expression data. (a) PCA plot showing sample separ https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g003.jpg Figure 3. Bar plots showing the number of enriched GO BP (a) and KEGG (b) pathways. The bars represe https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g004.jpg Figure 4. Venn diagrams showing pathway overlap between Ct inoculation conditions in HCjE and THP-1 https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g005.jpg Figure 5. GSEA pathway enrichment in HCjE cells at 4 hpi. Lollipop dot plots of the top 10 enriched https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g006.jpg Figure 6. GSEA pathway enrichment in HCjE cells at 24 hpi. Lollipop dot plots of the top 10 enriched https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g007.jpg Figure 7. GSEA pathway enrichment in THP1 cells at 4 hpi. Lollipop dot plots of the top 10 enriched https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g008.jpg Figure 8. GSEA pathway enrichment in THP-1 cells at 24 hpi. Lollipop dot plots of the top 10 enriche https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g009.jpg Figure 9. Differential expression patterns of pathway-associated genes in HCjE cells infected with C https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g010.jpg Figure 10. Differential expression patterns of pathway-associated genes in HCjE cells infected with https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g011.jpg Figure 11. Differential expression patterns of pathway-associated genes in THP-1 cells infected with https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g012.jpg Figure 12. Differential expression patterns of pathway-associated genes in THP-1 cells infected with https://www.frontiersin.org/files/Articles/1755017/fcimb-16-1755017-HTML/image_m/fcimb-16-1755017-g013.jpg Figure 13. Cytokine and chemokine concentrations in HCjE and THP-1 cells following Ct infection. Con https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1736086/full https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-t001.jpg Table 1. The targeting oligos of TRIP13. https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g001.jpg Figure 1. Acquisition and CNV analysis of differential MRGs in ccRCC. (A) Heatmap displaying the 174 https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g002.jpg Figure 2. Analyzing immunological responses and consensus clustering. (A–C) Consensus clustering mat https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g003.jpg Figure 3. Construction and evaluation of the mitophagy-related risk model. (A) LASSO coefficient pro https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g004.jpg Figure 4. Construction and validation of the prognostic nomogram. (A) Nomogram integrating risk scor https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g005.jpg Figure 5. Immune-related characteristics associated with the risk model. (A) Immune cell infiltratio https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g006.jpg Figure 6. Predicted drug sensitivity between high- and low-risk groups. (A) Representative drugs pre https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g007.jpg Figure 7. Dentification and preliminary validation of key prognostic genes. (A,B) Univariate and mul https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g008.jpg Figure 8. Expression of TRIP13 in ccRCC tissues and cell lines. (A) Immunohistochemistry was used to https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g009.jpg Figure 9. Regarding the validation of the prognostic function of TRIP13 in ccRCC. (A) Following the https://www.frontiersin.org/files/Articles/1736086/fphar-16-1736086-HTML/image_m/fphar-16-1736086-g010.jpg Figure 10. Effects of TRIP13 knockdown on tumor growth in vivo. (A,B) The mice were subcutaneously i https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1746993/full https://www.frontiersin.org/files/Articles/1746993/fendo-17-1746993-HTML-r1/image_m/fendo-17-1746993-g001.jpg Figure 1. Flow diagram of the study. https://www.frontiersin.org/files/Articles/1746993/fendo-17-1746993-HTML-r1/image_m/fendo-17-1746993-t001.jpg Table 1. Baseline characteristics for treatment groups. https://www.frontiersin.org/files/Articles/1746993/fendo-17-1746993-HTML-r1/image_m/fendo-17-1746993-t002.jpg Table 2. Comparison of lumbar spine BMD before and after 12 months of treatment in each groups. https://www.frontiersin.org/files/Articles/1746993/fendo-17-1746993-HTML-r1/image_m/fendo-17-1746993-t003.jpg Table 3. Between-group comparison of changes in lumbar spine bone mineral density after 12 months of https://www.frontiersin.org/files/Articles/1746993/fendo-17-1746993-HTML-r1/image_m/fendo-17-1746993-t004.jpg Table 4. Between-group comparison of change in modified K score at 3, 6, and 12 months. https://www.frontiersin.org/files/Articles/1746993/fendo-17-1746993-HTML-r1/image_m/fendo-17-1746993-t005.jpg Table 5. Comparison of hormone level changes between two groups at 3, 6, and 12 months. https://www.frontiersin.org/files/Articles/1746993/fendo-17-1746993-HTML-r1/image_m/fendo-17-1746993-t006.jpg Table 6. Comparison of changes in P1NP and β-CTX between two groups at 3, 6, and 12 months. https://www.frontiersin.org/files/Articles/1746993/fendo-17-1746993-HTML-r1/image_m/fendo-17-1746993-t007.jpg Table 7. Correlation analysis between bone mineral density and bone turnover markers. https://www.frontiersin.org/files/Articles/1746993/fendo-17-1746993-HTML-r1/image_m/fendo-17-1746993-g002.jpg Figure 2. Correlation analysis between bone mineral density and bone turnover markers. (A) P1NP and https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1724193/full https://www.frontiersin.org/files/Articles/1724193/fonc-16-1724193-HTML/image_m/fonc-16-1724193-t001.jpg Table 1. Patient and disease characteristics. https://www.frontiersin.org/files/Articles/1724193/fonc-16-1724193-HTML/image_m/fonc-16-1724193-t002.jpg Table 2. Treatment characteristics. https://www.frontiersin.org/files/Articles/1724193/fonc-16-1724193-HTML/image_m/fonc-16-1724193-t003.jpg Table 3. Weight loss comparison. https://www.frontiersin.org/files/Articles/1724193/fonc-16-1724193-HTML/image_m/fonc-16-1724193-g001.jpg Figure 1. Temporal changes in weight percentage among groups by nutrition type. T1: Last day of radi https://www.frontiersin.org/files/Articles/1724193/fonc-16-1724193-HTML/image_m/fonc-16-1724193-g002.jpg Figure 2. Survival probability based on 3- month FDG-PET-CT nodal response assessment. https://www.frontiersin.org/files/Articles/1724193/fonc-16-1724193-HTML/image_m/fonc-16-1724193-g003.jpg Figure 3. Kaplan-Meier curves showing local disease-free (A), regional-free (B), distant-free (C), p https://www.frontiersin.org/files/Articles/1724193/fonc-16-1724193-HTML/image_m/fonc-16-1724193-t004.jpg Table 4. Local, regional and distant failures and outcomes. https://www.frontiersin.org/files/Articles/1724193/fonc-16-1724193-HTML/image_m/fonc-16-1724193-t005.jpg Table 5. Comparison of our study outcomes with selected series from non-endemic regions. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1684836/full https://www.frontiersin.org/files/Articles/1684836/fimmu-16-1684836-HTML/image_m/fimmu-16-1684836-g001.jpg Figure 1. CXCL9 and CXCL10 contribute to inflammatory cell infiltration and impair spermatogenesis d https://www.frontiersin.org/files/Articles/1684836/fimmu-16-1684836-HTML/image_m/fimmu-16-1684836-g002.jpg Figure 2. Macrophages are a predominant cellular source of CXCL9 and CXCL10 in the testis during LPS https://www.frontiersin.org/files/Articles/1684836/fimmu-16-1684836-HTML/image_m/fimmu-16-1684836-g003.jpg Figure 3. Lactylation promotes the transcriptional expression of CXCL9 and CXCL10 in macrophages. (A https://www.frontiersin.org/files/Articles/1684836/fimmu-16-1684836-HTML/image_m/fimmu-16-1684836-g004.jpg Figure 4. Lactylation in macrophages inhibited ubiquitin–proteasome pathway-mediated STAT1 degradati https://www.frontiersin.org/files/Articles/1684836/fimmu-16-1684836-HTML/image_m/fimmu-16-1684836-g005.jpg Figure 5. TRIM21 lactylation inhibits STAT1 degradation by constraining the binding of TRIM21 to STA https://www.frontiersin.org/files/Articles/1684836/fimmu-16-1684836-HTML/image_m/fimmu-16-1684836-g006.jpg Figure 6. TRIM21 K345 lactylation impairs the binding of TRIM21 to STAT1. (A) The interaction model https://www.frontiersin.org/files/Articles/1684836/fimmu-16-1684836-HTML/image_m/fimmu-16-1684836-t001.jpg https://www.frontiersin.org/journals/antibiotics/articles/10.3389/frabi.2026.1764314/full https://www.frontiersin.org/files/Articles/1764314/frabi-05-1764314-HTML-r1/image_m/frabi-05-1764314-g001.jpg Figure 1. Figure displaying the progression of a variable (e.g., periodontal disease prevalence or h https://www.frontiersin.org/files/Articles/1764314/frabi-05-1764314-HTML-r1/image_m/frabi-05-1764314-g002.jpg Figure 2. Illustration of how periodontal disease progresses. Healthy gums, with a balanced biofilm https://www.frontiersin.org/files/Articles/1764314/frabi-05-1764314-HTML-r1/image_m/frabi-05-1764314-g003.jpg Figure 3. The bacterial community found in healthy (A), gingivitis (B), and periodontitis (C) condit https://www.frontiersin.org/files/Articles/1764314/frabi-05-1764314-HTML-r1/image_m/frabi-05-1764314-g004.jpg Figure 4. The mechanism of action of antioxidants on periodontal tissue. Reprinted from 'Oxidative s https://www.frontiersin.org/files/Articles/1764314/frabi-05-1764314-HTML-r1/image_m/frabi-05-1764314-t001.jpg Table 1. Medicinal plants and part of the plant used for the clinical trials. https://www.frontiersin.org/journals/nanotechnology/articles/10.3389/fnano.2026.1778616/full https://www.frontiersin.org/files/Articles/1778616/fnano-08-1778616-HTML/image_m/fnano-08-1778616-t001.jpg Table 1. Applications of selenium nanostructures in disease detection. https://www.frontiersin.org/files/Articles/1778616/fnano-08-1778616-HTML/image_m/fnano-08-1778616-g001.jpg Figure 1. Biomedical potential of selenium nanoparticle. https://www.frontiersin.org/files/Articles/1778616/fnano-08-1778616-HTML/image_m/fnano-08-1778616-g002.jpg Figure 2. Role of selenium nanoparticles in preventing the proliferation of cancer cells. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1585197/full https://www.frontiersin.org/files/Articles/1585197/fimmu-16-1585197-HTML/image_m/fimmu-16-1585197-g001.jpg Figure 1. Constructing of full-length cDNA clones by segment and fusion PCR (A), followed by enzyme https://www.frontiersin.org/files/Articles/1585197/fimmu-16-1585197-HTML/image_m/fimmu-16-1585197-g002.jpg Figure 2. The scheme of the animal trial. https://www.frontiersin.org/files/Articles/1585197/fimmu-16-1585197-HTML/image_m/fimmu-16-1585197-g003.jpg Figure 3. The clinical data of inoculated pigs. The rectal temperatures (A), average daily weight ga https://www.frontiersin.org/files/Articles/1585197/fimmu-16-1585197-HTML/image_m/fimmu-16-1585197-g004.jpg Figure 4. Viremia kinetics in pigs. Data are shown as a histogram with each spot representing an ind https://www.frontiersin.org/files/Articles/1585197/fimmu-16-1585197-HTML/image_m/fimmu-16-1585197-g005.jpg Figure 5. Lung lesions and immunohistochemistry examination in the immune phase. Representative imag https://www.frontiersin.org/files/Articles/1585197/fimmu-16-1585197-HTML/image_m/fimmu-16-1585197-g006.jpg Figure 6. Scores of gross lung lesions (A), microscopic lung lesions (B), PRRSV antigen in the lung https://www.frontiersin.org/files/Articles/1585197/fimmu-16-1585197-HTML/image_m/fimmu-16-1585197-g007.jpg Figure 7. Lung lesions and immunohistochemistry examination in the challenge phase. Representative i https://www.frontiersin.org/files/Articles/1585197/fimmu-16-1585197-HTML/image_m/fimmu-16-1585197-t001.jpg Table 1. Primers and probe used in this study. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1629262/full https://www.frontiersin.org/files/Articles/1629262/fimmu-16-1629262-HTML/image_m/fimmu-16-1629262-g001.jpg Figure 1. The life cycle of P. falciparum. The parasite alternates between human and Anopheles mosqu https://www.frontiersin.org/files/Articles/1629262/fimmu-16-1629262-HTML/image_m/fimmu-16-1629262-t001.jpg Table 1. The role and geographic distributioN OF MAJOR TAXONOMIC SPECIES Within the An. https://www.frontiersin.org/files/Articles/1629262/fimmu-16-1629262-HTML/image_m/fimmu-16-1629262-g002.jpg Figure 2. Schematic Diagram of TEP Mechanisms. TEPs are broadly categorized into three principal cla https://www.frontiersin.org/files/Articles/1629262/fimmu-16-1629262-HTML/image_m/fimmu-16-1629262-t002.jpg Table 2. Structural and functional diversity of the An. gambiae TEP gene family: immune roles in pat https://www.frontiersin.org/files/Articles/1629262/fimmu-16-1629262-HTML/image_m/fimmu-16-1629262-g003.jpg Figure 3. The schistosome life cycle with humans as the definitive host. Eggs released by adult worm https://www.frontiersin.org/files/Articles/1629262/fimmu-16-1629262-HTML/image_m/fimmu-16-1629262-t003.jpg Table 3. Functional and evolutionary diversity of the B. glabrata TEP family: Complement-like pathwa https://www.frontiersin.org/files/Articles/1629262/fimmu-16-1629262-HTML/image_m/fimmu-16-1629262-g004.jpg Figure 4. BgTEP as a synergistic immune pathogen eliminator in B. glabrata snails. In B. glabrata sn https://www.frontiersin.org/files/Articles/1629262/fimmu-16-1629262-HTML/image_m/fimmu-16-1629262-g005.jpg Figure 5. The interactions between BgTEP, lectin-like molecules BgFREPs, and pore-forming toxin Biom https://www.frontiersin.org/files/Articles/1629262/fimmu-16-1629262-HTML/image_m/fimmu-16-1629262-t004.jpg Table 4. Comparison of AgTEP1 and BgTEP1: Structural Features, and Immune Functions in An. gambiae a https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1811652/full https://www.frontiersin.org/files/Articles/1811652/fpsyg-17-1811652-HTML-r1/image_m/fpsyg-17-1811652-g001.jpg Figure 1. Research framework. https://www.frontiersin.org/files/Articles/1811652/fpsyg-17-1811652-HTML-r1/image_m/fpsyg-17-1811652-t001.jpg Table 1. Sample characteristics (N = 287). https://www.frontiersin.org/files/Articles/1811652/fpsyg-17-1811652-HTML-r1/image_m/fpsyg-17-1811652-t002.jpg Table 2. Confirmatory factor analysis results. https://www.frontiersin.org/files/Articles/1811652/fpsyg-17-1811652-HTML-r1/image_m/fpsyg-17-1811652-t003.jpg Table 3. Regression results for hypothesis testing. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1815899/full https://www.frontiersin.org/files/Articles/1815899/fneur-17-1815899-HTML/image_m/fneur-17-1815899-g001.jpg Figure 1. Interactions between gut microbiota-derived metabolites, immune responses, and neuroprotec https://www.frontiersin.org/files/Articles/1815899/fneur-17-1815899-HTML/image_m/fneur-17-1815899-t001.jpg Table 1. Gut microbiota– and host metabolism–derived factors influencing immune regulation and neuro https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1756506/full https://www.frontiersin.org/files/Articles/1756506/fvets-13-1756506-HTML/image_m/fvets-13-1756506-g001.jpg Figure 1. Identification of relevant studies via databases and registers. https://www.frontiersin.org/files/Articles/1756506/fvets-13-1756506-HTML/image_m/fvets-13-1756506-t001.jpg Table 1. Prevalence of Marburg virus RNA and antibodies in bats. https://www.frontiersin.org/files/Articles/1756506/fvets-13-1756506-HTML/image_m/fvets-13-1756506-g002.jpg Figure 2. Seroprevalence of Marburg virus antibodies and RNA in livestock, dogs, and rodents. https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2026.1761290/full https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g001.jpg Figure 1. Schematic representation of vesicles. https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g002.jpg Figure 2. Chemical structure of various surfactants used in the study. https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g003.jpg Figure 3. Mechanisms for acid and alkaline hydrolysis of methyl-N-nitroso-p-toluenesulfonamide (MNTS https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-t001.jpg Table 1. Kinetic parameters for hydrolysis in water. https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g004.jpg Figure 4. Basic hydrolysis of diazepam (la) and N-alkyl nitrazepam derivatives (1b–e). https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g005.jpg Figure 5. SN2 reaction of 2-alkylnaphthtalenesulfonates (AlkONS) with nucleophiles. https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-t002.jpg Table 2. Kinetic parameter of SN2 reaction of MeONS with water and bromide ions in a mixed vesicular https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g006.jpg Figure 6. (A) Conversion of Ellman’s anion (2) to Ellman’s reagent (3) by o-iodosobenzoate (4). (B) https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-t003.jpg Table 3. Yield of the oxidation and multicomponent reactions in different solvents. https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g007.jpg Figure 7. Photooxidation of α-PE sensitized by DCA. https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-t004.jpg Table 4. Product distribution in the DCA-sensitized photooxidation of α-PE. https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g008.jpg Figure 8. (A) Interconversion of NADH and NAD+. (B) Photosensitized NADH to NAD+ conversion. https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g009.jpg Figure 9. (A) Structure of PEG-b-PPFMA/Ru(bpy)2(phen-NH2)Cl2; (PPFMA = poly(pentafluorophenyl methac https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g010.jpg Figure 10. (A) Aerobic oxidative hydroxylation of arylboronic acids. (B) Conversion of 5-hydroxymeth https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g011.jpg Figure 11. Decarboxylation reaction of 6-nitrobenzisoxazole-3-carboxylate (6-NBIC). https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g012.jpg Figure 12. (A) Reaction of cyclopentadiene with 3-phenyl-1-(2-pyridyl)-2-propen-1-one (1a) and 3-(4- https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g013.jpg Figure 13. Kemp elimination reaction. https://www.frontiersin.org/files/Articles/1761290/fchem-14-1761290-HTML-r1/image_m/fchem-14-1761290-g014.jpg Figure 14. (A) Passerini reaction of carboxylic acid (1), aldehyde (2), and an isocyanide (3) to for https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1749856/full https://www.frontiersin.org/files/Articles/1749856/fmed-13-1749856-HTML/image_m/fmed-13-1749856-g001.jpg Figure 1. A flow-chart of this study. https://www.frontiersin.org/files/Articles/1749856/fmed-13-1749856-HTML/image_m/fmed-13-1749856-g002.jpg Figure 2. Network pharmacology. (A) Venn diagram showing the composition of aging-related targets re https://www.frontiersin.org/files/Articles/1749856/fmed-13-1749856-HTML/image_m/fmed-13-1749856-t001.jpg Table 1. OBand DL values of the candidate compounds identified from black soybean. https://www.frontiersin.org/files/Articles/1749856/fmed-13-1749856-HTML/image_m/fmed-13-1749856-t002.jpg Table 2. Top 20 targets information of PPI network. https://www.frontiersin.org/files/Articles/1749856/fmed-13-1749856-HTML/image_m/fmed-13-1749856-g003.jpg Figure 3. Bioinformatics-driven feature selection. (A) Performance evaluation of the SVM-RFE algorit https://www.frontiersin.org/files/Articles/1749856/fmed-13-1749856-HTML/image_m/fmed-13-1749856-g004.jpg Figure 4. Virtual molecular docking results. https://www.frontiersin.org/files/Articles/1749856/fmed-13-1749856-HTML/image_m/fmed-13-1749856-g005.jpg Figure 5. β-sitosterol attenuates UVA-induced photoaging in HSFs. (A) Cell viability assay determini https://www.frontiersin.org/files/Articles/1749856/fmed-13-1749856-HTML/image_m/fmed-13-1749856-g006.jpg Figure 6. HSP90AA1 mediates β-sitosterol’s anti-photoaging effects. (A) HSP90AA1 expression upregula https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1808415/full https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-t001.jpg Table 1. List of RT-PCR primers. https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-t002.jpg Table 2. List of primary antibodies. https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-t003.jpg Table 3. Mathematical conditions for gene categories. https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-g001.jpg Figure 1. Silencing PPP Components and YAP Alter YAP Target Gene Expression in HEK293Ts and MDA-MB-2 https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-g002.jpg Figure 2. Whole Transcriptome Analysis for NELFA-regulated gene expression. (A) Gene Set Enrichment https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-t004.jpg Table 4. Representative YAP-target genes subset with coordinated loss of NELF-C. https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-t005.jpg Table 5. Demographic table of the breast cancer cohort. https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-g003.jpg Figure 3. NELFA expression and its association with survival outcomes. NELFA expression and its asso https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-t006.jpg Table 6. Association of NELFA expression with the clinical features of the IDC patients. https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-g004.jpg Figure 4. High YAP expression correlates with poor survival. (A) Representative immunohistochemistry https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-t007.jpg Table 7. Association of YAP expression with the clinical features of the IDC patients. https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-g005.jpg Figure 5. YAP and NELFA expression and its association with patient survival outcomes. (A) Represent https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-t008.jpg Table 8. Association of YAP and NELFA (combined) expression with the clinical features of the IDC pa https://www.frontiersin.org/files/Articles/1808415/fonc-16-1808415-HTML/image_m/fonc-16-1808415-g006.jpg Figure 6. Overall survival and disease-free survival in IDC breast cancer cohort of TCGA. (A–C) KM p https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1789907/full https://www.frontiersin.org/files/Articles/1789907/fimmu-17-1789907-HTML/image_m/fimmu-17-1789907-g001.jpg Figure 1. Single cell transcriptome analysis of epithelial cells and immune cells in IPF. (A) UMAP o https://www.frontiersin.org/files/Articles/1789907/fimmu-17-1789907-HTML/image_m/fimmu-17-1789907-g002.jpg Figure 2. Single cell transcriptome analysis of mesenchymal cells in IPF. (A) UMAPs of all mesenchym https://www.frontiersin.org/files/Articles/1789907/fimmu-17-1789907-HTML/image_m/fimmu-17-1789907-g003.jpg Figure 3. Inference, analysis and visualization of cellular communication networks and pseudotime tr https://www.frontiersin.org/files/Articles/1789907/fimmu-17-1789907-HTML/image_m/fimmu-17-1789907-g004.jpg Figure 4. Multiomics analysis revealed spatial positioning of MDMs and myofibroblast type 2 in IPF. https://www.frontiersin.org/files/Articles/1789907/fimmu-17-1789907-HTML/image_m/fimmu-17-1789907-g005.jpg Figure 5. Infiltration of MDMs was negatively associated with lung function of IPF. (A) Correlations https://www.frontiersin.org/files/Articles/1789907/fimmu-17-1789907-HTML/image_m/fimmu-17-1789907-g006.jpg Figure 6. Construction of an MDMs based outcome prediction model. (A) Wayne diagram displaying the i https://www.frontiersin.org/files/Articles/1789907/fimmu-17-1789907-HTML/image_m/fimmu-17-1789907-g007.jpg Figure 7. External validation of the prediction model. (A) The curve of survival status distribution https://www.frontiersin.org/files/Articles/1789907/fimmu-17-1789907-HTML/image_m/fimmu-17-1789907-g008.jpg Figure 8. LGMN is primarily expressed in M2 macrophages and promotes the secretion of TGF-β1. (A) Do https://www.frontiersin.org/files/Articles/1789907/fimmu-17-1789907-HTML/image_m/fimmu-17-1789907-g009.jpg Figure 9. Pharmacological blockade of LGMN ameliorated PF by BLM. (A) Experimental flow chart of the https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1782734/full https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-t001.jpg Table 1. Grid box dimensions. https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-t002.jpg Table 2. Primer sequences of genes analyzed by RT-qPCR. https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-t003.jpg Table 3. Rutin physicochemical parameters. https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g001.jpg Figure 1. Radar of physicochemical parameters of rutin. https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-t004.jpg Table 4. Absorption and distribution properties of rutin. https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-t005.jpg Table 5. Toxicity prediction by Protox-3.0. https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-t006.jpg Table 6. Binding energy and interactions of rutin with 5-HT2A receptor (PDB: 8ZMG), human dopamine D https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g002.jpg Figure 2. Docking interactions of rutin with multiple target proteins. Rutin binding modes with (X) https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g003.jpg Figure 3. (A) Particles size through dynamic light scattering (DLS) and (B) zeta potential analyses https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g004.jpg Figure 4. TEM micrograph of biosynthesized RUT-SeNPs showing predominantly spherical, well-dispersed https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g005.jpg Figure 5. FTIR spectrum of biosynthesized rutin-conjugated selenium nanoparticles (RUT-SeNPs) showin https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g006.jpg Figure 6. Effect of Rutin, Sodium selenite and RUT-SeNPs on in social isolation subjected rats (A) R https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g007.jpg Figure 7. Effect of Rutin, Sodium selenite and RUT-SeNPs on (A) Number of Contacts, (B) Latency peri https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g008.jpg Figure 8. Impact of treatments on OS and antioxidant biomarkers in the prefrontal cortex. (A) Nrf2 m https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g009.jpg Figure 9. Effect of Rutin, sodium selenite, Rutin-SeNP on (A) TNF-α, (B) IL-1β and (C) NF-κB in the https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g010.jpg Figure 10. RUT-SeNPs protect against NF-κB immunoreactivity in the prefrontal cortex during SCH in r https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g011.jpg Figure 11. Effect of Rutin, sodium selenite, Rutin-SeNP on (A) BCL-2, (B) Bax, and (C) Caspase-3 exp https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g012.jpg Figure 12. Effect of Rutin, sodium selenite, Rutin-SeNP on (A) Serotonin, (B) Dopamine, (C) GABA, (D https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g013.jpg Figure 13. Effect of treatments on neuronal and glial function biomarkers in the prefrontal cortex. https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g014.jpg Figure 14. RUT-SeNPs protect against GFAP staining in the prefrontal cortex of rats with SI. Photomi https://www.frontiersin.org/files/Articles/1782734/fphar-17-1782734-HTML/image_m/fphar-17-1782734-g015.jpg Figure 15. Photomicrographs of male prefrontal cerebral cortex of all groups. (A) Control group show https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1592290/full https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-g001.jpg Figure 1. Flow chart of the current study. N=200 chronic kidney disease patients were evaluated and https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-t001.jpg Table 1. Baseline characteristics of the study population stratified according to Urate-lowering the https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-t002.jpg Table 2. Comparison of kidney function markers according to Urate lowering therapies in the study po https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-g002.jpg Figure 2. Mean comparisons of renal function biomarkers in the study population. (A) Comparison of S https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-t003.jpg Table 3. Comparison of lipid profiles according to Urate-lowering therapy in the study population. https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-g003.jpg Figure 3. Mean changes of lipid markers in the study population. (A) Changes of LDL-c, (B) Changes o https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-t004.jpg Table 4. Subgroup analysis of lipid profiles according to sex in the study population. https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-g004.jpg Figure 4. Mean changes of lipid profile levels over time according to gender. (A1) LDL-c in males, ( https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-t005.jpg Table 5. Correlation analysis of serum uric acid with LDL-c and HDL-c pre and post treatment in the https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-g005.jpg Figure 5. Scatter plots correlation between serum uric acid and lipid profiles Pre- and Post-Treatme https://www.frontiersin.org/files/Articles/1592290/fendo-16-1592290-HTML-r1/image_m/fendo-16-1592290-g006.jpg Figure 6. Hazard ratio of renal function decline according to changes in estimated glomerular filtra https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2026.1802499/full https://www.frontiersin.org/files/Articles/1802499/ffgc-09-1802499-HTML-r2/image_m/ffgc-09-1802499-g001.jpg Figure 1. Relative trap catches of Taphrorychus bicolor (%). The black dot represents the fitted val https://www.frontiersin.org/files/Articles/1802499/ffgc-09-1802499-HTML-r2/image_m/ffgc-09-1802499-t001.jpg Table 1. Pairwise contrast comparison of variants in the generalized linear mixed-effect model (GLMM https://www.frontiersin.org/files/Articles/1802499/ffgc-09-1802499-HTML-r2/image_m/ffgc-09-1802499-g002.jpg Figure 2. Relative male proportion of Taphrorychus bicolor (%). The black dot represents the fitted https://www.frontiersin.org/files/Articles/1802499/ffgc-09-1802499-HTML-r2/image_m/ffgc-09-1802499-t002.jpg Table 2. Pairwise contrast comparison of variants in the generalized linear mixed-effect model (GLMM https://www.frontiersin.org/files/Articles/1802499/ffgc-09-1802499-HTML-r2/image_m/ffgc-09-1802499-g003.jpg Figure 3. Gas chromatography coupled to electro-antenographic detection (GC–EAD) responses of Taphro https://www.frontiersin.org/files/Articles/1802499/ffgc-09-1802499-HTML-r2/image_m/ffgc-09-1802499-g004.jpg Figure 4. Gas chromatography coupled to electro-antenographic detection (GC–EAD) responses to volati https://www.frontiersin.org/journals/imaging/articles/10.3389/fimag.2026.1725794/full https://www.frontiersin.org/files/Articles/1725794/fimag-05-1725794-HTML/image_m/fimag-05-1725794-g001.jpg Figure 1. Osteoblasts and osteocytes ultrastructure. The microenvironment of osteoblasts is shown in https://www.frontiersin.org/files/Articles/1725794/fimag-05-1725794-HTML/image_m/fimag-05-1725794-g002.jpg Figure 2. Process of VOI selection for trabecular (a, b) and cortical (c) bone. A cylinder (2 mm d x https://www.frontiersin.org/files/Articles/1725794/fimag-05-1725794-HTML/image_m/fimag-05-1725794-g003.jpg Figure 3. Distribution of 2D slices BV/TV throughout the analyzed VOI. Slices at negative depths cor https://www.frontiersin.org/files/Articles/1725794/fimag-05-1725794-HTML/image_m/fimag-05-1725794-g004.jpg Figure 4. Tridimensional colormaps of Tb.Th and Tb.Sp for Control (a, b) and Al (c, d) groups, respe https://www.frontiersin.org/files/Articles/1725794/fimag-05-1725794-HTML/image_m/fimag-05-1725794-t001.jpg Table 1. Micro-CT morphometric parameters in the tibia of newborn rats. https://www.frontiersin.org/files/Articles/1725794/fimag-05-1725794-HTML/image_m/fimag-05-1725794-g005.jpg Figure 5. Original 2D slices from Control (a) and Al (e) groups; red arrows indicate trabecular form https://www.frontiersin.org/files/Articles/1725794/fimag-05-1725794-HTML/image_m/fimag-05-1725794-g006.jpg Figure 6. Tridimensional colormaps of Ct.Th for Control (a) and Al (b) groups, respectively. Cortica https://www.frontiersin.org/journals/environmental-chemistry/articles/10.3389/fenvc.2026.1797359/full https://www.frontiersin.org/files/Articles/1797359/fenvc-07-1797359-HTML/image_m/fenvc-07-1797359-g001.jpg Figure 1. Enrichment factors of several elements in burned and ash-affected soils. Bars represent me https://www.frontiersin.org/files/Articles/1797359/fenvc-07-1797359-HTML/image_m/fenvc-07-1797359-g002.jpg Figure 2. Nanoparticle concentrations (particles g-1 soil, mean ± SD) for Fe, Ti, Mn, Zn, Pb and Ce https://www.frontiersin.org/files/Articles/1797359/fenvc-07-1797359-HTML/image_m/fenvc-07-1797359-g003.jpg Figure 3. Elemental distribution of single nanoparticles across post-fire plots. (A) Total number-ba https://www.frontiersin.org/files/Articles/1797359/fenvc-07-1797359-HTML/image_m/fenvc-07-1797359-g004.jpg Figure 4. Normalized mass distributions of metal-bearing nanoparticles across fire-affected soils: ( https://www.frontiersin.org/files/Articles/1797359/fenvc-07-1797359-HTML/image_m/fenvc-07-1797359-g005.jpg Figure 5. Box plots illustrating fire-induced alterations in the size distribution of element-bearin https://www.frontiersin.org/files/Articles/1797359/fenvc-07-1797359-HTML/image_m/fenvc-07-1797359-g006.jpg Figure 6. Elemental ratios in bimetallic nanoparticles as indicators of fire transformation pathways https://www.frontiersin.org/files/Articles/1797359/fenvc-07-1797359-HTML/image_m/fenvc-07-1797359-g007.jpg Figure 7. Schematic overview showing the potential use of bimetallic nanoparticle (NP) ratios as geo https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1679981/full https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-t001.jpg Table 1. Source information of GSB. https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-g001.jpg Figure 1. HPLC fingerprint of 44 GSB samples (A) Mixed control HPLC fingerprint (B). https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-t002.jpg Table 2. Similarity evaluation results of GSB samples. https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-t003.jpg Table 3. GSB PCA eigenvalues and variance contribution rate. https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-t004.jpg Table 4. Principal component factor loading matrix for common peaks in GSB samples. https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-g002.jpg Figure 2. PCA of GSB from different origins (A) OPLS-DA scores of GSB from different origins (B) Imp https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-g003.jpg Figure 3. Effect of hydrogen peroxide on NCTC-1469 cell viability line chart. Data are shown as mean https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-g004.jpg Figure 4. A: Effect of different concentrations of swertiamarin (A), gentiopicroside (B) sweroside ( https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-g005.jpg Figure 5. Effects of swertiamarin, gentiopicroside, and sweroside on the activities of MDA (A), SOD https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-g006.jpg Figure 6. HPLC chromatograms of reference (A) and sample (B). https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-t005.jpg Table 5. Results of Q-markers content (mg/g, n = 3). https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-t006.jpg Table 6. Eigenvalues and variance contributions. https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-t007.jpg Table 7. Principal component scores, composite scores and rankings. https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-t008.jpg Table 8. Normalized raw data of 44 batches of GSB samples. https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-t009.jpg Table 9. Specific gravity calculations for 44 batches of GSB samples. https://www.frontiersin.org/files/Articles/1679981/fphar-16-1679981-HTML/image_m/fphar-16-1679981-g007.jpg Figure 7. HCA of 44 batches of GSB samples. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1751860/full https://www.frontiersin.org/files/Articles/1751860/fpls-16-1751860-HTML/image_m/fpls-16-1751860-g001.jpg Figure 1. Atractylodes chinensis (DC.) Koidz. https://www.frontiersin.org/files/Articles/1751860/fpls-16-1751860-HTML/image_m/fpls-16-1751860-g002.jpg Figure 2. Influence of different drought stress levels on atractylodin, β-eudesmol, and atractylenol https://www.frontiersin.org/files/Articles/1751860/fpls-16-1751860-HTML/image_m/fpls-16-1751860-t001.jpg Table 1. Functional annotation of unigenes in different databases. https://www.frontiersin.org/files/Articles/1751860/fpls-16-1751860-HTML/image_m/fpls-16-1751860-g003.jpg Figure 3. Functional enrichment analysis of DGEs. (A) GO enrichment analysis of DEGs in CK vs LDS. ( https://www.frontiersin.org/files/Articles/1751860/fpls-16-1751860-HTML/image_m/fpls-16-1751860-g004.jpg Figure 4. QRT-PCR validation of sesquiterpenoids biosynthesis-related genes in A. chinensis under dr https://www.frontiersin.org/files/Articles/1751860/fpls-16-1751860-HTML/image_m/fpls-16-1751860-g005.jpg Figure 5. PCA analysis of all samples and correlation diagram between samples. (A) PCA Analysis of A https://www.frontiersin.org/files/Articles/1751860/fpls-16-1751860-HTML/image_m/fpls-16-1751860-g006.jpg Figure 6. Analysis of differential metabolites under drought stress. (A) Volcano plot of DAMs in CK https://www.frontiersin.org/files/Articles/1751860/fpls-16-1751860-HTML/image_m/fpls-16-1751860-g007.jpg Figure 7. Exploration of the relationship between DEGs and DAMs using nine quadrant and co-expressio https://www.frontiersin.org/files/Articles/1751860/fpls-16-1751860-HTML/image_m/fpls-16-1751860-g008.jpg Figure 8. DEGs and DAMs involved in sesquiterpenoid and triterpenoid biosynthesis pathway (MVA, MEP/ https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1761588/full https://www.frontiersin.org/files/Articles/1761588/fpls-17-1761588-HTML-r1/image_m/fpls-17-1761588-t001.jpg Table 1. Summary of Gentiana scabra samples used in this study. https://www.frontiersin.org/files/Articles/1761588/fpls-17-1761588-HTML-r1/image_m/fpls-17-1761588-g001.jpg Figure 1. Metabolite distribution of G. scabra. from three habitats. (A) Principal component analysi https://www.frontiersin.org/files/Articles/1761588/fpls-17-1761588-HTML-r1/image_m/fpls-17-1761588-g002.jpg Figure 2. Accumulation patterns of differentially accumulated metabolites (DMs) in Gentiana scabra a https://www.frontiersin.org/files/Articles/1761588/fpls-17-1761588-HTML-r1/image_m/fpls-17-1761588-g003.jpg Figure 3. Transcriptional Profiles of G. scabra. Roots from Three Habitats (A) Cluster heatmap of DE https://www.frontiersin.org/files/Articles/1761588/fpls-17-1761588-HTML-r1/image_m/fpls-17-1761588-g004.jpg Figure 4. KEGG enrichment pathways of DEGs in G. scabra from three habitats. (A) KEGG enrichment pat https://www.frontiersin.org/files/Articles/1761588/fpls-17-1761588-HTML-r1/image_m/fpls-17-1761588-g005.jpg Figure 5. Boxplots showing the relative expression levels of three key genes in the terpenoid backbo https://www.frontiersin.org/files/Articles/1761588/fpls-17-1761588-HTML-r1/image_m/fpls-17-1761588-g006.jpg Figure 6. Quantitative real-time PCR (qRT-PCR) validation of six key differentially expressed genes https://www.frontiersin.org/files/Articles/1761588/fpls-17-1761588-HTML-r1/image_m/fpls-17-1761588-g007.jpg Figure 7. Integrated analysis of transcriptome and metabolome based on KEGG pathways in G. scabra ac https://www.frontiersin.org/files/Articles/1761588/fpls-17-1761588-HTML-r1/image_m/fpls-17-1761588-g008.jpg Figure 8. Correlation network analysis of DEGs and DMs in G. scabra across three comparison groups. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1759196/full https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-g001.jpg Figure 1. Colony morphology and SEM of strain HMX112. Morphological characteristics of strain HMX112 https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-g002.jpg Figure 2. Phylogenetic tree constructed by neighbor-joining method between strain HMX112 and its sim https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-t001.jpg Table 1. Differentiation characteristics of strain HMX112 and type strains of phylogenetically close https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-g003.jpg Figure 3. Polar lipid profile of strain HMX112. Chemical detection of polar lipids on the TLC plate https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-t002.jpg Table 2. Cellular fatty acid composition of strain HMX112 and its closest type strains. https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-g004.jpg Figure 4. Circos plot of the genome of strain HMX112T. https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-t003.jpg Table 3. 16S rRNA identity, ANI, and DDH genomic comparisons between strains HMX112 and their closes https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-g005.jpg Figure 5. Pan-genomic analysis and genomic covariance analysis plots of four Streptomyces species. ( https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-g006.jpg Figure 6. EggNOG functional classification chart for strain HMX112T. https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-t004.jpg Table 4. The resistance gene of HMX112T. https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-t005.jpg Table 5. Potential BGCs for secondary metabolites in Streptomyces flavimicrosus sp. nov. HMX112T. https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-g007.jpg Figure 7. Mass-to-core ratio [(M+Na)+] and chemical structure formula of the compound. https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-t006.jpg Table 6. Comparison of 1H-NMR and 13C-NMR data of compound and compound thiolutin. https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-t007.jpg Table 7. Thiolutin bacteriostatic activity test results. https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-g008.jpg Figure 8. Comparative analysis of the thiolutin biosynthetic gene cluster (cluster thi) in Streptomy https://www.frontiersin.org/files/Articles/1759196/fmicb-17-1759196-HTML/image_m/fmicb-17-1759196-g009.jpg Figure 9. The biosynthetic pathway of thiolutin in Streptomyces flavimicrosus HMX112T. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1721157/full https://www.frontiersin.org/files/Articles/1721157/fneur-17-1721157-HTML-r1/image_m/fneur-17-1721157-g001.jpg Figure 1. Occurrence of constipation in acute stroke patients. https://www.frontiersin.org/files/Articles/1721157/fneur-17-1721157-HTML-r1/image_m/fneur-17-1721157-t001.jpg Table 1. Demographic and clinical information collection in PSC. https://www.frontiersin.org/files/Articles/1721157/fneur-17-1721157-HTML-r1/image_m/fneur-17-1721157-t002.jpg Table 2. Demographic and clinical information collection in new-onset constipation. https://www.frontiersin.org/files/Articles/1721157/fneur-17-1721157-HTML-r1/image_m/fneur-17-1721157-t003.jpg Table 3. Multi-factor binary logistic regression analysis on the association between new-onset const https://www.frontiersin.org/files/Articles/1721157/fneur-17-1721157-HTML-r1/image_m/fneur-17-1721157-t004.jpg Table 4. Multivariable logistic regression analysis for poor outcome at discharge (all patients). https://www.frontiersin.org/files/Articles/1721157/fneur-17-1721157-HTML-r1/image_m/fneur-17-1721157-t005.jpg Table 5. Subgroup analysis: association between new-onset constipation and poor outcome stratified b https://www.frontiersin.org/files/Articles/1721157/fneur-17-1721157-HTML-r1/image_m/fneur-17-1721157-g002.jpg Figure 2. (A–C) Unadjusted comparison of discharge Barthel index between patients with and without n https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1731547/full https://www.frontiersin.org/files/Articles/1731547/fnagi-18-1731547-HTML/image_m/fnagi-18-1731547-t001.jpg Table 1. Basic information of ApoE ε4 and Non-ApoE ε4 patients. https://www.frontiersin.org/files/Articles/1731547/fnagi-18-1731547-HTML/image_m/fnagi-18-1731547-g001.jpg Figure 1. (A) The relationship between ApoE ε4 gene and BMI. (B) The relationship between ApoE ε4 ge https://www.frontiersin.org/files/Articles/1731547/fnagi-18-1731547-HTML/image_m/fnagi-18-1731547-g002.jpg Figure 2. (A) The relationship between ApoE ε4 gene and BMI in males. (B) The relationship between A https://www.frontiersin.org/files/Articles/1731547/fnagi-18-1731547-HTML/image_m/fnagi-18-1731547-g003.jpg Figure 3. (A) The relationship between ApoE ε4 gene and BMI in age ≤ 65. (B) The relationship betwee https://www.frontiersin.org/files/Articles/1731547/fnagi-18-1731547-HTML/image_m/fnagi-18-1731547-g004.jpg Figure 4. (A) The relationship between ApoE ε4 gene and BMI in MMSE > 20. (B) The relationship betwe https://www.frontiersin.org/files/Articles/1731547/fnagi-18-1731547-HTML/image_m/fnagi-18-1731547-g005.jpg Figure 5. The mediation effect of ApoE ε4 gene on cognitive function through metabolic pathway. *P < https://www.frontiersin.org/files/Articles/1731547/fnagi-18-1731547-HTML/image_m/fnagi-18-1731547-t002.jpg Table 2. The effect of ApoE ε4 gene on cognitive function through metabolic pathway. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1736338/full https://www.frontiersin.org/files/Articles/1736338/fnagi-17-1736338-HTML/image_m/fnagi-17-1736338-g001.jpg Figure 1. Flowchart of the participant selection process. https://www.frontiersin.org/files/Articles/1736338/fnagi-17-1736338-HTML/image_m/fnagi-17-1736338-t001.jpg Table 1. Analysis of risk factors for PSCI. https://www.frontiersin.org/files/Articles/1736338/fnagi-17-1736338-HTML/image_m/fnagi-17-1736338-t002.jpg Table 2. Results of the multivariate logistic regression analysis. https://www.frontiersin.org/files/Articles/1736338/fnagi-17-1736338-HTML/image_m/fnagi-17-1736338-g002.jpg Figure 2. ROC curves for PSCI prediction. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1767088/full https://www.frontiersin.org/files/Articles/1767088/fneur-17-1767088-HTML/image_m/fneur-17-1767088-g001.jpg Figure 1. Flow diagram of the study. https://www.frontiersin.org/files/Articles/1767088/fneur-17-1767088-HTML/image_m/fneur-17-1767088-t001.jpg Table 1. Demographic, clinical and neuroimaging characteristics in CAA-ICH patients with posterior c https://www.frontiersin.org/files/Articles/1767088/fneur-17-1767088-HTML/image_m/fneur-17-1767088-t002.jpg Table 2. Characteristics of patients with vs. without ICH recurrence during follow-up. https://www.frontiersin.org/files/Articles/1767088/fneur-17-1767088-HTML/image_m/fneur-17-1767088-t003.jpg Table 3. Neuroimaging characteristics in probable CAA and mixed cerebral small vessel disease. https://www.frontiersin.org/files/Articles/1767088/fneur-17-1767088-HTML/image_m/fneur-17-1767088-g002.jpg Figure 2. Kaplan-Meier curves for ICH recurrence according to the presence of WMH-PC on MRI. https://www.frontiersin.org/files/Articles/1767088/fneur-17-1767088-HTML/image_m/fneur-17-1767088-t004.jpg Table 4. Univariable and multivariable Cox regression analyses for predictors of recurrent ICH in to https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1766738/full https://www.frontiersin.org/files/Articles/1766738/fenvs-14-1766738-HTML/image_m/fenvs-14-1766738-g001.jpg Figure 1. Temporal trends in climate change scientific production by country income group. (a) Time- https://www.frontiersin.org/files/Articles/1766738/fenvs-14-1766738-HTML/image_m/fenvs-14-1766738-g002.jpg Figure 2. Conceptual framework of scientific coherence in climate science. The figure illustrates th https://www.frontiersin.org/files/Articles/1766738/fenvs-14-1766738-HTML/image_m/fenvs-14-1766738-t001.jpg Table 1. Illustrative operationalization of the four pillars of scientific coherence in climate scie https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1659801/full https://www.frontiersin.org/files/Articles/1659801/fpubh-13-1659801-HTML-r1/image_m/fpubh-13-1659801-t001.jpg Table 1. Key characteristics of autistic children. https://www.frontiersin.org/files/Articles/1659801/fpubh-13-1659801-HTML-r1/image_m/fpubh-13-1659801-t002.jpg Table 2. Out-of-pocket costs for autism-related medical care, therapies and intervention treatment i https://www.frontiersin.org/files/Articles/1659801/fpubh-13-1659801-HTML-r1/image_m/fpubh-13-1659801-t003.jpg Table 3. Total out-of-pocket costs for autism-related medical care, therapies and intervention treat https://www.frontiersin.org/files/Articles/1659801/fpubh-13-1659801-HTML-r1/image_m/fpubh-13-1659801-t004.jpg Table 4. Total out-of-pocket autism-related medical and therapy costs in the past 12 months by fundi https://www.frontiersin.org/files/Articles/1659801/fpubh-13-1659801-HTML-r1/image_m/fpubh-13-1659801-t005.jpg Table 5. Extra costs directly related to supporting the autistic child in the past 12 months (in Can https://www.frontiersin.org/files/Articles/1659801/fpubh-13-1659801-HTML-r1/image_m/fpubh-13-1659801-t006.jpg Table 6. Total extra costs directly related to supporting the autistic child in the past 12 months b https://www.frontiersin.org/files/Articles/1659801/fpubh-13-1659801-HTML-r1/image_m/fpubh-13-1659801-t007.jpg Table 7. Changes in employment status for caregivers. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1714070/full https://www.frontiersin.org/files/Articles/1714070/fcvm-13-1714070-HTML/image_m/fcvm-13-1714070-g001.jpg Figure 1. Flowchart of study selection. https://www.frontiersin.org/files/Articles/1714070/fcvm-13-1714070-HTML/image_m/fcvm-13-1714070-t001.jpg Table 1. Pooled incidence of heart failure per 1000 person-years, stratified by population character https://www.frontiersin.org/files/Articles/1714070/fcvm-13-1714070-HTML/image_m/fcvm-13-1714070-g002.jpg Figure 2. Meta-regression of HF incidence rates by study midpoint (A) and publication year (B). https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1719549/full https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-t001.jpg Table 1. Paired t-test results comparing values at baseline (T0) and after the intervention period ( https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-g001.jpg Figure 1. Linear regression analysis showing the relationship between serum AdoMet levels (pg/mL) an https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-g002.jpg Figure 2. Linear regression analysis illustrating the relationship between serum AdoMet levels (pg/m https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-g003.jpg Figure 3. Linear regression analysis depicting the relationship between serum AdoMet levels (pg/mL) https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-g004.jpg Figure 4. Linear regression analysis illustrating the relationship between serum AdoMet levels (pg/m https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-g005.jpg Figure 5. Linear regression analysis showing the relationship between serum AdoMet levels (pg/mL) an https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-g006.jpg Figure 6. Linear regression analysis illustrating the relationship between serum AdoMet levels (pg/m https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-g007.jpg Figure 7. Linear regression analysis showing the relationship between serum AdoMet levels (pg/mL) an https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-g008.jpg Figure 8. Linear regression analysis illustrating the relationship between serum AdoMet levels (pg/m https://www.frontiersin.org/files/Articles/1719549/fphys-16-1719549-HTML/image_m/fphys-16-1719549-g009.jpg Figure 9. Schematic overview of study design, sample workflow, and main findings. The diagram illust https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1787419/full https://www.frontiersin.org/files/Articles/1787419/fnut-13-1787419-HTML/image_m/fnut-13-1787419-t001.jpg Table 1. Demographic and clinical characteristic of the participants (n = 495). https://www.frontiersin.org/files/Articles/1787419/fnut-13-1787419-HTML/image_m/fnut-13-1787419-t002.jpg Table 2. Phase angle values according to SLD. https://www.frontiersin.org/files/Articles/1787419/fnut-13-1787419-HTML/image_m/fnut-13-1787419-t003.jpg Table 3. Baseline characteristics of participants stratified by SLD status. https://www.frontiersin.org/files/Articles/1787419/fnut-13-1787419-HTML/image_m/fnut-13-1787419-g001.jpg Figure 1. ROC curve evaluating the ability of phase angle (PhA) to discriminate SLD. https://www.frontiersin.org/files/Articles/1787419/fnut-13-1787419-HTML/image_m/fnut-13-1787419-t004.jpg Table 4. Logistic regression model predicting SLD. https://www.frontiersin.org/files/Articles/1787419/fnut-13-1787419-HTML/image_m/fnut-13-1787419-t005.jpg Table 5. Correlation between CAP value and various variables. https://www.frontiersin.org/files/Articles/1787419/fnut-13-1787419-HTML/image_m/fnut-13-1787419-t006.jpg Table 6. Linear regression models predicting CAP values. https://www.frontiersin.org/files/Articles/1787419/fnut-13-1787419-HTML/image_m/fnut-13-1787419-g002.jpg Figure 2. ROC curve evaluating the ability of predicted CAP values to identify SLD using the cutoff https://www.frontiersin.org/files/Articles/1787419/fnut-13-1787419-HTML/image_m/fnut-13-1787419-g003.jpg Figure 3. Bland–Altman plot showing the agreement between measured and predicted CAP values. https://www.frontiersin.org/journals/neuroergonomics/articles/10.3389/fnrgo.2026.1757738/full https://www.frontiersin.org/files/Articles/1757738/fnrgo-07-1757738-HTML/image_m/fnrgo-07-1757738-t001.jpg Table 1. Search concept blocks and synonyms (Title/Abstract). https://www.frontiersin.org/files/Articles/1757738/fnrgo-07-1757738-HTML/image_m/fnrgo-07-1757738-g001.jpg Figure 1. PRISMA flow diagram (combined across the three research questions: EEG-CST, CST-ID, and EE https://www.frontiersin.org/files/Articles/1757738/fnrgo-07-1757738-HTML/image_m/fnrgo-07-1757738-g002.jpg Figure 2. Geographic and temporal distribution of included studies. (A) Country of origin for public https://www.frontiersin.org/files/Articles/1757738/fnrgo-07-1757738-HTML/image_m/fnrgo-07-1757738-g003.jpg Figure 3. Venn diagram of search concepts and included publications. Overlaps show the number of stu https://www.frontiersin.org/files/Articles/1757738/fnrgo-07-1757738-HTML/image_m/fnrgo-07-1757738-t002.jpg Table 2. Participant overview of included studies in CST and ID. https://www.frontiersin.org/files/Articles/1757738/fnrgo-07-1757738-HTML/image_m/fnrgo-07-1757738-t003.jpg Table 3. Outcome measures reported by each study. https://www.frontiersin.org/files/Articles/1757738/fnrgo-07-1757738-HTML/image_m/fnrgo-07-1757738-t004.jpg Table 4. Cognitive measures used and significance results for each study. https://www.frontiersin.org/files/Articles/1757738/fnrgo-07-1757738-HTML/image_m/fnrgo-07-1757738-g004.jpg Figure 4. Distribution of EEG measurement types across the included studies. Bars indicate occurrenc https://www.frontiersin.org/files/Articles/1757738/fnrgo-07-1757738-HTML/image_m/fnrgo-07-1757738-t005.jpg Table 5. Summary of the metrics' occurrences across the 14 EEG in ID studies included in the review. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1757798/full https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-g001.jpg Figure 1. Location of the study area and layout of the experimental plots. https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-t001.jpg Table 1. Experimental treatments and irrigation–fertilization levels. https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-t002.jpg Table 2. Parameters of the multispectral camera and reflectance of the gray reference panel. https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-t003.jpg Table 3. Comparison of weighting schemes for constructing INDI based on LAI and LNWupperz. https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-t004.jpg Table 4. Vegetation index and calculation formula. https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-g002.jpg Figure 2. Correlation analysis between cotton nitrogen diagnosis indices and commonly used vegetatio https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-t005.jpg Table 5. Comparison of model prediction accuracy. https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-g003.jpg Figure 3. Training and validation results of machine learning algorithms: (A, D, G) represent the RF https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-g004.jpg Figure 4. Spatial distribution of INDI at different cotton growth stages. (A-D) correspond to the IN https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-g005.jpg Figure 5. Critical nitrogen dilution curves of upper-canopy leaves under different irrigation levels https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-t006.jpg Table 6. Model parameters for the critical nitrogen concentration dilution curve for cotton. https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-g006.jpg Figure 6. Comparison of simulated and observed critical nitrogen concentrations. https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-g007.jpg Figure 7. (A–D) represent irrigation levels of 60% ETc,80% ETc, and 100% ETc, respectively; N0, N245 https://www.frontiersin.org/files/Articles/1757798/fpls-17-1757798-HTML-r1/image_m/fpls-17-1757798-g008.jpg Figure 8. Correlation between INDI and NNI at different growth stages. (A-D) correspond to the squar https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1750479/full https://www.frontiersin.org/files/Articles/1750479/fcvm-13-1750479-HTML/image_m/fcvm-13-1750479-t001.jpg Table 1. Clinical characteristics of the cCHD patients. https://www.frontiersin.org/files/Articles/1750479/fcvm-13-1750479-HTML/image_m/fcvm-13-1750479-t002.jpg Table 2. CPB-related data and postoperative complications among cCHD patients. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1810135/full https://www.frontiersin.org/files/Articles/1810135/fonc-16-1810135-HTML/image_m/fonc-16-1810135-g001.jpg Figure 1. (A) Adeno-associated virus (AAV) illustration composed of a mixture of AAV serotypes (2, 5 https://www.frontiersin.org/files/Articles/1810135/fonc-16-1810135-HTML/image_m/fonc-16-1810135-t001.jpg Table 1. Summary of experimental parameters for each pig. https://www.frontiersin.org/files/Articles/1810135/fonc-16-1810135-HTML/image_m/fonc-16-1810135-g002.jpg Figure 2. Longitudinal CE-MRI for a representative Oncopig. Tumor, depicted by the red arrow, was fi https://www.frontiersin.org/files/Articles/1810135/fonc-16-1810135-HTML/image_m/fonc-16-1810135-g003.jpg Figure 3. (A) Representative coronal T1-weighted CE-MRI slices for all pigs that exhibited tumor gro https://www.frontiersin.org/files/Articles/1810135/fonc-16-1810135-HTML/image_m/fonc-16-1810135-g004.jpg Figure 4. Images of H&E stained tumor specimens for OPs 1-6. Scale bars are 100 μm and 10 μm in (A, https://www.frontiersin.org/files/Articles/1810135/fonc-16-1810135-HTML/image_m/fonc-16-1810135-t002.jpg Table 2. Histopathological analysis for all tumor samples per animal. https://www.frontiersin.org/files/Articles/1810135/fonc-16-1810135-HTML/image_m/fonc-16-1810135-g005.jpg Figure 5. Images of H&E stained tumor specimens showing infiltrative tumor boundaries in OPs 1-6. https://www.frontiersin.org/files/Articles/1810135/fonc-16-1810135-HTML/image_m/fonc-16-1810135-g006.jpg Figure 6. Image-guided resection of glioma in an Oncopig. (A) Intraoperative MRI, (B) widefield RGB, https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2026.1814006/full https://www.frontiersin.org/files/Articles/1814006/fnimg-05-1814006-HTML/image_m/fnimg-05-1814006-t001.jpg Table 1. Demographics, disease metrics, self-report measures. https://www.frontiersin.org/files/Articles/1814006/fnimg-05-1814006-HTML/image_m/fnimg-05-1814006-g001.jpg Figure 1. Simple main effect of fatigue within physical, cognitive, and psychosocial domains on cort https://www.frontiersin.org/files/Articles/1814006/fnimg-05-1814006-HTML/image_m/fnimg-05-1814006-t002.jpg Table 2. Within- and between- group cortical complexity associations with the impact of fatigue acro https://www.frontiersin.org/files/Articles/1814006/fnimg-05-1814006-HTML/image_m/fnimg-05-1814006-g002.jpg Figure 2. Results of the between group contrast HC>CD showing differences between groups in the asso https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1818425/full https://www.frontiersin.org/files/Articles/1818425/fped-14-1818425-HTML/image_m/fped-14-1818425-t001.jpg Table 1. Characteristics of the study sample. https://www.frontiersin.org/files/Articles/1818425/fped-14-1818425-HTML/image_m/fped-14-1818425-t002.jpg Table 2. Comparison of EDI data of very preterm children and their Ontario peers. https://www.frontiersin.org/files/Articles/1818425/fped-14-1818425-HTML/image_m/fped-14-1818425-t003.jpg Table 3. Comparison of EDI data of very preterm children with typical development and with NDI. https://www.frontiersin.org/files/Articles/1818425/fped-14-1818425-HTML/image_m/fped-14-1818425-t004.jpg Table 4. Association between BSID-III cognitive and language scores and EDI vulnerability in very pr https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1699500/full https://www.frontiersin.org/files/Articles/1699500/fimmu-17-1699500-HTML/image_m/fimmu-17-1699500-g001.jpg Figure 1. The cellular microenvironment of thyroid cancer (drawn using Figdraw; www.figdraw.com). VE https://www.frontiersin.org/files/Articles/1699500/fimmu-17-1699500-HTML/image_m/fimmu-17-1699500-g002.jpg Figure 2. The tumor immune microenvironment of thyroid cancer (drawn using Figdraw; www.figdraw.com) https://www.frontiersin.org/files/Articles/1699500/fimmu-17-1699500-HTML/image_m/fimmu-17-1699500-g003.jpg Figure 3. The metabolic reprogramming of thyroid cancer (drawn using Figdraw; www.figdraw.com). PD-1 https://www.frontiersin.org/files/Articles/1699500/fimmu-17-1699500-HTML/image_m/fimmu-17-1699500-t001.jpg Table 1. Clinical studies for ICIs in thyroid cancers. https://www.frontiersin.org/files/Articles/1699500/fimmu-17-1699500-HTML/image_m/fimmu-17-1699500-t002.jpg Table 2. Clinical studies for ICIs combined with TKIs in thyroid cancers. https://www.frontiersin.org/files/Articles/1699500/fimmu-17-1699500-HTML/image_m/fimmu-17-1699500-t003.jpg Table 3. Clinical studies for ICIs combined with chemoradiotherapy in thyroid cancers. https://www.frontiersin.org/files/Articles/1699500/fimmu-17-1699500-HTML/image_m/fimmu-17-1699500-t004.jpg Table 4. Clinical studies for ICIs combined with BRAFi in thyroid cancers. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1780686/full https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-t001.jpg Table 1. The clinical baseline characteristics of the study participants. https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-t002.jpg Table 2. MR analysis of the causal effect of obesity on NAFLD. https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-g001.jpg Figure 1. MR analysis of the causal link between obesity and NAFLD. (A,B) Scatter and Forest plots c https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-t003.jpg Table 3. Heterogeneity test of the causal effect of obesity on NAFLD. https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-t004.jpg Table 4. Pleiotropy test of the causal effect of obesity on NAFLD. https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-t005.jpg Table 5. MR analysis of the causal effect of NAFLD on obesity. https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-g002.jpg Figure 2. Identification of shared DEGs and functional enrichment. (A,B) Volcano plots and heatmaps https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-g003.jpg Figure 3. Biomarker selection via machine learning. (A) Intersection analysis of candidate DEGs. (B) https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-g004.jpg Figure 4. Validation and diagnostic efficacy of shared biomarkers. (A–D) Expression levels of NCAPH https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-g005.jpg Figure 5. Diagnostic nomogram construction and evaluation. (A,B) Nomograms predicting obesity and NA https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-g006.jpg Figure 6. Single-gene GSEA and interaction network. (A–D) Significant KEGG pathways enriched in NAFL https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-g007.jpg Figure 7. Chromosomal localization and regulatory networks. (A,B) Chromosomal distribution and subce https://www.frontiersin.org/files/Articles/1780686/fnut-13-1780686-HTML/image_m/fnut-13-1780686-g008.jpg Figure 8. Experimental validation. qRT-PCR confirms significant up-regulation of NCAPH in clinical o https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1731085/full https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-g001.jpg Figure 1. (A) Schematic description of the experiment set-up; (B) photo of the optical set-up used i https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-g002.jpg Figure 2. (A) The RI real part of crude oil, condensate, and seawater; (B) imaginary part of the RI https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-t001.jpg Table 1. Lists the results of the thickness calculation for each method used (Geometric thickness, M https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-t002.jpg Table 2. Lists the results of the thickness calculation for each method used (Geometric thickness, M https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-t003.jpg Table 3. Lists the relative error% compared to the expected thickness (geometrical thickness) of the https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-t004.jpg Table 4. Lists the relative error% compared to the expected thickness (geometrical thickness) of the https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-g003.jpg Figure 3. (A) Condensate - Measured reflectivity spectrum (solid red) and best-fit reflectivity spec https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-g004.jpg Figure 4. (A) Condensate – geometrical thickness versus optically measured thickness (Mean-FSR-based https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-g005.jpg Figure 5. (A–C) Condensate–measured reflectivity spectrum for 5.5, 18.8, and 23.3 (μm) thickness of https://www.frontiersin.org/files/Articles/1731085/fmars-13-1731085-HTML/image_m/fmars-13-1731085-g006.jpg Figure 6. (A) The modified optical set-up with increased distance between the source and the measure https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1673112/full https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-g001.jpg Figure 1. Flow diagram. https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-t001.jpg Table 1. Enrollment process of patients. https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-t002.jpg Table 2. Baseline characteristics of patients in the treatment and control groups. https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-g002.jpg Figure 2. Trends over time in the clinical symptom score (A), EASI (B), pruritus VAS score (C), and https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-g003.jpg Figure 3. Comparison of clinical indicators at baseline (A), 2-week (B), and 4-week (C) between the https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-t003.jpg Table 3. Comparison of clinical indicators between the two groups following 2-week treatment. https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-t004.jpg Table 4. Comparison of clinical indicators between the two groups following 4-week treatment. https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-g004.jpg Figure 4. Total significant efficacy of both groups after a 4-week treatment course (A); occurrences https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-t005.jpg Table 5. Comparison of total significant efficacy between the two groups with a 4-week treatment cou https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-t006.jpg Table 6. Evaluation of drug safety grades in both groups. https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-t007.jpg Table 7. Comparison of EASI rebound occurrences during a 12-week follow-up between the two groups. https://www.frontiersin.org/files/Articles/1673112/fphar-16-1673112-HTML-r1/image_m/fphar-16-1673112-g005.jpg Figure 5. The proposed mechanism for combining APL and pimecrolimus cream in the treatment of CPE. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1813306/full https://www.frontiersin.org/files/Articles/1813306/feduc-11-1813306-HTML/image_m/feduc-11-1813306-g001.jpg Figure 1. Study design and data collection. https://www.frontiersin.org/files/Articles/1813306/feduc-11-1813306-HTML/image_m/feduc-11-1813306-t001.jpg Table 1. Outcome of the viva voce examination stratified by self-reported use of GAI. https://www.frontiersin.org/files/Articles/1813306/feduc-11-1813306-HTML/image_m/feduc-11-1813306-t002.jpg Table 2. Themes and analytical definitions from the coded interview data. https://www.frontiersin.org/files/Articles/1813306/feduc-11-1813306-HTML/image_m/feduc-11-1813306-g002.jpg Figure 2. Thematic tree illustrating students’ reasoning for non-use of GAI. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1800924/full https://www.frontiersin.org/files/Articles/1800924/feduc-11-1800924-HTML/image_m/feduc-11-1800924-g001.jpg Figure 1. Disease-centered SPOC structure in clinical molecular diagnostics. https://www.frontiersin.org/files/Articles/1800924/feduc-11-1800924-HTML/image_m/feduc-11-1800924-t001.jpg Table 1. Course modules and topics. https://www.frontiersin.org/files/Articles/1800924/feduc-11-1800924-HTML/image_m/feduc-11-1800924-t002.jpg Table 2. Quiz and final examination performance across. https://www.frontiersin.org/files/Articles/1800924/feduc-11-1800924-HTML/image_m/feduc-11-1800924-t003.jpg Table 3. Student perceptions of the SPOC course. https://www.frontiersin.org/files/Articles/1800924/feduc-11-1800924-HTML/image_m/feduc-11-1800924-t004.jpg Table 4. Representative student quotes by thematic category. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1641542/full https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-t001.jpg Table 1. List of types of drug susceptibility, reference standards and drug susceptibility results. https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-g001.jpg Figure 1. (a) Colony Morphology of C. auris strain CAS20503 Incubated on Sabouraud dextrose agar med https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-g002.jpg Figure 2. (a) Morphological Characteristics of C. auris strain CAS20503 Observed by Gram Staining Mi https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-g003.jpg Figure 3. Identification of the Protein Fingerprint of C. auris strain CAS20503 Using MALDI-TOF MS. https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-g004.jpg Figure 4. Antifungal Susceptibility Test Results of C. auris strain CAS20503. https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-t002.jpg Table 2. Table of drug-resistant gene distribution in C. auris strain CAS20503. https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-t003.jpg Table 3. Virulence gene annotation of C. auris strain CAS20503 based on the DFVF database. https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-t004.jpg Table 4. Virulence gene annotation of C. auris strain CAS20503 based on the PHI database. https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-g005.jpg Figure 5. KEGG Pathway Classification of C. auris strain CAS20503. The pathway was composed of six b https://www.frontiersin.org/files/Articles/1641542/fcimb-15-1641542-HTML/image_m/fcimb-15-1641542-g006.jpg Figure 6. A phylogenetic tree was constructed through analysis of the 18S rRNA nucleotide sequence. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1672132/full https://www.frontiersin.org/files/Articles/1672132/fpsyg-17-1672132-HTML/image_m/fpsyg-17-1672132-t001.jpg Table 1. Proposed active ingredients of clinical EFT. https://www.frontiersin.org/files/Articles/1672132/fpsyg-17-1672132-HTML/image_m/fpsyg-17-1672132-g001.jpg Figure 1. EFT mechanism of change 1: reduction of physiogical and emotional dysregulation. https://www.frontiersin.org/files/Articles/1672132/fpsyg-17-1672132-HTML/image_m/fpsyg-17-1672132-g002.jpg Figure 2. EFT mechanism of change 2: reduction of emotional avoidance. https://www.frontiersin.org/files/Articles/1672132/fpsyg-17-1672132-HTML/image_m/fpsyg-17-1672132-g003.jpg Figure 3. Mechanism of change 3: cognitive and emotional restructuring. https://www.frontiersin.org/files/Articles/1672132/fpsyg-17-1672132-HTML/image_m/fpsyg-17-1672132-g004.jpg Figure 4. Mechanism of change 4: memory reconsolidation. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1722591/full https://www.frontiersin.org/files/Articles/1722591/fmicb-17-1722591-HTML-r1/image_m/fmicb-17-1722591-g001.jpg Figure 1. Maximum-likelihood phylogenetic reconstruction of 16S rRNA gene sequences from Nitrosospha https://www.frontiersin.org/files/Articles/1722591/fmicb-17-1722591-HTML-r1/image_m/fmicb-17-1722591-g002.jpg Figure 2. Relative abundance of AOA and non-AOA clades in the sampled circumpolar Arctic locations. https://www.frontiersin.org/files/Articles/1722591/fmicb-17-1722591-HTML-r1/image_m/fmicb-17-1722591-g003.jpg Figure 3. CCA biplot of the relative abundances of Nitrososphaerales ASVs on each sampling site and https://www.frontiersin.org/files/Articles/1722591/fmicb-17-1722591-HTML-r1/image_m/fmicb-17-1722591-g004.jpg Figure 4. Temperature and pH range of Ca. N. arcticus. Nitrite production (A,C) and corresponding gr https://www.frontiersin.org/files/Articles/1722591/fmicb-17-1722591-HTML-r1/image_m/fmicb-17-1722591-g005.jpg Figure 5. Visualization of aggregates, single cells, and putative extracellular polymeric substances https://www.frontiersin.org/files/Articles/1722591/fmicb-17-1722591-HTML-r1/image_m/fmicb-17-1722591-t001.jpg Table 1. Genomic and physiological features of Nitrosocosmicus arcticus Kfb and other representative https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1785264/full https://www.frontiersin.org/files/Articles/1785264/fmed-13-1785264-HTML-r1/image_m/fmed-13-1785264-t001.jpg Table 1. Data collection methods. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1819930/full https://www.frontiersin.org/files/Articles/1819930/fneur-17-1819930-HTML/image_m/fneur-17-1819930-t001.jpg Table 1. Comparison of baseline characteristics between the two groups. https://www.frontiersin.org/files/Articles/1819930/fneur-17-1819930-HTML/image_m/fneur-17-1819930-t002.jpg Table 2. Comparison of Hcy and UA levels between the two groups. https://www.frontiersin.org/files/Articles/1819930/fneur-17-1819930-HTML/image_m/fneur-17-1819930-t003.jpg Table 3. Correlation of Hcy and UA with MMSE (overall and by group). https://www.frontiersin.org/files/Articles/1819930/fneur-17-1819930-HTML/image_m/fneur-17-1819930-t004.jpg Table 4. Nonlinear relationship between UA and MMSE: quadratic regression analysis by group. https://www.frontiersin.org/files/Articles/1819930/fneur-17-1819930-HTML/image_m/fneur-17-1819930-t005.jpg Table 5. Multiple linear regression analysis: testing the interaction effects of Hcy and UA between https://www.frontiersin.org/files/Articles/1819930/fneur-17-1819930-HTML/image_m/fneur-17-1819930-t006.jpg Table 6. Binary logistic regression analysis: independent correlates with group as the dependent var https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1634771/full https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-t001.jpg Table 1. Summary statistics of patient characteristics. https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-g001.jpg Figure 1. Distribution of the devices that used in the study. https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-t002.jpg Table 2. Operation types of patients. https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-t003.jpg Table 3. Comparison of preoperative and operative variables based on readmission Status. https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-t004.jpg Table 4. Factors associated with hospital readmission after LVAD implantation (Cox regression analys https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-t005.jpg Table 5. Distribution of readmission causes. https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-g002.jpg Figure 2. Kaplan–Meier survival curve based on LVAD types. https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-g003.jpg Figure 3. Survival analysis based on readmission Status: in the survival analysis of LVAD-implanted https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-g004.jpg Figure 4. Distribution of readmission trends based on LVAD types. https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-g005.jpg Figure 5. Device-specific complication profiles after LVAD implantation. Three horizontal bar panels https://www.frontiersin.org/files/Articles/1634771/fcvm-13-1634771-HTML/image_m/fcvm-13-1634771-g006.jpg Figure 6. Cumulative and yearly incidence of hospital readmissions following LVAD implantation. The https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1593217/full https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-g001.jpg Figure 1. Geography of the study region. (A) Location of the Guangdong in China; (B) Study Area Loca https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-t001.jpg Table 1. Data description and sources. https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-g002.jpg Figure 2. Analysis scheme of the study. (CS: carbon storage; HQ: habitat quality; SR: Soil retention https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-g003.jpg Figure 3. Spatial distribution of ES between 2000 and 2020. (A) CS; (B) HQ; (C) SR; (D) WR; (E) ES ( https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-t002.jpg Table 2. Temporal statistics of ES. https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-t003.jpg Table 3. Fitting results of the OLS and MGWR model. https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-t004.jpg Table 4. Bandwidth of ES driving factors. https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-t005.jpg Table 5. The MGWR coefficients between ES driving factors. https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-g004.jpg Figure 4. Quantitative effects of drivers in ES through MGWR between 2000 from 2020. (A) 2000; (B) 2 https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-g005.jpg Figure 5. Mean ES values for each zone between 2000 from 2020. (A) 2000; (B) 2010; (C) 2020. https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-g006.jpg Figure 6. Spatiotemporal changes and structural transitions of ecological zones in eastern Guangdong https://www.frontiersin.org/files/Articles/1593217/feart-13-1593217-HTML/image_m/feart-13-1593217-g007.jpg Figure 7. The driving factors of different ecological functional regions. (A) 2000; (B) 2010; (C) 20 https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1781818/full https://www.frontiersin.org/files/Articles/1781818/fmed-13-1781818-HTML/image_m/fmed-13-1781818-t001.jpg Table 1. Stage based framework: how AI supports ophthalmic surgical competence development (Dreyfus https://www.frontiersin.org/files/Articles/1781818/fmed-13-1781818-HTML/image_m/fmed-13-1781818-t002.jpg Table 2. AI applications in ophthalmic surgery training and practice, organized by surgical phase. https://www.frontiersin.org/files/Articles/1781818/fmed-13-1781818-HTML/image_m/fmed-13-1781818-g001.jpg Figure 1. Conceptual framework of AI-assisted ophthalmic surgical training and competence developmen https://www.frontiersin.org/files/Articles/1781818/fmed-13-1781818-HTML/image_m/fmed-13-1781818-t003.jpg Table 3. Categorization of AI methodologies in ophthalmic surgical training and their validation sta https://www.frontiersin.org/files/Articles/1781818/fmed-13-1781818-HTML/image_m/fmed-13-1781818-g002.jpg Figure 2. Novice stage—evidence dashboard. Comparative summary of AI tools for early motor-skill acq https://www.frontiersin.org/files/Articles/1781818/fmed-13-1781818-HTML/image_m/fmed-13-1781818-g003.jpg Figure 3. Advanced beginner stage—evidence dashboard. Comparative summary of AI tools supporting att https://www.frontiersin.org/files/Articles/1781818/fmed-13-1781818-HTML/image_m/fmed-13-1781818-g004.jpg Figure 4. Competent stage—evidence dashboard. Comparative summary of AI tools for risk prediction, c https://www.frontiersin.org/files/Articles/1781818/fmed-13-1781818-HTML/image_m/fmed-13-1781818-g005.jpg Figure 5. Expert stage—evidence dashboard. Comparative summary of AI tools for performance profiling https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1810098/full https://www.frontiersin.org/files/Articles/1810098/fmed-13-1810098-HTML-r1/image_m/fmed-13-1810098-g001.jpg Figure 1. Literature search and study selection workflow. https://www.frontiersin.org/files/Articles/1810098/fmed-13-1810098-HTML-r1/image_m/fmed-13-1810098-g002.jpg Figure 2. Conceptual framework linking education deficit to low self-efficacy and missed-diagnosis r https://www.frontiersin.org/files/Articles/1810098/fmed-13-1810098-HTML-r1/image_m/fmed-13-1810098-g003.jpg Figure 3. LLM-driven solutions for primary care ophthalmic education barriers. https://www.frontiersin.org/files/Articles/1810098/fmed-13-1810098-HTML-r1/image_m/fmed-13-1810098-t001.jpg Table 1. Summary of key studies on LLM-assisted triage in primary care and emergency ophthalmology s https://www.frontiersin.org/files/Articles/1810098/fmed-13-1810098-HTML-r1/image_m/fmed-13-1810098-t002.jpg Table 2. Key evidence for LLMs in primary care chronic eye disease management and interdisciplinary https://www.frontiersin.org/files/Articles/1810098/fmed-13-1810098-HTML-r1/image_m/fmed-13-1810098-t003.jpg Table 3. Key evidence for LLM-driven simulation teaching and interactive learning. https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1684333/full https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-t001.jpg Table 1. Research on the yellow river basin. https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-g001.jpg Figure 1. The Yellow River Basin research area. https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-t002.jpg Table 2. Land-use transfer cost matrix. https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-t003.jpg Table 3. Carbon density values of different land types in the YRB as revised by annual precipitation https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-t004.jpg Table 4. Land-use Types (km2) and Carbon storage (million tons) in the YRB. https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-g002.jpg Figure 2. The spatial-temporal distribution of YRB’s carbon storage in 1980 (a), 1990 (b), 2000 (c), https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-g003.jpg Figure 3. Variations in carbon storage of the YRB from 1980 to 1990 (a), 1990 to 2000 (b), 2000 to 2 https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-g004.jpg Figure 4. Simulated and measured land-use of the YRB in 2020. https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-g005.jpg Figure 5. Land-use simulation map of the YRB in 2030 (A), 2040 (B), 2050 (C), 2060 (D), 2070 (E), 20 https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-g006.jpg Figure 6. The proportion of different land-use types in the YRB under SSP1-2.6 (a), SSP2-4.5 (b), SS https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-g007.jpg Figure 7. Temporal-spatial distribution of the YRB’s carbon storage in 2030 (A), 2040 (B), 2050 (C), https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-g008.jpg Figure 8. Variations in carbon storage in YRB under different scenarios (2030–2100). https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-t005.jpg Table 5. Variations in carbon storage in the YRB in 2020–2030 and 2030–2100 under different scenario https://www.frontiersin.org/files/Articles/1684333/feart-13-1684333-HTML/image_m/feart-13-1684333-g009.jpg Figure 9. Variations in carbon storage of YR from 2020–2030 and 2030–2100 under SSP1-2.6 (A), SSP2-4 https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1816268/full https://www.frontiersin.org/files/Articles/1816268/fpubh-14-1816268-HTML/image_m/fpubh-14-1816268-g001.jpg Figure 1. Identification of studies flow diagram for literature search. This figure was generated us https://www.frontiersin.org/files/Articles/1816268/fpubh-14-1816268-HTML/image_m/fpubh-14-1816268-g002.jpg Figure 2. The impact of hip fracture and surgery on cognitive function. This figure was generated us https://www.frontiersin.org/files/Articles/1816268/fpubh-14-1816268-HTML/image_m/fpubh-14-1816268-g003.jpg Figure 3. Forest plot illustrating the association between severity of CI and 1-year mortality follo https://www.frontiersin.org/files/Articles/1816268/fpubh-14-1816268-HTML/image_m/fpubh-14-1816268-g004.jpg Figure 4. Management strategies for CI after hip fracture surgery. This figure was generated using A https://www.frontiersin.org/files/Articles/1816268/fpubh-14-1816268-HTML/image_m/fpubh-14-1816268-g005.jpg Figure 5. Rehabilitation strategies for patients with hip fractures and CI adaptations to rehabilita https://www.frontiersin.org/files/Articles/1816268/fpubh-14-1816268-HTML/image_m/fpubh-14-1816268-t001.jpg Table 1. Stratified rehabilitation framework for geriatric hip fracture patients based on cognitive https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2025.1709762/full https://www.frontiersin.org/files/Articles/1709762/fcomm-10-1709762-HTML-r2/image_m/fcomm-10-1709762-t001.jpg Table 1. Participants. https://www.frontiersin.org/files/Articles/1709762/fcomm-10-1709762-HTML-r2/image_m/fcomm-10-1709762-t002.jpg Table 2. The biggest eco-social problems in the Baduy society according to Papagahan participants. https://www.frontiersin.org/files/Articles/1709762/fcomm-10-1709762-HTML-r2/image_m/fcomm-10-1709762-t003.jpg Table 3. Thematic summary table. https://www.frontiersin.org/files/Articles/1709762/fcomm-10-1709762-HTML-r2/image_m/fcomm-10-1709762-t004.jpg Table 4. Eco-social literacy levels of Baduy Youngsters. https://www.frontiersin.org/files/Articles/1709762/fcomm-10-1709762-HTML-r2/image_m/fcomm-10-1709762-t005.jpg Table 5. Papagahan sessions. https://www.frontiersin.org/files/Articles/1709762/fcomm-10-1709762-HTML-r2/image_m/fcomm-10-1709762-t006.jpg Table 6. Relationship between Papagahan sessions and the eco-social literacy level of Baduy youngste https://www.frontiersin.org/files/Articles/1709762/fcomm-10-1709762-HTML-r2/image_m/fcomm-10-1709762-g001.jpg Figure 1. Implementation of the first Papagahan session. Source: Authors’ own analysis. https://www.frontiersin.org/files/Articles/1709762/fcomm-10-1709762-HTML-r2/image_m/fcomm-10-1709762-g002.jpg Figure 2. Papagahan and improving eco-social literacy among Baduy youngsters. Source: Authors’ own a https://www.frontiersin.org/files/Articles/1709762/fcomm-10-1709762-HTML-r2/image_m/fcomm-10-1709762-g003.jpg Figure 3. Active community participation in Papagahan. Source: Authors’ own analysis. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1673229/full https://www.frontiersin.org/files/Articles/1673229/fcimb-15-1673229-HTML/image_m/fcimb-15-1673229-t001.jpg Table 1. Primers used for qRT-PCR. https://www.frontiersin.org/files/Articles/1673229/fcimb-15-1673229-HTML/image_m/fcimb-15-1673229-g001.jpg Figure 1. Presence of CMV in CMV infected SD rats. DNA loads were detected by quantitative PCR. Plot https://www.frontiersin.org/files/Articles/1673229/fcimb-15-1673229-HTML/image_m/fcimb-15-1673229-g002.jpg Figure 2. Dynamic changes of autophagy in CD8+ T cells after CMV infection. (A) Flow cytometry analy https://www.frontiersin.org/files/Articles/1673229/fcimb-15-1673229-HTML/image_m/fcimb-15-1673229-g003.jpg Figure 3. Dynamic alteration of metabolism in CD8+ T cells following CMV infection. (A) mRNA levels https://www.frontiersin.org/files/Articles/1673229/fcimb-15-1673229-HTML/image_m/fcimb-15-1673229-g004.jpg Figure 4. Expression of exhaustion markers in splenocytes after CMV infection. (A) The expression of https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1778885/full https://www.frontiersin.org/files/Articles/1778885/fphys-17-1778885-HTML/image_m/fphys-17-1778885-t001.jpg Table 1. Age and anthropometric characteristics of U18 handball players (mean ± SEM; n = 30). https://www.frontiersin.org/files/Articles/1778885/fphys-17-1778885-HTML/image_m/fphys-17-1778885-g001.jpg Figure 1. Schematic diagram of the study design illustrating the flowchart of participant recruitmen https://www.frontiersin.org/files/Articles/1778885/fphys-17-1778885-HTML/image_m/fphys-17-1778885-g002.jpg Figure 2. Schematic diagram of the study design showing the five testing time points (Week 0, Week 1 https://www.frontiersin.org/files/Articles/1778885/fphys-17-1778885-HTML/image_m/fphys-17-1778885-t002.jpg Table 2. Testing procedures and warm-up protocol. https://www.frontiersin.org/files/Articles/1778885/fphys-17-1778885-HTML/image_m/fphys-17-1778885-g003.jpg Figure 3. Diagram depicting the movement sequence of the MAT, adapted from a previously validated pr https://www.frontiersin.org/files/Articles/1778885/fphys-17-1778885-HTML/image_m/fphys-17-1778885-g004.jpg Figure 4. Schematic representation of the RAT setup and pathway, adapted from a previously validated https://www.frontiersin.org/files/Articles/1778885/fphys-17-1778885-HTML/image_m/fphys-17-1778885-t003.jpg Table 3. Body Mass and Body Mass Index over time (mean ± SEM) (n = 30). https://www.frontiersin.org/files/Articles/1778885/fphys-17-1778885-HTML/image_m/fphys-17-1778885-t004.jpg Table 4. Sprint, Modified Agility Test (MAT) and Reactive Agility Test (RAT) performances across mea https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1713299/full https://www.frontiersin.org/files/Articles/1713299/fgene-17-1713299-HTML-r1/image_m/fgene-17-1713299-g001.jpg Figure 1. Overview of the used workflow. Twenty-two unsolved IEI cases were selected and clinical da https://www.frontiersin.org/files/Articles/1713299/fgene-17-1713299-HTML-r1/image_m/fgene-17-1713299-t001.jpg Table 1. Genetic variants classified as strong candidates (AIM score >0.1 and/or AION Smoking Guns) https://www.frontiersin.org/files/Articles/1713299/fgene-17-1713299-HTML-r1/image_m/fgene-17-1713299-g002.jpg Figure 2. Overlap of prioritized variants identified by manual curation, AIMARRVEL, and AION. Varian https://www.frontiersin.org/files/Articles/1713299/fgene-17-1713299-HTML-r1/image_m/fgene-17-1713299-t002.jpg Table 2. Genetic variants in IEI genes detected by AI approaches and by manual curation with a relev https://www.frontiersin.org/files/Articles/1713299/fgene-17-1713299-HTML-r1/image_m/fgene-17-1713299-t003.jpg Table 3. Genetic variants in genes not previously associated with IEI detected by AI approaches. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1531866/full https://www.frontiersin.org/files/Articles/1531866/fendo-16-1531866-HTML-r1/image_m/fendo-16-1531866-g001.jpg Figure 1. Flowchart of 250 prostate cancer patients. Groups were divided by bone metastasis (No: n=1 https://www.frontiersin.org/files/Articles/1531866/fendo-16-1531866-HTML-r1/image_m/fendo-16-1531866-t001.jpg Table 1. Comparison of sE-cadherin, tPSA, fPSA, p2PSA levels, and PHI between BPH and PCa patients. https://www.frontiersin.org/files/Articles/1531866/fendo-16-1531866-HTML-r1/image_m/fendo-16-1531866-t002.jpg Table 2. Comparison of sE-cadherin, tPSA, fPSA, p2PSA levels, and PHI in patients with prostate canc https://www.frontiersin.org/files/Articles/1531866/fendo-16-1531866-HTML-r1/image_m/fendo-16-1531866-t003.jpg Table 3. Comparison of sE-cadherin, tPSA, fPSA, p2PSA levels, and PHI between prostate cancer patien https://www.frontiersin.org/files/Articles/1531866/fendo-16-1531866-HTML-r1/image_m/fendo-16-1531866-t004.jpg Table 4. Comparison of sE-cadherin, tPSA, fPSA, p2PSA levels, and PHI in prostate cancer patients wi https://www.frontiersin.org/files/Articles/1531866/fendo-16-1531866-HTML-r1/image_m/fendo-16-1531866-t005.jpg Table 5. ROC curve analysis of the diagnostic value of combined sE-cadherin and PHI for prostate can https://www.frontiersin.org/files/Articles/1531866/fendo-16-1531866-HTML-r1/image_m/fendo-16-1531866-g002.jpg Figure 2. Receiver Operating Characteristic (ROC) curves comparing the diagnostic performance of sE- https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1607656/full https://www.frontiersin.org/files/Articles/1607656/fpubh-13-1607656-HTML-r1/image_m/fpubh-13-1607656-t001.jpg Table 1. Prevalence and correlates of ONP awareness and use. https://www.frontiersin.org/files/Articles/1607656/fpubh-13-1607656-HTML-r1/image_m/fpubh-13-1607656-t002.jpg Table 2. ONP ever used descriptive statistics. https://www.frontiersin.org/files/Articles/1607656/fpubh-13-1607656-HTML-r1/image_m/fpubh-13-1607656-t003.jpg Table 3. Comparative ONP-beliefs to susceptibility and use. https://www.frontiersin.org/files/Articles/1607656/fpubh-13-1607656-HTML-r1/image_m/fpubh-13-1607656-g001.jpg Figure 1. Correlation between ONP use frequency and the likelihood of experiencing associated sympto https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1725080/full https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g001.jpg Figure 1. Digital pathways to intercultural competence, integrating technological formats, theoretic https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g002.jpg Figure 2. Keyword co-occurrence network (VOSviewer). Nodes represent author keywords (size = frequen https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g003.jpg Figure 3. Overlay visualization by average publication year. The same co-occurrence network is color https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g004.jpg Figure 4. Thematic performance map (centrality × impact). Bubbles plot clusters by Callon cen-tralit https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g005.jpg Figure 5. Correspondence analysis of descriptors and outcome terms. A two-dimension solution (percen https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g006.jpg Figure 6. Alluvial diagram linking author countries, descriptors, and abstract terms. Flows connect https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g007.jpg Figure 7. Co-citation network of the intellectual base. Nodes represent cited references (size = cit https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g008.jpg Figure 8. PRISMA flow chart. Identification (n = 133), duplicates removed (n = 23), screened (n = 11 https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-t001.jpg Table 1. Summarizes, for each study, author (year), doi, topic, brief description, methodology, and https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-t002.jpg Table 2. Crosswalk of implementation logics, dominant mechanisms, boundary conditions, and assessmen https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g009.jpg Figure 9. Effect-direction plot (SWiM). Stacked horizontal bars show counts of ↑/ns/mixed/↓ per stra https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-t003.jpg Table 3. Strategies × effectiveness (SWiM direction-of-effect synthesis). https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g010.jpg Figure 10. Risk-of-bias appraisal (% of applicable maximum). Horizontal bars show the percentage of https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g011.jpg Figure 11. Cumulative occurrences by source journal. Step lines show the number of included articles https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g012.jpg Figure 12. Geographic distribution of included studies (choropleth). Shading indicates the number of https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-g013.jpg Figure 13. Country-level citation impact within the inclusion set. Bars display cumulative citations https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-t004.jpg Table 4. Digital technologies used across studies. https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-t005.jpg Table 5. Technology families × intercultural-competence domains. https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-t006.jpg Table 6. Pedagogical approaches, implementation, and limitations. https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-t007.jpg Table 7. Mechanism codebook for the mechanism-oriented synthesis of digital interventions targeting https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-t008.jpg Table 8. Outcome instruments and measurement characteristics (“Evidence level/Quality” reflects the https://www.frontiersin.org/files/Articles/1725080/feduc-11-1725080-HTML/image_m/feduc-11-1725080-t009.jpg Table 9. Contextual/Equity constraints and enabling conditions by approach (“Yes” indicates the them https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1675473/full https://www.frontiersin.org/files/Articles/1675473/fpubh-13-1675473-HTML/image_m/fpubh-13-1675473-g001.jpg Figure 1. Flow diagram illustrating the search strategy, screening process, and final selection of s https://www.frontiersin.org/files/Articles/1675473/fpubh-13-1675473-HTML/image_m/fpubh-13-1675473-t001.jpg Table 1. Multivariable logistic regression analysis of factors associated with the usefulness of You https://www.frontiersin.org/files/Articles/1675473/fpubh-13-1675473-HTML/image_m/fpubh-13-1675473-g002.jpg Figure 2. Distribution of misleading and useful video content by COVID-19 periods (n) (p = 0.017). P https://www.frontiersin.org/files/Articles/1675473/fpubh-13-1675473-HTML/image_m/fpubh-13-1675473-t002.jpg Table 2. Viewing and engagement characteristics of videos by COVID-19 pandemic periods. https://www.frontiersin.org/files/Articles/1675473/fpubh-13-1675473-HTML/image_m/fpubh-13-1675473-t003.jpg Table 3. Content quality by information type and video source across the COVID-19 periods. https://www.frontiersin.org/files/Articles/1675473/fpubh-13-1675473-HTML/image_m/fpubh-13-1675473-g003.jpg Figure 3. Distribution of JAMA and GQS scores (%) by information type (Educational, Personal, Promot https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1564018/full https://www.frontiersin.org/files/Articles/1564018/fpsyg-17-1564018-HTML-r1/image_m/fpsyg-17-1564018-t001.jpg Table 1. Demographics of United States prisoner population. https://www.frontiersin.org/files/Articles/1564018/fpsyg-17-1564018-HTML-r1/image_m/fpsyg-17-1564018-t002.jpg Table 2. Keyness results (RQ1). https://www.frontiersin.org/files/Articles/1564018/fpsyg-17-1564018-HTML-r1/image_m/fpsyg-17-1564018-g001.jpg Figure 1. Collocates of the node “Module” in the CBI-CA manual (RQ2). https://www.frontiersin.org/files/Articles/1564018/fpsyg-17-1564018-HTML-r1/image_m/fpsyg-17-1564018-g002.jpg Figure 2. Collocates of the Node “Crim*” in the CBI-CA manual (RQ3). https://www.frontiersin.org/files/Articles/1564018/fpsyg-17-1564018-HTML-r1/image_m/fpsyg-17-1564018-g003.jpg Figure 3. Collocates of the Node “Crim*” in the T4C manual (RQ4). https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1694620/full https://www.frontiersin.org/files/Articles/1694620/fpsyt-17-1694620-HTML-r1/image_m/fpsyt-17-1694620-t001.jpg Table 1. PAPA loading with four factors. https://www.frontiersin.org/files/Articles/1694620/fpsyt-17-1694620-HTML-r1/image_m/fpsyt-17-1694620-t002.jpg Table 2. PAPA loading with 3 factors. https://www.frontiersin.org/files/Articles/1694620/fpsyt-17-1694620-HTML-r1/image_m/fpsyt-17-1694620-g001.jpg Figure 1. Confirmatory factor analysis of PAPA structure in male sample. https://www.frontiersin.org/files/Articles/1694620/fpsyt-17-1694620-HTML-r1/image_m/fpsyt-17-1694620-g002.jpg Figure 2. Confirmatory factor analysis of PAPA structure in the male sample. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1790264/full https://www.frontiersin.org/files/Articles/1790264/fpsyg-17-1790264-HTML/image_m/fpsyg-17-1790264-t001.jpg Table 1. Background characteristics of the participants (N = 345). https://www.frontiersin.org/files/Articles/1790264/fpsyg-17-1790264-HTML/image_m/fpsyg-17-1790264-t002.jpg Table 2. Fit indices for the LPAs. https://www.frontiersin.org/files/Articles/1790264/fpsyg-17-1790264-HTML/image_m/fpsyg-17-1790264-g001.jpg Figure 1. Scree plot of the Bayesian information criterion (BIC), sample-size adjusted Bayesian info https://www.frontiersin.org/files/Articles/1790264/fpsyg-17-1790264-HTML/image_m/fpsyg-17-1790264-g002.jpg Figure 2. The selected 3-profile solution. The measures used in the LPA are presented on the x-axis. https://www.frontiersin.org/files/Articles/1790264/fpsyg-17-1790264-HTML/image_m/fpsyg-17-1790264-t003.jpg Table 3. Regression coefficients for predicting sexting behaviors by profiles and disclosure of info https://www.frontiersin.org/files/Articles/1790264/fpsyg-17-1790264-HTML/image_m/fpsyg-17-1790264-g003.jpg Figure 3. Spearman rho correlations between the sexting behaviors. https://www.frontiersin.org/files/Articles/1790264/fpsyg-17-1790264-HTML/image_m/fpsyg-17-1790264-g004.jpg Figure 4. (a) Simple slope test to probe the moderating effect of adolescents’ disclosure of informa https://www.frontiersin.org/files/Articles/1790264/fpsyg-17-1790264-HTML/image_m/fpsyg-17-1790264-g005.jpg Figure 5. (a) Simple slope test to probe the moderating effect of adolescents’ disclosure of informa https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1746879/full https://www.frontiersin.org/files/Articles/1746879/fpubh-14-1746879-HTML/image_m/fpubh-14-1746879-g001.jpg Figure 1. Patient enrolment and outcomes. https://www.frontiersin.org/files/Articles/1746879/fpubh-14-1746879-HTML/image_m/fpubh-14-1746879-t001.jpg Table 1. Baseline clinical characteristics of patients with XDR-TB alone (XDR) and those with XDR-TB https://www.frontiersin.org/files/Articles/1746879/fpubh-14-1746879-HTML/image_m/fpubh-14-1746879-g002.jpg Figure 2. Clinical impact of diabetes on disease severity and treatment response. (A) Representative https://www.frontiersin.org/files/Articles/1746879/fpubh-14-1746879-HTML/image_m/fpubh-14-1746879-t002.jpg Table 2. Comparison of treatment regimens and drug resistance profiles between groups. https://www.frontiersin.org/files/Articles/1746879/fpubh-14-1746879-HTML/image_m/fpubh-14-1746879-g003.jpg Figure 3. Comparison of laboratory and immunological parameters between XDR and DM+XDR. (A–F) Scatte https://www.frontiersin.org/files/Articles/1746879/fpubh-14-1746879-HTML/image_m/fpubh-14-1746879-t003.jpg Table 3. Generalized linear model analysis of serum biomarkers associated with time to bacterial cle https://www.frontiersin.org/files/Articles/1746879/fpubh-14-1746879-HTML/image_m/fpubh-14-1746879-g004.jpg Figure 4. Hyperglycaemia mediates the negative effect of diabetes on bacterial clearance. (A) Progre https://www.frontiersin.org/files/Articles/1746879/fpubh-14-1746879-HTML/image_m/fpubh-14-1746879-g005.jpg Figure 5. Gradient effect of blood glucose levels on bacterial clearance and treatment success. (A) https://www.frontiersin.org/files/Articles/1746879/fpubh-14-1746879-HTML/image_m/fpubh-14-1746879-t004.jpg Table 4. Multivariable Cox proportional hazards analysis of blood glucose strata as a risk factor fo https://www.frontiersin.org/journals/audiology-and-otology/articles/10.3389/fauot.2025.1690547/full https://www.frontiersin.org/files/Articles/1690547/fauot-03-1690547-HTML/image_m/fauot-03-1690547-g001.jpg Figure 1. Illustration of the continuum of counseling and psychotherapy from low intensity and least https://www.frontiersin.org/files/Articles/1690547/fauot-03-1690547-HTML/image_m/fauot-03-1690547-g002.jpg Figure 2. PRISMA flowchart for database searching and study inclusion. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1794298/full https://www.frontiersin.org/files/Articles/1794298/fgene-17-1794298-HTML/image_m/fgene-17-1794298-g001.jpg Figure 1. Mechanistic schematic of disulfidptosis-associated osteogenic impairment in osteoporosis. https://www.frontiersin.org/files/Articles/1794298/fgene-17-1794298-HTML/image_m/fgene-17-1794298-g002.jpg Figure 2. Weighted gene co-expression network analysis (WGCNA) reveals gene modules associated with https://www.frontiersin.org/files/Articles/1794298/fgene-17-1794298-HTML/image_m/fgene-17-1794298-g003.jpg Figure 3. Identification of disulfidptosis-related hub genes in osteoporosis. (A) Volcano plot displ https://www.frontiersin.org/files/Articles/1794298/fgene-17-1794298-HTML/image_m/fgene-17-1794298-g004.jpg Figure 4. Disulfidptosis-related molecular subtyping of osteoporosis samples. (A) Cumulative distrib https://www.frontiersin.org/files/Articles/1794298/fgene-17-1794298-HTML/image_m/fgene-17-1794298-g005.jpg Figure 5. Single-cell atlas of the bone marrow microenvironment and disulfidptosis features in osteo https://www.frontiersin.org/files/Articles/1794298/fgene-17-1794298-HTML/image_m/fgene-17-1794298-g006.jpg Figure 6. Disulfidptosis characterization across BM-MSC subpopulations. (A) Dot plot summarizing the https://www.frontiersin.org/files/Articles/1794298/fgene-17-1794298-HTML/image_m/fgene-17-1794298-g007.jpg Figure 7. Identification of the BHLHE41/SLC7A11 regulatory axis in BM-MSCs. (A) Heatmap summarizing https://www.frontiersin.org/files/Articles/1794298/fgene-17-1794298-HTML/image_m/fgene-17-1794298-g008.jpg Figure 8. BHLHE41 may influence the chromatin remodeling state of osteoblast subpopulations via SIRT https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1807561/full https://www.frontiersin.org/files/Articles/1807561/fmed-13-1807561-HTML/image_m/fmed-13-1807561-t001.jpg Table 1. General information [n (%), χ ± s]. https://www.frontiersin.org/files/Articles/1807561/fmed-13-1807561-HTML/image_m/fmed-13-1807561-t002.jpg Table 2. Comparison of SIRI levels between the two patient groups. https://www.frontiersin.org/files/Articles/1807561/fmed-13-1807561-HTML/image_m/fmed-13-1807561-t003.jpg Table 3. Evaluation of SIRI in assessing the severity of influenza A-induced viral pneumonia among e https://www.frontiersin.org/files/Articles/1807561/fmed-13-1807561-HTML/image_m/fmed-13-1807561-t004.jpg Table 4. Severity assessment of influenza A virus-induced viral pneumonia in elderly patients: predi https://www.frontiersin.org/files/Articles/1807561/fmed-13-1807561-HTML/image_m/fmed-13-1807561-g001.jpg Figure 1. ROC curve of SIRI for predicting the severity of influenza A virus-induced viral pneumonia https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1673283/full https://www.frontiersin.org/files/Articles/1673283/fmed-12-1673283-HTML-r1/image_m/fmed-12-1673283-g001.jpg Figure1. Schematic illustration of copper metabolism in healthy individuals compared to Wilson disea https://www.frontiersin.org/files/Articles/1673283/fmed-12-1673283-HTML-r1/image_m/fmed-12-1673283-g002.jpg Figure 2. The ATP7B gene. (A) The ATP7B gene (OMIM: 606882) is located on the long arm (q-arm) of hu https://www.frontiersin.org/files/Articles/1673283/fmed-12-1673283-HTML-r1/image_m/fmed-12-1673283-g003.jpg Figure 3. Depiction of the potential hepatotoxic effects of copper accumulation in Wilson disease. I https://www.frontiersin.org/files/Articles/1673283/fmed-12-1673283-HTML-r1/image_m/fmed-12-1673283-t001.jpg Table 1. Leipzig scoring system for the diagnosis of Wilson diseasea. https://www.frontiersin.org/files/Articles/1673283/fmed-12-1673283-HTML-r1/image_m/fmed-12-1673283-t002.jpg Table 2. Adverse effects in treatment with D-penicillamine, trientine, and zinc. https://www.frontiersin.org/files/Articles/1673283/fmed-12-1673283-HTML-r1/image_m/fmed-12-1673283-g004.jpg Figure 4. Schematic overview of the proposed diagnostic workflow for Wilson disease. The pathway int https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2026.1776255/full https://www.frontiersin.org/files/Articles/1776255/fams-12-1776255-HTML/image_m/fams-12-1776255-t005.jpg Algorithm 1. Hierarchical Stratified SVM (HS–SVM). https://www.frontiersin.org/files/Articles/1776255/fams-12-1776255-HTML/image_m/fams-12-1776255-t006.jpg Algorithm 2. Hierarchical Stratified Support Vector Regression (HS-SVR). https://www.frontiersin.org/files/Articles/1776255/fams-12-1776255-HTML/image_m/fams-12-1776255-t001.jpg Table 1. Performance comparison of classical SVM and proposed hSVM (wCCR-based) on the INTERPRET–DD https://www.frontiersin.org/files/Articles/1776255/fams-12-1776255-HTML/image_m/fams-12-1776255-t002.jpg Table 2. Kernel-wise comparison of classical SVM and proposed hSVM on INTERPRET–DD–type data. https://www.frontiersin.org/files/Articles/1776255/fams-12-1776255-HTML/image_m/fams-12-1776255-t003.jpg Table 3. SVR evaluation of balanced and unbalanced datasets. https://www.frontiersin.org/files/Articles/1776255/fams-12-1776255-HTML/image_m/fams-12-1776255-t004.jpg Table 4. Classical SVR aganist hSVR (wMSER). https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1823265/full https://www.frontiersin.org/files/Articles/1823265/fpubh-14-1823265-HTML-r1/image_m/fpubh-14-1823265-g001.jpg Figure 1. PRISMA. https://www.frontiersin.org/files/Articles/1823265/fpubh-14-1823265-HTML-r1/image_m/fpubh-14-1823265-t001.jpg Table 1. Study characteristics and key findings. https://www.frontiersin.org/files/Articles/1823265/fpubh-14-1823265-HTML-r1/image_m/fpubh-14-1823265-t002.jpg Table 2. Methodological quality assessment of included studies. https://www.frontiersin.org/files/Articles/1823265/fpubh-14-1823265-HTML-r1/image_m/fpubh-14-1823265-t003.jpg Table 3. Summary of evidence and supporting references. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1683174/full https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-t001.jpg Table 1. Characteristics of the participants. https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-g001.jpg Figure 1. Typical cases. MRI sagittal (A), coronal (B) and arthroscopic (C) manifestations of latera https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-t002.jpg Table 2. Measurement results of gait phase analysis parameters [% (medians/IQRs)]. https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-g002.jpg Figure 2. Gait phase analysis. (A) Gait phase analysis involves the stance phase, neutral phase, pro https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-t003.jpg Table 3. Measurement results of general parameters of gait analysis parameters [(x¯±s) (medians/IQRs https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-g003.jpg Figure 3. General parameters of gait. (A) Parameters of step velocity. No significant differences we https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-t004.jpg Table 4. Measurement results of sagittal plane foot-to-ground angle analysis parameters [(x¯±s) (med https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-g004.jpg Figure 4. Changes in the sagittal plane foot to ground angle. (A) Parameters of foot landing angle. https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-t005.jpg Table 5. Measurement results of coronal plane foot-to-ground angle analysis parameters (medians/IQRs https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-g005.jpg Figure 5. Changes in the coronal plane foot to ground angle. (A) Parameters of angle of pronation at https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-t006.jpg Table 6. Measurement results of foot long axis and forward direction angle. https://www.frontiersin.org/files/Articles/1683174/fmed-12-1683174-HTML/image_m/fmed-12-1683174-g006.jpg Figure 6. Measurement results of foot long axis and forward direction angle. (A) Parameters of swing https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1687231/full https://www.frontiersin.org/files/Articles/1687231/fcvm-12-1687231-HTML/image_m/fcvm-12-1687231-g001.jpg Figure 1. Study flowchart. https://www.frontiersin.org/files/Articles/1687231/fcvm-12-1687231-HTML/image_m/fcvm-12-1687231-t001.jpg Table 1. Baseline characteristics of patients receiving PCI who have CKM. https://www.frontiersin.org/files/Articles/1687231/fcvm-12-1687231-HTML/image_m/fcvm-12-1687231-t002.jpg Table 2. Link between the TyG index and cardiovascular risk factors. https://www.frontiersin.org/files/Articles/1687231/fcvm-12-1687231-HTML/image_m/fcvm-12-1687231-g002.jpg Figure 2. Kaplan–Meier survival curves for ACM (A) and CM (B) based on TyG index stratification. https://www.frontiersin.org/files/Articles/1687231/fcvm-12-1687231-HTML/image_m/fcvm-12-1687231-t003.jpg Table 3. Findings from multivariate Cox regression assessing the link between the TyG index and endp https://www.frontiersin.org/files/Articles/1687231/fcvm-12-1687231-HTML/image_m/fcvm-12-1687231-g003.jpg Figure 3. RCS curves of the TyG index connected to ACM (A) and CM (B) risks. https://www.frontiersin.org/files/Articles/1687231/fcvm-12-1687231-HTML/image_m/fcvm-12-1687231-g004.jpg Figure 4. Forest plot depicting TyG index measurements and ACM risk across distinct patient subgroup https://www.frontiersin.org/files/Articles/1687231/fcvm-12-1687231-HTML/image_m/fcvm-12-1687231-g005.jpg Figure 5. Forest plot depicting TyG index measurements and CM risk across distinct patient subgroups https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2025.1718390/full https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g001.jpg Figure 1. Selected electrode channel locations on the Emotiv Flex Gel helmet. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-t001.jpg Table 1. Selected deep learning architectures for motor imagery EEG decoding. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g002.jpg Figure 2. Subject 2 wearing the Emotiv Flex Gel helmet and seated in front of the PC screen during t https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g003.jpg Figure 3. Timing scheme of the data registration session for: (a) Left and right classes, (b) neutra https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g004.jpg Figure 4. Timing scheme of the online BCI session. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-t002.jpg Table 2. Accuracy (%) of the selected DL EEG decoders in both Offline and Online BCI sessions. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g005.jpg Figure 5. Comparison of selected model accuracies across BCI sessions. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g006.jpg Figure 6. Online Confusion matrices across Subjects for FBLight ConvNet. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g007.jpg Figure 7. Online left (a) and right (b) classes sensitivity across models and subjects. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g008.jpg Figure 8. Online left (a) and right (b) classes precision across models and subjects. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g009.jpg Figure 9. Online left and right classes miss-as-neutral rate (MANR) (a), false alarm rate (FAR) (b), https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g010.jpg Figure 10. Participants' feedback scores in the NASA-TLX evaluation form. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-t003.jpg Table 3. Offline accuracy of additional models. https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g011.jpg Figure 11. Offline Lateralization Index (LI) per class across subjects. (a) Subject 1—LI across freq https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g012.jpg Figure 12. Offline C3 vs. C4 Mu-band power per class across subjects. (a) Subject 1–C3 vs. C4 mu pow https://www.frontiersin.org/files/Articles/1718390/fnsys-19-1718390-HTML/image_m/fnsys-19-1718390-g013.jpg Figure 13. Offline percentage change in band power of MI vs. neutral across subjects. (a) Subject 1— https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1838708/full https://www.frontiersin.org/files/Articles/1838708/fpsyg-17-1838708-HTML/image_m/fpsyg-17-1838708-t001.jpg Table 1. One-way ANOVA (Fisher’s)—automatic aggression and the presence of nevi in future sports’ co https://www.frontiersin.org/files/Articles/1838708/fpsyg-17-1838708-HTML/image_m/fpsyg-17-1838708-t002.jpg Table 2. Tukey post-hoc test—future sports coaches’ indirect/latency-based measure of aggression dep https://www.frontiersin.org/files/Articles/1838708/fpsyg-17-1838708-HTML/image_m/fpsyg-17-1838708-t003.jpg Table 3. Descriptive statistics—implicit (automatic) aggression of future sports coaches (N = 89). https://www.frontiersin.org/files/Articles/1838708/fpsyg-17-1838708-HTML/image_m/fpsyg-17-1838708-t004.jpg Table 4. Binomial logistic regressions analysis—automatic aggression of future sports coaches as cri https://www.frontiersin.org/files/Articles/1838708/fpsyg-17-1838708-HTML/image_m/fpsyg-17-1838708-t005.jpg Table 5. Variables in the equation—size of facial nevi (predictor) and sports coaches’ automatic agg https://www.frontiersin.org/files/Articles/1838708/fpsyg-17-1838708-HTML/image_m/fpsyg-17-1838708-t006.jpg Table 6. Variables in the equation—number of nevi on upper and lower limbs (predictor) and future sp https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1741907/full https://www.frontiersin.org/files/Articles/1741907/fpsyt-17-1741907-HTML-r1/image_m/fpsyt-17-1741907-t001.jpg Table 1. Baseline demographic characteristics of study participants by treatment cohort. https://www.frontiersin.org/files/Articles/1741907/fpsyt-17-1741907-HTML-r1/image_m/fpsyt-17-1741907-t002.jpg Table 2. Hazard ratios (HR) for opioid remission by treatment cohort. https://www.frontiersin.org/files/Articles/1741907/fpsyt-17-1741907-HTML-r1/image_m/fpsyt-17-1741907-g001.jpg Figure 1. Forest plot of adjusted hazard ratios (aHRs) for 1-year remission outcomes by treatment co https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1692890/full https://www.frontiersin.org/files/Articles/1692890/fams-11-1692890-HTML/image_m/fams-11-1692890-g001.jpg Figure 1. Integration of higher-derivative mechanical concepts within components of an optimal feedb https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1769223/full https://www.frontiersin.org/files/Articles/1769223/fimmu-17-1769223-HTML/image_m/fimmu-17-1769223-g001.jpg Figure 1. CONSORT flow diagram. The number of participants enrolled, followed-up from year 1 to year https://www.frontiersin.org/files/Articles/1769223/fimmu-17-1769223-HTML/image_m/fimmu-17-1769223-t001.jpg Table 1a. Characteristics of study participants at the ISS T-003 trial baseline. https://www.frontiersin.org/files/Articles/1769223/fimmu-17-1769223-HTML/image_m/fimmu-17-1769223-t002.jpg Table 1b. Characteristics of vaccinees by gender at the ISS T-003 trial baseline. https://www.frontiersin.org/files/Articles/1769223/fimmu-17-1769223-HTML/image_m/fimmu-17-1769223-t003.jpg Table 1c. Characteristics of placebos by gender at the ISS T-003 trial baseline. https://www.frontiersin.org/files/Articles/1769223/fimmu-17-1769223-HTML/image_m/fimmu-17-1769223-g002.jpg Figure 2. Anti-Tat Ab persistence in vaccinees and in anti-Tat Ab seroconverters placebos up to year https://www.frontiersin.org/files/Articles/1769223/fimmu-17-1769223-HTML/image_m/fimmu-17-1769223-g003.jpg Figure 3. Changes from baseline of CD4+ T-cells and blood HIV DNA during 12 years of follow-up by tr https://www.frontiersin.org/files/Articles/1769223/fimmu-17-1769223-HTML/image_m/fimmu-17-1769223-g004.jpg Figure 4. Viremia rebound, CD4+ T-cells and blood HIV DNA changes from ISS T-003 trial baseline by t https://www.frontiersin.org/files/Articles/1769223/fimmu-17-1769223-HTML/image_m/fimmu-17-1769223-g005.jpg Figure 5. Long term follow-up of volunteers non-compliant to therapy during the first 48 weeks since https://www.frontiersin.org/files/Articles/1769223/fimmu-17-1769223-HTML/image_m/fimmu-17-1769223-t004.jpg Table 2. Summary of key immunological and virological findings. https://www.frontiersin.org/journals/behavioral-economics/articles/10.3389/frbhe.2025.1539771/full https://www.frontiersin.org/files/Articles/1539771/frbhe-04-1539771-HTML/image_m/frbhe-04-1539771-g001.jpg Figure 1. This figure depicts an illustration of the coefficients from the regressions described in https://www.frontiersin.org/files/Articles/1539771/frbhe-04-1539771-HTML/image_m/frbhe-04-1539771-t001.jpg Table 1. Sample characteristics. https://www.frontiersin.org/files/Articles/1539771/frbhe-04-1539771-HTML/image_m/frbhe-04-1539771-t002.jpg Table 2. Regression models for actual hours worked, including overtime by generations (2007–2022). https://www.frontiersin.org/files/Articles/1539771/frbhe-04-1539771-HTML/image_m/frbhe-04-1539771-t003.jpg Table 3. Regression models for actual hours worked, including overtime by generations and gender (20 https://www.frontiersin.org/files/Articles/1539771/frbhe-04-1539771-HTML/image_m/frbhe-04-1539771-t004.jpg Table 4. Regression models for actual hours worked, including overtime by generations and education https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1787784/full https://www.frontiersin.org/files/Articles/1787784/fcell-14-1787784-HTML/image_m/fcell-14-1787784-g001.jpg Figure 1. Cellular origin and histological phenotype of cHCC-CCA Two main theories explain cHCC-CCA https://www.frontiersin.org/files/Articles/1787784/fcell-14-1787784-HTML/image_m/fcell-14-1787784-g002.jpg Figure 2. Schematic diagram of the ADSL–fumarate–KDM8–Beclin1 axis regulating autophagy under lipid https://www.frontiersin.org/files/Articles/1787784/fcell-14-1787784-HTML/image_m/fcell-14-1787784-t001.jpg Table 1. Testable hypotheses arising from the organelle dysfunction model in cHCC-CCA. https://www.frontiersin.org/files/Articles/1787784/fcell-14-1787784-HTML/image_m/fcell-14-1787784-t002.jpg Table 2. Classic immunohistochemical markers of cHCC-CCA(3-5, 13, 67). https://www.frontiersin.org/files/Articles/1787784/fcell-14-1787784-HTML/image_m/fcell-14-1787784-g003.jpg Figure 3. Diagnostic algorithm for cHCC-CCA based on histomorphology and immunohistochemistry. This https://www.frontiersin.org/files/Articles/1787784/fcell-14-1787784-HTML/image_m/fcell-14-1787784-t003.jpg Table 3. Summary of major retrospective studies on systemic therapy for cHCC-CCA. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1606543/full https://www.frontiersin.org/files/Articles/1606543/fpsyt-16-1606543-HTML/image_m/fpsyt-16-1606543-t001.jpg Table 1. Correlation analysis between negative emotions and family intimacy and psychological resili https://www.frontiersin.org/files/Articles/1606543/fpsyt-16-1606543-HTML/image_m/fpsyt-16-1606543-t002.jpg Table 2. Regression analysis among negative emotions, family intimacy, and psychological resilience. https://www.frontiersin.org/files/Articles/1606543/fpsyt-16-1606543-HTML/image_m/fpsyt-16-1606543-g001.jpg Figure 1. Family intimacy between negative emotions and psychological resilience in adolescent inpat https://www.frontiersin.org/files/Articles/1606543/fpsyt-16-1606543-HTML/image_m/fpsyt-16-1606543-t003.jpg Table 3. Results of the mediating effect test of family intimacy. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1710381/full https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-t001.jpg Table 1. Comparison of baseline characteristics of included patients [n (%), M (P25, P75)]. https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-g001.jpg Figure 1. Selection of characterizing variables based on LASSO regression. https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-g002.jpg Figure 2. Characteristic correlation rectangles for each variable. https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-t002.jpg Table 2. Comparison of predictive performance metrics for five machine learning algorithms. https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-g003.jpg Figure 3. Histogram comparing the performance of the models. https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-g004.jpg Figure 4. Characterization curves of subjects’ work for five machine learning models. https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-g005.jpg Figure 5. Confusion matrix plots for each model. Five labeled confusion matrix heatmaps (a-e) compar https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-g006.jpg Figure 6. Evaluation of the RFC model. Learning curve plot (a) showing ROC AUC for a random forest c https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-g007.jpg Figure 7. Evaluation of each model. Four-panel figure showing evaluation of machine learning classif https://www.frontiersin.org/files/Articles/1710381/fneur-17-1710381-HTML-r1/image_m/fneur-17-1710381-g008.jpg Figure 8. SHAP feature analysis of RFC. https://www.frontiersin.org/journals/sensors/articles/10.3389/fsens.2026.1800053/full https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g001.jpg Figure 1. Experimental setup. https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g002.jpg Figure 2. Software design of horizontal (A) and vertical (B) electrode lines. https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g003.jpg Figure 3. Cross-section of a piezo-resistive fabric (C), electro-conductive thread (B), non-conducti https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g004.jpg Figure 4. Needle (red) and bobbin (light brown) threads location example before thread tension adjus https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g005.jpg Figure 5. Needle (red) and bobbin (light brown) threads location example before thread tension adjus https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g006.jpg Figure 6. Finished sensor on Sefar Carbotex 03–205 CF with Thermotech N-30 thread in embroidery hoop https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g007.jpg Figure 7. Fabrication of EeonTex LTT-SLPA 60 kOhm with Thermotech N-30 thread. https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g008.jpg Figure 8. Vertical sensor lines on EeonTex LTT-SLPA 60 kOhm with Thermotech N-30 thread: (A) fabric https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g009.jpg Figure 9. Finished sensor on EeonTex LTT-SLPA 60 kOhm with Thermotech N-30 thread. https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g010.jpg Figure 10. Sensor 1 (Carbotex fabric) test results. (a) resistance point 2 × 6, 0,5-5 N force range. https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g011.jpg Figure 11. Sensor 2 (Eontex fabric) test results. (a) resistance point 1 × 3, 0,5-5 N force range. ( https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-t001.jpg Table 1. Sensitivity of the piezoresistive fabric sensors. https://www.frontiersin.org/files/Articles/1800053/fsens-07-1800053-HTML/image_m/fsens-07-1800053-g012.jpg Figure 12. Testing of the single-layer textile-based matrix-type pressure sensor prototype. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1786387/full https://www.frontiersin.org/files/Articles/1786387/fpubh-14-1786387-HTML/image_m/fpubh-14-1786387-t001.jpg Table 1. Demographic characteristics of the studied population; Zielona Gora, Poland, 2023 (n = 837) https://www.frontiersin.org/files/Articles/1786387/fpubh-14-1786387-HTML/image_m/fpubh-14-1786387-g001.jpg Figure 1. COVID-19 vaccine uptake by the number of doses; Zielona Gora, Poland, 2023 (n = 837). https://www.frontiersin.org/files/Articles/1786387/fpubh-14-1786387-HTML/image_m/fpubh-14-1786387-t002.jpg Table 2. COVID-19 vaccination by selected demographic variables; Zielona Gora, Poland, 2023 (n = 837 https://www.frontiersin.org/files/Articles/1786387/fpubh-14-1786387-HTML/image_m/fpubh-14-1786387-t003.jpg Table 3. COVID-19 vaccination by selected variables; Zielona Gora, Poland, 2023 (n = 837). https://www.frontiersin.org/files/Articles/1786387/fpubh-14-1786387-HTML/image_m/fpubh-14-1786387-t004.jpg Table 4. Reasons for taking the next dose of the COVID-19 vaccine, Zielona Gora, Poland (N = 804). https://www.frontiersin.org/files/Articles/1786387/fpubh-14-1786387-HTML/image_m/fpubh-14-1786387-t005.jpg Table 5. Multivariable logistic regression analysis: factors associated with the chances of being va https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1658987/full https://www.frontiersin.org/files/Articles/1658987/fimmu-16-1658987-HTML-r1/image_m/fimmu-16-1658987-g001.jpg Figure 1. CXCR4 antagonism corrects peripheral blood neutropenia in a pharmacological mouse model of https://www.frontiersin.org/files/Articles/1658987/fimmu-16-1658987-HTML-r1/image_m/fimmu-16-1658987-g002.jpg Figure 2. CXCR4 antagonism normalizes neutrophil accumulation in BM in a pharmacological mouse model https://www.frontiersin.org/files/Articles/1658987/fimmu-16-1658987-HTML-r1/image_m/fimmu-16-1658987-g003.jpg Figure 3. CXCR4 antagonism reduces incidence of myelokethaxis phenotype in a pharmacological mouse m https://www.frontiersin.org/files/Articles/1658987/fimmu-16-1658987-HTML-r1/image_m/fimmu-16-1658987-g004.jpg Figure 4. CXCR4 antagonism reduces pneumonia severity in a pharmacological mouse model of CXCR2 LOF. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2025.1603575/full https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g001.jpg Figure 1. Selection process to identify relevant literature. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g002.jpg Figure 2. Publishing organization of empirical studies over time. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g003.jpg Figure 3. Countries investigated in the empirical studies categorised according to ISO Norm 3166. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g004.jpg Figure 4. Number of empirical studies per country. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g005.jpg Figure 5. Spatial resolution data of empirical studies over time. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g006.jpg Figure 6. RS provider category in empirical studies over time. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g007.jpg Figure 7. ML used in Empirical studies over time. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g008.jpg Figure 8. Prevalence of Non-ML approaches used in empirical studies. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g009.jpg Figure 9. RS observation characteristics from empirical studies documenting conflict. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g010.jpg Figure 10. Validation data used in empirical studies over time. https://www.frontiersin.org/files/Articles/1603575/frsen-06-1603575-HTML/image_m/frsen-06-1603575-g011.jpg Figure 11. Declared challenges in empirical studies. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1789806/full https://www.frontiersin.org/files/Articles/1789806/fspor-08-1789806-HTML-r1/image_m/fspor-08-1789806-t001.jpg Table 1. Sociodemographic characteristics and general habits (n = 29). https://www.frontiersin.org/files/Articles/1789806/fspor-08-1789806-HTML-r1/image_m/fspor-08-1789806-t002.jpg Table 2. Descriptive statistics and sex differences for individual items and total score of the IPAQ https://www.frontiersin.org/files/Articles/1789806/fspor-08-1789806-HTML-r1/image_m/fspor-08-1789806-t003.jpg Table 3. Descriptive statistics and sex differences for score of the PSS-14 (n = 29). https://www.frontiersin.org/files/Articles/1789806/fspor-08-1789806-HTML-r1/image_m/fspor-08-1789806-t004.jpg Table 4. Descriptive statistics and sex differences for score of the PSQI (n = 29). https://www.frontiersin.org/files/Articles/1789806/fspor-08-1789806-HTML-r1/image_m/fspor-08-1789806-t005.jpg Table 5. Linear regression predicting IPAQ-SF from PSS-14 and sex. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1827732/full https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g001.jpg Figure 1. Geographic location of the study area. (A) Shows the standard map of China (Map Examinatio https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g002.jpg Figure 2. Slope (A), Aspect (B) and Elevation (C) of the study area. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-t001.jpg Table 1. Statistical summary of FSV in Pinus kesiya var. langbianensis sample plots. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-t002.jpg Table 2. Description of GEDI L2B parameters used for modeling and analysis. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-t003.jpg Table 3. Description of Landsat-8 parameters used for modeling and analysis. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g003.jpg Figure 3. Overall technical workflow of the study. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-t004.jpg Table 4. Optimal model and fitting parameters for GEDI characteristic variables. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-t005.jpg Table 5. Comparisons of accuracy under different interpolation conditions. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g004.jpg Figure 4. Results of OK interpolation of GEDI variables. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g005.jpg Figure 5. Results of SGCS interpolation of GEDI variables. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g006.jpg Figure 6. Results of IDW interpolation of GEDI variables. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g007.jpg Figure 7. Results of PSO-IDW interpolation of GEDI variables. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g008.jpg Figure 8. Results of GS-IDW interpolation of GEDI variables. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g009.jpg Figure 9. Results of GA-IDW interpolation of GEDI variables. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g010.jpg Figure 10. Results of SHAP analysis. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g011.jpg Figure 11. Scatterplots for different models. (A) is Adaboost; (B) is XGBoost; (C) is RF; (D) is Lig https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g012.jpg Figure 12. FSV of Pinus kesiya var. Langbianensis wall-to-wall mapping. https://www.frontiersin.org/files/Articles/1827732/fpls-17-1827732-HTML/image_m/fpls-17-1827732-g013.jpg Figure 13. Comparison of the final stacking model and the pass-through baseline. (A) is Final Stacki https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1658595/full https://www.frontiersin.org/files/Articles/1658595/fonc-15-1658595-HTML-r3/image_m/fonc-15-1658595-g001.jpg Figure 1. Study Cohort Selection flowchart. https://www.frontiersin.org/files/Articles/1658595/fonc-15-1658595-HTML-r3/image_m/fonc-15-1658595-t001.jpg Table 1. Chi-square of patients’ baseline sociodemographic and clinical characteristics stratified b https://www.frontiersin.org/files/Articles/1658595/fonc-15-1658595-HTML-r3/image_m/fonc-15-1658595-g002.jpg Figure 2. Kaplan–Meier plot of overall survival in patients with esophageal cancer, stratified by ne https://www.frontiersin.org/files/Articles/1658595/fonc-15-1658595-HTML-r3/image_m/fonc-15-1658595-t002.jpg Table 2. Multivariate cox regression analysis of clinical and demographic predictors of overall surv https://www.frontiersin.org/files/Articles/1658595/fonc-15-1658595-HTML-r3/image_m/fonc-15-1658595-g003.jpg Figure 3. Kaplan–Meier plot showing overall survival among patients with thoracic ESCC (C15.3), stra https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1666673/full https://www.frontiersin.org/files/Articles/1666673/fonc-15-1666673-HTML/image_m/fonc-15-1666673-t001.jpg Table 1. Chi-square of baseline sociodemographic and Clinical Characteristics of patients stratified https://www.frontiersin.org/files/Articles/1666673/fonc-15-1666673-HTML/image_m/fonc-15-1666673-t002.jpg Table 2. Chi-square of baseline sociodemographic and Clinical Characteristics of patients receiving https://www.frontiersin.org/files/Articles/1666673/fonc-15-1666673-HTML/image_m/fonc-15-1666673-g001.jpg Figure 1. Kaplan Meier Plot of Overall Survival for patients with Early-stage breast cancer stratifi https://www.frontiersin.org/files/Articles/1666673/fonc-15-1666673-HTML/image_m/fonc-15-1666673-t003.jpg Table 3. Overall Survival comparison by time to adjuvant chemotherapy, Groups (1-4). https://www.frontiersin.org/files/Articles/1666673/fonc-15-1666673-HTML/image_m/fonc-15-1666673-t004.jpg Table 4. Multivariate Cox Regression Analysis of Clinical and Demographic Predictors of Overall Surv https://www.frontiersin.org/files/Articles/1666673/fonc-15-1666673-HTML/image_m/fonc-15-1666673-g002.jpg Figure 2. Kaplan Meier Plot of Overall Survival for patients with Early stage breast cancer stratifi https://www.frontiersin.org/files/Articles/1666673/fonc-15-1666673-HTML/image_m/fonc-15-1666673-t005.jpg Table 5. Multivariate Cox Regression Analysis of Clinical and Demographic Predictors of Overall Surv https://www.frontiersin.org/files/Articles/1666673/fonc-15-1666673-HTML/image_m/fonc-15-1666673-g003.jpg Figure 3. Kaplan Meier Plot of Overall Survival for patients with TNBC early stage breast cancer str https://www.frontiersin.org/files/Articles/1666673/fonc-15-1666673-HTML/image_m/fonc-15-1666673-t006.jpg Table 6. Multivariate Cox Regression Analysis of HER2 positive and Triple Negative Breast cancer. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1740851/full https://www.frontiersin.org/files/Articles/1740851/fcell-14-1740851-HTML/image_m/fcell-14-1740851-g001.jpg Figure 1. Single-cell transcriptome profiling analysis of fetal hNSCs across cell culture passages. https://www.frontiersin.org/files/Articles/1740851/fcell-14-1740851-HTML/image_m/fcell-14-1740851-g002.jpg Figure 2. Fetal hNSCs are mainly composed of cycling cells with subpopulations committed to pre-diff https://www.frontiersin.org/files/Articles/1740851/fcell-14-1740851-HTML/image_m/fcell-14-1740851-g003.jpg Figure 3. Heterogeneity in the cellular composition of fetal hNSCs reflects changes in gene expressi https://www.frontiersin.org/files/Articles/1740851/fcell-14-1740851-HTML/image_m/fcell-14-1740851-g004.jpg Figure 4. Cluster-specific expression patterns of stemness, proliferative, and differentiation marke https://www.frontiersin.org/files/Articles/1740851/fcell-14-1740851-HTML/image_m/fcell-14-1740851-g005.jpg Figure 5. Trajectory inference highlights a gradual transition of fetal hNSCs along pseudotime. (A) https://www.frontiersin.org/files/Articles/1740851/fcell-14-1740851-HTML/image_m/fcell-14-1740851-g006.jpg Figure 6. Gene expression dynamics across pseudotime reveal coordinated activation and repression of https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1716793/full https://www.frontiersin.org/files/Articles/1716793/fgene-17-1716793-HTML/image_m/fgene-17-1716793-t001.jpg Table 1. The laws pertaining to PMS in different GCC countries and related aspects. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1788831/full https://www.frontiersin.org/files/Articles/1788831/fimmu-17-1788831-HTML/image_m/fimmu-17-1788831-t001.jpg Table 1. Demographic data and clinical characteristics of AD patients (n = 120). https://www.frontiersin.org/files/Articles/1788831/fimmu-17-1788831-HTML/image_m/fimmu-17-1788831-g001.jpg Figure 1. Heatmap displaying the distribution of 195 SNPs present in over 5% of the AD patient cohor https://www.frontiersin.org/files/Articles/1788831/fimmu-17-1788831-HTML/image_m/fimmu-17-1788831-g002.jpg Figure 2. Constrained analysis of principal coordinates (CAP) performed on the genotype matrix of 36 https://www.frontiersin.org/files/Articles/1788831/fimmu-17-1788831-HTML/image_m/fimmu-17-1788831-g003.jpg Figure 3. Genomic location of SNPs within the FLG and KIF3A loci across clusters A–D. A schematic re https://www.frontiersin.org/files/Articles/1788831/fimmu-17-1788831-HTML/image_m/fimmu-17-1788831-g004.jpg Figure 4. Principal component analysis (PCA) of patients based on 367 SNPs, revealing four genetic c https://www.frontiersin.org/files/Articles/1788831/fimmu-17-1788831-HTML/image_m/fimmu-17-1788831-t002.jpg Table 2. Logistic regression analysis of clinical variables and treatment response after 48 weeks of https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1807903/full https://www.frontiersin.org/files/Articles/1807903/fpubh-14-1807903-HTML/image_m/fpubh-14-1807903-g001.jpg Figure 1. PRISMA flow diagram of study identification, screening, eligibility assessment, and inclus https://www.frontiersin.org/files/Articles/1807903/fpubh-14-1807903-HTML/image_m/fpubh-14-1807903-g002.jpg Figure 2. Forest plots of pairwise meta-analysis and subgroup analyses examining the effects of aero https://www.frontiersin.org/files/Articles/1807903/fpubh-14-1807903-HTML/image_m/fpubh-14-1807903-g003.jpg Figure 3. Dose–response relationship between aerobic exercise volume and improvements in postpartum https://www.frontiersin.org/files/Articles/1807903/fpubh-14-1807903-HTML/image_m/fpubh-14-1807903-g004.jpg Figure 4. Dose–response relationship between aerobic exercise volume and improvements in postpartum https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1648314/full https://www.frontiersin.org/files/Articles/1648314/fnut-12-1648314-HTML-r1/image_m/fnut-12-1648314-g001.jpg Figure 1. Study profile. This flowchart illustrates the study duration, inclusion and exclusion crit https://www.frontiersin.org/files/Articles/1648314/fnut-12-1648314-HTML-r1/image_m/fnut-12-1648314-t001.jpg Table 1. Baseline characteristics of ICR and control cohorts. https://www.frontiersin.org/files/Articles/1648314/fnut-12-1648314-HTML-r1/image_m/fnut-12-1648314-t002.jpg Table 2. Univariate analysis of outcomes for ICR and control cohorts. https://www.frontiersin.org/files/Articles/1648314/fnut-12-1648314-HTML-r1/image_m/fnut-12-1648314-t003.jpg Table 3. Outcomes after IPW. https://www.frontiersin.org/files/Articles/1648314/fnut-12-1648314-HTML-r1/image_m/fnut-12-1648314-t004.jpg Table 4. Subgroup analysis of outcomes After IPW for ICR and control cohorts. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1744017/full https://www.frontiersin.org/files/Articles/1744017/fnut-13-1744017-HTML-r1/image_m/fnut-13-1744017-t001.jpg Table 1. Baseline characteristics of IER and control cohorts. https://www.frontiersin.org/files/Articles/1744017/fnut-13-1744017-HTML-r1/image_m/fnut-13-1744017-t002.jpg Table 2. Univariate analysis of outcomes for IER and control cohorts. https://www.frontiersin.org/files/Articles/1744017/fnut-13-1744017-HTML-r1/image_m/fnut-13-1744017-t003.jpg Table 3. Multivariate analysis of outcome indicators. https://www.frontiersin.org/files/Articles/1744017/fnut-13-1744017-HTML-r1/image_m/fnut-13-1744017-t004.jpg Table 4. Outcomes after IPW. https://www.frontiersin.org/files/Articles/1744017/fnut-13-1744017-HTML-r1/image_m/fnut-13-1744017-t005.jpg Table 5. Subgroup analysis of outcomes after IPW for IER and control cohorts. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1759595/full https://www.frontiersin.org/files/Articles/1759595/fmars-13-1759595-HTML-r1/image_m/fmars-13-1759595-g001.jpg Figure 1. Sampling areas. Sampling areas BEL and GER in the CCZ, and DISCOL Experimental area in the https://www.frontiersin.org/files/Articles/1759595/fmars-13-1759595-HTML-r1/image_m/fmars-13-1759595-g002.jpg Figure 2. Principal Component Analysis of environmental parameters from three areas investigated. Bo https://www.frontiersin.org/files/Articles/1759595/fmars-13-1759595-HTML-r1/image_m/fmars-13-1759595-g003.jpg Figure 3. Cumulative rarefaction curves for the different substrates in Belgian (BEL) and German (GE https://www.frontiersin.org/files/Articles/1759595/fmars-13-1759595-HTML-r1/image_m/fmars-13-1759595-g004.jpg Figure 4. Non-metric multidimensional scaling (NMDS) plot based on Jaccard dissimilarity matrix of b https://www.frontiersin.org/files/Articles/1759595/fmars-13-1759595-HTML-r1/image_m/fmars-13-1759595-g005.jpg Figure 5. Number of ASVs in sediments shared between sampling sites based on the full data set. Conn https://www.frontiersin.org/files/Articles/1759595/fmars-13-1759595-HTML-r1/image_m/fmars-13-1759595-g006.jpg Figure 6. Graphs of predicted values of dissimilarities in sediment bacterial communities as a funct https://www.frontiersin.org/files/Articles/1759595/fmars-13-1759595-HTML-r1/image_m/fmars-13-1759595-g007.jpg Figure 7. Non-metric multidimensional scaling (NMDS) plot based on robust Aitchison distance calcula https://www.frontiersin.org/files/Articles/1759595/fmars-13-1759595-HTML-r1/image_m/fmars-13-1759595-g008.jpg Figure 8. Distance-based Redundancy Analysis plot showing the relationship between environmental var https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1764757/full https://www.frontiersin.org/files/Articles/1764757/fmed-13-1764757-HTML/image_m/fmed-13-1764757-g001.jpg Figure 1. Trajectories of lung ultrasound score (LUS) over time. Lines indicate mean LUS trajectorie https://www.frontiersin.org/files/Articles/1764757/fmed-13-1764757-HTML/image_m/fmed-13-1764757-g002.jpg Figure 2. Flow chart of patient selection. Exclusions: lack of serial pre-extubation LUS assessments https://www.frontiersin.org/files/Articles/1764757/fmed-13-1764757-HTML/image_m/fmed-13-1764757-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1764757/fmed-13-1764757-HTML/image_m/fmed-13-1764757-t002.jpg Table 2. Baseline characteristics of all patients between the training and validation cohorts. https://www.frontiersin.org/files/Articles/1764757/fmed-13-1764757-HTML/image_m/fmed-13-1764757-t003.jpg Table 3. Multivariate logistic analysis. https://www.frontiersin.org/files/Articles/1764757/fmed-13-1764757-HTML/image_m/fmed-13-1764757-g003.jpg Figure 3. Nomogram for predicting extubation outcome in neonates with NRDS. Predictors include LUS t https://www.frontiersin.org/files/Articles/1764757/fmed-13-1764757-HTML/image_m/fmed-13-1764757-g004.jpg Figure 4. Model development and performance. (a) ROC curve in the development cohort (AUC 0.914, 95% https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1780636/full https://www.frontiersin.org/files/Articles/1780636/fimmu-17-1780636-HTML/image_m/fimmu-17-1780636-g001.jpg Figure 1. Literature search flow chart. https://www.frontiersin.org/files/Articles/1780636/fimmu-17-1780636-HTML/image_m/fimmu-17-1780636-t001.jpg Table 1. Basic characteristics of the literature. https://www.frontiersin.org/files/Articles/1780636/fimmu-17-1780636-HTML/image_m/fimmu-17-1780636-g002.jpg Figure 2. (A) Forest plot of ORR, showing pooled rates with 95% CIs for anlotinib plus PD−1/PD−L1 in https://www.frontiersin.org/files/Articles/1780636/fimmu-17-1780636-HTML/image_m/fimmu-17-1780636-g003.jpg Figure 3. (A) Forest plot of DCR, showing pooled rates with 95% CIs for anlotinib plus PD−1/PD−L1 in https://www.frontiersin.org/files/Articles/1780636/fimmu-17-1780636-HTML/image_m/fimmu-17-1780636-g004.jpg Figure 4. Forest plot of OS (A) and PFS (B) from RCTs, showing the pooled HRs with 95% CIs for anlot https://www.frontiersin.org/files/Articles/1780636/fimmu-17-1780636-HTML/image_m/fimmu-17-1780636-t002.jpg Table 2. Meta-analysis results of TRAEs in RCTs. https://www.frontiersin.org/files/Articles/1780636/fimmu-17-1780636-HTML/image_m/fimmu-17-1780636-g005.jpg Figure 5. SUCAR plot for ORR (A) and DCR (B). SUCRA provides a numerical summary of the rank distrib https://www.frontiersin.org/files/Articles/1780636/fimmu-17-1780636-HTML/image_m/fimmu-17-1780636-g006.jpg Figure 6. The funnel plot for publication bias of ORR (A) and DCR (B). Egger’s test was performed to https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1684641/full https://www.frontiersin.org/files/Articles/1684641/fmed-12-1684641-HTML/image_m/fmed-12-1684641-t001.jpg Table 1. Demographic and clinical characteristics of our population sample. https://www.frontiersin.org/files/Articles/1684641/fmed-12-1684641-HTML/image_m/fmed-12-1684641-t002.jpg Table 2. Differences in demographic and clinical variables according to central obesity categorizati https://www.frontiersin.org/files/Articles/1684641/fmed-12-1684641-HTML/image_m/fmed-12-1684641-g001.jpg Figure 1. Multivariable analyses according to various outcome variables. csDMARDs, conventional synt https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1718828/full https://www.frontiersin.org/files/Articles/1718828/fnut-12-1718828-HTML/image_m/fnut-12-1718828-g001.jpg Figure 1. PRISMA 2020 study selection flowchart. This illustrates the selection process from 1,721 r https://www.frontiersin.org/files/Articles/1718828/fnut-12-1718828-HTML/image_m/fnut-12-1718828-g002.jpg Figure 2. Risk of bias assessment for included studies. This summarizes RoB 2 across 21 RCTs for ran https://www.frontiersin.org/files/Articles/1718828/fnut-12-1718828-HTML/image_m/fnut-12-1718828-g003.jpg Figure 3. (a–c) Network geometry for each outcome. (a) PASI; (b) DLQI; (c) adverse events. Nodes rep https://www.frontiersin.org/files/Articles/1718828/fnut-12-1718828-HTML/image_m/fnut-12-1718828-g004.jpg Figure 4. (a–c) Cumulative ranking curves and SUCRA values. (a) PASI; (b) DLQI; (c) adverse events. https://www.frontiersin.org/files/Articles/1718828/fnut-12-1718828-HTML/image_m/fnut-12-1718828-g005.jpg Figure 5. (a–c) Funnel plots for small-study effects. (a) PASI; (b) DLQI; (c) adverse events. Effect https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1766401/full https://www.frontiersin.org/files/Articles/1766401/feduc-11-1766401-HTML/image_m/feduc-11-1766401-t001.jpg Table 1. Participants’ background characteristics by gender (N = 78). https://www.frontiersin.org/files/Articles/1766401/feduc-11-1766401-HTML/image_m/feduc-11-1766401-t002.jpg Table 2. Distribution of heutagogy clusters and M scores and SD per component. https://www.frontiersin.org/files/Articles/1766401/feduc-11-1766401-HTML/image_m/feduc-11-1766401-t003.jpg Table 3. Hierarchical binary logistic regression of AI usage frequency on heutagogy orientation. https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2025.1694926/full https://www.frontiersin.org/files/Articles/1694926/fclim-07-1694926-HTML-r2/image_m/fclim-07-1694926-g001.jpg Figure 1. Location of ISR and its six communities in the Northwest Territories. Image reproduced fro https://www.frontiersin.org/files/Articles/1694926/fclim-07-1694926-HTML-r2/image_m/fclim-07-1694926-g002.jpg Figure 2. Themes depicted as aqpiit (cloudberries) and a changing landscape (sandbars) as barriers t https://www.frontiersin.org/files/Articles/1694926/fclim-07-1694926-HTML-r2/image_m/fclim-07-1694926-t001.jpg Table 1. Description of families interviewed by generation size and generations represented. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1828560/full https://www.frontiersin.org/files/Articles/1828560/fpubh-14-1828560-HTML/image_m/fpubh-14-1828560-t001.jpg Table 1. Characteristics of the study sample. https://www.frontiersin.org/files/Articles/1828560/fpubh-14-1828560-HTML/image_m/fpubh-14-1828560-t002.jpg Table 2. Results of hierarchical linear regression predicting parental satisfaction with pediatric c https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1753553/full https://www.frontiersin.org/files/Articles/1753553/fpls-16-1753553-HTML/image_m/fpls-16-1753553-g001.jpg Figure 1. Calcium, magnesium, nitrogen, phosphorus, and potassium uptake from solution for cultivar https://www.frontiersin.org/files/Articles/1753553/fpls-16-1753553-HTML/image_m/fpls-16-1753553-g002.jpg Figure 2. Boron, copper, iron, manganese, and zinc uptake from solution for cultivar CJ2 and First L https://www.frontiersin.org/files/Articles/1753553/fpls-16-1753553-HTML/image_m/fpls-16-1753553-t001.jpg Table 1. Mean boron [B], copper [Cu], calcium [Ca], iron [Fe], magnesium [Mg], manganese [Mn], nitro https://www.frontiersin.org/files/Articles/1753553/fpls-16-1753553-HTML/image_m/fpls-16-1753553-g003.jpg Figure 3. Comparison of calculated mean solution input concentrations derived from leaf, stem, root, https://www.frontiersin.org/files/Articles/1753553/fpls-16-1753553-HTML/image_m/fpls-16-1753553-g004.jpg Figure 4. Comparison of calculated mean solution input concentrations derived from leaf, stem, root, https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1665790/full https://www.frontiersin.org/files/Articles/1665790/fpsyg-16-1665790-HTML/image_m/fpsyg-16-1665790-t001.jpg Table 1. Descriptive statistics of the sample. https://www.frontiersin.org/files/Articles/1665790/fpsyg-16-1665790-HTML/image_m/fpsyg-16-1665790-t002.jpg Table 2. Descriptive statistics and Pearson’s bivariate correlations. https://www.frontiersin.org/files/Articles/1665790/fpsyg-16-1665790-HTML/image_m/fpsyg-16-1665790-t003.jpg Table 3. Results of backward regression analysis: childhood trauma on orthorexia nervosa symptoms (n https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1759849/full https://www.frontiersin.org/files/Articles/1759849/fpsyg-16-1759849-HTML-r2/image_m/fpsyg-16-1759849-g001.jpg Figure 1. Flowchart. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1744639/full https://www.frontiersin.org/files/Articles/1744639/fbioe-14-1744639-HTML/image_m/fbioe-14-1744639-g001.jpg Figure 1. The preparation scheme of GelMA and GO/GelMA composite hydrogel. https://www.frontiersin.org/files/Articles/1744639/fbioe-14-1744639-HTML/image_m/fbioe-14-1744639-g002.jpg Figure 2. Rheological characterization of GO. (A) AFM images. Arrowheads denote the height profile o https://www.frontiersin.org/files/Articles/1744639/fbioe-14-1744639-HTML/image_m/fbioe-14-1744639-g003.jpg Figure 3. Characterization of GO/GelMA hydrogels. (A) H-NMR analysis of gelatin and GelMA. (B) SEM i https://www.frontiersin.org/files/Articles/1744639/fbioe-14-1744639-HTML/image_m/fbioe-14-1744639-g004.jpg Figure 4. Characterization of GO/GelMA hydrogels. (A) Porosity of the hydrogels, showing a gradual d https://www.frontiersin.org/files/Articles/1744639/fbioe-14-1744639-HTML/image_m/fbioe-14-1744639-g005.jpg Figure 5. Evaluation of the antibacterial performance of GO/GelMA hydrogels against Porphyromonas gi https://www.frontiersin.org/files/Articles/1744639/fbioe-14-1744639-HTML/image_m/fbioe-14-1744639-g006.jpg Figure 6. Biocompatibility and proliferation of BMSCs on GO/GelMA hydrogels. (A–C) Cell viability of https://www.frontiersin.org/files/Articles/1744639/fbioe-14-1744639-HTML/image_m/fbioe-14-1744639-g007.jpg Figure 7. In vitro osteogenic differentiation of BMSCs on GO/GelMA hydrogels. (A) ALP and ARS staini https://www.frontiersin.org/files/Articles/1744639/fbioe-14-1744639-HTML/image_m/fbioe-14-1744639-g008.jpg Figure 8. In vivo evaluation of anti-inflammatory and osteogenic effects in a periodontitis mouse mo https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1744076/full https://www.frontiersin.org/files/Articles/1744076/fonc-16-1744076-HTML-r1/image_m/fonc-16-1744076-t001.jpg Table 1. Baseline patient characteristics and univariate analysis. https://www.frontiersin.org/files/Articles/1744076/fonc-16-1744076-HTML-r1/image_m/fonc-16-1744076-t002.jpg Table 2. Survival analysis. https://www.frontiersin.org/files/Articles/1744076/fonc-16-1744076-HTML-r1/image_m/fonc-16-1744076-g001.jpg Figure 1. (A, B) Efficacy (OS and PFS) of atezolizumab/bevacizumab plus interventional therapy in BC https://www.frontiersin.org/files/Articles/1744076/fonc-16-1744076-HTML-r1/image_m/fonc-16-1744076-t003.jpg Table 3. Clinical data of 25 patients stratified by the neutrophil-to-lymphocyte ratio (NLR) (≥ 3 vs https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1814953/full https://www.frontiersin.org/files/Articles/1814953/fimmu-17-1814953-HTML/image_m/fimmu-17-1814953-g001.jpg Figure 1. Inflammatory circuits and atherosclerosis progression in RMDs. A number of inflammatory pa https://www.frontiersin.org/files/Articles/1814953/fimmu-17-1814953-HTML/image_m/fimmu-17-1814953-t001.jpg Table 1. Characteristics of clinical studies concerning peripheral artery disease and rheumatoid art https://www.frontiersin.org/files/Articles/1814953/fimmu-17-1814953-HTML/image_m/fimmu-17-1814953-t002.jpg Table 2. Characteristics of clinical studies concerning peripheral artery disease and systemic lupus https://www.frontiersin.org/files/Articles/1814953/fimmu-17-1814953-HTML/image_m/fimmu-17-1814953-t003.jpg Table 3. Characteristics of clinical studies concerning peripheral artery disease and antiphospholip https://www.frontiersin.org/files/Articles/1814953/fimmu-17-1814953-HTML/image_m/fimmu-17-1814953-t004.jpg Table 4. Characteristics of the clinical studies concerning peripheral artery disease and systemic s https://www.frontiersin.org/files/Articles/1814953/fimmu-17-1814953-HTML/image_m/fimmu-17-1814953-t005.jpg Table 5. Characteristics of clinical studies concerning peripheral artery disease and polymyalgia rh https://www.frontiersin.org/files/Articles/1814953/fimmu-17-1814953-HTML/image_m/fimmu-17-1814953-t006.jpg Table 6. Characteristics of clinical studies concerning peripheral artery disease and psoriatic arth https://www.frontiersin.org/files/Articles/1814953/fimmu-17-1814953-HTML/image_m/fimmu-17-1814953-t007.jpg Table 7. Characteristics of the clinical studies concerning peripheral artery disease and Sjögren’s https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1676548/full https://www.frontiersin.org/files/Articles/1676548/fmed-12-1676548-HTML-r1/image_m/fmed-12-1676548-g001.jpg Figure 1. The patient’s brain CT examination after the car accident showed a small left subdural hem https://www.frontiersin.org/files/Articles/1676548/fmed-12-1676548-HTML-r1/image_m/fmed-12-1676548-g002.jpg Figure 2. (A,B) Axial T2-weighted/FLAIR and T1-weighted MRI of the head demonstrate a mass-like abno https://www.frontiersin.org/files/Articles/1676548/fmed-12-1676548-HTML-r1/image_m/fmed-12-1676548-g003.jpg Figure 3. The patient’s spinal MRI showed no abnormalities. https://www.frontiersin.org/files/Articles/1676548/fmed-12-1676548-HTML-r1/image_m/fmed-12-1676548-t001.jpg Table 1. Results of serial cerebrospinal fluid (CSF) examinations. https://www.frontiersin.org/files/Articles/1676548/fmed-12-1676548-HTML-r1/image_m/fmed-12-1676548-g004.jpg Figure 4. Diagnostic and treatment timeline of this patient with neurobrucellosis. https://www.frontiersin.org/files/Articles/1676548/fmed-12-1676548-HTML-r1/image_m/fmed-12-1676548-t002.jpg Table 2. Literature review of brucellosis-associated intracranial abscesses (1980–2025). https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1577199/full https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g001.jpg Figure 1. Lampung bay waters in south Lampung. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-t001.jpg Table 1. The value of the questionnaire questions. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g002.jpg Figure 2. The use of a coral health chart to obtain information on coral reef health. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g003.jpg Figure 3. Education level of dive tour guides. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g004.jpg Figure 4. Coastal community by work type. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g005.jpg Figure 5. Knowledge level of coastal communities. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g006.jpg Figure 6. Dependence level of coastal communities. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g007.jpg Figure 7. Perceptions of coastal community on current coral reef ecosystem management. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g008.jpg Figure 8. Perceptions of coastal community on future coral reef ecosystem management. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g009.jpg Figure 9. Perception level of GIS. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g010.jpg Figure 10. Community participation level. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g011.jpg Figure 11. Observed data based on coral color. https://www.frontiersin.org/files/Articles/1577199/fmars-12-1577199-HTML/image_m/fmars-12-1577199-g012.jpg Figure 12. Causal loop diagram (CLD) model for the sustainability of coral reef ecosystem management https://www.frontiersin.org/journals/epidemiology/articles/10.3389/fepid.2025.1547867/full https://www.frontiersin.org/files/Articles/1547867/fepid-05-1547867-HTML/image_m/fepid-05-1547867-g001.jpg Figure 1. Flowchart of the PRISMA selection process and inclusion criteria. https://www.frontiersin.org/files/Articles/1547867/fepid-05-1547867-HTML/image_m/fepid-05-1547867-t001.jpg Table 1. Study characteristics by interventions evaluated for retention outcomes. https://www.frontiersin.org/files/Articles/1547867/fepid-05-1547867-HTML/image_m/fepid-05-1547867-t002.jpg Table 2. Study characteristics for viral suppression outcomes. https://www.frontiersin.org/files/Articles/1547867/fepid-05-1547867-HTML/image_m/fepid-05-1547867-t003.jpg Table 3. Pooled historical evidence for adolescent and adult RCT data. https://www.frontiersin.org/files/Articles/1547867/fepid-05-1547867-HTML/image_m/fepid-05-1547867-g002.jpg Figure 2. Forest plot of posterior probability of a retention benefit. https://www.frontiersin.org/files/Articles/1547867/fepid-05-1547867-HTML/image_m/fepid-05-1547867-t004.jpg Table 4. Adjusted posterior odds ratio for adolescent improving retention outcomes with α representi https://www.frontiersin.org/files/Articles/1547867/fepid-05-1547867-HTML/image_m/fepid-05-1547867-g003.jpg Figure 3. Forest plot of posterior probability of a viral suppression benefit. https://www.frontiersin.org/files/Articles/1547867/fepid-05-1547867-HTML/image_m/fepid-05-1547867-t005.jpg Table 5. Adjusted adolescent posterior odds ratio for adolescent virological failure with α represen https://www.frontiersin.org/files/Articles/1547867/fepid-05-1547867-HTML/image_m/fepid-05-1547867-t006.jpg Table 6. Effects of adolescent and adult historical data on adolescent intervention estimates. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1673712/full https://www.frontiersin.org/files/Articles/1673712/fphar-16-1673712-HTML-r1/image_m/fphar-16-1673712-g001.jpg Figure 1. Bland-Altman plot. https://www.frontiersin.org/files/Articles/1673712/fphar-16-1673712-HTML-r1/image_m/fphar-16-1673712-g002.jpg Figure 2. Flow chart of study selection. https://www.frontiersin.org/files/Articles/1673712/fphar-16-1673712-HTML-r1/image_m/fphar-16-1673712-g003.jpg Figure 3. Summary of risk of bias assessment for included studies. https://www.frontiersin.org/files/Articles/1673712/fphar-16-1673712-HTML-r1/image_m/fphar-16-1673712-g004.jpg Figure 4. Forest plot of the effect of quercetin on (A) total IgE, (B) OVA-IgE. https://www.frontiersin.org/files/Articles/1673712/fphar-16-1673712-HTML-r1/image_m/fphar-16-1673712-g005.jpg Figure 5. Forest plot of the effect of quercetin on (A) macrophage counts, (B) lymphocyte counts, (C https://www.frontiersin.org/files/Articles/1673712/fphar-16-1673712-HTML-r1/image_m/fphar-16-1673712-g006.jpg Figure 6. Forest plot of the effect of quercetin on (A) IL-4, (B) IL-5, (C) TNF-α, (D) IFN-γ, (E) IL https://www.frontiersin.org/files/Articles/1673712/fphar-16-1673712-HTML-r1/image_m/fphar-16-1673712-g007.jpg Figure 7. Forest plot of the effect of quercetin on HIS. https://www.frontiersin.org/files/Articles/1673712/fphar-16-1673712-HTML-r1/image_m/fphar-16-1673712-g008.jpg Figure 8. Forest plot of the effect of quercetin on (A) total IgE, (B) OVA-IgE in the sensitivity an https://www.frontiersin.org/files/Articles/1673712/fphar-16-1673712-HTML-r1/image_m/fphar-16-1673712-g009.jpg Figure 9. Post-hoc sensitivity analysis. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1723393/full https://www.frontiersin.org/files/Articles/1723393/fphar-17-1723393-HTML/image_m/fphar-17-1723393-t001.jpg Table 1. Clinical characteristics and laboratory data of the patient. https://www.frontiersin.org/files/Articles/1723393/fphar-17-1723393-HTML/image_m/fphar-17-1723393-g001.jpg Figure 1. Platelet count over time. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1809699/full https://www.frontiersin.org/files/Articles/1809699/fpls-17-1809699-HTML/image_m/fpls-17-1809699-g001.jpg Figure 1. Tile plots of weather parameters including average weekly minimum temperature (°C), averag https://www.frontiersin.org/files/Articles/1809699/fpls-17-1809699-HTML/image_m/fpls-17-1809699-t001.jpg Table 1. Summary statistics per phenotype per trial and of the respective distribution of best linea https://www.frontiersin.org/files/Articles/1809699/fpls-17-1809699-HTML/image_m/fpls-17-1809699-g002.jpg Figure 2. Cumulative admixture proportions of each triticale accession to the four expected populati https://www.frontiersin.org/files/Articles/1809699/fpls-17-1809699-HTML/image_m/fpls-17-1809699-t002.jpg Table 2. Maximum, minimum and average admixture proportions to each population layer (i.e., Layer 1, https://www.frontiersin.org/files/Articles/1809699/fpls-17-1809699-HTML/image_m/fpls-17-1809699-t003.jpg Table 3. List of statistically significant marker-trait associations (p-value < 0.05 after Bonferron https://www.frontiersin.org/files/Articles/1809699/fpls-17-1809699-HTML/image_m/fpls-17-1809699-g003.jpg Figure 3. Manhattan plots showing the results of the GWAS for flour AX content of triticale. The abs https://www.frontiersin.org/files/Articles/1809699/fpls-17-1809699-HTML/image_m/fpls-17-1809699-g004.jpg Figure 4. Boxplot distributions of total arabinoxylan content [TOT-AX; % of dry matter [dm], (a)], o https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1792457/full https://www.frontiersin.org/files/Articles/1792457/fneur-17-1792457-HTML/image_m/fneur-17-1792457-t001.jpg Table 1. Studies evaluating tDCS and Related neuromodulation for attention and related cognitive out https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1701561/full https://www.frontiersin.org/files/Articles/1701561/fpubh-13-1701561-HTML/image_m/fpubh-13-1701561-g001.jpg Figure 1. Literature retrieval and screening process. https://www.frontiersin.org/files/Articles/1701561/fpubh-13-1701561-HTML/image_m/fpubh-13-1701561-t001.jpg Table 1. The basic characteristics of the included literature (n = 15). https://www.frontiersin.org/files/Articles/1701561/fpubh-13-1701561-HTML/image_m/fpubh-13-1701561-t002.jpg Table 2. Critical appraisal of the included studies (n = 15). https://www.frontiersin.org/files/Articles/1701561/fpubh-13-1701561-HTML/image_m/fpubh-13-1701561-t003.jpg Table 3. ConQual summary of the findings. https://www.frontiersin.org/files/Articles/1701561/fpubh-13-1701561-HTML/image_m/fpubh-13-1701561-g002.jpg Figure 2. Theme map of family caregivers' experiences and support intervention systems for patients https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1792083/full https://www.frontiersin.org/files/Articles/1792083/fspor-08-1792083-HTML-r1/image_m/fspor-08-1792083-t001.jpg Table 1. Descriptive statistics for social capital (trust, reciprocity, network), motivation (extrin https://www.frontiersin.org/files/Articles/1792083/fspor-08-1792083-HTML-r1/image_m/fspor-08-1792083-t002.jpg Table 2. Correlations for social capital (trust, reciprocity, network) and intention. https://www.frontiersin.org/files/Articles/1792083/fspor-08-1792083-HTML-r1/image_m/fspor-08-1792083-t003.jpg Table 3. Correlations for social capital (trust, reciprocity, network) and motivation (extrinsic mot https://www.frontiersin.org/files/Articles/1792083/fspor-08-1792083-HTML-r1/image_m/fspor-08-1792083-g001.jpg Figure 1. Direct and indirect effects from social capital (trust, reciprocity, network) to intention https://www.frontiersin.org/files/Articles/1792083/fspor-08-1792083-HTML-r1/image_m/fspor-08-1792083-t004.jpg Table 4. Direct paths and standardized regression coefficients (β). https://www.frontiersin.org/files/Articles/1792083/fspor-08-1792083-HTML-r1/image_m/fspor-08-1792083-t005.jpg Table 5. Indirect paths and regression coefficients (β). https://www.frontiersin.org/journals/systems-biology/articles/10.3389/fsysb.2025.1721019/full https://www.frontiersin.org/files/Articles/1721019/fsysb-05-1721019-HTML/image_m/fsysb-05-1721019-g001.jpg Figure 1. Spatial context of the study area. (A) Map of Brazil with the state of São Paulo. (B) Loca https://www.frontiersin.org/files/Articles/1721019/fsysb-05-1721019-HTML/image_m/fsysb-05-1721019-t001.jpg Table 1. Bat species captured, their respective ectoparasitic flies (Streblidae) and number of Trypa https://www.frontiersin.org/files/Articles/1721019/fsysb-05-1721019-HTML/image_m/fsysb-05-1721019-g002.jpg Figure 2. Photographs of bat flies: (A) Strebla guajiro and (B) Trichobius joblingi. https://www.frontiersin.org/files/Articles/1721019/fsysb-05-1721019-HTML/image_m/fsysb-05-1721019-t002.jpg Table 2. Absolute number of Trypanosoma reads detected by metagenomics. https://www.frontiersin.org/files/Articles/1721019/fsysb-05-1721019-HTML/image_m/fsysb-05-1721019-g003.jpg Figure 3. Maximum-likelihood phylogenetic tree of Trypanosoma spp. based on 18S SSU rRNA sequences o https://www.frontiersin.org/files/Articles/1721019/fsysb-05-1721019-HTML/image_m/fsysb-05-1721019-g004.jpg Figure 4. Maximum-likelihood phylogenetic tree of Trypanosoma spp. based on the 18S SSU rRNA region https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1750175/full https://www.frontiersin.org/files/Articles/1750175/fpsyg-17-1750175-HTML/image_m/fpsyg-17-1750175-g001.jpg Figure 1. The hypothesized model. https://www.frontiersin.org/files/Articles/1750175/fpsyg-17-1750175-HTML/image_m/fpsyg-17-1750175-t001.jpg Table 1. Participant demographics. https://www.frontiersin.org/files/Articles/1750175/fpsyg-17-1750175-HTML/image_m/fpsyg-17-1750175-t002.jpg Table 2. Descriptive statistics and correlation analyses (N = 693). https://www.frontiersin.org/files/Articles/1750175/fpsyg-17-1750175-HTML/image_m/fpsyg-17-1750175-t003.jpg Table 3. Moderated mediational analysis of nature relatedness, environmental ethics awareness, and e https://www.frontiersin.org/files/Articles/1750175/fpsyg-17-1750175-HTML/image_m/fpsyg-17-1750175-t004.jpg Table 4. Conditional indirect effects of environmental organization membership on environmental beha https://www.frontiersin.org/files/Articles/1750175/fpsyg-17-1750175-HTML/image_m/fpsyg-17-1750175-g002.jpg Figure 2. Environmental organization membership's moderating effect on the relationship between natu https://www.frontiersin.org/journals/dementia/articles/10.3389/frdem.2026.1737068/full https://www.frontiersin.org/files/Articles/1737068/frdem-05-1737068-HTML/image_m/frdem-05-1737068-g001.jpg Figure 1. Flow chart showing the process of study identification and selection according to Preferre https://www.frontiersin.org/files/Articles/1737068/frdem-05-1737068-HTML/image_m/frdem-05-1737068-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1737068/frdem-05-1737068-HTML/image_m/frdem-05-1737068-g002.jpg Figure 2. Meta-analysis results on the association between HSV-2 and Alzheimer's disease combining t https://www.frontiersin.org/files/Articles/1737068/frdem-05-1737068-HTML/image_m/frdem-05-1737068-g003.jpg Figure 3. Meta-analysis results on the association between HSV-2 and all-cause dementia combining on https://www.frontiersin.org/files/Articles/1737068/frdem-05-1737068-HTML/image_m/frdem-05-1737068-g004.jpg Figure 4. Meta-analysis results on the association between HSV-2 on all-cause dementia using studies https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2026.1616403/full https://www.frontiersin.org/files/Articles/1616403/fgwh-07-1616403-HTML/image_m/fgwh-07-1616403-g001.jpg Figure 1. Sample flow chart. https://www.frontiersin.org/files/Articles/1616403/fgwh-07-1616403-HTML/image_m/fgwh-07-1616403-t001.jpg Table 1. Results from bivariate analysis (column percentage). https://www.frontiersin.org/files/Articles/1616403/fgwh-07-1616403-HTML/image_m/fgwh-07-1616403-t002.jpg Table 2. Results from marginal analysis of adverse pregnancy outcomes (APOs), low birth weight (LBW) https://www.frontiersin.org/files/Articles/1616403/fgwh-07-1616403-HTML/image_m/fgwh-07-1616403-t003.jpg Table 3. Results from marginal analysis among women IPV survivors. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1777655/full https://www.frontiersin.org/files/Articles/1777655/fonc-16-1777655-HTML/image_m/fonc-16-1777655-g001.jpg Figure 1. Illustration of nasoseptal flap incisions, plane of harvest and anatomy of sphenopalatine https://www.frontiersin.org/files/Articles/1777655/fonc-16-1777655-HTML/image_m/fonc-16-1777655-g002.jpg Figure 2. (A–D). Endoscopic operative view right nasal cavity demonstrating incisions for flap harve https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2025.1624277/full https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-g001.jpg Figure 1. Experimental design of the pre-test stress-loading conditions. A schematic representation https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-g002.jpg Figure 2. Optimization of entry and freezing detection after tracking. (A) Determination of the opti https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-g003.jpg Figure 3. Effects of pre-test stress loading on the outcomes of the NTT. Values of latency to enter https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-t001.jpg Table 1. Statistical summary of F-tests for behavioral variability under different pre-test stress c https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-g004.jpg Figure 4. Effects of subtle temperature variations on outcomes of the NTT. (A–C) Values of latency t https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-t002.jpg Table 2. F-test summary for temperature effects on NTT outcomes. https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-g005.jpg Figure 5. Effects of net-chasing on freezing time and exploratory behavior. (A) Freezing time (FT) m https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-t003.jpg Table 3. F-test summary for net-chasing effects on FT. https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-t004.jpg Table 4. F-test summary for FT across 5-min intervals with net-chasing conditions. https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-g006.jpg Figure 6. Assessment of learning effects during repeated NTT trials. (A–C) Values of latency to ente https://www.frontiersin.org/files/Articles/1624277/fnbeh-19-1624277-HTML/image_m/fnbeh-19-1624277-g007.jpg Figure 7. Entry events into the upper half of the tank follow a Poisson process. (A) Vertical moveme https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1697922/full https://www.frontiersin.org/files/Articles/1697922/fgene-16-1697922-HTML/image_m/fgene-16-1697922-g001.jpg Figure 1. Overview of RNA sequencing workflow. RNA molecules are first extracted from cells or tissu https://www.frontiersin.org/files/Articles/1697922/fgene-16-1697922-HTML/image_m/fgene-16-1697922-g002.jpg Figure 2. Flowchart illustrating an RNA-Seq analysis pipeline, from raw FASTQ files to functional in https://www.frontiersin.org/files/Articles/1697922/fgene-16-1697922-HTML/image_m/fgene-16-1697922-g003.jpg Figure 3. Quality control (QC) of RNA-Seq data. Sequencing reads undergo QC to identify adapter cont https://www.frontiersin.org/files/Articles/1697922/fgene-16-1697922-HTML/image_m/fgene-16-1697922-g004.jpg Figure 4. Schematic overview of normalization in RNA-Seq. (A) Normalization across experiments. Raw https://www.frontiersin.org/files/Articles/1697922/fgene-16-1697922-HTML/image_m/fgene-16-1697922-t001.jpg Table 1. Summary of normalization techniques. https://www.frontiersin.org/files/Articles/1697922/fgene-16-1697922-HTML/image_m/fgene-16-1697922-g005.jpg Figure 5. Scaling data. (A) Mean and variance of gene expression level. Variance is larger for large https://www.frontiersin.org/files/Articles/1697922/fgene-16-1697922-HTML/image_m/fgene-16-1697922-t002.jpg Table 2. Summary of scaling (transformation) techniques. https://www.frontiersin.org/files/Articles/1697922/fgene-16-1697922-HTML/image_m/fgene-16-1697922-t003.jpg Table 3. Summary of differential expression analysis techniques. https://www.frontiersin.org/files/Articles/1697922/fgene-16-1697922-HTML/image_m/fgene-16-1697922-g006.jpg Figure 6. Exploration of inferences of differential expression analysis. (A) Output of DESeq2’s diff https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1784235/full https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-g001.jpg Figure 1. Study flow chart. This study elucidated the mechanism by which the caffeine–CD4+CD39+ Treg https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-t001.jpg Table 1. Details of the datasets included in this study. https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-g002.jpg Figure 2. Caffeine promotes PTB by activating CD4+ Treg cells-mediated immunosuppression. (A) Schema https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-t002.jpg Table 2. PCR primer sequences. https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-g003.jpg Figure 3. Mediated MR analysis revealed that caffeine promotes PTB by activating CD39+CD4+ Tregs. (A https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-g004.jpg Figure 4. CD4+ Tregs from the lung tissues of patients with PTB were significantly activated and mor https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-g005.jpg Figure 5. Caffeine activates CD39+ Tregs by enhancing the CD39-mediated adenosine receptor binding c https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-g006.jpg Figure 6. Identification of the core markers with high adenosine receptor binding capacity based on https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-g007.jpg Figure 7. IF staining of PSMC5, AGPAT5, and BAG1 in FOXP3+ Tregs within mouse lung tissues. (A–C) IF https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-g008.jpg Figure 8. Multi-omics analyses of PSMC5 in PTB. (A–C) Volcano plots of MR effect estimates for eQTLs https://www.frontiersin.org/files/Articles/1784235/fimmu-17-1784235-HTML/image_m/fimmu-17-1784235-g009.jpg Figure 9. The mechanism by which caffeine modulates the risk of PTB via CD4+ Treg cells in patients https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2026.1776373/full https://www.frontiersin.org/files/Articles/1776373/fcomm-11-1776373-HTML-r1/image_m/fcomm-11-1776373-t001.jpg Table 1. Leftward Emphasis Spread within the Word (/tʕabag-na/). NOGAP V-[dor], MAXLINK V-[dor], R-A https://www.frontiersin.org/files/Articles/1776373/fcomm-11-1776373-HTML-r1/image_m/fcomm-11-1776373-t002.jpg Table 2. Leftward Emphasis Spread within the Word (/balliitʕ/). NOGAP V-[dor], MAXLINK V-[dor], L-AL https://www.frontiersin.org/files/Articles/1776373/fcomm-11-1776373-HTML-r1/image_m/fcomm-11-1776373-t003.jpg Table 3. Bidirectional Emphasis Spread within the Word (/batʕal/). NOGAP V-[dor], MAXLINK V-[dor], L https://www.frontiersin.org/files/Articles/1776373/fcomm-11-1776373-HTML-r1/image_m/fcomm-11-1776373-t004.jpg Table 4. Emphasis Spread Across Word Boundaries (/beet # tʕawa:big/). L/R- ALIGN V-[dor]o’, NOGAP V- https://www.frontiersin.org/files/Articles/1776373/fcomm-11-1776373-HTML-r1/image_m/fcomm-11-1776373-t005.jpg Table 5. Opaque Rightward Emphasis Spread Across Word Boundaries (/ʕaatʕ # taani/). DEPLINK V-[dor]ω https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1784775/full https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-g001.jpg Figure 1. Research hypothesis model. https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-t001.jpg Table 1. Correlation analysis results. https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-t002.jpg Table 2. Differences in bedtime procrastination by demographic characteristics. https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-t003.jpg Table 3. Results of latent profile analysis of academic stress among college students. https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-g002.jpg Figure 2. The four academic stress profiles and relative size of the profiles. AX1, Prospect stress; https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-t004.jpg Table 4. Analysis of differences in academic stress dimensions across profiles. https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-t005.jpg Table 5. Relations of the four latent profiles to BPS in the full sample. https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-t006.jpg Table 6. Relative mediation effects of symptom rumination and reflection. https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-t007.jpg Table 7. Results of moderated mediation analysis. https://www.frontiersin.org/files/Articles/1784775/fpsyg-17-1784775-HTML/image_m/fpsyg-17-1784775-g003.jpg Figure 3. Simple slope plot of the moderating effect. https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2026.1784806/full https://www.frontiersin.org/files/Articles/1784806/ffgc-09-1784806-HTML/image_m/ffgc-09-1784806-g001.jpg Figure 1. Study area map showing: (A) Map showing the study area’s location in Zimbabwe’s eastern hi https://www.frontiersin.org/files/Articles/1784806/ffgc-09-1784806-HTML/image_m/ffgc-09-1784806-t001.jpg Table 1. Hyper-parameter grids, selection criterion (lowest spatially blocked CV RMSE), and final se https://www.frontiersin.org/files/Articles/1784806/ffgc-09-1784806-HTML/image_m/ffgc-09-1784806-g002.jpg Figure 2. Empirical and modeled semivariograms of log-transformed carbon stock (Mg C ha−1) in planta https://www.frontiersin.org/files/Articles/1784806/ffgc-09-1784806-HTML/image_m/ffgc-09-1784806-t002.jpg Table 2. Statistical summaries of the Bayesian geostatistical hierarchical analysis for carbon stock https://www.frontiersin.org/files/Articles/1784806/ffgc-09-1784806-HTML/image_m/ffgc-09-1784806-g003.jpg Figure 3. Spatial prediction of forest carbon stock using the BGHM: (A) predicted carbon stock (MgC https://www.frontiersin.org/files/Articles/1784806/ffgc-09-1784806-HTML/image_m/ffgc-09-1784806-t003.jpg Table 3. Relative variable importance (scaled to 100) of predictors for carbon stock modeling using https://www.frontiersin.org/files/Articles/1784806/ffgc-09-1784806-HTML/image_m/ffgc-09-1784806-g004.jpg Figure 4. Spatial prediction of forest carbon stock using machine-learning models: (A) random forest https://www.frontiersin.org/files/Articles/1784806/ffgc-09-1784806-HTML/image_m/ffgc-09-1784806-g005.jpg Figure 5. Model diagnostic assessment for: (A) Bayesian geostatistical and (B) ML based models. ErIM https://www.frontiersin.org/files/Articles/1784806/ffgc-09-1784806-HTML/image_m/ffgc-09-1784806-g006.jpg Figure 6. Trace plots for Bayesian model fixed-effect parameters. Trace plots from the Markov chain https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1608558/full https://www.frontiersin.org/files/Articles/1608558/fnut-12-1608558-HTML/image_m/fnut-12-1608558-t001.jpg Table 1. Strategy used for searching studies in PubMed. https://www.frontiersin.org/files/Articles/1608558/fnut-12-1608558-HTML/image_m/fnut-12-1608558-g001.jpg Figure 1. Literature screening process and results. https://www.frontiersin.org/files/Articles/1608558/fnut-12-1608558-HTML/image_m/fnut-12-1608558-t002.jpg Table 2. Chronological overview of the nutrition literacy assessment tool for the elderly. https://www.frontiersin.org/files/Articles/1608558/fnut-12-1608558-HTML/image_m/fnut-12-1608558-g002.jpg Figure 2. The core elements of the content of the Nutrition Literacy Assessment. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1743624/full https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-g001.jpg Figure 1. Study area localization. Sidi Toui National Park (protected area) and their surroundings ( https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-t001.jpg Table 1. Annual rainfall recorded in the study area during the period from September 2019 to August https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-g002.jpg Figure 2. Soil surface materials and Vegetation cover (%) in the monitored plots during spring of 20 https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-g003.jpg Figure 3. Floristic composition of the study area: Aboveground species and germinated soil seed bank https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-g004.jpg Figure 4. Comparison of aboveground annual (A) and perennial density (B) in protected and grazed are https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-g005.jpg Figure 5. Comparison of aboveground annual richness (A), perennial richness (B), and total richness https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-t002.jpg Table 2. Sørensen coefficient similarity index (SCSI) between aboveground species in protected and g https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-g006.jpg Figure 6. Comparison of annual and perennial densities of germinated soil seed bank (A) according to https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-g007.jpg Figure 7. Comparison of annual, perennial (A, B) and total richness (C) of germinated soil seed bank https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-t003.jpg Table 3. Germinated soil seed bank species in protected vs. grazed areas: Presence (+)/absence (-), https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-t004.jpg Table 4. Sørensen coefficient similarity index (SCSI) between aboveground vegetation (AGV) and germi https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-t005.jpg Table 5. Specific frequency (Percentage of sample plots where a similar species was found) of the ge https://www.frontiersin.org/files/Articles/1743624/fpls-17-1743624-HTML/image_m/fpls-17-1743624-g008.jpg Figure 8. Redundancy analysis (RDA) of aboveground vegetation and soil seed bank in protected vs. gr https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1646328/full https://www.frontiersin.org/files/Articles/1646328/fneur-16-1646328-HTML-r1/image_m/fneur-16-1646328-g001.jpg Figure 1. A summary of the evidence searches and selection process. https://www.frontiersin.org/files/Articles/1646328/fneur-16-1646328-HTML-r1/image_m/fneur-16-1646328-t001.jpg Table 1. Summary table of included reviews. https://www.frontiersin.org/files/Articles/1646328/fneur-16-1646328-HTML-r1/image_m/fneur-16-1646328-g002.jpg Figure 2. Risk of bias of included studies. https://www.frontiersin.org/files/Articles/1646328/fneur-16-1646328-HTML-r1/image_m/fneur-16-1646328-g003.jpg Figure 3. Network diagram. https://www.frontiersin.org/files/Articles/1646328/fneur-16-1646328-HTML-r1/image_m/fneur-16-1646328-t002.jpg Table 2. League table on interventions. https://www.frontiersin.org/files/Articles/1646328/fneur-16-1646328-HTML-r1/image_m/fneur-16-1646328-g004.jpg Figure 4. SUCRA plot. https://www.frontiersin.org/files/Articles/1646328/fneur-16-1646328-HTML-r1/image_m/fneur-16-1646328-g005.jpg Figure 5. Funnel plot on publication bias. https://www.frontiersin.org/files/Articles/1646328/fneur-16-1646328-HTML-r1/image_m/fneur-16-1646328-t003.jpg Table 3. Ranking of SUCRA probabilities. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1704980/full https://www.frontiersin.org/files/Articles/1704980/fpsyg-16-1704980-HTML-r1/image_m/fpsyg-16-1704980-g001.jpg Figure 1. A summary of the evidence searches and selection process. https://www.frontiersin.org/files/Articles/1704980/fpsyg-16-1704980-HTML-r1/image_m/fpsyg-16-1704980-t001.jpg Table 1. Summary table of included reviews. https://www.frontiersin.org/files/Articles/1704980/fpsyg-16-1704980-HTML-r1/image_m/fpsyg-16-1704980-g002.jpg Figure 2. Risk of bias of included studies. https://www.frontiersin.org/files/Articles/1704980/fpsyg-16-1704980-HTML-r1/image_m/fpsyg-16-1704980-g003.jpg Figure 3. Network diagram. https://www.frontiersin.org/files/Articles/1704980/fpsyg-16-1704980-HTML-r1/image_m/fpsyg-16-1704980-g004.jpg Figure 4. Forest plot. https://www.frontiersin.org/files/Articles/1704980/fpsyg-16-1704980-HTML-r1/image_m/fpsyg-16-1704980-t002.jpg Table 2. League table on interventions. https://www.frontiersin.org/files/Articles/1704980/fpsyg-16-1704980-HTML-r1/image_m/fpsyg-16-1704980-g005.jpg Figure 5. SUCRA plot. https://www.frontiersin.org/files/Articles/1704980/fpsyg-16-1704980-HTML-r1/image_m/fpsyg-16-1704980-g006.jpg Figure 6. Funnel plot on publication bias. https://www.frontiersin.org/files/Articles/1704980/fpsyg-16-1704980-HTML-r1/image_m/fpsyg-16-1704980-t003.jpg Table 3. Ranking of SUCRA probabilities. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1727315/full https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-g001.jpg Figure 1. PRISMA flow diagram of the study process. https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-t001.jpg Table 1. Summary table of included reviews. https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-g002.jpg Figure 2. Risk of bias of included studies. https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-g003.jpg Figure 3. Exercise type network diagram. https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-g004.jpg Figure 4. Exercise dose network diagram. https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-g005.jpg Figure 5. Dose–response relationships of different physical activity interventions. https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-g006.jpg Figure 6. Dose–response curves of physical activity modalities on menopausal depression. The green s https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-t002.jpg Table 2. SUCRA values for each model parameter and exercise modality. https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-g007.jpg Figure 7. Cumulative rank probability plots and SUCRA scores for each exercise modality. https://www.frontiersin.org/files/Articles/1727315/fpsyg-17-1727315-HTML-r1/image_m/fpsyg-17-1727315-g008.jpg Figure 8. Posterior rank distributions of intervention–dose on menopausal depression. X-axis: Rank o https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1800865/full https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-t001.jpg Table 1. Comparison of patient baseline characteristics before and after propensity score matching. https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-t002.jpg Table 2. Comparison of primary and secondary clinical outcomes in the matched cohort (n = 156). https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-t003.jpg Table 3. Comparison of postoperative plasma D-dimer levels in the matched cohort (n = 156). https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-g001.jpg Figure 1. Comparison of postoperative plasma D-dimer levels in the matched cohort. https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-t004.jpg Table 4. Comparison of postoperative pain scores and opioid consumption in the matched cohort (n = 1 https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-t005.jpg Table 5. Comparison of postoperative complications and resource utilization in the matched cohort (n https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-g002.jpg Figure 2. Comparison of postoperative complications and resource utilization in the matched cohort. https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-t006.jpg Table 6. Multivariable logistic regression analysis for postoperative deep vein thrombosis (Full coh https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-g003.jpg Figure 3. Cook’s distance plot. https://www.frontiersin.org/files/Articles/1800865/fmed-13-1800865-HTML/image_m/fmed-13-1800865-g004.jpg Figure 4. ROC curve analysis. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1805164/full https://www.frontiersin.org/files/Articles/1805164/fcell-14-1805164-HTML/image_m/fcell-14-1805164-g001.jpg Figure 1. Inhibition of vacuole fusion by LPLs. Vacuole fusion reactions were treated with concentra https://www.frontiersin.org/files/Articles/1805164/fcell-14-1805164-HTML/image_m/fcell-14-1805164-g002.jpg Figure 2. LPLs do not affect Vps33 enrichment at vertex microdomains. Purified vacuoles containing V https://www.frontiersin.org/files/Articles/1805164/fcell-14-1805164-HTML/image_m/fcell-14-1805164-g003.jpg Figure 3. Effect of LPLs on Ca2+ uptake. Vacuoles were isolated from BJ3505 and 2X fusion reactions https://www.frontiersin.org/files/Articles/1805164/fcell-14-1805164-HTML/image_m/fcell-14-1805164-g004.jpg Figure 4. Effect of LPLs on Ca2+ efflux. Vacuoles from BJ3505 were incubated as 2X fusion reactions https://www.frontiersin.org/files/Articles/1805164/fcell-14-1805164-HTML/image_m/fcell-14-1805164-g005.jpg Figure 5. Effect of LPLs on initial vacuole acidification. BJ3505 vacuoles fusion reactions (2X) wer https://www.frontiersin.org/files/Articles/1805164/fcell-14-1805164-HTML/image_m/fcell-14-1805164-g006.jpg Figure 6. Effect of LPLs on maintaining vacuole acidification. Vacuoles from BJ3505 were incubated a https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1757576/full https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-g001.jpg Figure 1. Experimental timeline. Timeline of the differentiation process of C2C12 cells (A). Timelin https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-t001.jpg Table 1. Composition of mixture of SCFAs. https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-t002.jpg Table 2. List of sequences of PCR primers. https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-g002.jpg Figure 2. Gene expression response of Pgc1α and Tfam in C2C12 myotubes after exposure to acetate and https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-g003.jpg Figure 3. Gene expression response of Pgc1α and Tfam in C2C12 myotubes after exposure to acetate and https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-g004.jpg Figure 4. Gene expression responses of Pparα and Pparδ in C2C12 myotubes after exposure to acetate a https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-g005.jpg Figure 5. Gene expression response of Pparα and Pparδ in C2C12 myotubes after exposure to acetate an https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-g006.jpg Figure 6. Gene expression response of Myh7, Myh2, Myh1, and Myh4 in C2C12 myotubes after exposure to https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-g007.jpg Figure 7. MyHC I and MyHC II fold change in C2C12 myotubes after exposure to acetate and a mixture o https://www.frontiersin.org/files/Articles/1757576/fphys-17-1757576-HTML/image_m/fphys-17-1757576-g008.jpg Figure 8. MyHC I and MyHC II fold change compared with 0-mM SCFA exposure in C2C12 myotubes after ex https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1731276/full https://www.frontiersin.org/files/Articles/1731276/fonc-16-1731276-HTML/image_m/fonc-16-1731276-g001.jpg Figure 1. (A) Frequency of glioma subtypes according to the 2007 WHO classification in a cohort of 2 https://www.frontiersin.org/files/Articles/1731276/fonc-16-1731276-HTML/image_m/fonc-16-1731276-g002.jpg Figure 2. The figure depicts the annual distribution of gliomas operated in our center. Blue bars de https://www.frontiersin.org/files/Articles/1731276/fonc-16-1731276-HTML/image_m/fonc-16-1731276-g003.jpg Figure 3. The figure displays the annual frequency of testing (tests performed with either positive https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1785458/full https://www.frontiersin.org/files/Articles/1785458/fcvm-13-1785458-HTML/image_m/fcvm-13-1785458-g001.jpg Figure 1. STROBE flow diagram of patient enrollment. https://www.frontiersin.org/files/Articles/1785458/fcvm-13-1785458-HTML/image_m/fcvm-13-1785458-t001.jpg Table 1. Basic information. Comparison of baseline data between the dapagliflozin group and the cont https://www.frontiersin.org/files/Articles/1785458/fcvm-13-1785458-HTML/image_m/fcvm-13-1785458-t002.jpg Table 2. Observation indicators. Comparison of pre- and post-treatment data in the dapagliflozin gro https://www.frontiersin.org/files/Articles/1785458/fcvm-13-1785458-HTML/image_m/fcvm-13-1785458-t003.jpg Table 3. Observation indicators. Comparison of pre- and post-treatment data for the control group. https://www.frontiersin.org/files/Articles/1785458/fcvm-13-1785458-HTML/image_m/fcvm-13-1785458-t004.jpg Table 4. Observation indicators. Comparison of post-treatment data between the dapagliflozin group a https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2026.1712500/full https://www.frontiersin.org/files/Articles/1712500/fragi-07-1712500-HTML/image_m/fragi-07-1712500-g001.jpg Figure 1. Flowchart illustrating sample attrition and response frequency. https://www.frontiersin.org/files/Articles/1712500/fragi-07-1712500-HTML/image_m/fragi-07-1712500-t001.jpg Table 1. Response rates per survey phase. https://www.frontiersin.org/files/Articles/1712500/fragi-07-1712500-HTML/image_m/fragi-07-1712500-t002.jpg Table 2. Response frequency on closed-ended questions. https://www.frontiersin.org/files/Articles/1712500/fragi-07-1712500-HTML/image_m/fragi-07-1712500-t003.jpg Table 3. Participants’ satisfaction grouped by experience-related and benefit-related questions. https://www.frontiersin.org/files/Articles/1712500/fragi-07-1712500-HTML/image_m/fragi-07-1712500-g002.jpg Figure 2. Frequency of open-ended responses grouped by (sub-) themes. *not specified = different sta https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1771789/full https://www.frontiersin.org/files/Articles/1771789/fnagi-18-1771789-HTML/image_m/fnagi-18-1771789-t001.jpg Table 1. Instrument, assessment types, and variable names of the included constructs. https://www.frontiersin.org/files/Articles/1771789/fnagi-18-1771789-HTML/image_m/fnagi-18-1771789-t002.jpg Table 2. Key characteristics. https://www.frontiersin.org/files/Articles/1771789/fnagi-18-1771789-HTML/image_m/fnagi-18-1771789-t003.jpg Table 3. Baseline characteristics of non-carriers and carriers of variants considered as PD-risk or https://www.frontiersin.org/files/Articles/1771789/fnagi-18-1771789-HTML/image_m/fnagi-18-1771789-g001.jpg Figure 1. Association of variants considered PD-risk and severe variants with progression of non-mot https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1785729/full https://www.frontiersin.org/files/Articles/1785729/fmed-13-1785729-HTML/image_m/fmed-13-1785729-t001.jpg Table 1. Sample characteristics. https://www.frontiersin.org/files/Articles/1785729/fmed-13-1785729-HTML/image_m/fmed-13-1785729-t002.jpg Table 2. Instrument reliability analysis. https://www.frontiersin.org/files/Articles/1785729/fmed-13-1785729-HTML/image_m/fmed-13-1785729-g001.jpg Figure 1. Structural equation model (PLS-SEM) used to evaluate the measurement structure of the inst https://www.frontiersin.org/files/Articles/1785729/fmed-13-1785729-HTML/image_m/fmed-13-1785729-t003.jpg Table 3. Unstandardized factor loadings of the measurement model for dimensions related to the use o https://www.frontiersin.org/files/Articles/1785729/fmed-13-1785729-HTML/image_m/fmed-13-1785729-t004.jpg Table 4. Confirmatory factor model of the dimensions of the use of clinical simulators. https://www.frontiersin.org/files/Articles/1785729/fmed-13-1785729-HTML/image_m/fmed-13-1785729-t005.jpg Table 5. Evaluation of the structural model for convergent and discriminant validity of the instrume https://www.frontiersin.org/files/Articles/1785729/fmed-13-1785729-HTML/image_m/fmed-13-1785729-t006.jpg Table 6. Internal reliability and consistency analysis of the dimensions of the instrument on the us https://www.frontiersin.org/files/Articles/1785729/fmed-13-1785729-HTML/image_m/fmed-13-1785729-t007.jpg Table 7. Structural model of the impact of the use of clinical simulators on the development of prac https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1725594/full https://www.frontiersin.org/files/Articles/1725594/frai-08-1725594-HTML-r1/image_m/frai-08-1725594-g001.jpg Figure 9 https://www.frontiersin.org/files/Articles/1725594/frai-08-1725594-HTML-r1/image_m/frai-08-1725594-g002.jpg Figure 10 https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1698123/full https://www.frontiersin.org/files/Articles/1698123/fams-11-1698123-HTML-r1/image_m/fams-11-1698123-g001.jpg Figure 1. Conceptual model on the relationship between drought, human health and diseases. https://www.frontiersin.org/files/Articles/1698123/fams-11-1698123-HTML-r1/image_m/fams-11-1698123-g002.jpg Figure 2. Map showing southern African countries that were included in the review. https://www.frontiersin.org/files/Articles/1698123/fams-11-1698123-HTML-r1/image_m/fams-11-1698123-g003.jpg Figure 3. PRISMA diagram for literature search. Source: Author. https://www.frontiersin.org/files/Articles/1698123/fams-11-1698123-HTML-r1/image_m/fams-11-1698123-g004.jpg Figure 4. Center for disease control framework in BRACE. Adopted from Hess et al. [86]. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1677685/full https://www.frontiersin.org/files/Articles/1677685/fmed-12-1677685-HTML/image_m/fmed-12-1677685-g001.jpg Figure 1. Targeting cerebral ischemia via the bone-brain axis: therapeutic potential of Sinomenine. https://www.frontiersin.org/files/Articles/1677685/fmed-12-1677685-HTML/image_m/fmed-12-1677685-g002.jpg Figure 2. Pathophysiological mechanisms of neurovascular injury in cerebral ischemia. Neurovascular https://www.frontiersin.org/files/Articles/1677685/fmed-12-1677685-HTML/image_m/fmed-12-1677685-g003.jpg Figure 3. The pathological relationship between cerebral ischemia and the bone-brain axis. https://www.frontiersin.org/files/Articles/1677685/fmed-12-1677685-HTML/image_m/fmed-12-1677685-g004.jpg Figure 4. Pharmacokinetics and physicochemical properties of sinomenine. https://www.frontiersin.org/files/Articles/1677685/fmed-12-1677685-HTML/image_m/fmed-12-1677685-t001.jpg Table 1. Summary of the cardiovascular protective effects exerted by SIN. https://www.frontiersin.org/files/Articles/1677685/fmed-12-1677685-HTML/image_m/fmed-12-1677685-g005.jpg Figure 5. The pathological relationship between cerebral ischemia and the bone-brain axis. https://www.frontiersin.org/files/Articles/1677685/fmed-12-1677685-HTML/image_m/fmed-12-1677685-g006.jpg Figure 6. Potential mechanisms of SIN in treating cerebral ischemia through the bone-brain axis. https://www.frontiersin.org/files/Articles/1677685/fmed-12-1677685-HTML/image_m/fmed-12-1677685-t002.jpg Table 2. Application of sinomenine nano-delivery system in the treatment of cerebral ischemia. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1716827/full https://www.frontiersin.org/files/Articles/1716827/feduc-11-1716827-HTML/image_m/feduc-11-1716827-t001.jpg Table 1. Sociodemographic characteristics of the sample. https://www.frontiersin.org/files/Articles/1716827/feduc-11-1716827-HTML/image_m/feduc-11-1716827-g001.jpg Figure 1. (A) Ranking of reasons for parents’ concerns about their child’s development (the lower th https://www.frontiersin.org/files/Articles/1716827/feduc-11-1716827-HTML/image_m/feduc-11-1716827-g002.jpg Figure 2. (A) Greatest challenges in caring for a child with developmental delay. (B) Greatest chall https://www.frontiersin.org/files/Articles/1716827/feduc-11-1716827-HTML/image_m/feduc-11-1716827-t002.jpg Table 2. Group differences (ASD vs. NDD) in caregiving challenges and family priorities: Chi-square https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2026.1735411/full https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-g001.jpg Figure 1. Machine learning methodology flowchart for recreational therapy participation prediction. https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-t001.jpg Table 1. Summary of participant characteristics (N = 56). https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-t002.jpg Table 2. Self-reported frequency of participation in recreational therapy activities (N = 56). https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-t003.jpg Table 3. Top predictive features identified using SelectKBest with f_classif scoring. https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-t004.jpg Table 4. Machine learning model performance for high participation prediction (n = 57). https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-g002.jpg Figure 2. Learning curves for overfitting detection - high participation prediction [learning curves https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-g003.jpg Figure 3. Random forest model performance - high participation prediction (four-panel figure showing https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-t005.jpg Table 5. Class-weighted machine learning model performance for any participation prediction (n = 57) https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-g004.jpg Figure 4. Learning curves for class-weighted models - any participation prediction learning curves s https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-g005.jpg Figure 5. Class-weighted Random Forest model performance - any participation prediction. Four-panel https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-g006.jpg Figure 6. Feature importance analysis - high participation prediction Gini importance scores from Ra https://www.frontiersin.org/files/Articles/1735411/frhs-06-1735411-HTML/image_m/frhs-06-1735411-g007.jpg Figure 7. Feature importance analysis - any participation prediction Gini importance scores from cla https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1783588/full https://www.frontiersin.org/files/Articles/1783588/fimmu-17-1783588-HTML-r1/image_m/fimmu-17-1783588-g001.jpg Figure 1. Primary epigenetic mechanisms regulating Open/Closed chromatin state transitions and gene https://www.frontiersin.org/files/Articles/1783588/fimmu-17-1783588-HTML-r1/image_m/fimmu-17-1783588-g002.jpg Figure 2. Main epigenetic effectors: writers, erasers and readers, involved in chromatin conformatio https://www.frontiersin.org/files/Articles/1783588/fimmu-17-1783588-HTML-r1/image_m/fimmu-17-1783588-t001.jpg Table 1. Epigenetic effectors and affected pathways entailed in KCs function. https://www.frontiersin.org/files/Articles/1783588/fimmu-17-1783588-HTML-r1/image_m/fimmu-17-1783588-t002.jpg Table 2. Epigenetic effectors and affected pathways entailed in LSECs function. https://www.frontiersin.org/files/Articles/1783588/fimmu-17-1783588-HTML-r1/image_m/fimmu-17-1783588-t003.jpg Table 3. Epigenetic effectors and affected pathways entailed in DCs function. https://www.frontiersin.org/files/Articles/1783588/fimmu-17-1783588-HTML-r1/image_m/fimmu-17-1783588-g003.jpg Figure 3. Epigenetic mechanisms with evidence reported in human hAPCs. Epigenetic gene expression re https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1767112/full https://www.frontiersin.org/files/Articles/1767112/fped-14-1767112-HTML/image_m/fped-14-1767112-g001.jpg Figure 1. systemic-to-pulmonary shunt consists of a conduit connecting the right subclavian artery t https://www.frontiersin.org/files/Articles/1767112/fped-14-1767112-HTML/image_m/fped-14-1767112-t001.jpg Table 1. Recommended diameter for systemic-to-pulmonary artery shunt (16). https://www.frontiersin.org/files/Articles/1767112/fped-14-1767112-HTML/image_m/fped-14-1767112-t002.jpg Table 2. Comparative characteristics of the main systemic-to-pulmonary shunts. https://www.frontiersin.org/files/Articles/1767112/fped-14-1767112-HTML/image_m/fped-14-1767112-t003.jpg Table 3. Comparison between systemic-to-pulmonary shunt and ductal stent placement. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1700552/full https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-g001.jpg Figure 1. Characteristic panoramic views of Area A (left) and Area B (bottom) used in the experiment https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t001.jpg Table 1. Participants’ familiarity with each video. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-g002.jpg Figure 2. The diagram of the familiarity slider (Illustrated in English; Chinese version applied dur https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t002.jpg Table 2. Main HRV metrics in this study. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-g003.jpg Figure 3. Experimental equipment. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-g004.jpg Figure 4. Experimental procedure. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t003.jpg Table 3. Various statistical test methods. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t004.jpg Table 4. Parameters of multiple linear regression model. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t005.jpg Table 5. Correlation between the PANAS scale and HRV metrics. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t006.jpg Table 6. Correlation between big five personality traits and PANAS scores. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t007.jpg Table 7. Data analysis results of PANAS scale across familiarity levels. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t008.jpg Table 8. Comparative analysis of PPG data under different familiarity levels. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t009.jpg Table 9. Correlation between personality traits and the PANAS across familiarity degrees. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t010.jpg Table 10. Statistical comparison of PANAS item scores between high- and low-familiarity urban parks https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-g005.jpg Figure 5. PANAS items with significant differences in different subgroups. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-t011.jpg Table 11. Regression analysis results. https://www.frontiersin.org/files/Articles/1700552/fpsyg-17-1700552-HTML/image_m/fpsyg-17-1700552-g006.jpg Figure 6. Residual distribution of the model. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1791804/full https://www.frontiersin.org/files/Articles/1791804/fpsyg-17-1791804-HTML/image_m/fpsyg-17-1791804-g001.jpg Figure 1. The hypothesized model of career planning clarity affecting learning engagement. https://www.frontiersin.org/files/Articles/1791804/fpsyg-17-1791804-HTML/image_m/fpsyg-17-1791804-t001.jpg Table 1. Descriptive statistics and correlation analysis among variables. https://www.frontiersin.org/files/Articles/1791804/fpsyg-17-1791804-HTML/image_m/fpsyg-17-1791804-t002.jpg Table 2. Regression analysis of variables in the model. https://www.frontiersin.org/files/Articles/1791804/fpsyg-17-1791804-HTML/image_m/fpsyg-17-1791804-t003.jpg Table 3. Test of the chain mediating effect of self-efficacy and learning motivation. https://www.frontiersin.org/files/Articles/1791804/fpsyg-17-1791804-HTML/image_m/fpsyg-17-1791804-g002.jpg Figure 2. The chain mediating effect of self-efficacy and learning motivation. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1708828/full https://www.frontiersin.org/files/Articles/1708828/fcimb-15-1708828-HTML/image_m/fcimb-15-1708828-t001.jpg Table 1. Differential expression and acting target of lncRNA and circRNA that promote viral immune e https://www.frontiersin.org/files/Articles/1708828/fcimb-15-1708828-HTML/image_m/fcimb-15-1708828-g001.jpg Figure 1. Major proposed working mechanisms of long non-coding RNAs (lncRNAs). (a) guiding ribonucle https://www.frontiersin.org/files/Articles/1708828/fcimb-15-1708828-HTML/image_m/fcimb-15-1708828-g002.jpg Figure 2. Main biological functions of circular non-coding RNAs (circRNAs). (a) miRNA sponging throu https://www.frontiersin.org/files/Articles/1708828/fcimb-15-1708828-HTML/image_m/fcimb-15-1708828-g003.jpg Figure 3. Schematic diagram of the roles that long non-coding RNAs (lncRNAs) and circular non-coding https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2026.1666241/full https://www.frontiersin.org/files/Articles/1666241/falgy-07-1666241-HTML-r1/image_m/falgy-07-1666241-t001.jpg Table 1. Demographic characteristics of participants included in the analysis and frequency of use o https://www.frontiersin.org/files/Articles/1666241/falgy-07-1666241-HTML-r1/image_m/falgy-07-1666241-t002.jpg Table 2. Reasons to use, training needs, and barriers in implementing patient-reported outcome measu https://www.frontiersin.org/files/Articles/1666241/falgy-07-1666241-HTML-r1/image_m/falgy-07-1666241-t003.jpg Table 3. Frequency of use of specific PROMs across asthma, allergic rhinitis, and chronic rhinosinus https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1671806/full https://www.frontiersin.org/files/Articles/1671806/fimmu-17-1671806-HTML/image_m/fimmu-17-1671806-g001.jpg Figure 1. Basic information and structure of TBX21. (A) Location of the TBX21 gene on the human chro https://www.frontiersin.org/files/Articles/1671806/fimmu-17-1671806-HTML/image_m/fimmu-17-1671806-g002.jpg Figure 2. T-bet is associated with the development of multiple immune cells. (A) Mechanism of T-bet https://www.frontiersin.org/files/Articles/1671806/fimmu-17-1671806-HTML/image_m/fimmu-17-1671806-g003.jpg Figure 3. The relationship between single nucleotide polymorphisms (SNPs) in the TBX21 gene and the https://www.frontiersin.org/files/Articles/1671806/fimmu-17-1671806-HTML/image_m/fimmu-17-1671806-g004.jpg Figure 4. Role of T-bet in non-tumorigenic diseases. https://www.frontiersin.org/files/Articles/1671806/fimmu-17-1671806-HTML/image_m/fimmu-17-1671806-g005.jpg Figure 5. The function and molecular mechanisms of T-bet in tumor pathologies. https://www.frontiersin.org/files/Articles/1671806/fimmu-17-1671806-HTML/image_m/fimmu-17-1671806-g006.jpg Figure 6. The role of T-bet-associated immune cells and signaling pathways in the process of tumorig https://www.frontiersin.org/files/Articles/1671806/fimmu-17-1671806-HTML/image_m/fimmu-17-1671806-g007.jpg Figure 7. Immunotherapeutic approaches associated with T-bet. https://www.frontiersin.org/files/Articles/1671806/fimmu-17-1671806-HTML/image_m/fimmu-17-1671806-g008.jpg Figure 8. Proposed model of T-bet functions, mechanisms and applications. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1753305/full https://www.frontiersin.org/files/Articles/1753305/fpls-17-1753305-HTML/image_m/fpls-17-1753305-g001.jpg Figure 1. Chromosomal distribution of MsDCL, MsAGO, and MsRDR genes in the alfalfa genome. Chromosom https://www.frontiersin.org/files/Articles/1753305/fpls-17-1753305-HTML/image_m/fpls-17-1753305-g002.jpg Figure 2. (A) Phylogenetic tree of the DCL gene family. (B) Phylogenetic tree of the AGO gene family https://www.frontiersin.org/files/Articles/1753305/fpls-17-1753305-HTML/image_m/fpls-17-1753305-g003.jpg Figure 3. Syntenic relationships of MsAGO genes in alfalfa. The outermost colored blocks represent t https://www.frontiersin.org/files/Articles/1753305/fpls-17-1753305-HTML/image_m/fpls-17-1753305-g004.jpg Figure 4. (A) Collinearity analysis of MsDCL, MsAGO and MsRDR genes between M. sativa and A. thalian https://www.frontiersin.org/files/Articles/1753305/fpls-17-1753305-HTML/image_m/fpls-17-1753305-g005.jpg Figure 5. Distribution of hormone- and stress-responsive cis-elements in the promoter regions of MsD https://www.frontiersin.org/files/Articles/1753305/fpls-17-1753305-HTML/image_m/fpls-17-1753305-g006.jpg Figure 6. Relative expression levels of RNA silencing pathway genes across six alfalfa tissues. The https://www.frontiersin.org/files/Articles/1753305/fpls-17-1753305-HTML/image_m/fpls-17-1753305-g007.jpg Figure 7. Expression profiles of six selected MsDCL, MsAGO, and MsRDR genes under salt and drought s https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1794792/full https://www.frontiersin.org/files/Articles/1794792/fonc-16-1794792-HTML-r1/image_m/fonc-16-1794792-g001.jpg Figure 1. Three complementary approaches of the Cuídalas Program, Preventive, Predictive, and Care-O https://www.frontiersin.org/files/Articles/1794792/fonc-16-1794792-HTML-r1/image_m/fonc-16-1794792-g002.jpg Figure 2. Cuídalas Program screening pathway: from population at risk to definitive diagnosis, integ https://www.frontiersin.org/files/Articles/1794792/fonc-16-1794792-HTML-r1/image_m/fonc-16-1794792-t001.jpg Table 1. Preliminary operational results of the Cuídalas program (March 2023 - September 2024). https://www.frontiersin.org/files/Articles/1794792/fonc-16-1794792-HTML-r1/image_m/fonc-16-1794792-g003.jpg Figure 3. Comparative analysis of breast cancer screening modalities by opportunity, cost, test comp https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2025.1694007/full https://www.frontiersin.org/files/Articles/1694007/fpain-06-1694007-HTML/image_m/fpain-06-1694007-t001.jpg Table 1. Organization of the study analysis including study aims and sub-aims along with the relevan https://www.frontiersin.org/files/Articles/1694007/fpain-06-1694007-HTML/image_m/fpain-06-1694007-t002.jpg Table 2. Participant demographic characteristics and select baseline measures in the mITT analysis S https://www.frontiersin.org/files/Articles/1694007/fpain-06-1694007-HTML/image_m/fpain-06-1694007-t003.jpg Table 3. RelieVRx between-group differences at end-of-treatment and 12-months post-treatment for the https://www.frontiersin.org/files/Articles/1694007/fpain-06-1694007-HTML/image_m/fpain-06-1694007-t004.jpg Table 4. RelieVRx pain impact group differences from baseline to end-of-treatment and baseline to 12 https://www.frontiersin.org/files/Articles/1694007/fpain-06-1694007-HTML/image_m/fpain-06-1694007-t005.jpg Table 5. VR program engagement in a real-world clinical sample of 2,400+ prescribed patients vs. a L https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2026.1783018/full https://www.frontiersin.org/files/Articles/1783018/fdmed-07-1783018-HTML/image_m/fdmed-07-1783018-g001.jpg Figure 1. Flow diagram of participant inclusion and data availability. https://www.frontiersin.org/files/Articles/1783018/fdmed-07-1783018-HTML/image_m/fdmed-07-1783018-t001.jpg Table 1. Patient-reported motivation and perceptions . https://www.frontiersin.org/files/Articles/1783018/fdmed-07-1783018-HTML/image_m/fdmed-07-1783018-t002.jpg Table 2. Associations with age group and gender . https://www.frontiersin.org/files/Articles/1783018/fdmed-07-1783018-HTML/image_m/fdmed-07-1783018-t003.jpg Table 3. Adherence outcomes. https://www.frontiersin.org/files/Articles/1783018/fdmed-07-1783018-HTML/image_m/fdmed-07-1783018-t004.jpg Table 4. Parent/guardian-reported perceptions. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1802842/full https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-g001.jpg Figure 1. Flowchart of participant selection. https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-t001.jpg Table 1. Comparison of baseline demographic characteristics, clinical features, and imaging data amo https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-t002.jpg Table 2. Comparison of cognitive function, imaging markers, and blood biomarkers across disease stag https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-t003.jpg Table 3. Predictive performance of pairwise classification models for different disease stages under https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-t004.jpg Table 4. Baseline characteristics of MCI conversion and non-conversion groups [mean ± SD, n (%), n = https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-t005.jpg Table 5. Predictive performance of models with different feature combinations for MCI-to-AD conversi https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-t006.jpg Table 6. Multivariable logistic regression analysis of predictors for MCI-to-AD conversion. https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-g002.jpg Figure 2. Calibration curve and decision curve analysis of the full model for predicting conversion https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-t007.jpg Table 7. Baseline characteristics and hippocampal volume comparison among different disease stages i https://www.frontiersin.org/files/Articles/1802842/fnagi-18-1802842-HTML-r1/image_m/fnagi-18-1802842-t008.jpg Table 8. Results of the thin-slice MRI sub-cohort analysis (n = 182). https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1739508/full https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-g001.jpg Figure 1. A schematic diagram of model development. https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-g002.jpg Figure 2. Basic characteristics of study participants. (A1, A2) Pittsburgh Sleep Quality Index (PSQI https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-t001.jpg Table 1. General information of rral cancer patients (n=385). https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-t002.jpg Table 2. Baseline comparison between the model training and validation sets (n = 385). https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-t003.jpg Table 3. Variables with statistically significant differences in the training set (n = 269). https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-t004.jpg Table 4. Assignment of Independent Variables. https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-t005.jpg Table 5. Multivariate Logistic Regression Analysis of the Training Set. https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-g003.jpg Figure 3. ROC curve of the prediction model, ROC curves for training/validation sets showed excellen https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-g004.jpg Figure 4. Calibration curve of the prediction model, Internal validation calibration curve (1,000 Bo https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-g005.jpg Figure 5. Decision Curve of the Prediction Model,Model yielded substantial net clinical benefit at t https://www.frontiersin.org/files/Articles/1739508/fonc-16-1739508-HTML/image_m/fonc-16-1739508-g006.jpg Figure 6. Nomogram of the Prediction Model,Built in R 4.3.3 using multivariate logistic regression-d https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1623250/full https://www.frontiersin.org/files/Articles/1623250/fbioe-13-1623250-HTML-r2/image_m/fbioe-13-1623250-g001.jpg Figure 1. Finite-element model of intact C2–T1 and components (MO). https://www.frontiersin.org/files/Articles/1623250/fbioe-13-1623250-HTML-r2/image_m/fbioe-13-1623250-t001.jpg Table 1. Material properties of model components. https://www.frontiersin.org/files/Articles/1623250/fbioe-13-1623250-HTML-r2/image_m/fbioe-13-1623250-g002.jpg Figure 2. Schematic diagram of the M1–M8 surgical model. (A1–A7) Schematic diagram of the bone defec https://www.frontiersin.org/files/Articles/1623250/fbioe-13-1623250-HTML-r2/image_m/fbioe-13-1623250-g003.jpg Figure 3. Comparison of the ROM of the intact three-dimensional finite-element models of C2–T1 with https://www.frontiersin.org/files/Articles/1623250/fbioe-13-1623250-HTML-r2/image_m/fbioe-13-1623250-g004.jpg Figure 4. The ROM and △ROM for C4/5–C6/7 segment among different groups during various movements. https://www.frontiersin.org/files/Articles/1623250/fbioe-13-1623250-HTML-r2/image_m/fbioe-13-1623250-g005.jpg Figure 5. (A) C6 pedicle stress during flexion (MPa). (B) C6 pedicle stress during extension (MPa). https://www.frontiersin.org/files/Articles/1623250/fbioe-13-1623250-HTML-r2/image_m/fbioe-13-1623250-g006.jpg Figure 6. C6 facet joint stress and changes of the number. https://www.frontiersin.org/files/Articles/1623250/fbioe-13-1623250-HTML-r2/image_m/fbioe-13-1623250-g007.jpg Figure 7. C5/6 nucleus pulposus stress and annulus fibrosus stress. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1770102/full https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-g001.jpg Figure 1. Schematic illustration of miRNA-mediated regulation of CD8+ T cell function in respiratory https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t001.jpg Table 1. miRNA-mediated regulation of CD8+ T cell function in respiratory system tumors. https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-g002.jpg Figure 2. Schematic representation of miRNA-mediated regulation of the immune microenvironment in di https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t002.jpg Table 2. miRNA-mediated regulation of CD8+ T cell function in digestive system tumors. https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-g003.jpg Figure 3. Schematic illustration of miRNA-mediated immune regulation in gynecological tumors. The up https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t003.jpg Table 3. miRNA-mediated regulation of CD8+ T cell function in gynecology tumors. https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t004.jpg Table 4. miRNA-mediated regulation of CD8+ T cell function in hematological tumors and neurological https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t005.jpg Table 5. miRNA-mediated regulation of CD8+ T cell function in urinary system tumors, head and neck t https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t006.jpg Table 6. miRNA-mediated regulation of CD8+ T cell function in melanoma. https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-g004.jpg Figure 4. Diverse miRNA delivery systems for tumor immunotherapy: carrier platforms and immune regul https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t007.jpg Table 7. Key information of miRNA delivery systems in tumors (miRNA, action location, regulatory pat https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-g005.jpg Figure 5. Drug-mediated miRNA regulation in cancer cells and CD8+ T cells for tumor immunotherapy. I https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t008.jpg Table 8. Key information of drugs regulating miRNAs to impact tumors and CD8+T cell. https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t009.jpg Table 9. Key information of non-miRNA RNAs regulating miRNAs and affecting tumors & CD8+T cells. https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-g006.jpg Figure 6. Nucleic acid drug-mediated miRNA network regulation in tumor immunotherapy across multiple https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t010.jpg Table 10. Pan-cancer miRNAs: key regulators of CD8+ T cell function with conserved roles across mult https://www.frontiersin.org/files/Articles/1770102/fimmu-17-1770102-HTML/image_m/fimmu-17-1770102-t011.jpg Table 11. High-confidence miRNA targets: independently validated by multiple studies. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1754026/full https://www.frontiersin.org/files/Articles/1754026/fpubh-14-1754026-HTML-r1/image_m/fpubh-14-1754026-g001.jpg Figure 1. Conceptual framework. https://www.frontiersin.org/files/Articles/1754026/fpubh-14-1754026-HTML-r1/image_m/fpubh-14-1754026-g002.jpg Figure 2. Theoretical framework. https://www.frontiersin.org/files/Articles/1754026/fpubh-14-1754026-HTML-r1/image_m/fpubh-14-1754026-g003.jpg Figure 3. Thematic analysis framework. https://www.frontiersin.org/files/Articles/1754026/fpubh-14-1754026-HTML-r1/image_m/fpubh-14-1754026-t001.jpg Table 1. Demographics of the participants (N = 27). https://www.frontiersin.org/files/Articles/1754026/fpubh-14-1754026-HTML-r1/image_m/fpubh-14-1754026-t002.jpg Table 2. Sub-themes analysis. https://www.frontiersin.org/files/Articles/1754026/fpubh-14-1754026-HTML-r1/image_m/fpubh-14-1754026-t003.jpg Table 3. Main themes analysis. https://www.frontiersin.org/files/Articles/1754026/fpubh-14-1754026-HTML-r1/image_m/fpubh-14-1754026-g004.jpg Figure 4. Main extracted themes. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1814069/full https://www.frontiersin.org/files/Articles/1814069/fped-14-1814069-HTML/image_m/fped-14-1814069-g001.jpg Figure 1. Multilevel barriers to telemedicine use among new immigrant children and their families. https://www.frontiersin.org/files/Articles/1814069/fped-14-1814069-HTML/image_m/fped-14-1814069-t001.jpg Table 1. Telemedicine: insight, evidence, gaps and future prospects. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1822069/full https://www.frontiersin.org/files/Articles/1822069/fendo-17-1822069-HTML/image_m/fendo-17-1822069-t001.jpg Table 1. Summary of exercise modalities and their clinical benefits in type 2 diabetes management. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1814063/full https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-g001.jpg Figure 1. Study selection flowchart following PRISMA guidelines. https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-t001.jpg Table 1. Search strategy components used in the systematic review. https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-t002.jpg Table 2. Database-specific search strings used in the systematic review. https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-t003.jpg Table 3. Inclusion and exclusion criteria for study selection. https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-g002.jpg Figure 2. Flow diagram of study selection process. https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-g003.jpg Figure 3. Categorization of barriers to equitable maternal healthcare access identified in the syste https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-g004.jpg Figure 4. Financial barriers to maternal healthcare access in the United States. This figure illustr https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-g005.jpg Figure 5. Systematic barriers to maternal healthcare access. The diagram illustrates the four major https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-g006.jpg Figure 6. Maternal health outcomes and disparities emerging from healthcare access barriers. The dia https://www.frontiersin.org/files/Articles/1814063/fpubh-14-1814063-HTML/image_m/fpubh-14-1814063-g007.jpg Figure 7. Interventions for reducing maternal health disparities identified in the systematic review https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1816599/full https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g001.jpg Figure 1. Study cohort flow diagram. https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-t001.jpg Table 1. Baseline characteristics of the development and validation sets. https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g002.jpg Figure 2. 5-Fold cross-validation: metric distributions. Box plots show the distributions of AUC, ac https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g003.jpg Figure 3. Hyperparameter tuning improves key performance metrics of the hypoglycemia risk prediction https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-t002.jpg Table 2. Model performance on external validation. https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g004.jpg Figure 4. Calibration plot of the random forest model. Calibration curve demonstrating agreement bet https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g005.jpg Figure 5. Precision-recall curve of the random forest model for hypoglycemia risk prediction. Precis https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g006.jpg Figure 6. Decision curve analysis of the random forest model for hypoglycemia risk prediction in hos https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-t003.jpg Table 3. Clinical risk stratification in validation. https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g007.jpg Figure 7. Clinical risk stratification performance Clinical risk stratification using prediction pro https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-t004.jpg Table 4. Feature importance from SHAP analysis. https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g008.jpg Figure 8. Restricted cubic spline plots illustrating nonlinear relationships between key continuous https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-t005.jpg Table 5. Priority risk triage categories based on biomarker abnormality flags. https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-t006.jpg Table 6. Joint biomarker effects. https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g009.jpg Figure 9. Monitoring bias and hypoglycemia incidence in T1DM. Abnormal biomarkers trigger clinical v https://www.frontiersin.org/files/Articles/1816599/fendo-17-1816599-HTML-r3/image_m/fendo-17-1816599-g010.jpg Figure 10. Decision curve analysis comparing the random forest model with biomarker abnormality-base https://www.frontiersin.org/journals/radiology/articles/10.3389/fradi.2025.1661522/full https://www.frontiersin.org/files/Articles/1661522/fradi-05-1661522-HTML/image_m/fradi-05-1661522-g001.jpg Figure 1. The graph represents the eligibility criteria and screening for included and excluded stud https://www.frontiersin.org/files/Articles/1661522/fradi-05-1661522-HTML/image_m/fradi-05-1661522-t001.jpg Table 1. Sociodemographic characteristics of the included participants. https://www.frontiersin.org/files/Articles/1661522/fradi-05-1661522-HTML/image_m/fradi-05-1661522-t002.jpg Table 2. Clinical Characteristics and Diagnostic Outcomes of Included Studies. https://www.frontiersin.org/files/Articles/1661522/fradi-05-1661522-HTML/image_m/fradi-05-1661522-t003.jpg Table 3. Detailed JBI risk of bias assessment. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1730300/full https://www.frontiersin.org/files/Articles/1730300/fmicb-17-1730300-HTML/image_m/fmicb-17-1730300-t001.jpg Table 1. Laboratory and clinical indexes of brucellosis patients. https://www.frontiersin.org/files/Articles/1730300/fmicb-17-1730300-HTML/image_m/fmicb-17-1730300-g001.jpg Figure 1. Illustrates the gating strategy for MDSCs and their subgroups in HC group, ABI group, and https://www.frontiersin.org/files/Articles/1730300/fmicb-17-1730300-HTML/image_m/fmicb-17-1730300-g002.jpg Figure 2. Correlation analysis between TLR4+MDSCs level and PD-L1+MDSCs levels and serum ALT, AST, A https://www.frontiersin.org/files/Articles/1730300/fmicb-17-1730300-HTML/image_m/fmicb-17-1730300-g003.jpg Figure 3. Histopathological staining of mouse organs, alongside bacterial colonization assessments. https://www.frontiersin.org/files/Articles/1730300/fmicb-17-1730300-HTML/image_m/fmicb-17-1730300-g004.jpg Figure 4. mIF staining results of mouse lungs, livers and spleens. (A) mIF staining in acute liver; https://www.frontiersin.org/files/Articles/1730300/fmicb-17-1730300-HTML/image_m/fmicb-17-1730300-g005.jpg Figure 5. (A) Flow cytometry results of liver MDSC in mice; (B) low cytometry results of lung MDSC i https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1708246/full https://www.frontiersin.org/files/Articles/1708246/fpubh-13-1708246-HTML-r3/image_m/fpubh-13-1708246-t001.jpg Table 1. Snapshot of intervention components and their implementation. https://www.frontiersin.org/files/Articles/1708246/fpubh-13-1708246-HTML-r3/image_m/fpubh-13-1708246-g001.jpg Figure 1. Illustration of implementation model (M0 model) for suicide prevention: Potential mechanis https://www.frontiersin.org/files/Articles/1708246/fpubh-13-1708246-HTML-r3/image_m/fpubh-13-1708246-t002.jpg Table 2. Logic model for comprehensive mental well-being package scale up. https://www.frontiersin.org/files/Articles/1708246/fpubh-13-1708246-HTML-r3/image_m/fpubh-13-1708246-g002.jpg Figure 2. SPIRIT figure of study phases. https://www.frontiersin.org/files/Articles/1708246/fpubh-13-1708246-HTML-r3/image_m/fpubh-13-1708246-g003.jpg Figure 3. Adapted from the Updated Consolidated Framework for Implementation Research. Adapted with https://www.frontiersin.org/files/Articles/1708246/fpubh-13-1708246-HTML-r3/image_m/fpubh-13-1708246-g004.jpg Figure 4. Flowchart for implementation and evaluation of Mref model. https://www.frontiersin.org/files/Articles/1708246/fpubh-13-1708246-HTML-r3/image_m/fpubh-13-1708246-t003.jpg Table 3. Description of study timelines for assessments. https://www.frontiersin.org/files/Articles/1708246/fpubh-13-1708246-HTML-r3/image_m/fpubh-13-1708246-t004.jpg Table 4. Implementation outcomes and data sources. https://www.frontiersin.org/files/Articles/1708246/fpubh-13-1708246-HTML-r3/image_m/fpubh-13-1708246-t005.jpg Table 5. Study timeline and Gantt chart. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1822959/full https://www.frontiersin.org/files/Articles/1822959/fmed-13-1822959-HTML/image_m/fmed-13-1822959-t001.jpg Table 1. Summary of baseline characteristics in children undergoing lower eyelid entropion surgery. https://www.frontiersin.org/files/Articles/1822959/fmed-13-1822959-HTML/image_m/fmed-13-1822959-t002.jpg Table 2. Correlation analysis between severity of lower eyelid entropion and refractive status. https://www.frontiersin.org/files/Articles/1822959/fmed-13-1822959-HTML/image_m/fmed-13-1822959-t003.jpg Table 3. Changes in cylinder power over time following lower eyelid entropion correction surgery. https://www.frontiersin.org/files/Articles/1822959/fmed-13-1822959-HTML/image_m/fmed-13-1822959-g001.jpg Figure 1. Changes in cylinder power over time following lower eyelid entropion correction surgery. https://www.frontiersin.org/files/Articles/1822959/fmed-13-1822959-HTML/image_m/fmed-13-1822959-t004.jpg Table 4. Temporal changes in corneal astigmatism following lower eyelid entropion correction surgery https://www.frontiersin.org/files/Articles/1822959/fmed-13-1822959-HTML/image_m/fmed-13-1822959-g002.jpg Figure 2. Temporal changes in corneal astigmatism following lower eyelid entropion correction surger https://www.frontiersin.org/files/Articles/1822959/fmed-13-1822959-HTML/image_m/fmed-13-1822959-g003.jpg Figure 3. Postoperative stabilization point of corneal astigmatism in pediatric patients with lower https://www.frontiersin.org/files/Articles/1822959/fmed-13-1822959-HTML/image_m/fmed-13-1822959-t005.jpg Table 5. Curve of best corrected visual acuity changes over time following lower eyelid entropion su https://www.frontiersin.org/files/Articles/1822959/fmed-13-1822959-HTML/image_m/fmed-13-1822959-g004.jpg Figure 4. Curve of best corrected visual acuity changes over time following lower eyelid entropion s https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1658319/full https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-t001.jpg Table 1. Ingredient and chemical composition of experimental diets on DM basis. https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-t002.jpg Table 2. Primers used in RT-qPCR. https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-g001.jpg Figure 1. Bar graphs comparing growth performance and meat quality in three categories: L, M, and H. https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-g002.jpg Figure 2. Panel (a) shows histological images of muscle fiber cross sections for conditions L, M, an https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-g003.jpg Figure 3. Quality assessment of epigenomics high-throughput sequencing data from Tibetan sheep muslc https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-g004.jpg Figure 4. Genome-wide distribution bias of ATAC-Seq and H3K27ac CUT&Tag peaks. (a) The distributed p https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-g005.jpg Figure 5. Identification and feature comparison of enhancers and promoters. (a) Enrichment for GO BP https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-g006.jpg Figure 6. Identification and functional analysis of up- and downregulated enhancers. (a) Up- and dow https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-g007.jpg Figure 7. TF motif enrichment analysis of up- and downregulated enhancers. (a) Top 30 enriched TF mo https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-t003.jpg Table 3. Comparison of quality indicators for sequencing different samples. https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-g008.jpg Figure 8. Transcriptomic analysis reveals differentially expressed genes associated with muscle deve https://www.frontiersin.org/files/Articles/1658319/fnut-12-1658319-HTML/image_m/fnut-12-1658319-g009.jpg Figure 9. Proposed molecular mechanism of NFC/NDF ratio-mediated skeletal muscle development in Tibe https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1681919/full https://www.frontiersin.org/files/Articles/1681919/fpls-16-1681919-HTML/image_m/fpls-16-1681919-g001.jpg Figure 1. NLR genes number, classification and localization in the chromosomes of A. officinalis, A. https://www.frontiersin.org/files/Articles/1681919/fpls-16-1681919-HTML/image_m/fpls-16-1681919-g002.jpg Figure 2. The domain pattern and exon-intron structure of NLR genes in (A) A. officinalis, (B) A. ki https://www.frontiersin.org/files/Articles/1681919/fpls-16-1681919-HTML/image_m/fpls-16-1681919-g003.jpg Figure 3. The conserved motifs of NLR gene family members (A) and the distribution of these conserve https://www.frontiersin.org/files/Articles/1681919/fpls-16-1681919-HTML/image_m/fpls-16-1681919-g004.jpg Figure 4. Diversity of NLR genes in three species. (A) Phylogenetic analysis of NLR genes from A. of https://www.frontiersin.org/files/Articles/1681919/fpls-16-1681919-HTML/image_m/fpls-16-1681919-g005.jpg Figure 5. Contraction events of NLR genes in A. officinalis. (A) Collinearity analyses of NLR genes https://www.frontiersin.org/files/Articles/1681919/fpls-16-1681919-HTML/image_m/fpls-16-1681919-g006.jpg Figure 6. Stem blight resistance of A. officinalis and A. setaceus and expression analysis of aspara https://www.frontiersin.org/journals/drug-delivery/articles/10.3389/fddev.2026.1771095/full https://www.frontiersin.org/files/Articles/1771095/fddev-06-1771095-HTML-r1/image_m/fddev-06-1771095-g001.jpg Figure 1. Flow chart of study selection. The screening, full text review and extraction were managed https://www.frontiersin.org/files/Articles/1771095/fddev-06-1771095-HTML-r1/image_m/fddev-06-1771095-g002.jpg Figure 2. Overview of TXA delivery strategies in trauma care, including conventional IV administrati https://www.frontiersin.org/files/Articles/1771095/fddev-06-1771095-HTML-r1/image_m/fddev-06-1771095-t001.jpg Table 1. Biomaterials for local delivery of TXA. https://www.frontiersin.org/files/Articles/1771095/fddev-06-1771095-HTML-r1/image_m/fddev-06-1771095-t002.jpg Table 2. Comparison of pharmacokinetics between IV and alternative route TXA in human and swine. https://www.frontiersin.org/files/Articles/1771095/fddev-06-1771095-HTML-r1/image_m/fddev-06-1771095-g003.jpg Figure 3. Comparison of key pharmacokinetic parameters among administration routes. Data were pooled https://www.frontiersin.org/files/Articles/1771095/fddev-06-1771095-HTML-r1/image_m/fddev-06-1771095-t003.jpg Table 3. Roles of biomaterials as intrinsic hemostatic agents in local TXA delivery systems. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1710461/full https://www.frontiersin.org/files/Articles/1710461/fmed-13-1710461-HTML/image_m/fmed-13-1710461-t001.jpg Table 1. Clinical characteristics of EIAS patients and controls. https://www.frontiersin.org/files/Articles/1710461/fmed-13-1710461-HTML/image_m/fmed-13-1710461-t002.jpg Table 2. Laboratory parameters between EIAS patients and controls. https://www.frontiersin.org/files/Articles/1710461/fmed-13-1710461-HTML/image_m/fmed-13-1710461-g001.jpg Figure 1. Fasting insulin/C-peptide ratio between IAA patients and controls. https://www.frontiersin.org/files/Articles/1710461/fmed-13-1710461-HTML/image_m/fmed-13-1710461-g002.jpg Figure 2. ROC of fasting insulin to C-peptide ratio in the diagnosis of EIAS. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1754172/full https://www.frontiersin.org/files/Articles/1754172/fphar-17-1754172-HTML-r1/image_m/fphar-17-1754172-g001.jpg Figure 1. Network pharmacology and functional enrichment analysis of GAA. (A) Input the overlapping https://www.frontiersin.org/files/Articles/1754172/fphar-17-1754172-HTML-r1/image_m/fphar-17-1754172-g002.jpg Figure 2. Validation of core targets in an SRLI model (GSE217695). (A) Heatmap of liver gene express https://www.frontiersin.org/files/Articles/1754172/fphar-17-1754172-HTML-r1/image_m/fphar-17-1754172-g003.jpg Figure 3. Molecular Docking of Primary Targets of GAA. (A) Molecular docking visualization of TNFα w https://www.frontiersin.org/files/Articles/1754172/fphar-17-1754172-HTML-r1/image_m/fphar-17-1754172-g004.jpg Figure 4. Biophysical Validation of the GAA-TNFα Interaction via SPR and Molecular Dynamics Simulati https://www.frontiersin.org/files/Articles/1754172/fphar-17-1754172-HTML-r1/image_m/fphar-17-1754172-g005.jpg Figure 5. GAA inhibits TNFα-driven inflammation in macrophages by suppressing NF-κB signaling. (A) U https://www.frontiersin.org/files/Articles/1754172/fphar-17-1754172-HTML-r1/image_m/fphar-17-1754172-g006.jpg Figure 6. GAA Ameliorates Septic Liver Injury by Targeting the TNFα/NF-κB Axis. (A) Schematic of the https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1729653/full https://www.frontiersin.org/files/Articles/1729653/fpubh-14-1729653-HTML/image_m/fpubh-14-1729653-t001.jpg Table 1. The global burden of IHD attributable to dietary risks among young adults in 1990 and 2021, https://www.frontiersin.org/files/Articles/1729653/fpubh-14-1729653-HTML/image_m/fpubh-14-1729653-g001.jpg Figure 1. Global gender-specific trends in dietary risk-attributable IHD burden in 1990–2021. https://www.frontiersin.org/files/Articles/1729653/fpubh-14-1729653-HTML/image_m/fpubh-14-1729653-t002.jpg Table 2. The burden of IHD attributable to dietary risks among young adults in 21 GBD regions and 5 https://www.frontiersin.org/files/Articles/1729653/fpubh-14-1729653-HTML/image_m/fpubh-14-1729653-g002.jpg Figure 2. Proportion of IHD mortality rate (A) and DALYs rate (B) attributable to 13 dietary risks a https://www.frontiersin.org/files/Articles/1729653/fpubh-14-1729653-HTML/image_m/fpubh-14-1729653-g003.jpg Figure 3. The EAPC of IHD attributable to 13 dietary risks among young adults by SDI regions from 19 https://www.frontiersin.org/files/Articles/1729653/fpubh-14-1729653-HTML/image_m/fpubh-14-1729653-g004.jpg Figure 4. The mortality rate (A) and DALYs rate (B) in 2021, and the EAPC of the mortality rate (C) https://www.frontiersin.org/files/Articles/1729653/fpubh-14-1729653-HTML/image_m/fpubh-14-1729653-g005.jpg Figure 5. Predicted global trends of IHD attributable to dietary risks among young adults over the n https://www.frontiersin.org/journals/ophthalmology/articles/10.3389/fopht.2025.1660483/full https://www.frontiersin.org/files/Articles/1660483/fopht-05-1660483-HTML/image_m/fopht-05-1660483-t001.jpg Table 1. Presents a breakdown of the stages of the treatment in IOD patients, which require specific https://www.frontiersin.org/files/Articles/1660483/fopht-05-1660483-HTML/image_m/fopht-05-1660483-g001.jpg Figure 1. Ethical framework flow diagram. This flow diagram outlines the ethical processes involved https://www.frontiersin.org/files/Articles/1660483/fopht-05-1660483-HTML/image_m/fopht-05-1660483-g002.jpg Figure 2. Family pedigree for an affected family, providing the preventive action for affected and c https://www.frontiersin.org/files/Articles/1660483/fopht-05-1660483-HTML/image_m/fopht-05-1660483-g003.jpg Figure 3. AI integration pipeline diagram. This diagram illustrates the data flow and decision-makin https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1835866/full https://www.frontiersin.org/files/Articles/1835866/fendo-17-1835866-HTML/image_m/fendo-17-1835866-g001.jpg Figure 1. Workflow for predicting occult DKD via clinlabomics combined with machine learning. (A) Fl https://www.frontiersin.org/files/Articles/1835866/fendo-17-1835866-HTML/image_m/fendo-17-1835866-t001.jpg Table 1. Baseline demographic, clinical, and laboratory characteristics of the training cohort. https://www.frontiersin.org/files/Articles/1835866/fendo-17-1835866-HTML/image_m/fendo-17-1835866-g002.jpg Figure 2. Model comparison and feature selection for occult DKD prediction. (A) AUCs of the eight ML https://www.frontiersin.org/files/Articles/1835866/fendo-17-1835866-HTML/image_m/fendo-17-1835866-g003.jpg Figure 3. Global explanation of the final 8-feature LR model. (A, B) SHAP summary plots displaying t https://www.frontiersin.org/files/Articles/1835866/fendo-17-1835866-HTML/image_m/fendo-17-1835866-g004.jpg Figure 4. Local explanation of the LR model for individualized prediction. (A) SHAP heatmap visualiz https://www.frontiersin.org/files/Articles/1835866/fendo-17-1835866-HTML/image_m/fendo-17-1835866-g005.jpg Figure 5. Clinical utility and application of the optimal LR model. (A) Net benefits of the LR model https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1740447/full https://www.frontiersin.org/files/Articles/1740447/fendo-16-1740447-HTML/image_m/fendo-16-1740447-g001.jpg Figure 1. Flowchart of patient selection. https://www.frontiersin.org/files/Articles/1740447/fendo-16-1740447-HTML/image_m/fendo-16-1740447-g002.jpg Figure 2. Relationship between HGI and HbA1c in the study cohort. https://www.frontiersin.org/files/Articles/1740447/fendo-16-1740447-HTML/image_m/fendo-16-1740447-t001.jpg Table 1. Patient baseline information. https://www.frontiersin.org/files/Articles/1740447/fendo-16-1740447-HTML/image_m/fendo-16-1740447-g003.jpg Figure 3. Pairwise differences in HGI across heart-failure phenotypes. https://www.frontiersin.org/files/Articles/1740447/fendo-16-1740447-HTML/image_m/fendo-16-1740447-t002.jpg Table 2. Association between HGI and EF-stratified HF phenotype. https://www.frontiersin.org/files/Articles/1740447/fendo-16-1740447-HTML/image_m/fendo-16-1740447-g004.jpg Figure 4. RCS analysis of the association between HGI and HF phenotype and in-hospital WHF (Models 1 https://www.frontiersin.org/files/Articles/1740447/fendo-16-1740447-HTML/image_m/fendo-16-1740447-t003.jpg Table 3. Association between HGI and in-hospital WHF: binary logistic regression. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1760909/full https://www.frontiersin.org/files/Articles/1760909/fonc-16-1760909-HTML/image_m/fonc-16-1760909-t001.jpg Table 1. Baseline characteristics of the patients V90% represents the percentage of the total target https://www.frontiersin.org/files/Articles/1760909/fonc-16-1760909-HTML/image_m/fonc-16-1760909-g001.jpg Figure 1. Schematic diagram of the field arrangement for radiotherapy planning. https://www.frontiersin.org/files/Articles/1760909/fonc-16-1760909-HTML/image_m/fonc-16-1760909-g002.jpg Figure 2. TBI target area. The red line segment in the figure encloses the PTV of the TBI target are https://www.frontiersin.org/files/Articles/1760909/fonc-16-1760909-HTML/image_m/fonc-16-1760909-t002.jpg Table 2. Dosimetric results for the 12 Gy in 6 fractions regimen. https://www.frontiersin.org/files/Articles/1760909/fonc-16-1760909-HTML/image_m/fonc-16-1760909-t003.jpg Table 3. Dosimetric results for the 3 Gy in 1 fractions regimen. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1756374/full https://www.frontiersin.org/files/Articles/1756374/fneur-17-1756374-HTML/image_m/fneur-17-1756374-g001.jpg Figure 1. Study flowchart. IA, intracranial aneurysm; PED, flow diverter; FU-DSA, follow up digital https://www.frontiersin.org/files/Articles/1756374/fneur-17-1756374-HTML/image_m/fneur-17-1756374-t001.jpg Table 1. Clinical characteristics and morphology and hemodynamic parameters in the training and vali https://www.frontiersin.org/files/Articles/1756374/fneur-17-1756374-HTML/image_m/fneur-17-1756374-g002.jpg Figure 2. Selection of potential predictive factors using the least absolute shrinkage and selection https://www.frontiersin.org/files/Articles/1756374/fneur-17-1756374-HTML/image_m/fneur-17-1756374-t002.jpg Table 2. Multivariate logistic regression analysis. https://www.frontiersin.org/files/Articles/1756374/fneur-17-1756374-HTML/image_m/fneur-17-1756374-g003.jpg Figure 3. Hemodynamics-based nomogram predicting the risk of incomplete occlusion of intracranial an https://www.frontiersin.org/files/Articles/1756374/fneur-17-1756374-HTML/image_m/fneur-17-1756374-g004.jpg Figure 4. The receiver operating characteristic (ROC) curve, the calibration curve, and the decision https://www.frontiersin.org/files/Articles/1756374/fneur-17-1756374-HTML/image_m/fneur-17-1756374-g005.jpg Figure 5. A representative case of an incompletely occluded intracranial aneurysm treated with a pip https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1669695/full https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-g001.jpg Figure 1. Theoretical framework of interaction between digital village construction and rural health https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-t001.jpg Table 1. Indicator system for digital village construction. https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-t002.jpg Table 2. Indicator system for the efficiency of rural healthcare services. https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-t003.jpg Table 3. Hierarchy of CCD between digital village development and rural healthcare service efficienc https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-t004.jpg Table 4. Factors affecting the CCD between two systems. https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-t005.jpg Table 5. Level of digital village construction, level of rural healthcare service efficiency, and th https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-g002.jpg Figure 2. Spatial distribution of CCD in 29 Chinese provinces, (a) 2015, (b) 2018, (c) 2020, (d) 202 https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-t006.jpg Table 6. Gini coefficient and decomposition of the CCD between two systems from 2015 to 2022. https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-g003.jpg Figure 3. Dynamic evolution of CCD between two systems. (a) National, (b) East, (c) Central, (d) Wes https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-t007.jpg Table 7. Traditional Markov transfer probability matrix for the CCD in China, 2015–2022. https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-t008.jpg Table 8. Spatial Markov transfer probability matrix for the CCD in China, 2015–2022. https://www.frontiersin.org/files/Articles/1669695/fpubh-13-1669695-HTML/image_m/fpubh-13-1669695-t009.jpg Table 9. Regression analysis of quantile results of influencing factors. https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2026.1678660/full https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-g001.jpg Figure 1. PRISMA diagram. CBM, Chinese biological medicine database; CENTRAL, Cochrane central regis https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-t001.jpg Table 1. Characteristics of included studies. https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-g002.jpg Figure 2. (a) risk of bias summary. (b) Risk of bias graph. https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-g007.jpg . https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-g003.jpg Figure 3. Forest plots of Tai Chi on chronic musculoskeletal pain. B, brief pain inventory; F, fibro https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-g004.jpg Figure 4. Subgroup analysis of Tai Chi on chronic musculoskeletal pain by disease category. B, Brief https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-g005.jpg Figure 5. Subgroup analysis of Tai Chi on chronic musculoskeletal pain by control type. B, brief pai https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-g006.jpg Figure 6. Funnel plot for included studies. FM, fibromyalgia; LBP, low back pain; OA, osteoarthritis https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-t002.jpg Table 2. Adverse events reporting form. https://www.frontiersin.org/files/Articles/1678660/fpain-07-1678660-HTML/image_m/fpain-07-1678660-t003.jpg Table 3. Quality of evidence of primary outcomes. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1765221/full https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g001.jpg Figure 1. The flow chart of analyzing public datasets. https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-t001.jpg Table 1. Detailed information of the GEO datasets used for key genes identification of the cardiac a https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g002.jpg Figure 2. Shared hallmarks and differentially expressed inflammatory fibrosis genes in heart failure https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g003.jpg Figure 3. Shared inflammatory fibrosis DEGs function enrichments and key diagnostic genes selection. https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g004.jpg Figure 4. Major cell types during heart and kidney tissue fibrosis. (A) Uniform Manifold Approximati https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g005.jpg Figure 5. IL-10 and PHLDA1 expression patterns and key regulon network selection in macrophages duri https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g006.jpg Figure 6. PHLDA1 and MAFF were significantly up-regulated in M1 macrophages during cardiac fibrosis. https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g007.jpg Figure 7. Cell communication analysis comparing heart failure (HF) and control groups reveals a sign https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g008.jpg Figure 8. PHLDA1 promotes renal fibrosis by regulating the crosstalk between proximal tubular epithe https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g009.jpg Figure 9. PHLDA1 regulates the inflammation levels of CD10+ PTE kidney cells via the NF-κB pathway. https://www.frontiersin.org/files/Articles/1765221/fimmu-17-1765221-HTML-r1/image_m/fimmu-17-1765221-g010.jpg Figure 10. Enhanced PTE-fibroblast communication in renal fibrosis via IL-1 signaling. (A) Global Ce https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1647853/full https://www.frontiersin.org/files/Articles/1647853/fsufs-09-1647853-HTML/image_m/fsufs-09-1647853-g001.jpg Figure 1. Research framework. https://www.frontiersin.org/files/Articles/1647853/fsufs-09-1647853-HTML/image_m/fsufs-09-1647853-g002.jpg Figure 2. Visual maps of different regional divisions. (a) Based on agricultural zoning and the gove https://www.frontiersin.org/files/Articles/1647853/fsufs-09-1647853-HTML/image_m/fsufs-09-1647853-t001.jpg Table 1. Emission coefficient for agricultural inputs in corn carbon and nitrogen footprint calculat https://www.frontiersin.org/files/Articles/1647853/fsufs-09-1647853-HTML/image_m/fsufs-09-1647853-g003.jpg Figure 3. Changes in corn planting area from 2014 to 2023 in the four major corn-producing regions. https://www.frontiersin.org/files/Articles/1647853/fsufs-09-1647853-HTML/image_m/fsufs-09-1647853-g004.jpg Figure 4. Changes in weighted carbon footprint per unit area in the four major corn-producing region https://www.frontiersin.org/files/Articles/1647853/fsufs-09-1647853-HTML/image_m/fsufs-09-1647853-g005.jpg Figure 5. Changes in weighted nitrogen footprint per unit area in the four major corn-producing regi https://www.frontiersin.org/files/Articles/1647853/fsufs-09-1647853-HTML/image_m/fsufs-09-1647853-t002.jpg Table 2. Weighted carbon footprint per unit area under different scenarios (kg CE/ha). https://www.frontiersin.org/files/Articles/1647853/fsufs-09-1647853-HTML/image_m/fsufs-09-1647853-t003.jpg Table 3. Weighted nitrogen footprint per unit area regions under different scenarios (kg N-eq/ha). https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1768388/full https://www.frontiersin.org/files/Articles/1768388/fmed-13-1768388-HTML-r1/image_m/fmed-13-1768388-g001.jpg Figure 1. AI-empowered intelligent teaching (AIEIT) sytem architecture. Created with BioRender.com. https://www.frontiersin.org/files/Articles/1768388/fmed-13-1768388-HTML-r1/image_m/fmed-13-1768388-t001.jpg Table 1. Baseline characteristics of participants. https://www.frontiersin.org/files/Articles/1768388/fmed-13-1768388-HTML-r1/image_m/fmed-13-1768388-g002.jpg Figure 2. Comprehensive Evaluation of Core Competencies. (A–D) Box plots showing the accuracy of mol https://www.frontiersin.org/files/Articles/1768388/fmed-13-1768388-HTML-r1/image_m/fmed-13-1768388-t002.jpg Table 2. Comparative assessment of training outcomes. https://www.frontiersin.org/files/Articles/1768388/fmed-13-1768388-HTML-r1/image_m/fmed-13-1768388-t003.jpg Table 3. Comparative analysis of professional competency, collaboration, and well-being. https://www.frontiersin.org/files/Articles/1768388/fmed-13-1768388-HTML-r1/image_m/fmed-13-1768388-g003.jpg Figure 3. Learning analytics and cognitive efficiency assessment. (A) Weekly learning time. Box plot https://www.frontiersin.org/files/Articles/1768388/fmed-13-1768388-HTML-r1/image_m/fmed-13-1768388-g004.jpg Figure 4. Long-term outcomes and professional development assessment. (A) Knowledge retention over t https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1680824/full https://www.frontiersin.org/files/Articles/1680824/fped-14-1680824-HTML/image_m/fped-14-1680824-g001.jpg Figure 1. Flowchart depicting the study population and the development and validation of the nomogra https://www.frontiersin.org/files/Articles/1680824/fped-14-1680824-HTML/image_m/fped-14-1680824-t001.jpg Table 1. Baseline characteristics of All patients in the training and validation sets. https://www.frontiersin.org/files/Articles/1680824/fped-14-1680824-HTML/image_m/fped-14-1680824-t002.jpg Table 2. Univariate logistic regression analysis of predictors for BPD in preterm infants in the tra https://www.frontiersin.org/files/Articles/1680824/fped-14-1680824-HTML/image_m/fped-14-1680824-t003.jpg Table 3. Multivariate logistic regression analysis of the predictive factors. https://www.frontiersin.org/files/Articles/1680824/fped-14-1680824-HTML/image_m/fped-14-1680824-g002.jpg Figure 2. A nomogram model for predicting the occurrence of BPD during hospitalization among very pr https://www.frontiersin.org/files/Articles/1680824/fped-14-1680824-HTML/image_m/fped-14-1680824-g003.jpg Figure 3. Validation of the prediction model using the receiver operating characteristic (ROC) curve https://www.frontiersin.org/files/Articles/1680824/fped-14-1680824-HTML/image_m/fped-14-1680824-g004.jpg Figure 4. The calibration scatter plot of the predictive model. (A) Training set(n=801); (B) Interna https://www.frontiersin.org/files/Articles/1680824/fped-14-1680824-HTML/image_m/fped-14-1680824-g005.jpg Figure 5. Decision curve analysis of the predictive model. (A) Training set(n=801); (B) Internal val https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1653442/full https://www.frontiersin.org/files/Articles/1653442/fimmu-16-1653442-HTML/image_m/fimmu-16-1653442-g001.jpg Figure 1. Metabolic reprogramming in colorectal cancer liver metastasis. The relationship between th https://www.frontiersin.org/files/Articles/1653442/fimmu-16-1653442-HTML/image_m/fimmu-16-1653442-t001.jpg Table 1. Mechanistic links between core components of metabolic syndrome and colorectal cancer liver https://www.frontiersin.org/files/Articles/1653442/fimmu-16-1653442-HTML/image_m/fimmu-16-1653442-t002.jpg Table 2. Synergistic mechanisms between metabolic syndrome components in driving colorectal cancer l https://www.frontiersin.org/files/Articles/1653442/fimmu-16-1653442-HTML/image_m/fimmu-16-1653442-t003.jpg Table 3. Application of metabolomics in the study of colorectal cancer liver metastasis. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1745307/full https://www.frontiersin.org/files/Articles/1745307/fonc-16-1745307-HTML/image_m/fonc-16-1745307-g001.jpg Figure 1. Patient flowchart. ESD, endoscopic submucosal dissection; EGC, early gastric cancer; MEGCs https://www.frontiersin.org/files/Articles/1745307/fonc-16-1745307-HTML/image_m/fonc-16-1745307-g002.jpg Figure 2. Process of endoscopic MEGCs. MEGCs, missed early gastric cancers; ESD, endoscopic submucos https://www.frontiersin.org/files/Articles/1745307/fonc-16-1745307-HTML/image_m/fonc-16-1745307-t001.jpg Table 1. Clinicopathologic characteristics of missed early gastric cancers and EGCs without MEGCs. https://www.frontiersin.org/files/Articles/1745307/fonc-16-1745307-HTML/image_m/fonc-16-1745307-g003.jpg Figure 3. Classification of MEGCs pre-ESD. MEGC, missed early gastric cancer. ESD, endoscopic submuc https://www.frontiersin.org/files/Articles/1745307/fonc-16-1745307-HTML/image_m/fonc-16-1745307-g004.jpg Figure 4. Classification of MEGCs post-ESD. MEGC, missed early gastric cancer. ESD, endoscopic submu https://www.frontiersin.org/files/Articles/1745307/fonc-16-1745307-HTML/image_m/fonc-16-1745307-t002.jpg Table 2. Logistic regression analysis of risk factors associated with inadequate observation pre-ESD https://www.frontiersin.org/files/Articles/1745307/fonc-16-1745307-HTML/image_m/fonc-16-1745307-t003.jpg Table 3. Logistic regression analysis of risk factors associated with diagnosis error post-ESD. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1784420/full https://www.frontiersin.org/files/Articles/1784420/fmed-13-1784420-HTML/image_m/fmed-13-1784420-g001.jpg Figure 1. Flow chart for patients undergoing hysterectomy. The diagram illustrates the exclusion of https://www.frontiersin.org/files/Articles/1784420/fmed-13-1784420-HTML/image_m/fmed-13-1784420-t001.jpg Table 1. Demographic and clinical characteristics of the study population (n = 296). https://www.frontiersin.org/files/Articles/1784420/fmed-13-1784420-HTML/image_m/fmed-13-1784420-g002.jpg Figure 2. Distribution of AUB according to the PALM–COEIN classification and PMB. AUB, abnormal uter https://www.frontiersin.org/files/Articles/1784420/fmed-13-1784420-HTML/image_m/fmed-13-1784420-t002.jpg Table 2. Concordance and diagnostic performance of pre-operative endometrial biopsy with final hyste https://www.frontiersin.org/files/Articles/1784420/fmed-13-1784420-HTML/image_m/fmed-13-1784420-g003.jpg Figure 3. ROC curves of pre-operative endometrial biopsy findings for predicting final hysterectomy https://www.frontiersin.org/files/Articles/1784420/fmed-13-1784420-HTML/image_m/fmed-13-1784420-t003.jpg Table 3. Comparison of pre-operative endometrial biopsy and final endometrial histopathology in hyst https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1804849/full https://www.frontiersin.org/files/Articles/1804849/fmed-13-1804849-HTML/image_m/fmed-13-1804849-t001.jpg Table 1. Interview guide on patients’ experience with telemedicine. https://www.frontiersin.org/files/Articles/1804849/fmed-13-1804849-HTML/image_m/fmed-13-1804849-t002.jpg Table 2. Participant characteristics. https://www.frontiersin.org/files/Articles/1804849/fmed-13-1804849-HTML/image_m/fmed-13-1804849-g001.jpg Figure 1. Infographic summarizing key findings from a qualitative study on telemedicine in heart fai https://www.frontiersin.org/files/Articles/1804849/fmed-13-1804849-HTML/image_m/fmed-13-1804849-t003.jpg Table 3. Findings of qualitative analysis. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1642639/full https://www.frontiersin.org/files/Articles/1642639/fimmu-16-1642639-HTML/image_m/fimmu-16-1642639-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1642639/fimmu-16-1642639-HTML/image_m/fimmu-16-1642639-g001.jpg Figure 1. Overview on the multifaceted application and characteristics of extracellular vesicles in https://www.frontiersin.org/files/Articles/1642639/fimmu-16-1642639-HTML/image_m/fimmu-16-1642639-t001.jpg Table 1. Summary of commonly used techniques for EV isolation and characterization, highlighting key https://www.frontiersin.org/files/Articles/1642639/fimmu-16-1642639-HTML/image_m/fimmu-16-1642639-t002.jpg Table 2. Comparative overview of extracellular vesicle (EV) subtypes. https://www.frontiersin.org/files/Articles/1642639/fimmu-16-1642639-HTML/image_m/fimmu-16-1642639-g002.jpg Figure 2. Mechanisms by which HNSCC-derived EV promote tumor progression, immune suppression, and th https://www.frontiersin.org/files/Articles/1642639/fimmu-16-1642639-HTML/image_m/fimmu-16-1642639-g003.jpg Figure 3. Therapeutic strategies for targeting EVs in HNSCC. This figure summarizes new and establis https://www.frontiersin.org/journals/transplantation/articles/10.3389/frtra.2025.1714886/full https://www.frontiersin.org/files/Articles/1714886/frtra-04-1714886-HTML/image_m/frtra-04-1714886-g001.jpg Figure 1. PRISMA 2020 flowchart. https://www.frontiersin.org/files/Articles/1714886/frtra-04-1714886-HTML/image_m/frtra-04-1714886-t001.jpg Table 1. General study information. https://www.frontiersin.org/files/Articles/1714886/frtra-04-1714886-HTML/image_m/frtra-04-1714886-t002.jpg Table 2. Summary of immunosuppressive schemas, including induction and maintenance immunosuppression https://www.frontiersin.org/files/Articles/1714886/frtra-04-1714886-HTML/image_m/frtra-04-1714886-t003.jpg Table 3. Summary of infection prophylaxis and infectious complications. https://www.frontiersin.org/files/Articles/1714886/frtra-04-1714886-HTML/image_m/frtra-04-1714886-t004.jpg Table 4. Summary of rejection episodes in various immunosuppression schemas. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1738957/full https://www.frontiersin.org/files/Articles/1738957/fsurg-13-1738957-HTML/image_m/fsurg-13-1738957-i001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1738957/fsurg-13-1738957-HTML/image_m/fsurg-13-1738957-g001.jpg Figure 1. PRISMA 2020 flowchart highlighting the study selection process. https://www.frontiersin.org/files/Articles/1738957/fsurg-13-1738957-HTML/image_m/fsurg-13-1738957-t001.jpg Table 1. Patient demographics. https://www.frontiersin.org/files/Articles/1738957/fsurg-13-1738957-HTML/image_m/fsurg-13-1738957-t002.jpg Table 2. Nerve reconstruction details. https://www.frontiersin.org/files/Articles/1738957/fsurg-13-1738957-HTML/image_m/fsurg-13-1738957-t003.jpg Table 3. Complications after facial nerve reconstruction. https://www.frontiersin.org/files/Articles/1738957/fsurg-13-1738957-HTML/image_m/fsurg-13-1738957-t004.jpg Table 4. Preclinical evidence on facial nerve reconstruction in facial VCA. https://www.frontiersin.org/files/Articles/1738957/fsurg-13-1738957-HTML/image_m/fsurg-13-1738957-g002.jpg Figure 2. Facial-nerve repair in facial VCA—techniques, recovery arc, and clinical impact: direct br https://www.frontiersin.org/journals/transplantation/articles/10.3389/frtra.2026.1750905/full https://www.frontiersin.org/files/Articles/1750905/frtra-05-1750905-HTML/image_m/frtra-05-1750905-t001.jpg Table 1. Recipient demographics, medical, and immunological information. Reported as n (%), unless o https://www.frontiersin.org/files/Articles/1750905/frtra-05-1750905-HTML/image_m/frtra-05-1750905-t002.jpg Table 2. Surgical and transplant characteristics. https://www.frontiersin.org/files/Articles/1750905/frtra-05-1750905-HTML/image_m/frtra-05-1750905-t003.jpg Table 3. Donor demographics and immunological information. Reported as n (%), unless otherwise state https://www.frontiersin.org/files/Articles/1750905/frtra-05-1750905-HTML/image_m/frtra-05-1750905-t004.jpg Table 4. Postoperative outcomes in UTx transplant recipients. https://www.frontiersin.org/files/Articles/1750905/frtra-05-1750905-HTML/image_m/frtra-05-1750905-t005.jpg Table 5. Numerical risk-associated factors for complications. https://www.frontiersin.org/files/Articles/1750905/frtra-05-1750905-HTML/image_m/frtra-05-1750905-t006.jpg Table 6. Categorical risk-associated factors for complications. https://www.frontiersin.org/files/Articles/1750905/frtra-05-1750905-HTML/image_m/frtra-05-1750905-t007.jpg Table 7. Categorical results and post-hoc Dunn's test with Bonferroni correction of significant cate https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1732906/full https://www.frontiersin.org/files/Articles/1732906/fphar-17-1732906-HTML-r1/image_m/fphar-17-1732906-g002.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1732906/fphar-17-1732906-HTML-r1/image_m/fphar-17-1732906-g001.jpg Figure 1. (A) TSLP four-helix bundle structure with tezepelumab Fab blocking Site I/TSLPR-binding in https://www.frontiersin.org/files/Articles/1732906/fphar-17-1732906-HTML-r1/image_m/fphar-17-1732906-t001.jpg Table 1. Pharmacokinetic parameters of tezepelumab. SC, subcutaneous; Q4W, every 4 weeks; AUC, area https://www.frontiersin.org/files/Articles/1732906/fphar-17-1732906-HTML-r1/image_m/fphar-17-1732906-t002.jpg Table 2. Safety profile of tezepelumab (pooled data from Phase 2/3 trials). https://www.frontiersin.org/files/Articles/1732906/fphar-17-1732906-HTML-r1/image_m/fphar-17-1732906-t003.jpg Table 3. Systematic mechanistic comparison of approved biologics for severe asthma (revised: 6 colum https://www.frontiersin.org/files/Articles/1732906/fphar-17-1732906-HTML-r1/image_m/fphar-17-1732906-t004.jpg Table 4. Real-world evidence for tezepelumab: key studies and subgroup findings (new table, added in https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1726439/full https://www.frontiersin.org/files/Articles/1726439/fonc-15-1726439-HTML/image_m/fonc-15-1726439-t001.jpg Table 1. Baseline characteristics according to NOTCH1 mutational status. https://www.frontiersin.org/files/Articles/1726439/fonc-15-1726439-HTML/image_m/fonc-15-1726439-g001.jpg Figure 1. Impact of NOTCH1 mutation status on key clinical outcomes (A) Kaplan-Meier curves showing https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2025.1681039/full https://www.frontiersin.org/files/Articles/1681039/fnmol-18-1681039-HTML-r1/image_m/fnmol-18-1681039-g001.jpg Figure 1. Lipid droplet (LD) composition and structure. (A) Schematic representation of a LD. The co https://www.frontiersin.org/files/Articles/1681039/fnmol-18-1681039-HTML-r1/image_m/fnmol-18-1681039-g002.jpg Figure 2. LD biogenesis, key steps and proteins involved: schematic representation illustrating the https://www.frontiersin.org/files/Articles/1681039/fnmol-18-1681039-HTML-r1/image_m/fnmol-18-1681039-g003.jpg Figure 3. LD functions. Energy reservoir: LDs store neutral lipids and cholesterol, serving as energ https://www.frontiersin.org/files/Articles/1681039/fnmol-18-1681039-HTML-r1/image_m/fnmol-18-1681039-t001.jpg Table 1. Summary of the main LD functions. https://www.frontiersin.org/files/Articles/1681039/fnmol-18-1681039-HTML-r1/image_m/fnmol-18-1681039-g004.jpg Figure 4. LDs in the brain and their potential functions. Schematic representation of LDs size and f https://www.frontiersin.org/files/Articles/1681039/fnmol-18-1681039-HTML-r1/image_m/fnmol-18-1681039-g005.jpg Figure 5. α-Syn, lipids and LD interaction. (A) Schematic representation of α-Syn primary structure, https://www.frontiersin.org/files/Articles/1681039/fnmol-18-1681039-HTML-r1/image_m/fnmol-18-1681039-g006.jpg Figure 6. LDs involvement in PD. (A) Illustration of the interaction of LDs and α-Syn leading to its https://www.frontiersin.org/files/Articles/1681039/fnmol-18-1681039-HTML-r1/image_m/fnmol-18-1681039-g007.jpg Figure 7. LDs dysregulation in PD. (Left panel) Schematic representation of the neuroprotective path https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1780580/full https://www.frontiersin.org/files/Articles/1780580/fendo-17-1780580-HTML/image_m/fendo-17-1780580-g001.jpg Figure 1. PRISMA flow diagram of the study processes. https://www.frontiersin.org/files/Articles/1780580/fendo-17-1780580-HTML/image_m/fendo-17-1780580-t001.jpg Table 1. Characteristics of studies included in the network meta-analysis. https://www.frontiersin.org/files/Articles/1780580/fendo-17-1780580-HTML/image_m/fendo-17-1780580-g002.jpg Figure 2. Bias risk assessment of the RCTs. https://www.frontiersin.org/files/Articles/1780580/fendo-17-1780580-HTML/image_m/fendo-17-1780580-g003.jpg Figure 3. Network evidence plots for (A) healing rate, (B) healing time, (C) wound area reduction. T https://www.frontiersin.org/files/Articles/1780580/fendo-17-1780580-HTML/image_m/fendo-17-1780580-g004.jpg Figure 4. Forest plots for (A) healing rate; (B) healing time; (C) wound area reduction. https://www.frontiersin.org/files/Articles/1780580/fendo-17-1780580-HTML/image_m/fendo-17-1780580-g005.jpg Figure 5. Cumulative ranking curves (SUCRA plots) for (A) healing rate, (B) healing time, (C) wound https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1615563/full https://www.frontiersin.org/files/Articles/1615563/fped-13-1615563-HTML/image_m/fped-13-1615563-g001.jpg Figure 1. The ROC and AUC of GLS in identifying left ventricle dysfunction. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1797220/full https://www.frontiersin.org/files/Articles/1797220/fphar-17-1797220-HTML/image_m/fphar-17-1797220-g006.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1797220/fphar-17-1797220-HTML/image_m/fphar-17-1797220-g001.jpg Figure 1. Overview. https://www.frontiersin.org/files/Articles/1797220/fphar-17-1797220-HTML/image_m/fphar-17-1797220-g002.jpg Figure 2. Schematic diagram of CHMs modulating the NF-κB signaling pathway NF-κB: nuclear factor kap https://www.frontiersin.org/files/Articles/1797220/fphar-17-1797220-HTML/image_m/fphar-17-1797220-g003.jpg Figure 3. Crocin and Safranal through SIRT1-mediated antioxidant and anti-inflammatory effects CAT: https://www.frontiersin.org/files/Articles/1797220/fphar-17-1797220-HTML/image_m/fphar-17-1797220-g004.jpg Figure 4. PI3K: Tetrandrine and Kemengfang Exert Opposite Regulation on PI3K and AKT Phosphorylation https://www.frontiersin.org/files/Articles/1797220/fphar-17-1797220-HTML/image_m/fphar-17-1797220-g005.jpg Figure 5. Microbiome and Immunomodulatory Effects of CHMs 24h-UPE:24-h urine protein excretion TC: t https://www.frontiersin.org/files/Articles/1797220/fphar-17-1797220-HTML/image_m/fphar-17-1797220-t001.jpg Table 1. The mechanisms of CHMs to treat MN. https://www.frontiersin.org/files/Articles/1797220/fphar-17-1797220-HTML/image_m/fphar-17-1797220-t002.jpg Table 2. Summary of CHMs, active metabolites, dosages, and experimental models for MN treatment. https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1726469/full https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-g001.jpg Figure 1. Flow diagram in accordance with PRISMA statement (www.prisma-statement.org). https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-t001.jpg Table 1. Characteristics of the 23 studies included in the systematic review and meta-analysis. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-g002.jpg Figure 2. Forest plot for global cognition. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-t002.jpg Table 2. Results for subgroup analysis of general cognition. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-g003.jpg Figure 3. Forest plot for language ability. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-t003.jpg Table 3. Results for subgroup analysis of language. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-g004.jpg Figure 4. Forest plot for memory. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-t004.jpg Table 4. Results for subgroup analysis of memory. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-g005.jpg Figure 5. Forest plot for executive function. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-t005.jpg Table 5. Results for subgroup analysis of execution. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-g006.jpg Figure 6. Forest plot for emotional and psychological status. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-t006.jpg Table 6. Results for subgroup analysis of emotional and psychological status. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-g007.jpg Figure 7. Risk of bias graph. https://www.frontiersin.org/files/Articles/1726469/fnagi-18-1726469-HTML/image_m/fnagi-18-1726469-g008.jpg Figure 8. Risk of bias summary. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1823348/full https://www.frontiersin.org/files/Articles/1823348/fphar-17-1823348-HTML/image_m/fphar-17-1823348-t001.jpg Table 1. qRT-PCR primer sequence. https://www.frontiersin.org/files/Articles/1823348/fphar-17-1823348-HTML/image_m/fphar-17-1823348-g001.jpg Figure 1. TNF-α upregulates TRPM4 expression in HUVECs. (A,B) HUVECs were treated with increasing co https://www.frontiersin.org/files/Articles/1823348/fphar-17-1823348-HTML/image_m/fphar-17-1823348-g002.jpg Figure 2. Inhibition of TRPM4 attenuates TNF-α-induced inflammatory response in HUVECs. (A,B) HUVECs https://www.frontiersin.org/files/Articles/1823348/fphar-17-1823348-HTML/image_m/fphar-17-1823348-g003.jpg Figure 3. TRPM4 inhibition attenuates TNF-α-induced pyroptosis in HUVECs. (A–P) The mRNA expressions https://www.frontiersin.org/files/Articles/1823348/fphar-17-1823348-HTML/image_m/fphar-17-1823348-g004.jpg Figure 4. TRPM4 inhibition attenuates TNF-α-induced inflammatory response via HSP60 regulation. (A,B https://www.frontiersin.org/files/Articles/1823348/fphar-17-1823348-HTML/image_m/fphar-17-1823348-g005.jpg Figure 5. HSP60 knockdown attenuates TNF-α-induced pyroptosis in HUVECs. (A) Following transfection https://www.frontiersin.org/files/Articles/1823348/fphar-17-1823348-HTML/image_m/fphar-17-1823348-g006.jpg Figure 6. TRPM4 promotes NF-κB signaling by modulating the HSP60–IKKα/β interaction. (A) Following t https://www.frontiersin.org/files/Articles/1823348/fphar-17-1823348-HTML/image_m/fphar-17-1823348-g007.jpg Figure 7. Proposed mechanism of the TRPM4-HSP60-NF-κB pathway in regulating inflammation and pyropto https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1795707/full https://www.frontiersin.org/files/Articles/1795707/fphar-17-1795707-HTML-r1/image_m/fphar-17-1795707-g001.jpg Figure 1. Effects of ClC-3 on Ach-mediated relaxation in aortas from AngII-induced hypertensive mice https://www.frontiersin.org/files/Articles/1795707/fphar-17-1795707-HTML-r1/image_m/fphar-17-1795707-g002.jpg Figure 2. Cl− channel blockers restored NO production in HUVECs. (A,B) HUVECs were pretreated with C https://www.frontiersin.org/files/Articles/1795707/fphar-17-1795707-HTML-r1/image_m/fphar-17-1795707-g003.jpg Figure 3. Effect of ClC-3 on the expression and phosphorylation of eNOS at serine 1177. (A) HUVECs w https://www.frontiersin.org/files/Articles/1795707/fphar-17-1795707-HTML-r1/image_m/fphar-17-1795707-g004.jpg Figure 4. Effect of ClC-3 on the expression and phosphorylation of Akt and AMPK in AngII-treated HUV https://www.frontiersin.org/files/Articles/1795707/fphar-17-1795707-HTML-r1/image_m/fphar-17-1795707-g005.jpg Figure 5. Effect of ClC-3 on the interaction of eNOS with Akt, Hsp90 and caveolin-1 in HUVECs. (A,B) https://www.frontiersin.org/files/Articles/1795707/fphar-17-1795707-HTML-r1/image_m/fphar-17-1795707-g006.jpg Figure 6. ClC-3 interacted with eNOS through its oxygenase domain. (A,B) The interaction of endogeno https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1802700/full https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g001.jpg Figure 1. BRG1 was upregulated in mice after 4 weeks of MI and in CFs exposed to TGF-β1. (A,B) Level https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g002.jpg Figure 2. BRG1 knockdown in mice prevented cardiac fibrosis after MI. (A) Schematic diagram of the e https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g003.jpg Figure 3. The effect of BRG1 overexpression or knockdown on collagen synthesis in CFs. (A) BRG1 over https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g004.jpg Figure 4. BRG1 knockdown attenuated TGF-β1-induced profibrotic responses in CFs, including collagen https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g005.jpg Figure 5. BRG1 interacts with ZEB1 and is required to maintain ZEB1 expression. (A) Immunofluorescen https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g006.jpg Figure 6. The effects of ZEB1 knockdown in CFs. (A) Western blot analysis of ZEB1, Col-I and FN1 pro https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g007.jpg Figure 7. ZEB1 regulated the effects of BRG1 on cardiac fibrosis. (A) Western blot analysis of ZEB1, https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g008.jpg Figure 8. BRG1 promoted Smad3 phosphorylation by regulating Ppp2r1a transcription. (A) Western blot https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g009.jpg Figure 9. BRG1 silencing inhibited TGF-β1-induced activation of human CFs. (A) Western blot analysis https://www.frontiersin.org/files/Articles/1802700/fphar-17-1802700-HTML/image_m/fphar-17-1802700-g010.jpg Figure 10. Schematic diagram depicting the role of BRG1 on cardiac fibrosis. Myocardial infarction u https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1684395/full https://www.frontiersin.org/files/Articles/1684395/fendo-16-1684395-HTML/image_m/fendo-16-1684395-t001.jpg Table 1. General information. https://www.frontiersin.org/files/Articles/1684395/fendo-16-1684395-HTML/image_m/fendo-16-1684395-t002.jpg Table 2. Multi-factor analysis. https://www.frontiersin.org/files/Articles/1684395/fendo-16-1684395-HTML/image_m/fendo-16-1684395-g001.jpg Figure 1. Column line diagram model. The nomogram incorporates three independent predictors determin https://www.frontiersin.org/files/Articles/1684395/fendo-16-1684395-HTML/image_m/fendo-16-1684395-t003.jpg Table 3. Scores for the column-line diagram model. https://www.frontiersin.org/files/Articles/1684395/fendo-16-1684395-HTML/image_m/fendo-16-1684395-g002.jpg Figure 2. The ROC curve and calibration curve. (A) The ROC curve serves as a tool to assess the disc https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1693441/full https://www.frontiersin.org/files/Articles/1693441/fphar-16-1693441-HTML/image_m/fphar-16-1693441-g001.jpg Figure 1. Physiological redox balance versus oxidative stress in the heart. Under physiological cond https://www.frontiersin.org/files/Articles/1693441/fphar-16-1693441-HTML/image_m/fphar-16-1693441-g002.jpg Figure 2. Timeline of pathological events in MIRI. The progression from ischemia (low ROS) to a mass https://www.frontiersin.org/files/Articles/1693441/fphar-16-1693441-HTML/image_m/fphar-16-1693441-g003.jpg Figure 3. The pathophysiological cascade of MIRI. Ischemic triggers lead to a reperfusion-induced ox https://www.frontiersin.org/files/Articles/1693441/fphar-16-1693441-HTML/image_m/fphar-16-1693441-g004.jpg Figure 4. Primary sources of ROS in cardiomyocytes during MIRI. Key sources include mitochondria, NA https://www.frontiersin.org/files/Articles/1693441/fphar-16-1693441-HTML/image_m/fphar-16-1693441-t001.jpg Table 1. Clinical trials evaluating oxidative stress-related interventions in MI. https://www.frontiersin.org/files/Articles/1693441/fphar-16-1693441-HTML/image_m/fphar-16-1693441-g005.jpg Figure 5. Mechanisms of therapeutic agents targeting oxidative stress in MIRI. Strategies include: ( https://www.frontiersin.org/files/Articles/1693441/fphar-16-1693441-HTML/image_m/fphar-16-1693441-g006.jpg Figure 6. A precision medicine framework for targeting oxidative stress in MIRI. This framework inte https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1694060/full https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-g009.jpg https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-t001.jpg Table 1. Primer sequences for quantitative real-time PCR. https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-g001.jpg Figure 1. Illustrative effect of CIS and AVN-C treatments on survival rats that showed a reduction i https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-g002.jpg Figure 2. The effects of CIS and AVN-C on the body weight of rats were systematically recorded for a https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-g003.jpg Figure 3. Effect of AVN-C in combination with CIS on cardiac biomarkers. (A) Influence of AVN-C with https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-g004.jpg Figure 4. Effect of AVN-C in combination with CIS on MDA and ROS levels. (A) Influence of AVN-C with https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-g005.jpg Figure 5. Effect of AVN-C on CIS-induced alterations in inflammatory markers. (A) Interleukin (IL)-1 https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-g006.jpg Figure 6. Effect of AVN-C on CIS-induced alterations to Nrf2, P62, and Keap1 levels. (A) Nrf2, (B) P https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-g007.jpg Figure 7. Effect of AVN-C on CIS-induced alterations to Nrf2, P62, and Keap1 levels. (A) Nrf2, (B) P https://www.frontiersin.org/files/Articles/1694060/fphar-16-1694060-HTML-r2/image_m/fphar-16-1694060-g008.jpg Figure 8. Photomicrographs of histological sections from cardiac tissues across different experiment https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1726458/full https://www.frontiersin.org/files/Articles/1726458/fphar-16-1726458-HTML/image_m/fphar-16-1726458-g001.jpg Figure 1. Schematic representation of TEC family kinases and their domains. PH, pleckstrin homology; https://www.frontiersin.org/files/Articles/1726458/fphar-16-1726458-HTML/image_m/fphar-16-1726458-t001.jpg Table 1. Research status of BTK inhibitors launched in the market. https://www.frontiersin.org/files/Articles/1726458/fphar-16-1726458-HTML/image_m/fphar-16-1726458-t002.jpg Table 2. Cardiovascular toxicities associated with BTK inhibitors. https://www.frontiersin.org/files/Articles/1726458/fphar-16-1726458-HTML/image_m/fphar-16-1726458-g002.jpg Figure 2. Mechanisms of Ibrutinib and Acalabrutinib-Induced Cardiotoxicity. NOX2/4,NADPH oxidase; RO https://www.frontiersin.org/files/Articles/1726458/fphar-16-1726458-HTML/image_m/fphar-16-1726458-t003.jpg Table 3. Research status of BTK inhibitors in the clinical trials. https://www.frontiersin.org/files/Articles/1726458/fphar-16-1726458-HTML/image_m/fphar-16-1726458-t004.jpg Table 4. Research status of BTK inhibitors in the preclinical trials. https://www.frontiersin.org/files/Articles/1726458/fphar-16-1726458-HTML/image_m/fphar-16-1726458-t005.jpg Table 5. Research status of BMX, ITK, TXK and TEC inhibitors. https://www.frontiersin.org/files/Articles/1726458/fphar-16-1726458-HTML/image_m/fphar-16-1726458-t006.jpg Table 6. Summary of cardiotoxicity of TEC family kinase inhibitors. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1725208/full https://www.frontiersin.org/files/Articles/1725208/fphar-17-1725208-HTML/image_m/fphar-17-1725208-g001.jpg Figure 1. Involvement of miRNA101a in the process of AF myocardial fibrosis. (A) miRNA101a qPCR resu https://www.frontiersin.org/files/Articles/1725208/fphar-17-1725208-HTML/image_m/fphar-17-1725208-g002.jpg Figure 2. miRNA101a is released by exosomes from macrophages in epicardial adipose tissue. (A) Human https://www.frontiersin.org/files/Articles/1725208/fphar-17-1725208-HTML/image_m/fphar-17-1725208-g003.jpg Figure 3. miRNA101a negatively regulates PDGF-DD expression. (A) Immunofluorescence staining for ide https://www.frontiersin.org/files/Articles/1725208/fphar-17-1725208-HTML/image_m/fphar-17-1725208-g004.jpg Figure 4. miRNA101a regulates cardiac fibroblast migration. (A) Cell assay Transwell; (B) Immunofluo https://www.frontiersin.org/files/Articles/1725208/fphar-17-1725208-HTML/image_m/fphar-17-1725208-g005.jpg Figure 5. miRNA101a promotes degradation of collagen and extracellular matrix under AF. (A) WB detec https://www.frontiersin.org/files/Articles/1725208/fphar-17-1725208-HTML/image_m/fphar-17-1725208-g006.jpg Figure 6. miRNA101a promotes macrophage phenotypic transformation under AF and plays a protective ro https://www.frontiersin.org/files/Articles/1725208/fphar-17-1725208-HTML/image_m/fphar-17-1725208-g007.jpg Figure 7. EATMs-derived miR101a regulates AF through the PI3K-AKT pathway. (A) WB detection of human https://www.frontiersin.org/files/Articles/1725208/fphar-17-1725208-HTML/image_m/fphar-17-1725208-g008.jpg Figure 8. Graphical abstract. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1792549/full https://www.frontiersin.org/files/Articles/1792549/fpls-17-1792549-HTML/image_m/fpls-17-1792549-g001.jpg Figure 1. Morphology and color variation of the parental lines and representative F1 plants. (A) Cle https://www.frontiersin.org/files/Articles/1792549/fpls-17-1792549-HTML/image_m/fpls-17-1792549-g002.jpg Figure 2. Schematic representation of phenotypic trait measurement methods in Clematis hybrids. https://www.frontiersin.org/files/Articles/1792549/fpls-17-1792549-HTML/image_m/fpls-17-1792549-t001.jpg Table 1. Standard grading and scoring criteria for flower color in Clematis hybrids. https://www.frontiersin.org/files/Articles/1792549/fpls-17-1792549-HTML/image_m/fpls-17-1792549-t002.jpg Table 2. Segregation of flower color scores in F1 from two crosses. https://www.frontiersin.org/files/Articles/1792549/fpls-17-1792549-HTML/image_m/fpls-17-1792549-t003.jpg Table 3. Heterosis analysis of quantitative traits in F1 of two cross combinations. https://www.frontiersin.org/files/Articles/1792549/fpls-17-1792549-HTML/image_m/fpls-17-1792549-g003.jpg Figure 3. Frequency distribution of phenotypic traits in F1 populations. (A) Cross combination I (TT https://www.frontiersin.org/files/Articles/1792549/fpls-17-1792549-HTML/image_m/fpls-17-1792549-t004.jpg Table 4. AIC values of candidate genetic models for ten quantitative traits in F1. https://www.frontiersin.org/files/Articles/1792549/fpls-17-1792549-HTML/image_m/fpls-17-1792549-t005.jpg Table 5. Adaptability test results of F1 representative quantitative traits selection model. https://www.frontiersin.org/files/Articles/1792549/fpls-17-1792549-HTML/image_m/fpls-17-1792549-t006.jpg Table 6. Estimation of genetic parameters of quantitative traits of F1 under the optimal model. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1634656/full https://www.frontiersin.org/files/Articles/1634656/fspor-07-1634656-HTML/image_m/fspor-07-1634656-t001.jpg Table 1. Key neuromechanical and EMG variables showing significant differences across experimental g https://www.frontiersin.org/files/Articles/1634656/fspor-07-1634656-HTML/image_m/fspor-07-1634656-t002.jpg Table 2. Descriptive statistics of key performance and EMG variables (mean ± SD). https://www.frontiersin.org/files/Articles/1634656/fspor-07-1634656-HTML/image_m/fspor-07-1634656-t003.jpg Table 3. Within-group pre–post comparisons of key variables (mean ± SD; Δ = post–pre). https://www.frontiersin.org/files/Articles/1634656/fspor-07-1634656-HTML/image_m/fspor-07-1634656-g001.jpg Figure 1. Neuromechanical biplot of principal components. Biplot of the first two principal componen https://www.frontiersin.org/files/Articles/1634656/fspor-07-1634656-HTML/image_m/fspor-07-1634656-g002.jpg Figure 2. Canonical discriminant scatterplot. Linear discriminant space visualized across the first https://www.frontiersin.org/files/Articles/1634656/fspor-07-1634656-HTML/image_m/fspor-07-1634656-g003.jpg Figure 3. Time-Series trajectory of reactive strength Index during EMG-guided stretch-shortening cyc https://www.frontiersin.org/files/Articles/1634656/fspor-07-1634656-HTML/image_m/fspor-07-1634656-t004.jpg Table 4. Performance comparison between random forest and multilayer perceptron classifiers for EMG- https://www.frontiersin.org/files/Articles/1634656/fspor-07-1634656-HTML/image_m/fspor-07-1634656-g004.jpg Figure 4. Predictive modeling framework for post-intervention classification using PCA-reduced neuro https://www.frontiersin.org/files/Articles/1634656/fspor-07-1634656-HTML/image_m/fspor-07-1634656-g005.jpg Figure 5. Receiver operating characteristic (ROC) curves for random forest and multilayer perceptron https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1676448/full https://www.frontiersin.org/files/Articles/1676448/fspor-07-1676448-HTML-r1/image_m/fspor-07-1676448-g001.jpg Figure 1. Retroreflective marker placement shown from three views: (A) sagittal, (B) anterior, and (C https://www.frontiersin.org/files/Articles/1676448/fspor-07-1676448-HTML-r1/image_m/fspor-07-1676448-g002.jpg Figure 2. Experimental setup for the stop-jump task. The global coordinate system is defined as: X ( https://www.frontiersin.org/files/Articles/1676448/fspor-07-1676448-HTML-r1/image_m/fspor-07-1676448-g003.jpg Figure 3. Stop-jump task sequences showing (A) forefoot landing and (B) rearfoot landing techniques. https://www.frontiersin.org/files/Articles/1676448/fspor-07-1676448-HTML-r1/image_m/fspor-07-1676448-g004.jpg Figure 4. Mean ± SD and SPM t-values of hip, knee, and ankle joint angles between forefoot landing ( https://www.frontiersin.org/files/Articles/1676448/fspor-07-1676448-HTML-r1/image_m/fspor-07-1676448-g005.jpg Figure 5. Mean ± SD and SPM t-values of hip, knee, and ankle joint moments comparing forefoot landin https://www.frontiersin.org/files/Articles/1676448/fspor-07-1676448-HTML-r1/image_m/fspor-07-1676448-g006.jpg Figure 6. Mean ± SD and SPM t-values of GRF in posterior and vertical direction, GRF inclination ang https://www.frontiersin.org/files/Articles/1676448/fspor-07-1676448-HTML-r1/image_m/fspor-07-1676448-t001.jpg Table 1. Ankle joint kinematics and kinetics at initial contact. https://www.frontiersin.org/files/Articles/1676448/fspor-07-1676448-HTML-r1/image_m/fspor-07-1676448-t002.jpg Table 2. Performance variables during stop-jumping task. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1703099/full https://www.frontiersin.org/files/Articles/1703099/feduc-10-1703099-HTML/image_m/feduc-10-1703099-g001.jpg Figure 1. The flowchart of SPAR-4-SLR protocol. https://www.frontiersin.org/files/Articles/1703099/feduc-10-1703099-HTML/image_m/feduc-10-1703099-t001.jpg Table 1. Reviewed studies. https://www.frontiersin.org/files/Articles/1703099/feduc-10-1703099-HTML/image_m/feduc-10-1703099-g002.jpg Figure 2. Chart depicting the phrases. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1785625/full https://www.frontiersin.org/files/Articles/1785625/fimmu-17-1785625-HTML/image_m/fimmu-17-1785625-g001.jpg Figure 1. Existing treatment methods for pancreatic cancer. Schematic overview of current PC treatme https://www.frontiersin.org/files/Articles/1785625/fimmu-17-1785625-HTML/image_m/fimmu-17-1785625-t001.jpg Table 1. Drug carrier specific information. https://www.frontiersin.org/files/Articles/1785625/fimmu-17-1785625-HTML/image_m/fimmu-17-1785625-g002.jpg Figure 2. Classification and advantages/disadvantages of targeted drug carriers. Schematic classific https://www.frontiersin.org/files/Articles/1785625/fimmu-17-1785625-HTML/image_m/fimmu-17-1785625-g003.jpg Figure 3. Part of targeted drug delivery treatment methods for pancreatic cancer. Representative tar https://www.frontiersin.org/files/Articles/1785625/fimmu-17-1785625-HTML/image_m/fimmu-17-1785625-t002.jpg Table 2. Advances in research on targeted drug carriers. https://www.frontiersin.org/files/Articles/1785625/fimmu-17-1785625-HTML/image_m/fimmu-17-1785625-g004.jpg Figure 4. Factors affecting targeted drug delivery. Schematic illustration of major mechanisms gover https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1678525/full https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-t001.jpg Table 1. Baseline characteristics of the patient cohort (N = 403). https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-t002.jpg Table 2. Scoring criteria for surgical phases. https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-g001.jpg Figure 1. Integrated dataset and ISA model overview. (A) The dataset construction pipeline. The data https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-g002.jpg Figure 2. Dual-branch network architecture for joint prediction of stages and quality. (A) The top b https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-g003.jpg Figure 3. Flowchart of ISA development and evaluation. ISA refers to the Artificial Intelligence mod https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-t003.jpg Table 3. Performance of the model. https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-g004.jpg Figure 4. Phase recognition results for one laparoscopic hemi-hepatectomy video. For a representativ https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-t004.jpg Table 4. Confusion matrix of phase prediction (percent). https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-g005.jpg Figure 5. Heatmap visualizations of neural network activations. Heatmap visualizations of neural net https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-t005.jpg Table 5. Classification performance metrics by phase. https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-g006.jpg Figure 6. Evaluation of model classification performance. (A) ROC curves for the classification of a https://www.frontiersin.org/files/Articles/1678525/fonc-15-1678525-HTML-r1/image_m/fonc-15-1678525-t006.jpg Table 6. Key performance metrics on the external validation cohort (N=122). https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1791456/full https://www.frontiersin.org/files/Articles/1791456/fmolb-13-1791456-HTML/image_m/fmolb-13-1791456-g001.jpg Figure 1. Selection of Prostate Cancer Risk Genes by MR. (A) Manhattan plot of transcriptome-wide MR https://www.frontiersin.org/files/Articles/1791456/fmolb-13-1791456-HTML/image_m/fmolb-13-1791456-t001.jpg Table 1. Five candidate genes from TMR and SMR results. https://www.frontiersin.org/files/Articles/1791456/fmolb-13-1791456-HTML/image_m/fmolb-13-1791456-g002.jpg Figure 2. Single-cell expression profiling for PCa. (A,B) UMAP Plot categorizing single-cell data by https://www.frontiersin.org/files/Articles/1791456/fmolb-13-1791456-HTML/image_m/fmolb-13-1791456-g003.jpg Figure 3. PGAP3 is associated with PCa progression and prognosis. (A) Immunofluorescence imaging of https://www.frontiersin.org/files/Articles/1791456/fmolb-13-1791456-HTML/image_m/fmolb-13-1791456-g004.jpg Figure 4. The role of PGAP3 in special or unique metabolic pathways. (A–C) Enrichment analysis resul https://www.frontiersin.org/files/Articles/1791456/fmolb-13-1791456-HTML/image_m/fmolb-13-1791456-g005.jpg Figure 5. The interplay between PGAP3 and T cells. (A) CellChat analysis results of intercellular co https://www.frontiersin.org/files/Articles/1791456/fmolb-13-1791456-HTML/image_m/fmolb-13-1791456-g006.jpg Figure 6. In vitro validation of PGAP3 in PCa cell lines. (A,B) qRT-PCR and Western blot showing PGA https://www.frontiersin.org/journals/developmental-psychology/articles/10.3389/fdpys.2026.1748280/full https://www.frontiersin.org/files/Articles/1748280/fdpys-04-1748280-HTML/image_m/fdpys-04-1748280-g001.jpg Figure 1. Timeline including the eight waves of our panel and Dutch COVID-19 pandemic-related restri https://www.frontiersin.org/files/Articles/1748280/fdpys-04-1748280-HTML/image_m/fdpys-04-1748280-t001.jpg Table 1. Descriptives per dataset (large and imputed, see Methods) per number of targets. The first https://www.frontiersin.org/files/Articles/1748280/fdpys-04-1748280-HTML/image_m/fdpys-04-1748280-t002.jpg Table 2. Bivariate correlations among all variables used in the statistical analyses in the large da https://www.frontiersin.org/files/Articles/1748280/fdpys-04-1748280-HTML/image_m/fdpys-04-1748280-t003.jpg Table 3. Bivariate correlations among all variables used in the statistical analyses in the large da https://www.frontiersin.org/files/Articles/1748280/fdpys-04-1748280-HTML/image_m/fdpys-04-1748280-g002.jpg Figure 2. Giving behavior toward seven targets in the Pandemic Dictator Game across time (T1 to T8, https://www.frontiersin.org/files/Articles/1748280/fdpys-04-1748280-HTML/image_m/fdpys-04-1748280-g003.jpg Figure 3. Mean giving behavior to five targets in the Pandemic Dictator Game for all timepoints (T1 https://www.frontiersin.org/files/Articles/1748280/fdpys-04-1748280-HTML/image_m/fdpys-04-1748280-g004.jpg Figure 4. Mean giving to vaccinated (purple) and unvaccinated (blue) targets by participants who are https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1761397/full https://www.frontiersin.org/files/Articles/1761397/fimmu-17-1761397-HTML/image_m/fimmu-17-1761397-g001.jpg Figure 1. Assessment of factors affecting NK cell transduction. The impact of human AB serum and IL- https://www.frontiersin.org/files/Articles/1761397/fimmu-17-1761397-HTML/image_m/fimmu-17-1761397-g002.jpg Figure 2. Workflow of the Natural Killer Cell Transduction (NKCT) process on the CliniMACS Prodigy p https://www.frontiersin.org/files/Articles/1761397/fimmu-17-1761397-HTML/image_m/fimmu-17-1761397-g003.jpg Figure 3. Automation of CAR NK cell manufacturing using the NKCT process. Three independent runs wer https://www.frontiersin.org/files/Articles/1761397/fimmu-17-1761397-HTML/image_m/fimmu-17-1761397-g004.jpg Figure 4. CliniMACS Prodigy-manufactured BDCA2 CAR NK cells exhibit potent antitumor activity in vit https://www.frontiersin.org/files/Articles/1761397/fimmu-17-1761397-HTML/image_m/fimmu-17-1761397-g005.jpg Figure 5. CliniMACS Prodigy-manufactured BDCA2 CAR NK cells strongly inhibit tumor growth in vivo wh https://www.frontiersin.org/files/Articles/1761397/fimmu-17-1761397-HTML/image_m/fimmu-17-1761397-g006.jpg Figure 6. Flow cytometric analysis of the in vivo persistence of tumor cells and NK cells. At the co https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1734203/full https://www.frontiersin.org/files/Articles/1734203/fimmu-16-1734203-HTML/image_m/fimmu-16-1734203-g001.jpg Figure 1. Ndufs4 (-/-) mice are characterized by lymphopenia due to impaired homeostatic expansion. https://www.frontiersin.org/files/Articles/1734203/fimmu-16-1734203-HTML/image_m/fimmu-16-1734203-g002.jpg Figure 2. Impaired T-cell immune responses in Ndufs4(-/-) mice. (A) Diagram of the in-vivo immunizat https://www.frontiersin.org/files/Articles/1734203/fimmu-16-1734203-HTML/image_m/fimmu-16-1734203-g003.jpg Figure 3. Adoptive T-cell transfer model used to assess effector function of Ndufs4(-/-) CD8+ T cell https://www.frontiersin.org/files/Articles/1734203/fimmu-16-1734203-HTML/image_m/fimmu-16-1734203-g004.jpg Figure 4. Mitochondrial complex I deficiency and decreased cellular respiration in Ndufs4(-/-) CD8+ https://www.frontiersin.org/files/Articles/1734203/fimmu-16-1734203-HTML/image_m/fimmu-16-1734203-g005.jpg Figure 5. Increased aerobic glycolysis and reactive oxygen species production in Ndufs4(-/-) T cells https://www.frontiersin.org/files/Articles/1734203/fimmu-16-1734203-HTML/image_m/fimmu-16-1734203-g006.jpg Figure 6. Analysis of a human patient with NDUFS4-defieicnt Leigh syndrome. (A) Family pedigree of t https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1642750/full https://www.frontiersin.org/files/Articles/1642750/fdgth-07-1642750-HTML-r1/image_m/fdgth-07-1642750-t001.jpg Table 1. Inputs for the illustrative check (replace bracketed placeholders with best estimates). https://www.frontiersin.org/files/Articles/1642750/fdgth-07-1642750-HTML-r1/image_m/fdgth-07-1642750-t002.jpg Table 2. Illustrative AI-specific billing codes, with proposed relative point values, example moneta https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1786346/full https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-g001.jpg Figure 1. The process of inclusion and exclusion of study subjects. https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-g002.jpg Figure 2. Image preprocessing. https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-g003.jpg Figure 3. 3D ResNeXt network structure diagram. (A) Main framework of 3D ResNeXt. (B) Detailed compo https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-t001.jpg Table 1. The distribution of cases on the training and validation sets. https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-t002.jpg Table 2. Performance comparison of different deep learning models on the training and test sets. https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-t003.jpg Table 3. DeLong’s test for statistical significance of AUC differences among models on the test set. https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-g004.jpg Figure 4. Confusion matrix of our model. (A) Train set. (B) Test set. https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-g005.jpg Figure 5. Comparison of ROC performance among different models. (A) Train set. (B) Test set. https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-g006.jpg Figure 6. Grad-CAM heatmaps overlaid on sagittal, coronal, and axial chest CT views for NTM-LD case. https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-g007.jpg Figure 7. Grad-CAM heatmaps overlaid on sagittal, coronal, and axial chest CT views for PTB cases. https://www.frontiersin.org/files/Articles/1786346/fmed-13-1786346-HTML/image_m/fmed-13-1786346-t004.jpg Table 4. Performance of the 3D ResNeXt model with different ablated preprocessing strategies on the https://www.frontiersin.org/journals/nephrology/articles/10.3389/fneph.2025.1744454/full https://www.frontiersin.org/files/Articles/1744454/fneph-05-1744454-HTML/image_m/fneph-05-1744454-g001.jpg Figure 1. Identification of the study population. eGFR, estimated glomerular filtration rate; IgAN, https://www.frontiersin.org/files/Articles/1744454/fneph-05-1744454-HTML/image_m/fneph-05-1744454-t001.jpg Table 1. Annual rate of decline in estimated glomerular filtration rate (eGFR) in biopsy-proven immu https://www.frontiersin.org/files/Articles/1744454/fneph-05-1744454-HTML/image_m/fneph-05-1744454-g002.jpg Figure 2. Kaplan–Meier curves for the time to the composite kidney outcome (≥50% eGFR decline or kid https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1812290/full https://www.frontiersin.org/files/Articles/1812290/fimmu-17-1812290-HTML/image_m/fimmu-17-1812290-g001.jpg Figure 1. Workflow of the RA patient samples processing integrating bulk-RNA sequencing, single cell https://www.frontiersin.org/files/Articles/1812290/fimmu-17-1812290-HTML/image_m/fimmu-17-1812290-g002.jpg Figure 2. Illustration of random forest operation tree diagram. It is an ensemble learning algorithm https://www.frontiersin.org/files/Articles/1812290/fimmu-17-1812290-HTML/image_m/fimmu-17-1812290-g003.jpg Figure 3. Illustration of the operational diagram of gene set variation analysis operation. gene set https://www.frontiersin.org/files/Articles/1812290/fimmu-17-1812290-HTML/image_m/fimmu-17-1812290-g004.jpg Figure 4. Architecture of the XGBoost model. The algorithm constructs feature sets and, based on the https://www.frontiersin.org/files/Articles/1812290/fimmu-17-1812290-HTML/image_m/fimmu-17-1812290-g005.jpg Figure 5. Scheme for calculating the matthews correlation coefficient (MCC). The principle of using https://www.frontiersin.org/files/Articles/1812290/fimmu-17-1812290-HTML/image_m/fimmu-17-1812290-g006.jpg Figure 6. Illustration of artificial neural network operation tree diagram. Neural networks assign w https://www.frontiersin.org/files/Articles/1812290/fimmu-17-1812290-HTML/image_m/fimmu-17-1812290-t001.jpg Table 1. Candidate biomarkers identified in RA from transcriptomic studies, highlighting the GEO dat https://www.frontiersin.org/files/Articles/1812290/fimmu-17-1812290-HTML/image_m/fimmu-17-1812290-t002.jpg Table 2. Cell phenotypes discovery in RA by using ML. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1724489/full https://www.frontiersin.org/files/Articles/1724489/fonc-16-1724489-HTML-r1/image_m/fonc-16-1724489-g001.jpg Figure 1. Workflow for model development in this study. https://www.frontiersin.org/files/Articles/1724489/fonc-16-1724489-HTML-r1/image_m/fonc-16-1724489-g002.jpg Figure 2. Identifies differentially expressed genes associated with radiotherapy efficacy based on t https://www.frontiersin.org/files/Articles/1724489/fonc-16-1724489-HTML-r1/image_m/fonc-16-1724489-g003.jpg Figure 3. Evaluation of the prognostic risk model in the training cohort. (A, B) LASSO regression id https://www.frontiersin.org/files/Articles/1724489/fonc-16-1724489-HTML-r1/image_m/fonc-16-1724489-t001.jpg Table 1. A list of the 12 key differentially expressed genes from the model. https://www.frontiersin.org/files/Articles/1724489/fonc-16-1724489-HTML-r1/image_m/fonc-16-1724489-g004.jpg Figure 4. Prognostic model and nomogram validation in lung adenocarcinoma. (A) Kaplan-Meier curves f https://www.frontiersin.org/files/Articles/1724489/fonc-16-1724489-HTML-r1/image_m/fonc-16-1724489-g005.jpg Figure 5. Immune-characteristic comparison between TCGA-LUAD low- and high-risk groups (divided by m https://www.frontiersin.org/files/Articles/1724489/fonc-16-1724489-HTML-r1/image_m/fonc-16-1724489-g006.jpg Figure 6. Association of TSPAN32 with clinical features, survival, and immune infiltration. (A) Kapl https://www.frontiersin.org/files/Articles/1724489/fonc-16-1724489-HTML-r1/image_m/fonc-16-1724489-g007.jpg Figure 7. Overexpression of TSPAN32 inhibits proliferation and migration of lung cancer cells and en https://www.frontiersin.org/files/Articles/1724489/fonc-16-1724489-HTML-r1/image_m/fonc-16-1724489-g008.jpg Figure 8. The regulatory mechanism of TSPAN32 in LUAD through stabilization of PTEN. (A) KEGG enrich https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1752125/full https://www.frontiersin.org/files/Articles/1752125/fonc-16-1752125-HTML/image_m/fonc-16-1752125-g001.jpg Figure 1. Images of chest computed tomography. The arrows in (A, B) indicate the affected areas. pan https://www.frontiersin.org/files/Articles/1752125/fonc-16-1752125-HTML/image_m/fonc-16-1752125-g002.jpg Figure 2. Images of PET‐CT. (A) shows High metabolism of the multiple thick-walled pulmonary cavitie https://www.frontiersin.org/files/Articles/1752125/fonc-16-1752125-HTML/image_m/fonc-16-1752125-g003.jpg Figure 3. Pathological images of lymph node biopsy. (A) shows haematoxylin and eosin (HE) staining, https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1629156/full https://www.frontiersin.org/files/Articles/1629156/fnhum-19-1629156-HTML-r1/image_m/fnhum-19-1629156-g001.jpg Figure 1. Cranial CT and MRI scans of the patient. (A) CT revealing left hemisphere atrophy with Com https://www.frontiersin.org/files/Articles/1629156/fnhum-19-1629156-HTML-r1/image_m/fnhum-19-1629156-g002.jpg Figure 2. CTA and CTP analysis of the patient. (A) CTA showing a hypoplastic right VA (red arrow). ( https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1748916/full https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-t001.jpg Table 1. Sequence of primers. https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g001.jpg Figure 1. Venn diagram of the targets of QFXFD bioactive components and the primary relevant targets https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-t002.jpg Table 2. Core ingredients of QFXFD for the treatment of chronic cough. https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g002.jpg Figure 2. QFXFD network of relationships between the bioactive components and targets. https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g003.jpg Figure 3. PPI network analysis of potential therapeutic targets for QFXFD intervention in chronic co https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-t003.jpg Table 3. Information on core targets. https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g004.jpg Figure 4. GO and KEGG analyses of potential therapeutic targets for QFXFD intervention in chronic co https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-t004.jpg Table 4. Information on molecular docking binding energy. https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g005.jpg Figure 5. The outcomes of molecular docking between the core ingredients and targets: (A) Molecular https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g006.jpg Figure 6. The cough reflex sensitivity (CRS) of guinea pigs. *p < 0.05, **p < 0.01, vs. normal contr https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g007.jpg Figure 7. Differential leukocyte classification and counts in BALF across experimental groups. *p <  https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g008.jpg Figure 8. QFXFD attenuated the inflammatory injury of lung tissue. Representative images of HE-stain https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g009.jpg Figure 9. Effect of QFXFD on PLC-β/PKC, TRPA1, TRPV1, NK-1R protein expression in lung tissue. **p < https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g010.jpg Figure 10. The protein blots and quantitative analysis of PLC-β, PKC, TRPA1, TRPV1, NK-1R in the lun https://www.frontiersin.org/files/Articles/1748916/fmed-13-1748916-HTML/image_m/fmed-13-1748916-g011.jpg Figure 11. Effect of QFXFD on PLC-β/PKC, TRPA1, TRPV1, SP mRNA expression in lung tissue. **p < 0.01 https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1793302/full https://www.frontiersin.org/files/Articles/1793302/fimmu-17-1793302-HTML/image_m/fimmu-17-1793302-g001.jpg Figure 1. Study design. (A) Cohort design, sample acquisition, and timeline of assessment of allergi https://www.frontiersin.org/files/Articles/1793302/fimmu-17-1793302-HTML/image_m/fimmu-17-1793302-t001.jpg Table 1. Cohort demographics and allergic outcomes for samples used for IgA-SEQ. https://www.frontiersin.org/files/Articles/1793302/fimmu-17-1793302-HTML/image_m/fimmu-17-1793302-g002.jpg Figure 2. Unsorted microbiome composition and diversity. (A) Taxa summary plot per sample. (B) Alpha https://www.frontiersin.org/files/Articles/1793302/fimmu-17-1793302-HTML/image_m/fimmu-17-1793302-g003.jpg Figure 3. IgA coating of bacteria with respect to cohort, allergic outcomes, and demographic covaria https://www.frontiersin.org/files/Articles/1793302/fimmu-17-1793302-HTML/image_m/fimmu-17-1793302-g004.jpg Figure 4. IgA coating as a function of age. (A) Fecal IgA1 and IgA2 in relation to infant age. (B) B https://www.frontiersin.org/files/Articles/1793302/fimmu-17-1793302-HTML/image_m/fimmu-17-1793302-g005.jpg Figure 5. Fecal IgA levels with respect to dietary breastmilk and human milk IgA reactivity towards https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1786230/full https://www.frontiersin.org/files/Articles/1786230/fonc-16-1786230-HTML/image_m/fonc-16-1786230-g001.jpg Figure 1. Diagnostic imaging findings. (A) Endoscopy and Endoscopic Ultrasound (EUS) revealing a lon https://www.frontiersin.org/files/Articles/1786230/fonc-16-1786230-HTML/image_m/fonc-16-1786230-g002.jpg Figure 2. Histopathological and immunohistochemical characterization of the primary esophageal lesio https://www.frontiersin.org/files/Articles/1786230/fonc-16-1786230-HTML/image_m/fonc-16-1786230-g003.jpg Figure 3. Timeline of the patient’s diagnosis, treatment, and disease progression. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1737059/full https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-g001.jpg Figure 1. (A) Laparoscopic oocyte retrieval in goats. The animal was restrained in dorsal recumbency https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-g002.jpg Figure 2. Slaughterhouse-derived goat ovaries. https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-t001.jpg Table 1. Primer details of ZAR-1, MFN-2, BAX, BCL-2, and GAPDH genes. https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-t002.jpg Table 2. Comparison of percentages of usable quality oocytes (grade 1 & 2) retrieved. https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-t003.jpg Table 3. Comparison of maturation percentage between the groups. https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-g003.jpg Figure 3. (A) In vitro matured oocytes with cumulus cell mass showing expansion under culture condit https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-t004.jpg Table 4. Comparison of in vitro development between the groups. https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-g004.jpg Figure 4. (A) Cleaved caprine oocytes at different stages of development following in vitro fertiliz https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-g005.jpg Figure 5. Heatmap of fold-change (Live ÷ Abattoir) across genes (ZAR1, MFN2, BAX, BCL2) and developm https://www.frontiersin.org/files/Articles/1737059/fvets-13-1737059-HTML-r1/image_m/fvets-13-1737059-g006.jpg Figure 6. Forest plot of fold-change (Live ÷ Abattoir) for gene expression with 95% confidence inter https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1785017/full https://www.frontiersin.org/files/Articles/1785017/fdgth-08-1785017-HTML/image_m/fdgth-08-1785017-g001.jpg Figure 1. Integrated conceptual framework based on TAM and Louart S model. https://www.frontiersin.org/files/Articles/1785017/fdgth-08-1785017-HTML/image_m/fdgth-08-1785017-t001.jpg Table 1. Participants characteristics (N = 280). https://www.frontiersin.org/files/Articles/1785017/fdgth-08-1785017-HTML/image_m/fdgth-08-1785017-t002.jpg Table 2. Level of acceptance per end-users' qualification. https://www.frontiersin.org/files/Articles/1785017/fdgth-08-1785017-HTML/image_m/fdgth-08-1785017-t003.jpg Table 3. Average score by domain and end-user qualification (N = 280). https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1768595/full https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-g001.jpg Figure 1. Effects of straw return and phosphate fertilizer treatment on annual dry matter accumulati https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-t001.jpg Table 1. Effects of straw return and phosphate fertilizer management on rice yield and yield traits. https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-t002.jpg Table 2. Effects of straw return and phosphate fertilizer management on yield and yield traits of ra https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-g002.jpg Figure 2. Effects of straw return and phosphorus fertilizer treatments on rice ATPase activity after https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-g003.jpg Figure 3. Effects of straw return and phosphate fertilizer treatment on phosphorus uptake of rice in https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-g004.jpg Figure 4. Effects of straw return and phosphate fertilizer treatment on phosphorus uptake of rapesee https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-t003.jpg Table 3. Effects of straw return and phosphorus fertilizer management on annual phosphorus utilizati https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-g005.jpg Figure 5. Effects of straw return and phosphorus fertilizer treatments on soil organic matter conten https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-g006.jpg Figure 6. Effects of straw return and phosphorus fertilizer treatments on soil total P (TP) and Olse https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-g007.jpg Figure 7. Effects of straw return and phosphorus fertilizer treatments on soil total P (TP) and Olse https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-g008.jpg Figure 8. Effects of straw return and phosphorus fertilizer treatments on the relative abundance of https://www.frontiersin.org/files/Articles/1768595/fpls-17-1768595-HTML/image_m/fpls-17-1768595-g009.jpg Figure 9. The relationship of yield (a–c), phosphorus absorption (d–f), soil phosphorus content, org https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1797432/full https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g001.jpg Figure 1. PRISMA study flow diagram. https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g002.jpg Figure 2. Risk of bias summary. https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g003.jpg Figure 3. Risk of bias graph. https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g004.jpg Figure 4. Forest plot of meta-analysis on the effect of exercise on Quality of Life in Lung Cancer p https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g005.jpg Figure 5. Funnel plots of the meta-analysis on the effect of exercise in lung cancer patients. (A) Q https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g006.jpg Figure 6. Forest plot of meta-analysis on the effect of exercise on fatigue in lung cancer patients. https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g007.jpg Figure 7. Forest plot of meta-analysis on the effect of exercise on anxiety in lung cancer patients. https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g008.jpg Figure 8. Forest plot of meta-analysis on the effect of exercise on depression in lung cancer patien https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g009.jpg Figure 9. Forest plot of meta-analysis on the effect of exercise on pain in lung cancer patients. https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g010.jpg Figure 10. Forest plot of meta-analysis on the effect of exercise on sleep quality in lung cancer pa https://www.frontiersin.org/files/Articles/1797432/fphys-17-1797432-HTML/image_m/fphys-17-1797432-g011.jpg Figure 11. Sensitivity analysis of the effect of exercise on outcomes in lung cancer patients. (A) q https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1709677/full https://www.frontiersin.org/files/Articles/1709677/fimmu-16-1709677-HTML/image_m/fimmu-16-1709677-t001.jpg Table 1. Sequence of the primers. https://www.frontiersin.org/files/Articles/1709677/fimmu-16-1709677-HTML/image_m/fimmu-16-1709677-g001.jpg Figure 1. Phenotypic and histological characterization of the murine NEC model. (A) Gross appearance https://www.frontiersin.org/files/Articles/1709677/fimmu-16-1709677-HTML/image_m/fimmu-16-1709677-g002.jpg Figure 2. m6A modification in intestinal tissue from mouse. (A) Dot blot showed the overall level of https://www.frontiersin.org/files/Articles/1709677/fimmu-16-1709677-HTML/image_m/fimmu-16-1709677-t002.jpg Table 2. Basic information of infants included in the study. https://www.frontiersin.org/files/Articles/1709677/fimmu-16-1709677-HTML/image_m/fimmu-16-1709677-g003.jpg Figure 3. m6A modification in intestinal tissue from infants. (A) Number of peaks in two groups. (B) https://www.frontiersin.org/files/Articles/1709677/fimmu-16-1709677-HTML/image_m/fimmu-16-1709677-g004.jpg Figure 4. DEGs from RNA-seq. (A) The volcano plot distribution of DEGs. Upregulated genes are repres https://www.frontiersin.org/files/Articles/1709677/fimmu-16-1709677-HTML/image_m/fimmu-16-1709677-g005.jpg Figure 5. Integrated analysis of DEGs and differential m6A peaks related genes. (A) Four quadrant di https://www.frontiersin.org/files/Articles/1709677/fimmu-16-1709677-HTML/image_m/fimmu-16-1709677-g006.jpg Figure 6. Hub genes of DEGs with differential m6A modifications. (A) List of top 10 hub genes. The c https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1794137/full https://www.frontiersin.org/files/Articles/1794137/fped-14-1794137-HTML-r1/image_m/fped-14-1794137-g001.jpg Figure 1. EEG at 8 years showing slowing of the posterior dominant rhythm and paroxysms of slow thet https://www.frontiersin.org/files/Articles/1794137/fped-14-1794137-HTML-r1/image_m/fped-14-1794137-g002.jpg Figure 2. MRI at 8 years showing a type 1 Arnold-Chiari malformation, specifically a 9 mm herniation https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1733450/full https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-g001.jpg Figure 1. Expression of C. parvum rhomboids in sporozoites. (A) RT-PCR on sporozoite total RNA: lane https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-g002.jpg Figure 2. Evolutionary relationships of Apicomplexa rhomboids. (A) Neighbor-joining phylogenetic tre https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-t001.jpg Table 1. List of the C. parvum rhomboids in this study. https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-g003.jpg Figure 3. Schematic representation of the genomic regions that surround the PARL-like rhomboid genes https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-g004.jpg Figure 4. Purification on Ni-NTA-resin of recombinant rhomboids expressed in Escherichia coli. Fract https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-g005.jpg Figure 5. Western blot analysis of oocyst and sporozoite lysates at different times from the inducti https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-g006.jpg Figure 6. Localization of CpRom1 in excysted sporozoites by mouse anti-CpRom1 (IFA in A–D) and by ra https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-g007.jpg Figure 7. Immunolocalization of CpRom2 in excysted sporozoites and in infected HCT8 cells. (A–E) Col https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-g008.jpg Figure 8. Localization of CpRom3 in excysted sporozoites (1,000 ×). (A) Excysted sporozoites in whit https://www.frontiersin.org/files/Articles/1733450/fcimb-16-1733450-HTML/image_m/fcimb-16-1733450-t002.jpg Table 2. Putative targets of C. parvum rhomboids. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1805527/full https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-t001.jpg Table 1. Effect of fertilizer application on soil organic carbon (SOC), total nitrogen (TN), and tot https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-g001.jpg Figure 1. Changes in black soil pH under different fertilizer treatments. Data represent mean values https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-g002.jpg Figure 2. Changes in microbial biomass carbon (a), microbial biomass nitrogen (b), and microbial bio https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-t002.jpg Table 2. Effect of fertilizer application on copper (Cu), zinc (Zn), lead (Pb), and cadmium (Cd) con https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-t003.jpg Table 3. Effect of fertilizer application on chlorophyll content and leaf nitrogen (N) content at di https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-g003.jpg Figure 3. Effect of fertilizer application on (a) the number of pods per plant and (b) soybean grain https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-g004.jpg Figure 4. Correlation matrix showing relationships between soybean grain yield and black soil nutrie https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-g005.jpg Figure 5. Relative abundance of dominant soil bacterial (a) and fungal (b) phyla following fertilize https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-g006.jpg Figure 6. Venn diagrams showing the shared and unique OTUs of bacterial (a) and fungal (b) communiti https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-g007.jpg Figure 7. PCoA of soil Bacterial (a) and Fungal (b) communities under different fertilization treatm https://www.frontiersin.org/files/Articles/1805527/fmicb-17-1805527-HTML/image_m/fmicb-17-1805527-g008.jpg Figure 8. Heatmap of soil bacterial (a) and fungal (b) communities under different fertilization tre https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1703350/full https://www.frontiersin.org/files/Articles/1703350/fvets-12-1703350-HTML-r1/image_m/fvets-12-1703350-t001.jpg Table 1. Breakpoints used to define E. coli and S. pseudintermedius as resistant, intermediate or su https://www.frontiersin.org/files/Articles/1703350/fvets-12-1703350-HTML-r1/image_m/fvets-12-1703350-g001.jpg Figure 1. Percentage of resistant (R, red bars), intermediate (I, orange bars), and susceptible (S, https://www.frontiersin.org/files/Articles/1703350/fvets-12-1703350-HTML-r1/image_m/fvets-12-1703350-g002.jpg Figure 2. Percentage of resistant (R, red bars), intermediate (I, orange bars), and susceptible (S, https://www.frontiersin.org/files/Articles/1703350/fvets-12-1703350-HTML-r1/image_m/fvets-12-1703350-g003.jpg Figure 3. (A) Number of isolated E. coli strains (N) and percentage of ESBL E. coli in the different https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1676990/full https://www.frontiersin.org/files/Articles/1676990/fvets-13-1676990-HTML-r1/image_m/fvets-13-1676990-t001.jpg Table 1. Escherichia coli virulence characteristics (hemolysis and CNF detection) of the strains iso https://www.frontiersin.org/files/Articles/1676990/fvets-13-1676990-HTML-r1/image_m/fvets-13-1676990-t002.jpg Table 2. Histological characteristics of the organs: perforation of the uterine wall (perforated/int https://www.frontiersin.org/files/Articles/1676990/fvets-13-1676990-HTML-r1/image_m/fvets-13-1676990-t003.jpg Table 3. Categorization of the different parameters investigated: inflammatory reaction (mild, moder https://www.frontiersin.org/files/Articles/1676990/fvets-13-1676990-HTML-r1/image_m/fvets-13-1676990-g001.jpg Figure 1. Immunohistochemical expression of PI3K in canine uteri, hematoxylin counterstaining. Top l https://www.frontiersin.org/files/Articles/1676990/fvets-13-1676990-HTML-r1/image_m/fvets-13-1676990-t004.jpg Table 4. MIC values (mg/L) of the tested antimicrobials for E. coli isolates grouped according to he https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1809435/full https://www.frontiersin.org/files/Articles/1809435/fcimb-16-1809435-HTML/image_m/fcimb-16-1809435-g001.jpg Figure 1. Persistence of RG4 viral RNA in tissues. CNS, central nervous system; EBOV, Ebola virus; L https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1760230/full https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g001.jpg Figure 1. Distribution of harmonized prior quantitative grades in the UCI mathematics and SMARVUS da https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t001.jpg Table 1. Descriptive statistics of prior quantitative achievement. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g002.jpg Figure 2. Engineering math readiness score (EMRS) distributions for the UCI mathematics and SMARVUS https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t002.jpg Table 2. Performance of classification models in the UCI mathematics dataset. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t003.jpg Table 3. Performance of classification models in the SMARVUS dataset. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g003.jpg Figure 3. Area under the ROC curve (AUC) for four classification algorithms—logistic regression, dec https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g004.jpg Figure 4. Top ten predictors of at-risk status according to permutation-based feature importance in https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g005.jpg Figure 5. ROC AUC of the engineering math readiness score (EMRS) logistic-regression model across wi https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t004.jpg Table 4. Top predictors of at-risk status in the UCI mathematics dataset (random forest importance). https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t005.jpg Table 5. Top predictors of at-risk status in the SMARVUS dataset (random forest importance). https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t006.jpg Table 6. Cross-dataset performance of the EMRS logistic-regression model. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t007.jpg Table 7. Bootstrap-estimated performance of the EMRS logistic-regression model. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g006.jpg Figure 6. Calibration plot of the EMRS logistic-regression model in the UCI mathematics dataset. The https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g007.jpg Figure 7. Calibration plot of the EMRS logistic-regression model in the SMARVUS dataset. The x-axis https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t008.jpg Table 8. Fairness metrics by sex in the UCI mathematics dataset. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t009.jpg Table 9. Fairness metrics by school type in the UCI mathematics dataset. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t010.jpg Table 10. Fairness metrics by gender in the SMARVUS dataset. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t011.jpg Table 11. Fairness metrics by country in SMARVUS (countries with n ≥ 200). https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t012.jpg Table 12. Selected thresholds and cost–sensitive metrics for UCI mathematics. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t013.jpg Table 13. Recommended thresholds and levelling policies for UCI mathematics. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t014.jpg Table 14. Selected thresholds and cost–sensitive metrics for SMARVUS. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t015.jpg Table 15. Recommended threshold and levelling policy for SMARVUS. https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g008.jpg Figure 8. True positive rate (recall for the at-risk class) and true negative rate (specificity) of https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g009.jpg Figure 9. Expected cost per student in the UCI mathematics dataset under a cost function that weight https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g010.jpg Figure 10. True positive rate and true negative rate of the EMRS model in the SMARVUS dataset as a f https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g011.jpg Figure 11. Expected cost per student in the SMARVUS dataset under the same cost function. The cost i https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t016.jpg Table 16. Contingency table of EMRS readiness band by Calculus I outcome (UTN pilot, n = 30). https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-g012.jpg Figure 12. Percentage of students who passed or failed Calculus I within each EMRS readiness band fo https://www.frontiersin.org/files/Articles/1760230/feduc-11-1760230-HTML/image_m/feduc-11-1760230-t017.jpg Table 17. Example EMRS-CLI output for five incoming UTN students. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1775859/full https://www.frontiersin.org/files/Articles/1775859/fneur-17-1775859-HTML/image_m/fneur-17-1775859-g001.jpg Figure 1. Study workflow for identifying OS-related genes associated with tinnitus and subsequent fu https://www.frontiersin.org/files/Articles/1775859/fneur-17-1775859-HTML/image_m/fneur-17-1775859-g002.jpg Figure 2. Quality control summary for the GWAS meta-analysis and gene annotation and enrichment anal https://www.frontiersin.org/files/Articles/1775859/fneur-17-1775859-HTML/image_m/fneur-17-1775859-g003.jpg Figure 3. SMR and Colocalization results for the association between the protein abundance of OS-rel https://www.frontiersin.org/files/Articles/1775859/fneur-17-1775859-HTML/image_m/fneur-17-1775859-g004.jpg Figure 4. Integrative analysis of eQTLs for OS-related genes in relation to tinnitus risk. (A) SMR a https://www.frontiersin.org/files/Articles/1775859/fneur-17-1775859-HTML/image_m/fneur-17-1775859-g005.jpg Figure 5. Integrative analysis of mQTLs for OS-related genes in relation to tinnitus risk. (A) SMR a https://www.frontiersin.org/files/Articles/1775859/fneur-17-1775859-HTML/image_m/fneur-17-1775859-g006.jpg Figure 6. Validation using brain tissue eQTL data and experimentally confirmed,and downstream target https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2026.1772804/full https://www.frontiersin.org/files/Articles/1772804/frsus-07-1772804-HTML/image_m/frsus-07-1772804-t001.jpg Table 1. The main selected academic papers clustered by the relative domain(s). https://www.frontiersin.org/files/Articles/1772804/frsus-07-1772804-HTML/image_m/frsus-07-1772804-t002.jpg Table 2. The role, year of experience and related field of 10 participants. https://www.frontiersin.org/files/Articles/1772804/frsus-07-1772804-HTML/image_m/frsus-07-1772804-t003.jpg Table 3. Main themes and sub-themes identified from the integrated analysis of NLR and interviews. https://www.frontiersin.org/files/Articles/1772804/frsus-07-1772804-HTML/image_m/frsus-07-1772804-g001.jpg Figure 1. Synthesized e-clothing design workflow model based on literature and interview findings. T https://www.frontiersin.org/files/Articles/1772804/frsus-07-1772804-HTML/image_m/frsus-07-1772804-t004.jpg Table 4. Comparison of eight common eco-design tools from literature and interview results. https://www.frontiersin.org/files/Articles/1772804/frsus-07-1772804-HTML/image_m/frsus-07-1772804-g002.jpg Figure 2. Integrated eco-design intervention points within the e-clothing workflow model. The figure https://www.frontiersin.org/files/Articles/1772804/frsus-07-1772804-HTML/image_m/frsus-07-1772804-t005.jpg Table 5. Mapping eco-design stages to tools, limitations, and proposed support for e-clothing design https://www.frontiersin.org/files/Articles/1772804/frsus-07-1772804-HTML/image_m/frsus-07-1772804-g003.jpg Figure 3. Cyclical eco-design toolkit embedded in the e-clothing development workflow. This diagram https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1759488/full https://www.frontiersin.org/files/Articles/1759488/fonc-16-1759488-HTML/image_m/fonc-16-1759488-g000.jpg Graphical Abstract. Schematic representation of the modified MSRE-qPCR method for detecting methylat https://www.frontiersin.org/files/Articles/1759488/fonc-16-1759488-HTML/image_m/fonc-16-1759488-g001.jpg Figure 1. Differential DNA methylation in CESC. (A–D) Boxplots of DNA methylation for RXFP3, ZNF671, https://www.frontiersin.org/files/Articles/1759488/fonc-16-1759488-HTML/image_m/fonc-16-1759488-g002.jpg Figure 2. Combination enzyme treatment enriched the sequence template DNA electropherograms of genes https://www.frontiersin.org/files/Articles/1759488/fonc-16-1759488-HTML/image_m/fonc-16-1759488-g003.jpg Figure 3. (A) The cycle threshold (Ct) values of the RXFP3 - L1 gene were determined in patients wit https://www.frontiersin.org/files/Articles/1759488/fonc-16-1759488-HTML/image_m/fonc-16-1759488-g004.jpg Figure 4. (A-E) A comparison of Ct values for RXFP3-L1, RXFP3-L2, ZNF671, PAX1, SOX1 methylation det https://www.frontiersin.org/files/Articles/1759488/fonc-16-1759488-HTML/image_m/fonc-16-1759488-g005.jpg Figure 5. MethyLight-derived Ct values for PAX1, SOX1, and the reference gene ACTB. https://www.frontiersin.org/files/Articles/1759488/fonc-16-1759488-HTML/image_m/fonc-16-1759488-g006.jpg Figure 6. (A, B) Summarizes the area under the curve (AUC), standard error (SE), 95% confidence inte https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1751424/full https://www.frontiersin.org/files/Articles/1751424/fonc-16-1751424-HTML/image_m/fonc-16-1751424-t001.jpg Table 1. Diagnosis, treatment, and prognosis of patients with different types of liposarcoma. https://www.frontiersin.org/files/Articles/1751424/fonc-16-1751424-HTML/image_m/fonc-16-1751424-g001.jpg Figure 1. The timeline shows the onset of illness and treatment from March 2024 to June 2025. https://www.frontiersin.org/files/Articles/1751424/fonc-16-1751424-HTML/image_m/fonc-16-1751424-g002.jpg Figure 2. Axial CT images in the arterial (A), venous (B), and delayed (C) phases demonstrate four d https://www.frontiersin.org/files/Articles/1751424/fonc-16-1751424-HTML/image_m/fonc-16-1751424-g003.jpg Figure 3. (A) Sagittal abdominal CT scan demonstrating a large, heterogeneous intra-abdominal mass. https://www.frontiersin.org/files/Articles/1751424/fonc-16-1751424-HTML/image_m/fonc-16-1751424-g004.jpg Figure 4. Photomicrographs show histopathological features. (A, B) Same field at ×100 and ×200 magni https://www.frontiersin.org/files/Articles/1751424/fonc-16-1751424-HTML/image_m/fonc-16-1751424-g005.jpg Figure 5. Fluorescence in situ hybridization (FISH) analysis. (A, B) MDM2 amplification (red signals https://www.frontiersin.org/files/Articles/1751424/fonc-16-1751424-HTML/image_m/fonc-16-1751424-g006.jpg Figure 6. Complete macroscopic resection of the tumor. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1802177/full https://www.frontiersin.org/files/Articles/1802177/fonc-16-1802177-HTML/image_m/fonc-16-1802177-g001.jpg Figure 1. Contrast-enhanced abdominal CT. (A) Hepatic arterial phase; (B) Hepatic venous phase; (C) https://www.frontiersin.org/files/Articles/1802177/fonc-16-1802177-HTML/image_m/fonc-16-1802177-g002.jpg Figure 2. Non-contrast MRI. (A) T1w GRE (out-phase) sequence of the liver; (B) T2w sequence of the l https://www.frontiersin.org/files/Articles/1802177/fonc-16-1802177-HTML/image_m/fonc-16-1802177-g003.jpg Figure 3. Contrast−enhanced MRI. (A) T1−weighted imaging of the liver in the arterial phase; (B) T1− https://www.frontiersin.org/files/Articles/1802177/fonc-16-1802177-HTML/image_m/fonc-16-1802177-g004.jpg Figure 4. Pathological findings. (A–C) were obtained from the same histological section, while panel https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1811868/full https://www.frontiersin.org/files/Articles/1811868/fimmu-17-1811868-HTML/image_m/fimmu-17-1811868-g001.jpg Figure 1. eCasp-1 is elevated in patients and murine gut I/R. (A, B) Plasma eCasp-1 levels in health https://www.frontiersin.org/files/Articles/1811868/fimmu-17-1811868-HTML/image_m/fimmu-17-1811868-g002.jpg Figure 2. eCasp-1 release involves GSDMD-dependent membrane processes (A, B) WT peritoneal macrophag https://www.frontiersin.org/files/Articles/1811868/fimmu-17-1811868-HTML/image_m/fimmu-17-1811868-g003.jpg Figure 3. eCasp-1 functions as a DAMP to induce cytokine release and organ injury. (A, B) WT periton https://www.frontiersin.org/files/Articles/1811868/fimmu-17-1811868-HTML/image_m/fimmu-17-1811868-g004.jpg Figure 4. eCasp-1 binds to TLR4 to drive inflammation, which is effectively suppressed by the novel https://www.frontiersin.org/files/Articles/1811868/fimmu-17-1811868-HTML/image_m/fimmu-17-1811868-g005.jpg Figure 5. Treatment with C16 attenuates systemic inflammation, lung injury, and mortality in gut I/R https://www.frontiersin.org/files/Articles/1811868/fimmu-17-1811868-HTML/image_m/fimmu-17-1811868-g006.jpg Figure 6. Summary of findings. In gut I/R injury, inflammasomes activation promotes the cleavage of https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1723656/full https://www.frontiersin.org/files/Articles/1723656/fphar-17-1723656-HTML/image_m/fphar-17-1723656-t001.jpg Table 1. Baseline demographic and disease characteristics of patients included in the analysis (n = https://www.frontiersin.org/files/Articles/1723656/fphar-17-1723656-HTML/image_m/fphar-17-1723656-g001.jpg Figure 1. Individual rivaroxaban concentrations (µg/mL). The bar represents the average concentratio https://www.frontiersin.org/files/Articles/1723656/fphar-17-1723656-HTML/image_m/fphar-17-1723656-t002.jpg Table 2. Pharmacokinetic parameters for patients; preop, postop day 1, and 7-day postop the mean (SD https://www.frontiersin.org/files/Articles/1723656/fphar-17-1723656-HTML/image_m/fphar-17-1723656-t003.jpg Table 3. The GMR and CI for AUC and Cmax; preop, postop day 1, and 7-day postop. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1597281/full https://www.frontiersin.org/files/Articles/1597281/fimmu-16-1597281-HTML/image_m/fimmu-16-1597281-g001.jpg Figure 1. MGL expression by human MΦ is activated by mycobacteria and contributes to antibacterial f https://www.frontiersin.org/files/Articles/1597281/fimmu-16-1597281-HTML/image_m/fimmu-16-1597281-g002.jpg Figure 2. MΦ MGL is activated by both M1- and M2-polarizing conditions. (A) Monocytes from PBMC were https://www.frontiersin.org/files/Articles/1597281/fimmu-16-1597281-HTML/image_m/fimmu-16-1597281-g003.jpg Figure 3. MGL is abundant in human TB granulomas. (A) Human tissue specimens obtained from lymph nod https://www.frontiersin.org/files/Articles/1597281/fimmu-16-1597281-HTML/image_m/fimmu-16-1597281-g004.jpg Figure 4. HIV status and viral load correspond with MGL defects in human lung and peripheral blood. https://www.frontiersin.org/files/Articles/1597281/fimmu-16-1597281-HTML/image_m/fimmu-16-1597281-g005.jpg Figure 5. Mtb and HIV display differential binding affinity for MGL. A recombinant immunoglobulin mo https://www.frontiersin.org/files/Articles/1597281/fimmu-16-1597281-HTML/image_m/fimmu-16-1597281-g006.jpg Figure 6. Myeloid cells in peripheral blood of HIV+ donors retain MGL functional response to Mtb. (A https://www.frontiersin.org/files/Articles/1597281/fimmu-16-1597281-HTML/image_m/fimmu-16-1597281-g007.jpg Figure 7. Mtb exposure differentially activates cytokines in PBMC of HIV- and HIV+ donors. PBMC of 1 https://www.frontiersin.org/files/Articles/1597281/fimmu-16-1597281-HTML/image_m/fimmu-16-1597281-g008.jpg Figure 8. Subpopulation of human neutrophils express MGL that is impaired due to HIV. (A) Intravascu https://www.frontiersin.org/journals/stroke/articles/10.3389/fstro.2026.1751659/full https://www.frontiersin.org/files/Articles/1751659/fstro-05-1751659-HTML/image_m/fstro-05-1751659-t001.jpg Table 1. Data collection timeline*. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1768395/full https://www.frontiersin.org/files/Articles/1768395/fimmu-17-1768395-HTML-r1/image_m/fimmu-17-1768395-t001.jpg Table 1. Summary of analytical performance parameters of TCID50MN and FRNT assays for the ancestral https://www.frontiersin.org/files/Articles/1768395/fimmu-17-1768395-HTML-r1/image_m/fimmu-17-1768395-t002.jpg Table 2. Repeatability results of TCID50MN and FRNT assays for the ancestral strain and the Omicron https://www.frontiersin.org/files/Articles/1768395/fimmu-17-1768395-HTML-r1/image_m/fimmu-17-1768395-t003.jpg Table 3. Table of intermediate precision results of the TCID50MN and FRNT assays for the ancestral s https://www.frontiersin.org/files/Articles/1768395/fimmu-17-1768395-HTML-r1/image_m/fimmu-17-1768395-t004.jpg Table 4. Trueness/bias results of the TCID50MN assay, for the ancestral strain and the Omicron subva https://www.frontiersin.org/files/Articles/1768395/fimmu-17-1768395-HTML-r1/image_m/fimmu-17-1768395-t005.jpg Table 5. Trueness/bias results of FRNT assay, for the ancestral strain and the Omicron subvariants w https://www.frontiersin.org/files/Articles/1768395/fimmu-17-1768395-HTML-r1/image_m/fimmu-17-1768395-t006.jpg Table 6. Robustness results of TCID50MN and FRNT assays for the ancestral strain Wuhan and Omicron s https://www.frontiersin.org/files/Articles/1768395/fimmu-17-1768395-HTML-r1/image_m/fimmu-17-1768395-t007.jpg Table 7. Uncertainty results of TCID50MN and FRNT assays for the ancestral strain Wuhan and Omicron https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1706868/full https://www.frontiersin.org/files/Articles/1706868/fpsyg-17-1706868-HTML-r2/image_m/fpsyg-17-1706868-t001.jpg Table 1. Descriptives, skewness, kurtosis, factor loading, and item-rest correlation of the LCQ-EF i https://www.frontiersin.org/files/Articles/1706868/fpsyg-17-1706868-HTML-r2/image_m/fpsyg-17-1706868-t002.jpg Table 2. Fit indices of the measurement invariance models for the LCQ-EF between Mexico and Spain. https://www.frontiersin.org/files/Articles/1706868/fpsyg-17-1706868-HTML-r2/image_m/fpsyg-17-1706868-t003.jpg Table 3. Fit indices of the multigroup factorial invariance models by sex. https://www.frontiersin.org/files/Articles/1706868/fpsyg-17-1706868-HTML-r2/image_m/fpsyg-17-1706868-t004.jpg Table 4. Fit indices of the multigroup factorial invariance models by age groups. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1778538/full https://www.frontiersin.org/files/Articles/1778538/fmed-13-1778538-HTML/image_m/fmed-13-1778538-g001.jpg Figure 1. The patents body changes after onset of the disease. (A) The fingernails of the both toes https://www.frontiersin.org/files/Articles/1778538/fmed-13-1778538-HTML/image_m/fmed-13-1778538-g002.jpg Figure 2. Gastroscopy after admission (2021-01-07). (A,B) Esophageal mucosal lesions. (C,D). The muc https://www.frontiersin.org/files/Articles/1778538/fmed-13-1778538-HTML/image_m/fmed-13-1778538-g003.jpg Figure 3. Colonoscopy after admission (2021-01-07). (A) Multiple patchy congestion and edema of the https://www.frontiersin.org/files/Articles/1778538/fmed-13-1778538-HTML/image_m/fmed-13-1778538-g004.jpg Figure 4. High-grade intraepithelial neoplasia of esophageal squamous epithelium. https://www.frontiersin.org/files/Articles/1778538/fmed-13-1778538-HTML/image_m/fmed-13-1778538-g005.jpg Figure 5. Adenocarcinoma of the sigmoid colon. https://www.frontiersin.org/files/Articles/1778538/fmed-13-1778538-HTML/image_m/fmed-13-1778538-g006.jpg Figure 6. Take prednisone orally for 12 months discontinue for 3 months (2022-04-26)showed: the muco https://www.frontiersin.org/files/Articles/1778538/fmed-13-1778538-HTML/image_m/fmed-13-1778538-g007.jpg Figure 7. Second colonoscopy re-examination (2022-04-26): Upon inserting the endoscope through the s https://www.frontiersin.org/files/Articles/1778538/fmed-13-1778538-HTML/image_m/fmed-13-1778538-g008.jpg Figure 8. The third review of gastroscopy was performed (2024-08-29). (A,B) A neoplastic elevation i https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1647377/full https://www.frontiersin.org/files/Articles/1647377/fcimb-15-1647377-HTML/image_m/fcimb-15-1647377-g001.jpg Figure 1. Genomic characteristics of intestinal microbiota from wild rodents. (A) Workflow illustrat https://www.frontiersin.org/files/Articles/1647377/fcimb-15-1647377-HTML/image_m/fcimb-15-1647377-g002.jpg Figure 2. Bile acid transformation capacity of intestinal microbiota in wild rodents. (A) Taxonomic https://www.frontiersin.org/files/Articles/1647377/fcimb-15-1647377-HTML/image_m/fcimb-15-1647377-g003.jpg Figure 3. Host-specific bile acid metabolism by the intestinal microbiota. (A) Comparative analysis https://www.frontiersin.org/files/Articles/1647377/fcimb-15-1647377-HTML/image_m/fcimb-15-1647377-g004.jpg Figure 4. Functional enrichment of BSH-carrying genomes in the CAG-485 genus. Functional profiling o https://www.frontiersin.org/files/Articles/1647377/fcimb-15-1647377-HTML/image_m/fcimb-15-1647377-g005.jpg Figure 5. Gut microbiota and BA metabolism response to Enterocytozoon bieneusi infection. (A, B) Box https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1779448/full https://www.frontiersin.org/files/Articles/1779448/fendo-17-1779448-HTML/image_m/fendo-17-1779448-g001.jpg Figure 1. Pathophysiological mechanisms of bone-muscle “crosstalk” and the development of osteosarco https://www.frontiersin.org/files/Articles/1779448/fendo-17-1779448-HTML/image_m/fendo-17-1779448-g002.jpg Figure 2. Workflow of the “zero-cost” AI screening strategy (target: individuals without existing im https://www.frontiersin.org/files/Articles/1779448/fendo-17-1779448-HTML/image_m/fendo-17-1779448-g003.jpg Figure 3. Technical panorama of the AI-based automated muscle assessment framework. The workflow ill https://www.frontiersin.org/files/Articles/1779448/fendo-17-1779448-HTML/image_m/fendo-17-1779448-t001.jpg Table 1. Summary of AI-driven sarcopenia assessment across orthopedic subspecialties. https://www.frontiersin.org/files/Articles/1779448/fendo-17-1779448-HTML/image_m/fendo-17-1779448-g004.jpg Figure 4. Multi-scenario applications of AI-based opportunistic screening in geriatric orthopedics. https://www.frontiersin.org/files/Articles/1779448/fendo-17-1779448-HTML/image_m/fendo-17-1779448-g005.jpg Figure 5. Integrative mechanism of AI-based muscle assessment for clinical prognostic stratification https://www.frontiersin.org/files/Articles/1779448/fendo-17-1779448-HTML/image_m/fendo-17-1779448-g006.jpg Figure 6. Current challenges and limitations in AI-driven sarcopenia assessment for geriatric orthop https://www.frontiersin.org/files/Articles/1779448/fendo-17-1779448-HTML/image_m/fendo-17-1779448-g007.jpg Figure 7. Future directions in AI-driven sarcopenia assessment. Multi-modal Fusion: Integration of i https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1636007/full https://www.frontiersin.org/files/Articles/1636007/fmicb-16-1636007-HTML-r1/image_m/fmicb-16-1636007-gr0001.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1636007/fmicb-16-1636007-HTML-r1/image_m/fmicb-16-1636007-g001.jpg Figure 1. α-diversity and β-diversity. (A) Shannon diversity rarefaction curve. (B) Shannon index of https://www.frontiersin.org/files/Articles/1636007/fmicb-16-1636007-HTML-r1/image_m/fmicb-16-1636007-g002.jpg Figure 2. Microbial composition. (A) Phylum-level composition of the CeD group and CDFH group. (B) G https://www.frontiersin.org/files/Articles/1636007/fmicb-16-1636007-HTML-r1/image_m/fmicb-16-1636007-g003.jpg Figure 3. Metabolite changes in CeD patients. (A) PLS-DA scores of the CeD group vs. the CDFH group. https://www.frontiersin.org/files/Articles/1636007/fmicb-16-1636007-HTML-r1/image_m/fmicb-16-1636007-g004.jpg Figure 4. Differences in metabolite types between CeD groups. (A) PLS-DA scores for SCeD and CDF gro https://www.frontiersin.org/files/Articles/1636007/fmicb-16-1636007-HTML-r1/image_m/fmicb-16-1636007-g005.jpg Figure 5. Diagnostic Models Based on Differential Microorganisms and Metabolites. (A) Top 5 differen https://www.frontiersin.org/files/Articles/1636007/fmicb-16-1636007-HTML-r1/image_m/fmicb-16-1636007-g006.jpg Figure 6. Correlation analysis of differential microbes and differential metabolites. (A) Correlatio https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1699833/full https://www.frontiersin.org/files/Articles/1699833/fpubh-14-1699833-HTML-r1/image_m/fpubh-14-1699833-g001.jpg Figure 1. Flow chart of participants. https://www.frontiersin.org/files/Articles/1699833/fpubh-14-1699833-HTML-r1/image_m/fpubh-14-1699833-t001.jpg Table 1. Comparison of food allergy among preschool children with different characteristics in Haiko https://www.frontiersin.org/files/Articles/1699833/fpubh-14-1699833-HTML-r1/image_m/fpubh-14-1699833-t002.jpg Table 2. Univariate analysis of food allergy and indoor environmental variables (n = 3,049). https://www.frontiersin.org/files/Articles/1699833/fpubh-14-1699833-HTML-r1/image_m/fpubh-14-1699833-t003.jpg Table 3. Multivariate logistic regression analysis of risk factors for food allergy among preschool https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1570426/full https://www.frontiersin.org/files/Articles/1570426/fendo-16-1570426-HTML/image_m/fendo-16-1570426-g001.jpg Figure 1. Flow chart. https://www.frontiersin.org/files/Articles/1570426/fendo-16-1570426-HTML/image_m/fendo-16-1570426-t001.jpg Table 1. The fracture event number, follow-up person-year, the incidence rate and the hazard ratio a https://www.frontiersin.org/files/Articles/1570426/fendo-16-1570426-HTML/image_m/fendo-16-1570426-t002.jpg Table 2. Baseline demographic factors and comorbidities of endometrial cancer patients with and with https://www.frontiersin.org/files/Articles/1570426/fendo-16-1570426-HTML/image_m/fendo-16-1570426-g002.jpg Figure 2. Cumulative incidence of (A) osteoporotic fracture, (B) hip fracture, (C) vertebral fractur https://www.frontiersin.org/files/Articles/1570426/fendo-16-1570426-HTML/image_m/fendo-16-1570426-t003.jpg Table 3. The fracture event number, follow-up person-year, the incidence rate and the hazard ratio a https://www.frontiersin.org/files/Articles/1570426/fendo-16-1570426-HTML/image_m/fendo-16-1570426-t004.jpg Table 4. Subgroup analysis among endometrial cancer patients with and without DM. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1630303/full https://www.frontiersin.org/files/Articles/1630303/fpubh-13-1630303-HTML-r2/image_m/fpubh-13-1630303-g001.jpg Figure 1. Trial flow. https://www.frontiersin.org/files/Articles/1630303/fpubh-13-1630303-HTML-r2/image_m/fpubh-13-1630303-t001.jpg Table 1. Overview of assessment points and measurements used. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1732495/full https://www.frontiersin.org/files/Articles/1732495/fonc-16-1732495-HTML/image_m/fonc-16-1732495-t001.jpg Table 1. Baseline demographics and clinical characteristics of the cohort stratified by race. https://www.frontiersin.org/files/Articles/1732495/fonc-16-1732495-HTML/image_m/fonc-16-1732495-g001.jpg Figure 1. Kaplan-Meier curves for overall survival (in days) by race plotted against the cumulative https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1732088/full https://www.frontiersin.org/files/Articles/1732088/frai-08-1732088-HTML/image_m/frai-08-1732088-g001.jpg Figure 1. The hypothetical model. https://www.frontiersin.org/files/Articles/1732088/frai-08-1732088-HTML/image_m/frai-08-1732088-t001.jpg Table 1. Demographic statistics. https://www.frontiersin.org/files/Articles/1732088/frai-08-1732088-HTML/image_m/frai-08-1732088-t002.jpg Table 2. The standardized loadings, CR, AVE, and Cronbach’s alpha of the model. https://www.frontiersin.org/files/Articles/1732088/frai-08-1732088-HTML/image_m/frai-08-1732088-t003.jpg Table 3. Correlations among variables. https://www.frontiersin.org/files/Articles/1732088/frai-08-1732088-HTML/image_m/frai-08-1732088-t004.jpg Table 4. Model fitting index. https://www.frontiersin.org/files/Articles/1732088/frai-08-1732088-HTML/image_m/frai-08-1732088-t005.jpg Table 5. Hypothesis test result. https://www.frontiersin.org/files/Articles/1732088/frai-08-1732088-HTML/image_m/frai-08-1732088-t006.jpg Table 6. Direct and indirect effect analysis. https://www.frontiersin.org/files/Articles/1732088/frai-08-1732088-HTML/image_m/frai-08-1732088-g002.jpg Figure 2. The final study. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1722255/full https://www.frontiersin.org/files/Articles/1722255/fimmu-17-1722255-HTML/image_m/fimmu-17-1722255-g001.jpg Figure 1. Flowchart detailing the search methods and findings according to PRISMA guidelines. https://www.frontiersin.org/files/Articles/1722255/fimmu-17-1722255-HTML/image_m/fimmu-17-1722255-t001.jpg Table 1. Characteristics and main findings of the included studies. https://www.frontiersin.org/files/Articles/1722255/fimmu-17-1722255-HTML/image_m/fimmu-17-1722255-t002.jpg Table 2. NOS assessment of included studies. https://www.frontiersin.org/files/Articles/1722255/fimmu-17-1722255-HTML/image_m/fimmu-17-1722255-g002.jpg Figure 2. Heatmap summarizing direction of reported associations between gut microbial taxa and sero https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1773360/full https://www.frontiersin.org/files/Articles/1773360/fpsyg-17-1773360-HTML/image_m/fpsyg-17-1773360-g001.jpg Figure 1. Flow chart of study populations. Flow chart includes the number of individuals and percent https://www.frontiersin.org/files/Articles/1773360/fpsyg-17-1773360-HTML/image_m/fpsyg-17-1773360-t001.jpg Table 1. Descriptive characteristics of the study samples and multivariable logistic regression mode https://www.frontiersin.org/files/Articles/1773360/fpsyg-17-1773360-HTML/image_m/fpsyg-17-1773360-t002.jpg Table 2. High avoidant coping in the recently diagnosed sample and associations with participant cha https://www.frontiersin.org/files/Articles/1773360/fpsyg-17-1773360-HTML/image_m/fpsyg-17-1773360-t003.jpg Table 3. Multivariable logistic regressions of the associations between covariates and avoidant copi https://www.frontiersin.org/files/Articles/1773360/fpsyg-17-1773360-HTML/image_m/fpsyg-17-1773360-g002.jpg Figure 2. Receiver operating characteristic (ROC) curves for the predicted probabilities. The figure https://www.frontiersin.org/files/Articles/1773360/fpsyg-17-1773360-HTML/image_m/fpsyg-17-1773360-t004.jpg Table 4. Performance of the logistic regression models on the total study and the recently diagnosed https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1633967/full https://www.frontiersin.org/files/Articles/1633967/fneur-16-1633967-HTML/image_m/fneur-16-1633967-g001.jpg Figure 1. Representative cases of the NWU measurement. (A) The preoperative cranial CT of a ALVOS pa https://www.frontiersin.org/files/Articles/1633967/fneur-16-1633967-HTML/image_m/fneur-16-1633967-g002.jpg Figure 2. Flow diagram of subjects’ enrollment. ALVOS indicates acute large vessel occlusion stroke; https://www.frontiersin.org/files/Articles/1633967/fneur-16-1633967-HTML/image_m/fneur-16-1633967-t001.jpg Table 1. The baseline clinical characteristics of all participants. https://www.frontiersin.org/files/Articles/1633967/fneur-16-1633967-HTML/image_m/fneur-16-1633967-t002.jpg Table 2. The comparisons between participants with good and poor outcomes. https://www.frontiersin.org/files/Articles/1633967/fneur-16-1633967-HTML/image_m/fneur-16-1633967-t003.jpg Table 3. Clinical factors independently associated with poor outcomes in ALVOS patients with success https://www.frontiersin.org/files/Articles/1633967/fneur-16-1633967-HTML/image_m/fneur-16-1633967-g003.jpg Figure 3. The relationship of NWU (A) and NLR (B) with 90-day poor outcomes in the restricted cubic https://www.frontiersin.org/files/Articles/1633967/fneur-16-1633967-HTML/image_m/fneur-16-1633967-g004.jpg Figure 4. Distribution of 90-day mRS scores across NWU and NLR levels. (A) Along with increased NWU https://www.frontiersin.org/files/Articles/1633967/fneur-16-1633967-HTML/image_m/fneur-16-1633967-t004.jpg Table 4. Clinical factors independently associated with 90-day outcomes grade in ALVOS patients with https://www.frontiersin.org/files/Articles/1633967/fneur-16-1633967-HTML/image_m/fneur-16-1633967-g005.jpg Figure 5. The indicative values of NWU and NLR for poor outcomes of participants. The combination of https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1684466/full https://www.frontiersin.org/files/Articles/1684466/fcvm-12-1684466-HTML/image_m/fcvm-12-1684466-t001.jpg Table 1. Change in preoperative and postoperative morphological measurements. https://www.frontiersin.org/files/Articles/1684466/fcvm-12-1684466-HTML/image_m/fcvm-12-1684466-g001.jpg Figure 1. Preoperative and postoperative diameter-volume correlation. https://www.frontiersin.org/files/Articles/1684466/fcvm-12-1684466-HTML/image_m/fcvm-12-1684466-t002.jpg Table 2. Comparison of patients with and without positive remodeling. https://www.frontiersin.org/files/Articles/1684466/fcvm-12-1684466-HTML/image_m/fcvm-12-1684466-t003.jpg Table 3. Effect of significant parameters on positive remodeling. https://www.frontiersin.org/files/Articles/1684466/fcvm-12-1684466-HTML/image_m/fcvm-12-1684466-g002.jpg Figure 2. Secondary reintervention risk. https://www.frontiersin.org/files/Articles/1684466/fcvm-12-1684466-HTML/image_m/fcvm-12-1684466-g003.jpg Figure 3. Kaplan–Meier survival curve. https://www.frontiersin.org/files/Articles/1684466/fcvm-12-1684466-HTML/image_m/fcvm-12-1684466-g004.jpg Figure 4. Follow-up findings in patients with a decrease or no change in diameter. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1807684/full https://www.frontiersin.org/files/Articles/1807684/feduc-11-1807684-HTML/image_m/feduc-11-1807684-t001.jpg Table 1. Illustrative coding of value-related competences across selected curricular documents. https://www.frontiersin.org/files/Articles/1807684/feduc-11-1807684-HTML/image_m/feduc-11-1807684-t002.jpg Table 2. Analytical dimensions of the axiological framework of the national curriculum. https://www.frontiersin.org/files/Articles/1807684/feduc-11-1807684-HTML/image_m/feduc-11-1807684-g001.jpg Figure 1. Analytical model of the axiological structure of the national curriculum. https://www.frontiersin.org/files/Articles/1807684/feduc-11-1807684-HTML/image_m/feduc-11-1807684-t003.jpg Table 3. Correlating educational values with curricular competences, educational levels, and the nor https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1703586/full https://www.frontiersin.org/files/Articles/1703586/fcomp-07-1703586-HTML/image_m/fcomp-07-1703586-t001.jpg Table 1. Generic form of a two-players payoff matrix, when two strategies are viable. https://www.frontiersin.org/files/Articles/1703586/fcomp-07-1703586-HTML/image_m/fcomp-07-1703586-t002.jpg Table 2. Zero sum game payoff matrix. https://www.frontiersin.org/files/Articles/1703586/fcomp-07-1703586-HTML/image_m/fcomp-07-1703586-t003.jpg Table 3. Prisoner's Dilemma payoff matrix. https://www.frontiersin.org/files/Articles/1703586/fcomp-07-1703586-HTML/image_m/fcomp-07-1703586-g001.jpg Figure 1. Simulation and analysis workflow. After selecting the games, they are instantiated in LLM https://www.frontiersin.org/files/Articles/1703586/fcomp-07-1703586-HTML/image_m/fcomp-07-1703586-g002.jpg Figure 2. Final payoffs of agent 1 in the one-shot zero-sum game, for each LLM (see legend for color https://www.frontiersin.org/files/Articles/1703586/fcomp-07-1703586-HTML/image_m/fcomp-07-1703586-t004.jpg Table 4. Internal Variability (IV) and Cross-Language Inconsistency (CI) metrics for the zero-sum ga https://www.frontiersin.org/files/Articles/1703586/fcomp-07-1703586-HTML/image_m/fcomp-07-1703586-g003.jpg Figure 3. Aggregated final payoffs of the repeated Prisoner's Dilemma games over repeated experiment https://www.frontiersin.org/files/Articles/1703586/fcomp-07-1703586-HTML/image_m/fcomp-07-1703586-g004.jpg Figure 4. Evolution of normalized penalties (averaged over repeated experiments) over repeated round https://www.frontiersin.org/files/Articles/1703586/fcomp-07-1703586-HTML/image_m/fcomp-07-1703586-g005.jpg Figure 5. Radar plot mapping the three metrics described in Section 2.2.4, for Prisoner's Dilemma an https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2026.1749814/full https://www.frontiersin.org/files/Articles/1749814/fnagi-18-1749814-HTML/image_m/fnagi-18-1749814-g001.jpg Figure 1. (A) Schematic overview of the EEG recording setup. Sustained attention paradigms (Go-NoGo https://www.frontiersin.org/files/Articles/1749814/fnagi-18-1749814-HTML/image_m/fnagi-18-1749814-g002.jpg Figure 2. Overview of the study design and assessment procedure of the SENSE-AGE study. https://www.frontiersin.org/files/Articles/1749814/fnagi-18-1749814-HTML/image_m/fnagi-18-1749814-t001.jpg Table 1. The four different conditions of the Go-NoGo task. https://www.frontiersin.org/files/Articles/1749814/fnagi-18-1749814-HTML/image_m/fnagi-18-1749814-g003.jpg Figure 3. Example trials of the cued Go-NoGo task, adapted from Pershin et al. (2023). The division https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1744272/full https://www.frontiersin.org/files/Articles/1744272/fpls-17-1744272-HTML-r1/image_m/fpls-17-1744272-g001.jpg Figure 1. A graphic representation of the trait discovery stage gates, from product scoping to the i https://www.frontiersin.org/files/Articles/1744272/fpls-17-1744272-HTML-r1/image_m/fpls-17-1744272-t001.jpg Table 1. Marker-trait association studies in RTB crops in the last ten years. https://www.frontiersin.org/files/Articles/1744272/fpls-17-1744272-HTML-r1/image_m/fpls-17-1744272-t002.jpg Table 2. Summary of recent advances in genome editing of RTB crops mainly banana, cassava, and yam. https://www.frontiersin.org/journals/food-science-and-technology/articles/10.3389/frfst.2026.1794521/full https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-t001.jpg Table 1. Thin-layer drying models used to describe Nigerian walnut drying behavior. https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-t002.jpg Table 2. Comparative performance of thin-layer drying models for Nigerian walnut under different tem https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-t003.jpg Table 3. Best-fitting thin-layer drying models for Nigerian walnut under all drying conditions. https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g001.jpg Figure 1. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g002.jpg Figure 2. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g003.jpg Figure 3. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g004.jpg Figure 4. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g005.jpg Figure 5. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g006.jpg Figure 6. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g007.jpg Figure 7. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g008.jpg Figure 8. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g009.jpg Figure 9. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g010.jpg Figure 10. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g011.jpg Figure 11. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-g012.jpg Figure 12. Experimental and predicted moisture ratio curves for six thin-layer models (Newton, Page, https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-t004.jpg Table 4. Effective moisture diffusivity (Deff) for walnut samples under different drying conditions. https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-t005.jpg Table 5. Activation energy for moisture diffusion of Nigerian walnut under different drying conditio https://www.frontiersin.org/files/Articles/1794521/frfst-06-1794521-HTML/image_m/frfst-06-1794521-t006.jpg Table 6. Specific energy consumption (SEC) of oven (OD) and cabinet (CA) drying of Nigerian walnut. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1624327/full https://www.frontiersin.org/files/Articles/1624327/fgene-16-1624327-HTML/image_m/fgene-16-1624327-g001.jpg Figure 1. DP treatment protected Staphylococcus aureus-induced pneumonia and altered the transcripto https://www.frontiersin.org/files/Articles/1624327/fgene-16-1624327-HTML/image_m/fgene-16-1624327-g002.jpg Figure 2. Identification and functional enrichment analysis of DEGs. (A) The volcano map of DEGs bet https://www.frontiersin.org/files/Articles/1624327/fgene-16-1624327-HTML/image_m/fgene-16-1624327-t001.jpg Table 1. Classification of RASEs between DP vs. SA and SA vs. HL. https://www.frontiersin.org/files/Articles/1624327/fgene-16-1624327-HTML/image_m/fgene-16-1624327-g003.jpg Figure 3. Genome-wide profiling of RASGs induced by DP. (A) Venn diagram showing the overlapped gene https://www.frontiersin.org/files/Articles/1624327/fgene-16-1624327-HTML/image_m/fgene-16-1624327-g004.jpg Figure 4. Immune cell subpopulation analysis based on CIBERSORT algorithm. (A) The proportion of imm https://www.frontiersin.org/files/Articles/1624327/fgene-16-1624327-HTML/image_m/fgene-16-1624327-g005.jpg Figure 5. Selection and analysis of hub genes that were co-regulated by SA and DP. (A) Venn diagram https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1658113/full https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-g001.jpg Figure 1. Research roadmap. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-g002.jpg Figure 2. Study area–Qinghai Province, China. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-t001.jpg Table 1. Data sources. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-t002.jpg Table 2. Selection of suitability evaluation factors. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-t003.jpg Table 3. Subjective and objective weighting results table. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-t004.jpg Table 4. Game theory combination weighting results table. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-t005.jpg Table 5. Interval mapping of suitability evaluation factors. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-g003.jpg Figure 3. Suitability evaluation factors for PV industry development. (a) Distance to town, (b) Dist https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-g004.jpg Figure 4. Map of suitable areas for PV industry land use. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-g005.jpg Figure 5. Restricted area. (a) Nature reserves, (b) land use, (c) Slope, (d) Distance to transportat https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-g006.jpg Figure 6. Regional distribution of areas with realizable potential. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-g007.jpg Figure 7. Potential area for PV power generation in various regions. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-g008.jpg Figure 8. Correlation between PV potential and electricity consumption. (a) Spatial distribution of https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-t006.jpg Table 6. Power generation potential and emission reduction effects. https://www.frontiersin.org/files/Articles/1658113/fenvs-13-1658113-HTML/image_m/fenvs-13-1658113-g009.jpg Figure 9. Visualisation of carbon emission reduction benefits. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1824103/full https://www.frontiersin.org/files/Articles/1824103/fmicb-17-1824103-HTML/image_m/fmicb-17-1824103-g001.jpg Figure 1. Abundance of cyanobacteria from 16S rRNA amplicon sequencing. Description of the bacterial https://www.frontiersin.org/files/Articles/1824103/fmicb-17-1824103-HTML/image_m/fmicb-17-1824103-t001.jpg Table 1. Rare cyanobacterial ASV sequences from steam vents. https://www.frontiersin.org/files/Articles/1824103/fmicb-17-1824103-HTML/image_m/fmicb-17-1824103-g002.jpg Figure 2. Maximum likelihood phylogenomic tree of 381 cyanobacterial genomes, including newly sequen https://www.frontiersin.org/files/Articles/1824103/fmicb-17-1824103-HTML/image_m/fmicb-17-1824103-t002.jpg Table 2. Statistics and applied guidelines of selected cyanobacterial MAGs deposited in SeqCode. https://www.frontiersin.org/files/Articles/1824103/fmicb-17-1824103-HTML/image_m/fmicb-17-1824103-g003.jpg Figure 3. Guidelines for describing cyanobacterial species under the SeqCode. Two situations arise w https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2025.1643931/full https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-t001.jpg Table 1. Physical parameters of limestone tailings. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-t002.jpg Table 2. Chemical composition of limestone tailings. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g001.jpg Figure 1. XRD analysis of limestone tailings. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g002.jpg Figure 2. SEM images of the limestone tailings. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g003.jpg Figure 3. Gradation curve of limestone tailings and gravel. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g004.jpg Figure 4. Dry density and moisture content curve of limestone tailings. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g005.jpg Figure 5. The CBR of limestone tailings under different compactness. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g006.jpg Figure 6. Fitting curve of the direct shear test of limestone tailings. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g007.jpg Figure 7. Triaxial consolidation drainage shear test device. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g008.jpg Figure 8. Triaxial shear test fitting curves of limestone tailings under different confining pressur https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g009.jpg Figure 9. Strength envelope of limestone tailings sample. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-t003.jpg Table 3. Mechanical parameters of soil material. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g010.jpg Figure 10. Filling subgrade calculation model. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g011.jpg Figure 11. Filling subgrade model settlement diagram in 3 m. (a) Gravel. (b) Limestone tailings. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g012.jpg Figure 12. Filling subgrade model settlement diagram in 5 m. (a) Gravel. (b) Limestone tailings. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g013.jpg Figure 13. Filling subgrade model settlement diagram in 7 m. (a) Gravel. (b) Limestone tailings. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g014.jpg Figure 14. Stress and strain distribution. (a) Stress distribution. (b) Strain distribution. https://www.frontiersin.org/files/Articles/1643931/fbuil-11-1643931-HTML/image_m/fbuil-11-1643931-g015.jpg Figure 15. Filling subgrade settlement observation curve. https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2026.1748837/full https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-g001.jpg Figure 1. Methodological workflow of the proposed framework. https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-g002.jpg Figure 2. Express G Diagram of the multimodel elements (Fuchs et al., 2011). https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-g003.jpg Figure 3. An instance of link element (left) based on ISO 21597, (right) based on the proposed appro https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-g004.jpg Figure 4. Algorithm steps for the dynamic linking and version-aware update mechanism. https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-g005.jpg Figure 5. Dynamic multimodel framework architecture for environmental planning service based on (Al- https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-g006.jpg Figure 6. User interface for Environmental Planning Service. Documents browser (left) and documents https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-g007.jpg Figure 7. Business process model for the assessment of the environmental consideration. https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-t001.jpg Table 1. Quantitative evaluation indicators of the case study. https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-t002.jpg Table 2. Conceptual comparison with existing BIM–GIS and ICDD-Based approaches. https://www.frontiersin.org/files/Articles/1748837/fbuil-12-1748837-HTML/image_m/fbuil-12-1748837-g008.jpg Figure 8. Execution-time comparison between manual and automated semantic link creation. https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2025.1729043/full https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-t001.jpg Table 1. Chronological summary of key studies on wing geometry, spar configuration, and modal perfor https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-t002.jpg Table 2. Aerodynamic and geometric date for tapered wings at 9° angle of attack. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-t003.jpg Table 3. Aerodynamic and geometric date for swept wings at 9° angle of attack. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g001.jpg Figure 1. Geometrical characteristics of the studied NACA airfoils. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-t004.jpg Table 4. Box spar cross-section dimensions. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-t005.jpg Table 5. I section spar dimensions. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-t006.jpg Table 6. Excerpt from the APDL code for tapered NACA 2411 wing (box spar). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g002.jpg Figure 2. Samples of different 3-D wings models. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g003.jpg Figure 3. Taper wing layouts. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g004.jpg Figure 4. Swept wing layout. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-t007.jpg Table 7. Different wing parts mechanical properties, Aluminum alloys. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g005.jpg Figure 5. FE solution details; element type layout, discretized wing model and flowchart. (a) Solid https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g006.jpg Figure 6. Effect of wing element assembly on natural frequency and deflection for 6th mode, (a) spar https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g007.jpg Figure 7. Deflection of spar, spar and ribs and wing structure. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g008.jpg Figure 8. ωn of spar, spar and ribs and wing structure. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g009.jpg Figure 9. 0024 1st mode ωn and deflection (bending). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g010.jpg Figure 10. 0024 2nd mode ωn and deflection (bending). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g011.jpg Figure 11. 0024 3rd mode ωn and deflection, box spar (couple of bending and torsion). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g012.jpg Figure 12. 0024 4th mode ωn and deflection, box spar (couple of bending and torsion). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g013.jpg Figure 13. 0024 5th mode ωn and deflection, box spar (couple of bending and torsion). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g014.jpg Figure 14. 0024 6th mode ωn and deflection, box spar (bending). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g015.jpg Figure 15. 0024 1st mode ωn and deflection, I section (bending). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g016.jpg Figure 16. 0024 2nd mode ωn and deflection, I section (bending). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g017.jpg Figure 17. 0024 3rd mode ωn and deflection, I section (couple of bending and torsion). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g018.jpg Figure 18. 0024 4th mode ωn and deflection, I section (couple of bending and torsion). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g019.jpg Figure 19. 0024 5th mode ωn and deflection, I section (couple of bending and torsion). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g020.jpg Figure 20. 0024 6th mode ωn and deflection, I section (bending). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g021.jpg Figure 21. Variation of natural frequency for taper wings (box-spar configuration). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g022.jpg Figure 22. Variation of natural frequency for taper wing (I-section configuration). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g023.jpg Figure 23. Variation of natural frequency for Swept-back wings (box-spar configuration). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g024.jpg Figure 24. Variation of natural frequency for Swept-back wings (I-section configuration). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g025.jpg Figure 25. Deflection vs. mode number for taper wings (box-spar configuration). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g026.jpg Figure 26. Deflection vs. mode number for taper wing (I-section configuration). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g027.jpg Figure 27. Deflection vs. mode number for Swept-back wings (box-spar configuration). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g028.jpg Figure 28. Deflection vs. mode number for Swept-back wings (I-section configuration). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-t008.jpg Table 8. Two-way ANOVA results for geometry and spar type. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-t009.jpg Table 9. NACA 4412 model, Characteristics and natural frequency, for 1st and 2nd modes. https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g029.jpg Figure 29. Simulation to the 1st mode vibration for reference (Mostakim et al., 2020). https://www.frontiersin.org/files/Articles/1729043/fmech-11-1729043-HTML-r1/image_m/fmech-11-1729043-g030.jpg Figure 30. Simulation to the 2nd mode vibration for reference (Mostakim et al., 2020). https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1759284/full https://www.frontiersin.org/files/Articles/1759284/feduc-11-1759284-HTML-r1/image_m/feduc-11-1759284-t001.jpg Table 1. Descriptive statistics and reliability for student indices. https://www.frontiersin.org/files/Articles/1759284/feduc-11-1759284-HTML-r1/image_m/feduc-11-1759284-t002.jpg Table 2. Pearson correlations among key student indices. https://www.frontiersin.org/files/Articles/1759284/feduc-11-1759284-HTML-r1/image_m/feduc-11-1759284-g001.jpg Figure 1. Relationship between students’ AI_risk_index and Grammar_focus_index (1–5 Likert scales). https://www.frontiersin.org/files/Articles/1759284/feduc-11-1759284-HTML-r1/image_m/feduc-11-1759284-g002.jpg Figure 2. Students’ and teachers’ perceptions of grammar-focused instruction and AI (mean scores on https://www.frontiersin.org/files/Articles/1759284/feduc-11-1759284-HTML-r1/image_m/feduc-11-1759284-g003.jpg Figure 3. Instructional leadership model: AI as a teacher-led communicative compensation mechanism b https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1701763/full https://www.frontiersin.org/files/Articles/1701763/fpubh-13-1701763-HTML/image_m/fpubh-13-1701763-t001.jpg Table 1. Summary of included study outcomes: effect of exercise modalities on arterial stiffness (PW https://www.frontiersin.org/files/Articles/1701763/fpubh-13-1701763-HTML/image_m/fpubh-13-1701763-g001.jpg Figure 1. Flowchart of the study identification process. https://www.frontiersin.org/files/Articles/1701763/fpubh-13-1701763-HTML/image_m/fpubh-13-1701763-t002.jpg Table 2. Arterial stiffness responses to resistance, aerobic and/or concurrent aerobic plus resistan https://www.frontiersin.org/journals/neuroanatomy/articles/10.3389/fnana.2026.1754934/full https://www.frontiersin.org/files/Articles/1754934/fnana-20-1754934-HTML/image_m/fnana-20-1754934-g001.jpg Figure 1. Justo Gonzalo (second from left) at the “Hospital Provincial de Madrid” in 1933. He is loo https://www.frontiersin.org/files/Articles/1754934/fnana-20-1754934-HTML/image_m/fnana-20-1754934-g002.jpg Figure 2. Threshold excitability strength–duration curves (volts versus microfarads) obtained by ret https://www.frontiersin.org/files/Articles/1754934/fnana-20-1754934-HTML/image_m/fnana-20-1754934-g003.jpg Figure 3. Perceived orientation (in degrees) of a 10-cm upright white test arrow as a function of mu https://www.frontiersin.org/files/Articles/1754934/fnana-20-1754934-HTML/image_m/fnana-20-1754934-g004.jpg Figure 4. Room where Gonzalo taught the courses on Brain Pathophysiology (1945–1966), which formed p https://www.frontiersin.org/files/Articles/1754934/fnana-20-1754934-HTML/image_m/fnana-20-1754934-g005.jpg Figure 5. Upper part: Schematic diagram of the specific visual and tactile gradients. The curves tha https://www.frontiersin.org/files/Articles/1754934/fnana-20-1754934-HTML/image_m/fnana-20-1754934-g006.jpg Figure 6. Thirty-five cases of central syndrome of varying intensity. The symmetric concentric reduc https://www.frontiersin.org/files/Articles/1754934/fnana-20-1754934-HTML/image_m/fnana-20-1754934-g007.jpg Figure 7. Justo Gonzalo presenting his contribution at the IV Congress of Neuropsychiatry in Madrid, https://www.frontiersin.org/files/Articles/1754934/fnana-20-1754934-HTML/image_m/fnana-20-1754934-g008.jpg Figure 8. Allometry. Correlation curves of various visual perceptual functions (luminosity, image or https://www.frontiersin.org/journals/neuroanatomy/articles/10.3389/fnana.2025.1724830/full https://www.frontiersin.org/files/Articles/1724830/fnana-19-1724830-HTML/image_m/fnana-19-1724830-g001.jpg Figure 1. Cajal’s organization of the neuronal nucleus. (A–K) Nuclear structures illustrated by Caja https://www.frontiersin.org/files/Articles/1724830/fnana-19-1724830-HTML/image_m/fnana-19-1724830-g002.jpg Figure 2. Argyrophilic nucleolar spherules/“FC-DFC units”. (A) Cajal’s silver staining of argyrophil https://www.frontiersin.org/files/Articles/1724830/fnana-19-1724830-HTML/image_m/fnana-19-1724830-g003.jpg Figure 3. Hyaline grumes/“nuclear speckles”. (A) Cajal’s original drawing of hyaline grumes/“nuclear https://www.frontiersin.org/files/Articles/1724830/fnana-19-1724830-HTML/image_m/fnana-19-1724830-g004.jpg Figure 4. Neutrophil granules/“transcription factories”. (A) In vivo transcription assay based on th https://www.frontiersin.org/files/Articles/1724830/fnana-19-1724830-HTML/image_m/fnana-19-1724830-g005.jpg Figure 5. The accessory body/“Cajal body”. (A) Cajal’s original drawing of silver-stained accessory https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1685730/full https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-t001.jpg Table 1. Informed consent assessment scale (ICAS). https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-t002.jpg Table 2. Item difficulty estimates from the Rasch model and the 2-parameter logistic (2PL) model, al https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-g001.jpg Figure 1. Item characteristics curve for Rasch analysis. Each curve represents one of the 14 items o https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-g002.jpg Figure 2. Item information curve for the Rasch model. The Rasch TIF peaks around θ = −2, indicating https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-t003.jpg Table 3. Item fit statistics for the Rasch model. https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-t004.jpg Table 4. Person fit statistics for the Rasch model. https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-g003.jpg Figure 3. Item characteristics curves for 2PL model. Distinct slopes illustrate variable item discri https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-g004.jpg Figure 4. Item information curve for 2PL model. https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-g005.jpg Figure 5. Reliability plot for Rasch analysis. https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-g006.jpg Figure 6. Reliability plot for 2PL analysis. https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-g007.jpg Figure 7. Person-item map (Wright map) for ICAS. This map displays the distribution of item difficul https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-t005.jpg Table 5. Model fit comparison between the Rasch and 2PL models for the ICAS. https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-t006.jpg Table 6. Spearman’s rho correlation matrix between the Global ICAS assessment, CAS total scores (eva https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-t007.jpg Table 7. Comparison of ICAS item performance between academic years (Chi-square tests). https://www.frontiersin.org/files/Articles/1685730/fmed-12-1685730-HTML/image_m/fmed-12-1685730-t008.jpg Table 8. Comparison of continuous assessment variables between academic years. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1772860/full https://www.frontiersin.org/files/Articles/1772860/fonc-16-1772860-HTML/image_m/fonc-16-1772860-t001.jpg Table 1. Target genes and frequency in metastatic prostatic adenocarcinoma. https://www.frontiersin.org/files/Articles/1772860/fonc-16-1772860-HTML/image_m/fonc-16-1772860-t002.jpg Table 2. Targeted treatments and frequency in metastatic prostatic adenocarcinoma. https://www.frontiersin.org/files/Articles/1772860/fonc-16-1772860-HTML/image_m/fonc-16-1772860-g001.jpg Figure 1. Overall survival (OS) and progression-free survival (PFS) for p53 mutant patients with hor https://www.frontiersin.org/files/Articles/1772860/fonc-16-1772860-HTML/image_m/fonc-16-1772860-g002.jpg Figure 2. Overall survival (OS) and progression-free survival (PFS) for PTEN mutant patients with ho https://www.frontiersin.org/files/Articles/1772860/fonc-16-1772860-HTML/image_m/fonc-16-1772860-g003.jpg Figure 3. Overall survival (OS) and progression-free survival (PFS) for HRR mutant patients with hor https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1788049/full https://www.frontiersin.org/files/Articles/1788049/fendo-17-1788049-HTML-r1/image_m/fendo-17-1788049-t001.jpg Table 1. Longitudinal parameters at baseline and during high-dose vitamin D supplementation. https://www.frontiersin.org/files/Articles/1788049/fendo-17-1788049-HTML-r1/image_m/fendo-17-1788049-g001.jpg Figure 1. A graphical representation of the temporal relationship between serum 25-hydroxyvitamin D https://www.frontiersin.org/files/Articles/1788049/fendo-17-1788049-HTML-r1/image_m/fendo-17-1788049-g002.jpg Figure 2. A graphical representation of the temporal relationship between serum 25-hydroxyvitamin D https://www.frontiersin.org/files/Articles/1788049/fendo-17-1788049-HTML-r1/image_m/fendo-17-1788049-g003.jpg Figure 3. A graphical representation of the temporal relationship between serum 25-hydroxyvitamin D https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1752280/full https://www.frontiersin.org/files/Articles/1752280/fpsyg-17-1752280-HTML/image_m/fpsyg-17-1752280-g001.jpg Figure 1. Schematic overview of the proposed framework linking uncertainty dynamics, embodiment, and https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1706773/full https://www.frontiersin.org/files/Articles/1706773/fneur-17-1706773-HTML-r1/image_m/fneur-17-1706773-g001.jpg Figure 1. Wall-marker set-up for VVOR and VOR-S. Schematic of the central visual fixation target and https://www.frontiersin.org/files/Articles/1706773/fneur-17-1706773-HTML-r1/image_m/fneur-17-1706773-g002.jpg Figure 2. Representative head velocity profiles. (A) Shows representative sinusoidal VVOR and VOR-S https://www.frontiersin.org/files/Articles/1706773/fneur-17-1706773-HTML-r1/image_m/fneur-17-1706773-g003.jpg Figure 3. Relationship between intended and actual head rotation frequency and peak velocity. The co https://www.frontiersin.org/files/Articles/1706773/fneur-17-1706773-HTML-r1/image_m/fneur-17-1706773-g004.jpg Figure 4. Effect of head rotation frequency and direction on VVOR and VOR-S gain and refixation sacc https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1807534/full https://www.frontiersin.org/files/Articles/1807534/fmed-13-1807534-HTML/image_m/fmed-13-1807534-g001.jpg Figure 1. Representative OCTA images used in analysis. (A) The total retinal thickness map, (B) Five https://www.frontiersin.org/files/Articles/1807534/fmed-13-1807534-HTML/image_m/fmed-13-1807534-g002.jpg Figure 2. Representative flow signal diagram in an OCT B-scan of a control eye, a Group 1 eye, and a https://www.frontiersin.org/files/Articles/1807534/fmed-13-1807534-HTML/image_m/fmed-13-1807534-t001.jpg Table 1. Basic characteristics of the subjects. https://www.frontiersin.org/files/Articles/1807534/fmed-13-1807534-HTML/image_m/fmed-13-1807534-g003.jpg Figure 3. Representative OCT B-scan images of a retina and choroid of a control eye, a Group 1 eye, https://www.frontiersin.org/files/Articles/1807534/fmed-13-1807534-HTML/image_m/fmed-13-1807534-t002.jpg Table 2. The retinal capillary density (%) and choroid vessel index (CVI) among the three groups. https://www.frontiersin.org/files/Articles/1807534/fmed-13-1807534-HTML/image_m/fmed-13-1807534-t003.jpg Table 3. The thickness (μm) of retina and choroid among the three groups. https://www.frontiersin.org/files/Articles/1807534/fmed-13-1807534-HTML/image_m/fmed-13-1807534-g004.jpg Figure 4. Analysis of correlation between outer retinal thickness and choroidal index in patients wi https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1630913/full https://www.frontiersin.org/files/Articles/1630913/fenvs-14-1630913-HTML-r1/image_m/fenvs-14-1630913-g001.jpg Figure 1. (a) Represents global CO2 emissions in the transportation sector over the year (in gigaton https://www.frontiersin.org/files/Articles/1630913/fenvs-14-1630913-HTML-r1/image_m/fenvs-14-1630913-g002.jpg Figure 2. (a) Localization of Li-ion battery giga factories (e.g., India is taken as an example mode https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1772474/full https://www.frontiersin.org/files/Articles/1772474/fmed-13-1772474-HTML/image_m/fmed-13-1772474-g001.jpg Figure 1. This patient’s follow-up chest CT images for suspected Aspergillus infection. (A,B) Multip https://www.frontiersin.org/files/Articles/1772474/fmed-13-1772474-HTML/image_m/fmed-13-1772474-g002.jpg Figure 2. Chest CT images of the patient before and after bronchoscopy. (A,B) The manifestation of A https://www.frontiersin.org/files/Articles/1772474/fmed-13-1772474-HTML/image_m/fmed-13-1772474-g003.jpg Figure 3. Bronchoscopy image showing an Aspergillus ball in the posterior segment of the right upper https://www.frontiersin.org/files/Articles/1772474/fmed-13-1772474-HTML/image_m/fmed-13-1772474-g004.jpg Figure 4. Pathological findings of Aspergillus fumigatus. (A) Paraffin-embedded tissue section with https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1724283/full https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g001.jpg Figure 1. Geographical location of the Mu Us Sandy Land and spatial distribution of 11 meteorologica https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-t001.jpg Table 1. The Detailed information of data sources. https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g002.jpg Figure 2. Schematic of eight NPP change patterns identified by the BFAST algorithm: (A) Type1–monoto https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g003.jpg Figure 3. Schematic illustration of the overall workflow. Variable abbreviations are consistent with https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g004.jpg Figure 4. Spatial characteristic of environmental factors and LULC distribution. Specifically, it in https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g005.jpg Figure 5. (A) Annual average, (B) trend, and (C) significance of NPP in the MUSL (2001–2020); (D) sp https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g006.jpg Figure 6. The direct effect, indirect effect, and regression weight of climate factors (P, T, ET, VP https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g007.jpg Figure 7. Spatial distribution of correlation coefficients between (A) NPP and P and (C) NPP and ET, https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g008.jpg Figure 8. Variations of (A) WUE and (B) PUE in Type1, Type3, Type5, and Type7 regions. Scatter plots https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g009.jpg Figure 9. WUE and PUE quartile-based classifications in the MUSL from 2001 to 2020. (A) Frequency hi https://www.frontiersin.org/files/Articles/1724283/fpls-17-1724283-HTML/image_m/fpls-17-1724283-g010.jpg Figure 10. Timeline of ecological restoration stages and policy evolution in the MUSL. https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2026.1830223/full https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-g001.jpg Figure 1. Framework illustrating Key constructs and relationships. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t001.jpg Table 1. Instrument development. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t002.jpg Table 2. Measurement reliability and convergent validity. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t003.jpg Table 3. Discriminant validity. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t004.jpg Table 4. “KMO and bartlett's test”. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t005.jpg Table 5. Anti-image correlation & communality. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t006.jpg Table 6. Loading factor in EFA. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-g002.jpg Figure 2. Final model CFA. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t007.jpg Table 7. CFA model-fit summary. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t008.jpg Table 8. Demographic profile of respondents. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t009.jpg Table 9. Standardized direct effects on purchase intention. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-g003.jpg Figure 3. Direct effects model – influence of independent variables on purchase intention. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-g004.jpg Figure 4. Final structural model with mediating variables and standardized estimates. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t010.jpg Table 10. Model Fit indices. https://www.frontiersin.org/files/Articles/1830223/fcomm-11-1830223-HTML-r1/image_m/fcomm-11-1830223-t011.jpg Table 11. Path analysis. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1689991/full https://www.frontiersin.org/files/Articles/1689991/fimmu-17-1689991-HTML/image_m/fimmu-17-1689991-t001.jpg Table 1. Baseline demographic and clinical characteristics of the patients. https://www.frontiersin.org/files/Articles/1689991/fimmu-17-1689991-HTML/image_m/fimmu-17-1689991-g001.jpg Figure 1. The lesions of patients before treatment. https://www.frontiersin.org/files/Articles/1689991/fimmu-17-1689991-HTML/image_m/fimmu-17-1689991-g002.jpg Figure 2. The lesions of patients after 4 weeks treatment of stapokibart. https://www.frontiersin.org/files/Articles/1689991/fimmu-17-1689991-HTML/image_m/fimmu-17-1689991-t002.jpg Table 2. Comparison of BPDAI,DLQI,itching NRS,EO and anti-BP180 antibodies between before and after https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1747090/full https://www.frontiersin.org/files/Articles/1747090/fmed-13-1747090-HTML-r1/image_m/fmed-13-1747090-g001.jpg Figure 1. Flowchart of patient recruitment and study population selection. https://www.frontiersin.org/files/Articles/1747090/fmed-13-1747090-HTML-r1/image_m/fmed-13-1747090-t001.jpg Table 1. Baseline characteristics of RA patients with and without osteoporosis. https://www.frontiersin.org/files/Articles/1747090/fmed-13-1747090-HTML-r1/image_m/fmed-13-1747090-t002.jpg Table 2. Univariate and multivariate logistic regression analyses of risk factors for osteoporosis i https://www.frontiersin.org/files/Articles/1747090/fmed-13-1747090-HTML-r1/image_m/fmed-13-1747090-g002.jpg Figure 2. Nomogram for predicting the risk of osteoporosis in patients with RA. https://www.frontiersin.org/files/Articles/1747090/fmed-13-1747090-HTML-r1/image_m/fmed-13-1747090-g003.jpg Figure 3. Validation of the nomogram for predicting osteoporosis in RA patients. (a) ROC curve for t https://www.frontiersin.org/files/Articles/1747090/fmed-13-1747090-HTML-r1/image_m/fmed-13-1747090-g004.jpg Figure 4. DCA and CICA of the osteoporosis risk nomogram in RA patients. (a,b) DCA in the training c https://www.frontiersin.org/files/Articles/1747090/fmed-13-1747090-HTML-r1/image_m/fmed-13-1747090-g005.jpg Figure 5. Risk stratification based on nomogram scores. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1803471/full https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-t001.jpg Table 1. Eligibility criteria. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-t002.jpg Table 2. Complete search strategies across databases. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-g001.jpg Figure 1. Systematic review search and screening procedure. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-t003.jpg Table 3. Characteristics of studies included in the meta-analysis. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-t004.jpg Table 4. Descriptive characteristics of SSG intervention protocols. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-g002.jpg Figure 2. Risk of overall bias. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-g003.jpg Figure 3. RoB-2 assessment. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-t005.jpg Table 5. GRADE summary of evidence. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-g004.jpg Figure 4. Forest plot of SSG on maximal aerobic capacity. Weights are from random-effects model. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-g005.jpg Figure 5. Forest plot of SSG on intermittent high-intensity endurance. Weights are from random-effec https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-g006.jpg Figure 6. Forest plot of SSG on sprint acceleration. Weights are from random-effects model. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-g007.jpg Figure 7. Forest plot of SSG on maximal sprint speed. Weights are from random-effects model. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-g008.jpg Figure 8. Forest plot of SSG on change-of-direction ability. Weights are from random-effects model. https://www.frontiersin.org/files/Articles/1803471/fphys-17-1803471-HTML/image_m/fphys-17-1803471-g009.jpg Figure 9. Forest plot of SSG on lower-limb explosive power. Weights are from random-effects model. https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2026.1831688/full https://www.frontiersin.org/files/Articles/1831688/fagro-08-1831688-HTML/image_m/fagro-08-1831688-t001.jpg Table 1. Bioherbicide classification, examples, and mode of action (MOA) (adapted from Zhang et al., https://www.frontiersin.org/files/Articles/1831688/fagro-08-1831688-HTML/image_m/fagro-08-1831688-g001.jpg Figure 1. Challenges in bioherbicide applications. https://www.frontiersin.org/files/Articles/1831688/fagro-08-1831688-HTML/image_m/fagro-08-1831688-g002.jpg Figure 2. Common waterhemp (Amaranthus tuberculatus [Moq.] J.D. Sauer) regrowth after pelargonic aci https://www.frontiersin.org/files/Articles/1831688/fagro-08-1831688-HTML/image_m/fagro-08-1831688-t002.jpg Table 2. Summarized efficacy reports on bioherbicides. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1712006/full https://www.frontiersin.org/files/Articles/1712006/fcvm-13-1712006-HTML-r1/image_m/fcvm-13-1712006-g001.jpg Figure 1. Flow chart of study inclusion-exclusion criteria. https://www.frontiersin.org/files/Articles/1712006/fcvm-13-1712006-HTML-r1/image_m/fcvm-13-1712006-t001.jpg Table 1. Baseline characteristics of participants grouped according to incidence of CVD. https://www.frontiersin.org/files/Articles/1712006/fcvm-13-1712006-HTML-r1/image_m/fcvm-13-1712006-t002.jpg Table 2. Logistic regression analysis of TyG and physical measurement indicators with incidence of C https://www.frontiersin.org/files/Articles/1712006/fcvm-13-1712006-HTML-r1/image_m/fcvm-13-1712006-g002.jpg Figure 2. Restricted cubic spline curves showing the association between TyG index and ABSI and inci https://www.frontiersin.org/files/Articles/1712006/fcvm-13-1712006-HTML-r1/image_m/fcvm-13-1712006-g003.jpg Figure 3. Receiver operating characteristic curve for predicting CVD in overweight and obese patient https://www.frontiersin.org/files/Articles/1712006/fcvm-13-1712006-HTML-r1/image_m/fcvm-13-1712006-t003.jpg Table 3. ROC analysis of TyG and physical measurement indicators independently and in combination fo https://www.frontiersin.org/files/Articles/1712006/fcvm-13-1712006-HTML-r1/image_m/fcvm-13-1712006-g004.jpg Figure 4. Subgroup analysis of the association between the TyG index, the ABSI, and incident cardiov https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1771169/full https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-g001.jpg Figure 1. Flowchart of data collection for included studies. https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-g002.jpg Figure 2. Temporal distribution of publications from 2005 to 2025 (data extracted up to 1 November 2 https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-g003.jpg Figure 3. (A) World map showing the geographic distribution of publications. Darker shading indicate https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-t001.jpg Table 1. Top 10 countries/regions by publication volume. https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-g004.jpg Figure 4. Author co-authorship networks in neurotransmitter-related gut–brain axis research. (A) Ful https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-t002.jpg Table 2. Top 10 institutions of high contribution. https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-g005.jpg Figure 5. Institutional co-authorship network in neurotransmitter-related gut–brain axis research. E https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-g006.jpg Figure 6. Journal-level knowledge structure and relatedness networks. (A) Journal co-citation networ https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-t003.jpg Table 3. Top 10 citing and cited sources. https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-t004.jpg Table 4. Top 10 most cited references. https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-g007.jpg Figure 7. Keyword-based thematic structure and emerging topics in neurotransmitter-related gut–brain https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-g008.jpg Figure 8. CiteSpace timeline visualization of co-citation clusters (2005–2025). Each horizontal line https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-t005.jpg Table 5. Bacterial production of neurotransmitters. https://www.frontiersin.org/files/Articles/1771169/fmicb-17-1771169-HTML/image_m/fmicb-17-1771169-g009.jpg Figure 9. Mechanistic pathways linking gut microbiota-derived metabolites and neurotransmitters to c https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1791933/full https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-g001.jpg Figure 1. Flowchart of data collection for included studies. https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-g002.jpg Figure 2. The distribution of publication dates. https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-g003.jpg Figure 3. Co-authorship network among countries: (A) global collaboration. (B) International researc https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-t001.jpg Table 1. The top 10 productive countries. https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-g004.jpg Figure 4. Co-authorship network among core authors: (A) core authors collaboration, (B) core authors https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-t002.jpg Table 2. Top 10 institutions of high contribution. https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-g005.jpg Figure 5. Journal relationship analysis: (A) journal citation analysis; (B) journal coupling analysi https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-t003.jpg Table 3. Top 10 citing sources. https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-t004.jpg Table 4. Top 10 most cited references. https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-g006.jpg Figure 6. (A) Keyword co-occurrence network (top 30): node size, productivity; edges, co-keyword. (B https://www.frontiersin.org/files/Articles/1791933/fimmu-17-1791933-HTML/image_m/fimmu-17-1791933-g007.jpg Figure 7. (A) Top 7 references with the strongest citation bursts; (B) timeline visualization of co- https://www.frontiersin.org/journals/tuberculosis/articles/10.3389/ftubr.2026.1809118/full https://www.frontiersin.org/files/Articles/1809118/ftubr-04-1809118-HTML/image_m/ftubr-04-1809118-g001.jpg Figure 1. Implementation science frameworks. https://www.frontiersin.org/files/Articles/1809118/ftubr-04-1809118-HTML/image_m/ftubr-04-1809118-g002.jpg Figure 2. SWOT analysis of Pakistan's health system readiness for TB–comorbidity integrated care: in https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1737389/full https://www.frontiersin.org/files/Articles/1737389/fpls-17-1737389-HTML/image_m/fpls-17-1737389-g001.jpg Figure 1. Effect of geographical environments on Pinelliae Rhizoma (PR) from different origins. (A) https://www.frontiersin.org/files/Articles/1737389/fpls-17-1737389-HTML/image_m/fpls-17-1737389-g002.jpg Figure 2. Analysis of the differentially expressed genes (DEGs) between the PR from JZ and CX. (A) S https://www.frontiersin.org/files/Articles/1737389/fpls-17-1737389-HTML/image_m/fpls-17-1737389-g003.jpg Figure 3. Differential expression of the genes related to monoterpenoid indole alkaloid (MIA) pathwa https://www.frontiersin.org/files/Articles/1737389/fpls-17-1737389-HTML/image_m/fpls-17-1737389-g004.jpg Figure 4. Differential expression of the genes related to benzylisoquinoline alkaloid (BIA) pathway. https://www.frontiersin.org/files/Articles/1737389/fpls-17-1737389-HTML/image_m/fpls-17-1737389-g005.jpg Figure 5. Establishment of the callus induction system of P. ternata. (A) Analysis of the infection https://www.frontiersin.org/files/Articles/1737389/fpls-17-1737389-HTML/image_m/fpls-17-1737389-g006.jpg Figure 6. Effect of MAO, NCS II, and TyrAT genes on the alkaloid biosynthesis of P. ternata. (A) Clo https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1717245/full https://www.frontiersin.org/files/Articles/1717245/fcvm-13-1717245-HTML-r1/image_m/fcvm-13-1717245-t001.jpg Table 1. Baseline characteristics of the study patients. https://www.frontiersin.org/files/Articles/1717245/fcvm-13-1717245-HTML-r1/image_m/fcvm-13-1717245-t002.jpg Table 2. CMR parameters of study patients stratified by cardiopulmonary function. https://www.frontiersin.org/files/Articles/1717245/fcvm-13-1717245-HTML-r1/image_m/fcvm-13-1717245-g001.jpg Figure 1. Peak VO₂ between different clinical subgroups. Error bars indicate standard error of the m https://www.frontiersin.org/files/Articles/1717245/fcvm-13-1717245-HTML-r1/image_m/fcvm-13-1717245-g002.jpg Figure 2. Heatmap of pairwise Pearson correlations between CPET and clinical/CMR variables. *P < 0.0 https://www.frontiersin.org/files/Articles/1717245/fcvm-13-1717245-HTML-r1/image_m/fcvm-13-1717245-g003.jpg Figure 3. Peak VO₂ correlations with cardiac structural and functional parameters. Dotted lines indi https://www.frontiersin.org/files/Articles/1717245/fcvm-13-1717245-HTML-r1/image_m/fcvm-13-1717245-t003.jpg Table 3. Univariate analysis of factors associated with peak VO2. https://www.frontiersin.org/files/Articles/1717245/fcvm-13-1717245-HTML-r1/image_m/fcvm-13-1717245-t004.jpg Table 4. Multiple linear regression of factors associated with peak VO2. https://www.frontiersin.org/files/Articles/1717245/fcvm-13-1717245-HTML-r1/image_m/fcvm-13-1717245-g004.jpg Figure 4. ROC curve for LA reservoir strain in predicting reduced exercise tolerance. LA reservoir s https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1746894/full https://www.frontiersin.org/files/Articles/1746894/fphys-17-1746894-HTML/image_m/fphys-17-1746894-g001.jpg Figure 1. Flow diagram of the study design and participant analysis. The workflow illustrates the pr https://www.frontiersin.org/files/Articles/1746894/fphys-17-1746894-HTML/image_m/fphys-17-1746894-t001.jpg Table 1. Baseline demographic characteristics of study participants. Continuous variables are presen https://www.frontiersin.org/files/Articles/1746894/fphys-17-1746894-HTML/image_m/fphys-17-1746894-t002.jpg Table 2. Standard and wearable device measurements stratified by altitude and population group. Data https://www.frontiersin.org/files/Articles/1746894/fphys-17-1746894-HTML/image_m/fphys-17-1746894-g002.jpg Figure 2. Trends in physiological measurements across altitudes for Migrant and Resident groups. Lin https://www.frontiersin.org/files/Articles/1746894/fphys-17-1746894-HTML/image_m/fphys-17-1746894-g003.jpg Figure 3. Bland-Altman analysis of agreement between wearable device and standard measurements. Blan https://www.frontiersin.org/files/Articles/1746894/fphys-17-1746894-HTML/image_m/fphys-17-1746894-t003.jpg Table 3. Error metrics for wearable device measurements stratified by altitude and population group. https://www.frontiersin.org/files/Articles/1746894/fphys-17-1746894-HTML/image_m/fphys-17-1746894-g004.jpg Figure 4. Longitudinal analysis of measurement error in the Migrant group across altitudes. Boxplots https://www.frontiersin.org/files/Articles/1746894/fphys-17-1746894-HTML/image_m/fphys-17-1746894-g005.jpg Figure 5. Comparison of measurement error between Migrant (acute exposure) and Resident (chronic ada https://www.frontiersin.org/files/Articles/1746894/fphys-17-1746894-HTML/image_m/fphys-17-1746894-g006.jpg Figure 6. Comparison of measurement error between Han and Zang ethnicities within the Resident group https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1666837/full https://www.frontiersin.org/files/Articles/1666837/fnagi-17-1666837-HTML/image_m/fnagi-17-1666837-g001.jpg Figure 1. Diagram illustrating the process from primary tumor site to brain microenvironment. (A) Br https://www.frontiersin.org/files/Articles/1666837/fnagi-17-1666837-HTML/image_m/fnagi-17-1666837-g002.jpg Figure 2. Diagram of brain tumor microenvironment interactions with various cell types. Panels depic https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1691657/full https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g011.jpg Scheme 1. The synthetic pathway and structures of the investigated compounds (A–E). Ethyl 1-{[3-(ada https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-t001.jpg Table 1. Crystal data and structure refinement parameters for compounds B, D and E. https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g001.jpg Figure 1. (a–c) ORTEP representation with atomic labelling scheme for compounds B, D, and E. (d) Str https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g002.jpg Figure 2. Hirshfeld surfaces mapped over the normalized distance (dnorm) for (a) compound B, (b) com https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g003.jpg Figure 3. Decomposed 2D fingerprint plots showing the relative contributions of various intermolecul https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g004.jpg Figure 4. Columnar molecular arrangement in the solid state of compound B, and various molecular dim https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-t002.jpg Table 2. Intermolecular interactions observed in various dimers of compound B, with decomposed energ https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g005.jpg Figure 5. (a) Deformation electron density map highlighting a chalcogen bond between sulfur atoms. T https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-t003.jpg Table 3. Intermolecular interactions observed in various dimers of compound D, with decomposed energ https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g006.jpg Figure 6. (a) Columnar crystal packing of compound D viewed along the crystallographic ac plane, wit https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g007.jpg Figure 7. (a) Crystal packing of compound E projected onto the crystallographic ac plane, with the b https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-t004.jpg Table 4. Intermolecular interactions observed in various dimers of compound E, with decomposed energ https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-t005.jpg Table 5. Topological parameters for selected intermolecular interactions in different dimers of comp https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-t006.jpg Table 6. Cytotoxicity of compounds A-E and the anticancer drug Doxorubicin against hepatocellular ca https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-t007.jpg Table 7. Vina docking scores for the title compounds A-C and the co-crystallized inhibitor in kcal m https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g008.jpg Figure 8. (a) Experimental (pink) and predicted (yellow) conformations of the control inhibitor (AGB https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g009.jpg Figure 9. Highest occupied molecular orbitals (left panel) and lowest unoccupied molecular orbitals https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-t008.jpg Table 8. Calculated molecular descriptors and biological activity (pIC50 = 6−log10(IC50) of Compound https://www.frontiersin.org/files/Articles/1691657/fchem-13-1691657-HTML-r1/image_m/fchem-13-1691657-g010.jpg Figure 10. Structural similarity between different pairs of molecules. https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2026.1717075/full https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-t001.jpg Table 1. Sample characteristics. https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-g001.jpg Figure 1. Distribution by level of school attended (percentage values). https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-g002.jpg Figure 2. Professional status by gender (percentage values). https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-t002.jpg Table 2. Education and employment. https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-g003.jpg Figure 3. Perceived health status by gender (percentage values). https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-g004.jpg Figure 4. Smokers by gender (percentage values). https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-g005.jpg Figure 5. Alcohol consumers by gender (percentage values). https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-g006.jpg Figure 6. Physical activity by gender (percentage values). https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-t003.jpg Table 3. Lifestyle and perceived health. https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-g007.jpg Figure 7. Individuals receiving at least one vaccination by gender (percentage values). https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-g008.jpg Figure 8. Individuals receiving at least one vaccination by school attendance (percentage values). https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-t004.jpg Table 4. Preventive healthcare and screening. https://www.frontiersin.org/files/Articles/1717075/fsoc-11-1717075-HTML/image_m/fsoc-11-1717075-t005.jpg Table 5. Housing conditions. https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2025.1679179/full https://www.frontiersin.org/files/Articles/1679179/fcomm-10-1679179-HTML/image_m/fcomm-10-1679179-g001.jpg Figure 1. Model of acculturation. Own presentation based on Berry (1980, 2022) (see also, e.g., Kiyl https://www.frontiersin.org/files/Articles/1679179/fcomm-10-1679179-HTML/image_m/fcomm-10-1679179-t001.jpg Table 1. Participant information. https://www.frontiersin.org/files/Articles/1679179/fcomm-10-1679179-HTML/image_m/fcomm-10-1679179-g002.jpg Figure 2. Participants’ self-assessed acculturation trajectories. https://www.frontiersin.org/files/Articles/1679179/fcomm-10-1679179-HTML/image_m/fcomm-10-1679179-g003.jpg Figure 3. Repertoires of an online information seeker (left) and an online and interpersonal informa https://www.frontiersin.org/files/Articles/1679179/fcomm-10-1679179-HTML/image_m/fcomm-10-1679179-t002.jpg Table 2. Overview of participants’ acculturation trajectories and repertoire type classification. https://www.frontiersin.org/files/Articles/1679179/fcomm-10-1679179-HTML/image_m/fcomm-10-1679179-g004.jpg Figure 4. Repertoire of a participant experiencing assimilation (left) and separation (right). Healt https://www.frontiersin.org/files/Articles/1679179/fcomm-10-1679179-HTML/image_m/fcomm-10-1679179-g005.jpg Figure 5. Repertoires of participants experiencing integration. Health information repertoire of Pri https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1585178/full https://www.frontiersin.org/files/Articles/1585178/fpubh-13-1585178-HTML-r1/image_m/fpubh-13-1585178-t001.jpg Table 1. Overview of search categories and terms. https://www.frontiersin.org/files/Articles/1585178/fpubh-13-1585178-HTML-r1/image_m/fpubh-13-1585178-t002.jpg Table 2. Inclusion and exclusion criteria for PHR/P with migrants in Germany. https://www.frontiersin.org/files/Articles/1585178/fpubh-13-1585178-HTML-r1/image_m/fpubh-13-1585178-g001.jpg Figure 1. PRISMA-based flow diagram (65) depicting literature search, screening and selection proces https://www.frontiersin.org/files/Articles/1585178/fpubh-13-1585178-HTML-r1/image_m/fpubh-13-1585178-t003.jpg Table 3. Characteristics of iNcluded Projects. https://www.frontiersin.org/files/Articles/1585178/fpubh-13-1585178-HTML-r1/image_m/fpubh-13-1585178-t004.jpg Table 4. Coded participation of focus population in different research phases. https://www.frontiersin.org/files/Articles/1585178/fpubh-13-1585178-HTML-r1/image_m/fpubh-13-1585178-t005.jpg Table 5. Coded project-related reflections. https://www.frontiersin.org/files/Articles/1585178/fpubh-13-1585178-HTML-r1/image_m/fpubh-13-1585178-t006.jpg Table 6. Overview of inductively coded subcategories and their examplesa. https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2026.1688241/full https://www.frontiersin.org/files/Articles/1688241/froh-07-1688241-HTML-r1/image_m/froh-07-1688241-g001.jpg Figure 1. A study flowchart describing oversampling via SMOTE (synthetic minority oversampling techn https://www.frontiersin.org/files/Articles/1688241/froh-07-1688241-HTML-r1/image_m/froh-07-1688241-t001.jpg Table 1. Child-level demographic characteristics of the sample (N = 198,433). https://www.frontiersin.org/files/Articles/1688241/froh-07-1688241-HTML-r1/image_m/froh-07-1688241-t002.jpg Table 2. Parent-level demographic characteristics of the sample (N = 198,433). https://www.frontiersin.org/files/Articles/1688241/froh-07-1688241-HTML-r1/image_m/froh-07-1688241-t003.jpg Table 3. Logistic regression analyses predicting pediatric claims for treatment of dental pain. https://www.frontiersin.org/files/Articles/1688241/froh-07-1688241-HTML-r1/image_m/froh-07-1688241-t004.jpg Table 4. Logistic regression analyses predicting pediatric claims for extraction. https://www.frontiersin.org/files/Articles/1688241/froh-07-1688241-HTML-r1/image_m/froh-07-1688241-t005.jpg Table 5. Logistic regression analyses predicting pediatric claims for treatment under dental anesthe https://www.frontiersin.org/files/Articles/1688241/froh-07-1688241-HTML-r1/image_m/froh-07-1688241-t006.jpg Table 6. Logistic regression analyses predicting pediatric claims for emergency dental visits. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1741942/full https://www.frontiersin.org/files/Articles/1741942/fpubh-14-1741942-HTML/image_m/fpubh-14-1741942-g001.jpg Figure 1. Flow diagram describing definition of final analytic sample. https://www.frontiersin.org/files/Articles/1741942/fpubh-14-1741942-HTML/image_m/fpubh-14-1741942-t001.jpg Table 1. Demographic characteristics of commercially-insured patients with CVD (n = 192,500), strati https://www.frontiersin.org/files/Articles/1741942/fpubh-14-1741942-HTML/image_m/fpubh-14-1741942-g002.jpg Figure 2. Cost distributions of overall medical, outpatient, inpatient, and prescription costs by pa https://www.frontiersin.org/files/Articles/1741942/fpubh-14-1741942-HTML/image_m/fpubh-14-1741942-t002.jpg Table 2. Mean (standard deviation) overall medical, inpatient, outpatient, and prescription costs fo https://www.frontiersin.org/files/Articles/1741942/fpubh-14-1741942-HTML/image_m/fpubh-14-1741942-t003.jpg Table 3. Gamma regression analysis results predicting average overall medical, outpatient, inpatient https://www.frontiersin.org/files/Articles/1741942/fpubh-14-1741942-HTML/image_m/fpubh-14-1741942-t004.jpg Table 4. Multinomial propensity score matching analysis results for average overall medical, outpati https://www.frontiersin.org/files/Articles/1741942/fpubh-14-1741942-HTML/image_m/fpubh-14-1741942-t005.jpg Table 5. Standardized mean differences (SMD) for baseline covariates before and after multinomial pr https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1664397/full https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-g001.jpg Figure 1. Research model. https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-t001.jpg Table 1. Information on demographic characteristics of participants. https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-t002.jpg Table 2. Mean, standard deviation, skewness and kurtosis, and alpha values of the subscales in the s https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-t003.jpg Table 3. MANOVA results of digital citizenship and cyberbullying subscale scores according to gender https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-t004.jpg Table 4. Pearson correlation test results of digital citizenship and cyberbullying attitude subscale https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-t005.jpg Table 5. Fit index values of digital citizenship scale according to CFA results. https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-g002.jpg Figure 2. Data collection procedure. https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-t006.jpg Table 6. Fit index values of cyberbullying attitude scale according to CFA results. https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-g003.jpg Figure 3. Path diagram of digital citizenship scale second level CFA analysis. https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-t007.jpg Table 7. Pearson correlation analysis of digital citizenship and cyberbullying attitude subscales. https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-g004.jpg Figure 4. Path diagram of second level CFA analysis of cyberbullying attitude scale. https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-t008.jpg Table 8. Path analysis results for digital citizenship scale and identity concealment, enjoyment, ap https://www.frontiersin.org/files/Articles/1664397/fpsyg-16-1664397-HTML/image_m/fpsyg-16-1664397-g005.jpg Figure 5. Path analysis for the prediction of identity concealment, enjoyment, approval, and anxiety https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1666659/full https://www.frontiersin.org/files/Articles/1666659/fpubh-13-1666659-HTML-r1/image_m/fpubh-13-1666659-t001.jpg Table 1. Included studies not reporting prevalence and risk factors of burnout. https://www.frontiersin.org/files/Articles/1666659/fpubh-13-1666659-HTML-r1/image_m/fpubh-13-1666659-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1666659/fpubh-13-1666659-HTML-r1/image_m/fpubh-13-1666659-t002.jpg Table 2. Included studies reporting prevalence and risk factors of burnout. https://www.frontiersin.org/files/Articles/1666659/fpubh-13-1666659-HTML-r1/image_m/fpubh-13-1666659-g002.jpg Figure 2. Number of studies per continent. https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2025.1714841/full https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-g001.jpg Figure 1. From left to right, Souq area in a shopping mall in Bahrain compared to (right) Bab Al-Bah https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-g002.jpg Figure 2. Plan (left) for Bab Al Bahrain (Manama Souq Entrance [Red]) showing its proximity to port https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-g003.jpg Figure 3. The kingdom of Bahrain location. Source: Google Maps (2025). https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-t001.jpg Table 1. List of different factors impacting preference of shopping malls from different literature. https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-t002.jpg Table 2. Reliability statistics for survey. https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-t003.jpg Table 3. Respondents’ profile (n = 240). https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-g004.jpg Figure 4. Decomgraphic profile of respondents. https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-t004.jpg Table 4. Mean comparison of user perceptions across souqs and malls. https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-g005.jpg Figure 5. Scree plots for factor extraction in souq and mall items. https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-t005.jpg Table 5. Highest-loading items on principal components (PC1 and PC2) for souqs and malls. https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-t006.jpg Table 6. Cluster-Based Segmentation of users Preferences: Souq vs. Mall. https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-t007.jpg Table 7. Regression model of mall–souq preference difference. https://www.frontiersin.org/files/Articles/1714841/fbuil-11-1714841-HTML/image_m/fbuil-11-1714841-g006.jpg Figure 6. Distribution of responses to the question: “Would you like to see a hybrid space combining https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2026.1745473/full https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-g001.jpg Figure 1. Map of Naya Nazimabad (Source: Javedan corporation limited 2023). https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-t001.jpg Table 1. Reliability testing. https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-t002.jpg Table 2. Respondents’ profile (N = 141). https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-t003.jpg Table 3. Rotated component loadings (varimax), signed values; cells shown for ∣loading∣≥0.40; h2 = c https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-t004.jpg Table 4. PCA variance summary (rotated). https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-t005.jpg Table 5. Kruskal–Wallis tests. https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-t006.jpg Table 6. Ordinal logi (DV: belonging item). https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-t007.jpg Table 7. Mediation results: usage → satisfaction → belonging/comfort. https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-g002.jpg Figure 2. Satellite Image records. (a)-image 2001, (b)-image 2005, (c)-image 2010, (d)-image 2015, ( https://www.frontiersin.org/files/Articles/1745473/fbuil-12-1745473-HTML/image_m/fbuil-12-1745473-g003.jpg Figure 3. Satellite image records (Source: Google Earth). https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1560729/full https://www.frontiersin.org/files/Articles/1560729/fphar-16-1560729-HTML/image_m/fphar-16-1560729-g001.jpg Figure 1. PRISMA flow diagram or study screening and selection. https://www.frontiersin.org/files/Articles/1560729/fphar-16-1560729-HTML/image_m/fphar-16-1560729-t001.jpg Table 1. Baseline characteristics of included studies. https://www.frontiersin.org/files/Articles/1560729/fphar-16-1560729-HTML/image_m/fphar-16-1560729-g002.jpg Figure 2. Forest plot of the efficacy of different pharmaceutical forms of Curcuma longa extract or https://www.frontiersin.org/files/Articles/1560729/fphar-16-1560729-HTML/image_m/fphar-16-1560729-g003.jpg Figure 3. Forest plot of the efficacy of different pharmaceutical forms of Curcuma longa extract or https://www.frontiersin.org/files/Articles/1560729/fphar-16-1560729-HTML/image_m/fphar-16-1560729-g004.jpg Figure 4. Forest plot of the efficacy of different pharmaceutical forms of Curcuma longa extract or https://www.frontiersin.org/files/Articles/1560729/fphar-16-1560729-HTML/image_m/fphar-16-1560729-g005.jpg Figure 5. Forest plot of the efficacy of different pharmaceutical forms of Curcuma longa extract or https://www.frontiersin.org/files/Articles/1560729/fphar-16-1560729-HTML/image_m/fphar-16-1560729-g006.jpg Figure 6. Forest plot of the efficacy of different pharmaceutical forms of Curcuma longa extract or https://www.frontiersin.org/files/Articles/1560729/fphar-16-1560729-HTML/image_m/fphar-16-1560729-g007.jpg Figure 7. Forest plot of the efficacy of different pharmaceutical forms of Curcuma longa extract or https://www.frontiersin.org/files/Articles/1560729/fphar-16-1560729-HTML/image_m/fphar-16-1560729-g008.jpg Figure 8. Risk of bias for each included study. (A), risk of bias summary. (B), risk of bias graph. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1739222/full https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-g001.jpg Figure 1. AI-supported collaborative marking framework. https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-t001.jpg Table 1. Mapping between research paper assignment scoring rubric dimensions and corresponding chara https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-g002.jpg Figure 2. Experimental procedure. https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-t002.jpg Table 2. Coding scheme for AI-supported collaborative marking interaction behaviors. https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-g003.jpg Figure 3. Evaluation results of students, AI without RAG, AI with RAG, and experts on overall score, https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-t003.jpg Table 3. Differences in overall evaluation scores among students (N = 121), AI without RAG (N = 1), https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-t004.jpg Table 4. Differences in overall marking scores among students (N = 24), AI-supported collaborative m https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-g004.jpg Figure 4. Marking results of students, AI-supported collaborative marking, and experts across overal https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-t005.jpg Table 5. Correlation analysis between expert ratings and student ratings without RAG across all dime https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-t006.jpg Table 6. Summary statistics of score agreement between experts and ratings without RAG. https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-g005.jpg Figure 5. (A) Bland–Altman plot of overall score agreement between experts and AI ratings without RA https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-t007.jpg Table 7. Correlation analysis between expert ratings and AI-supported collaborative marking ratings https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-t008.jpg Table 8. Summary statistics of score agreement between experts and AI-supported collaborative markin https://www.frontiersin.org/files/Articles/1739222/fpsyg-17-1739222-HTML/image_m/fpsyg-17-1739222-g006.jpg Figure 6. Behavioral transition patterns of different student types in the AI-supported collaborativ https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1681250/full https://www.frontiersin.org/files/Articles/1681250/fnins-19-1681250-HTML/image_m/fnins-19-1681250-g001.jpg Figure 1. Experimental setup Illustration of task complexity and experimental procedure. (A1) In the https://www.frontiersin.org/files/Articles/1681250/fnins-19-1681250-HTML/image_m/fnins-19-1681250-g002.jpg Figure 2. Motor skill acquisition. Results of two-way ANOVA. (A) indicates during the pre-test, the https://www.frontiersin.org/files/Articles/1681250/fnins-19-1681250-HTML/image_m/fnins-19-1681250-g003.jpg Figure 3. PSD Patterns of each group for ipsilateral vs. contralateral M1 activation during left- an https://www.frontiersin.org/files/Articles/1681250/fnins-19-1681250-HTML/image_m/fnins-19-1681250-g004.jpg Figure 4. Bilateral M1 Gamma-Band Activation Before and After Practice. (A) Pre-test ipsilateral M1 https://www.frontiersin.org/files/Articles/1681250/fnins-19-1681250-HTML/image_m/fnins-19-1681250-g005.jpg Figure 5. EEG band power changes in iM1 following various practice protocols. (A) depicts the percen https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1729495/full https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-g001.jpg Figure 1. Schematic diagram of the experimental procedure and experimental paradigm. https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-g002.jpg Figure 2. Difference test of Visual Analog Scale (VAS) scores before and after Stroop task. https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-g003.jpg Figure 3. Heat map of the correlation between mental fatigue and cognitive flexibility. https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-g004.jpg Figure 4. Histogram of test results for accuracy and reaction time in the More-odd shifting task bef https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-t001.jpg Table 1. Normality test for the More-odd shifting task. https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-t002.jpg Table 2. Wilcoxon signed-rank test for the More-odd shifting task. https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-g005.jpg Figure 5. Changes in potential waveforms and topographic maps at each electrode site before and afte https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-t003.jpg Table 3. ANOVA of repeated measurements of accuracy under the More-odd shift task. https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-t004.jpg Table 4. ANOVA of repeated measures of reaction time under the More-odd shifting task. https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-t005.jpg Table 5. Latency of N2 component under mental fatigue intervention in More-odd shift task. https://www.frontiersin.org/files/Articles/1729495/fnins-19-1729495-HTML/image_m/fnins-19-1729495-t006.jpg Table 6. Repeated measures ANOVA of N2 component latency under More odd shift tasks. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1760782/full https://www.frontiersin.org/files/Articles/1760782/fimmu-17-1760782-HTML/image_m/fimmu-17-1760782-g001.jpg Figure 1. Spatial and functional heterogeneity of the breast cancer tumor microenvironment. This sch https://www.frontiersin.org/files/Articles/1760782/fimmu-17-1760782-HTML/image_m/fimmu-17-1760782-t001.jpg Table 1. Major cellular and molecular components of the breast cancer TIME and their immunomodulator https://www.frontiersin.org/files/Articles/1760782/fimmu-17-1760782-HTML/image_m/fimmu-17-1760782-g002.jpg Figure 2. Mechanisms of CD8+ T cell dysfunction in breast cancer. (A) CD8+ T cells interact in many https://www.frontiersin.org/files/Articles/1760782/fimmu-17-1760782-HTML/image_m/fimmu-17-1760782-t002.jpg Table 2. TAM subpopulations in breast cancer subtypes. https://www.frontiersin.org/files/Articles/1760782/fimmu-17-1760782-HTML/image_m/fimmu-17-1760782-t003.jpg Table 3. Metabolic targets in breast cancer immunotherapy. https://www.frontiersin.org/files/Articles/1760782/fimmu-17-1760782-HTML/image_m/fimmu-17-1760782-g003.jpg Figure 3. Tumor cells (red) drive immunosuppression through metabolic reprogramming. Aerobic glycoly https://www.frontiersin.org/files/Articles/1760782/fimmu-17-1760782-HTML/image_m/fimmu-17-1760782-t004.jpg Table 4. Rational combination strategies in clinical development. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1765825/full https://www.frontiersin.org/files/Articles/1765825/fimmu-17-1765825-HTML/image_m/fimmu-17-1765825-g001.jpg Figure 1. The flowchart of the present study. GC, gastric cancer; QC, quality control; QTL, quantita https://www.frontiersin.org/files/Articles/1765825/fimmu-17-1765825-HTML/image_m/fimmu-17-1765825-t001.jpg Table 1. Three independent SNPs were identified in the multivariable Cox proportional hazards regres https://www.frontiersin.org/files/Articles/1765825/fimmu-17-1765825-HTML/image_m/fimmu-17-1765825-t002.jpg Table 2. Associations of three independent SNPs with overall survival in both discovery and validati https://www.frontiersin.org/files/Articles/1765825/fimmu-17-1765825-HTML/image_m/fimmu-17-1765825-t003.jpg Table 3. Associations between three independent SNPs and survival of GC in the Shanghai cohort (N = https://www.frontiersin.org/files/Articles/1765825/fimmu-17-1765825-HTML/image_m/fimmu-17-1765825-g002.jpg Figure 2. Prediction of GC survival with combined unfavorable genotypes. KM survival curves in the S https://www.frontiersin.org/files/Articles/1765825/fimmu-17-1765825-HTML/image_m/fimmu-17-1765825-g003.jpg Figure 3. The results of the eQTL analyses for three independent SNPs. The CD160 rs9728526 G allele https://www.frontiersin.org/files/Articles/1765825/fimmu-17-1765825-HTML/image_m/fimmu-17-1765825-g004.jpg Figure 4. The results of differential mRNA expression and survival analyses for SNP-associated genes https://www.frontiersin.org/files/Articles/1765825/fimmu-17-1765825-HTML/image_m/fimmu-17-1765825-g005.jpg Figure 5. The results of immune infiltration analyses in GC. The box plots showed the significant di https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1823003/full https://www.frontiersin.org/files/Articles/1823003/fcell-14-1823003-HTML/image_m/fcell-14-1823003-g001.jpg Figure 1. Graphical abstract. This figure systematically illustrates the comprehensive pathological https://www.frontiersin.org/files/Articles/1823003/fcell-14-1823003-HTML/image_m/fcell-14-1823003-g002.jpg Figure 2. The mechanisms of apoptosis in sepsis. Sepsis stimulates the aggregation of pro-Bax/Bak on https://www.frontiersin.org/files/Articles/1823003/fcell-14-1823003-HTML/image_m/fcell-14-1823003-g003.jpg Figure 3. The mechanism of pyroptosis in sepsis. PAMPs/DAMPs activate the NLRP3 inflammasome, recrui https://www.frontiersin.org/files/Articles/1823003/fcell-14-1823003-HTML/image_m/fcell-14-1823003-t001.jpg Table 1. Major types of cell death in sepsis and their characteristics. https://www.frontiersin.org/files/Articles/1823003/fcell-14-1823003-HTML/image_m/fcell-14-1823003-g004.jpg Figure 4. The immune status in sepsis exhibits a biphasic pattern characterized by early hyperinflam https://www.frontiersin.org/files/Articles/1823003/fcell-14-1823003-HTML/image_m/fcell-14-1823003-g005.jpg Figure 5. Sepsis-induced cardiomyopathy involves ferroptosis, metabolic reprogramming, and mitochond https://www.frontiersin.org/files/Articles/1823003/fcell-14-1823003-HTML/image_m/fcell-14-1823003-g006.jpg Figure 6. Targeted modulation of cell death pathways and immune balance holds therapeutic potential https://www.frontiersin.org/files/Articles/1823003/fcell-14-1823003-HTML/image_m/fcell-14-1823003-g007.jpg Figure 7. Molecular mechanism of sepsis-induced organ injury. Damaged cells release HMGB1, DNA, ATP, https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1807272/full https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t001.jpg Table 1. Factor definition and explanation. https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t002.jpg Table 2. Leadership effectiveness factors in elementary school administration and supervision. https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t003.jpg Table 3. Linguistic scales for the evaluation of decision criteria (adapted from Bongo and Seva, 202 https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t004.jpg Table 4. Consistency Index. https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t005.jpg Table 5. Demographic information of the experts (N = 9). https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t006.jpg Table 6. Sample evaluation of best criterion to others (expert 1). https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t007.jpg Table 7. Evaluation of other criteria to worst (expert 1). https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t008.jpg Table 8. Optimal weights of leadership effectiveness factors (expert 1). https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t009.jpg Table 9. Revised optimal weights of leadership effectiveness factors (expert 1). https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t010.jpg Table 10. Summary of priority weights for leadership effectiveness factor. https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t011.jpg Table 11. Aggregated crisp optimal weights of leadership effectiveness factors. https://www.frontiersin.org/files/Articles/1807272/feduc-11-1807272-HTML/image_m/feduc-11-1807272-t012.jpg Table 12. Consistency ratio of each experts' evaluation. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1734218/full https://www.frontiersin.org/files/Articles/1734218/fpsyg-17-1734218-HTML-r1/image_m/fpsyg-17-1734218-t001.jpg Table 1. Model fit coefficient (construct validity). https://www.frontiersin.org/files/Articles/1734218/fpsyg-17-1734218-HTML-r1/image_m/fpsyg-17-1734218-t002.jpg Table 2. Factor loadings, AVE, and CR of each item. https://www.frontiersin.org/files/Articles/1734218/fpsyg-17-1734218-HTML-r1/image_m/fpsyg-17-1734218-t003.jpg Table 3. Descriptive statistics for soft skills dimensions and academic performance. https://www.frontiersin.org/files/Articles/1734218/fpsyg-17-1734218-HTML-r1/image_m/fpsyg-17-1734218-g001.jpg Figure 1. Pearson correlations between soft skills dimensions and academic performance. https://www.frontiersin.org/files/Articles/1734218/fpsyg-17-1734218-HTML-r1/image_m/fpsyg-17-1734218-g002.jpg Figure 2. Hierarchical regression analysis of soft skills and academic performance. https://www.frontiersin.org/files/Articles/1734218/fpsyg-17-1734218-HTML-r1/image_m/fpsyg-17-1734218-t004.jpg Table 4. Mediation effect results (N = 627). https://www.frontiersin.org/files/Articles/1734218/fpsyg-17-1734218-HTML-r1/image_m/fpsyg-17-1734218-g003.jpg Figure 3. Standardized path coefficients of the dual-mechanism model. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1795301/full https://www.frontiersin.org/files/Articles/1795301/fpsyg-17-1795301-HTML-r1/image_m/fpsyg-17-1795301-t001.jpg Table 1. Descriptive statistics and reliability of the study variables (N = 392). https://www.frontiersin.org/files/Articles/1795301/fpsyg-17-1795301-HTML-r1/image_m/fpsyg-17-1795301-t002.jpg Table 2. Exploratory factor analysis results (PAF with Promax rotation). https://www.frontiersin.org/files/Articles/1795301/fpsyg-17-1795301-HTML-r1/image_m/fpsyg-17-1795301-t003.jpg Table 3. CFA model fit indices. https://www.frontiersin.org/files/Articles/1795301/fpsyg-17-1795301-HTML-r1/image_m/fpsyg-17-1795301-g001.jpg Figure 1. Structural equation model. https://www.frontiersin.org/files/Articles/1795301/fpsyg-17-1795301-HTML-r1/image_m/fpsyg-17-1795301-t004.jpg Table 4. Model fit indices and evaluation criteria. https://www.frontiersin.org/files/Articles/1795301/fpsyg-17-1795301-HTML-r1/image_m/fpsyg-17-1795301-t005.jpg Table 5. Path coefficients of the structural equation model. https://www.frontiersin.org/files/Articles/1795301/fpsyg-17-1795301-HTML-r1/image_m/fpsyg-17-1795301-t006.jpg Table 6. Mediation analysis results. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1815902/full https://www.frontiersin.org/files/Articles/1815902/fmed-13-1815902-HTML/image_m/fmed-13-1815902-g001.jpg Figure 1. iPSC differentiation method reproduced from “Generation of kidney organoid from human indu https://www.frontiersin.org/files/Articles/1815902/fmed-13-1815902-HTML/image_m/fmed-13-1815902-t001.jpg Table 1. Technical challenges and strategies for quality control in kidney organoids. https://www.frontiersin.org/files/Articles/1815902/fmed-13-1815902-HTML/image_m/fmed-13-1815902-t002.jpg Table 2. Applications of kidney organoids in modeling various renal diseases. https://www.frontiersin.org/files/Articles/1815902/fmed-13-1815902-HTML/image_m/fmed-13-1815902-g002.jpg Figure 2. Multifaceted applications of kidney organoids in biomedical research. https://www.frontiersin.org/files/Articles/1815902/fmed-13-1815902-HTML/image_m/fmed-13-1815902-t003.jpg Table 3. Kidney organoid culture systems. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1758356/full https://www.frontiersin.org/files/Articles/1758356/fimmu-17-1758356-HTML/image_m/fimmu-17-1758356-g001.jpg Figure 1. Intracellular mechanisms underlying resveratrol-mediated neuroprotection. Resveratrol acti https://www.frontiersin.org/files/Articles/1758356/fimmu-17-1758356-HTML/image_m/fimmu-17-1758356-t001.jpg Table 1. Summary of studies investigating the neuroprotective and memory-enhancing effects of resver https://www.frontiersin.org/files/Articles/1758356/fimmu-17-1758356-HTML/image_m/fimmu-17-1758356-g002.jpg Figure 2. Systems-level model of resveratrol’s modulation of the inflammation–immunity–memory axis a https://www.frontiersin.org/files/Articles/1758356/fimmu-17-1758356-HTML/image_m/fimmu-17-1758356-t002.jpg Table 2. Summary of studies on resveratrol and inflammation–immunity axis in Alzheimer’s disease. https://www.frontiersin.org/files/Articles/1758356/fimmu-17-1758356-HTML/image_m/fimmu-17-1758356-t003.jpg Table 3. Summary of key findings of resveratrol on the inflammation–immunity axis in Parkinson’s dis https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1651324/full https://www.frontiersin.org/files/Articles/1651324/fcvm-12-1651324-HTML/image_m/fcvm-12-1651324-t001.jpg Table 1. Summary of current limitations in cardiovascular AI, proposed cognitive alignment strategie https://www.frontiersin.org/files/Articles/1651324/fcvm-12-1651324-HTML/image_m/fcvm-12-1651324-g001.jpg Figure 1. From current cardiovascular AI to cognitively aligned AI, illustrating the transformation https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1797378/full https://www.frontiersin.org/files/Articles/1797378/fpls-17-1797378-HTML/image_m/fpls-17-1797378-g001.jpg Figure 1. WGCNA analysis of the proteome: (A) Proteomic profiling of safflower at 48 hpi and 72 hpi https://www.frontiersin.org/files/Articles/1797378/fpls-17-1797378-HTML/image_m/fpls-17-1797378-t001.jpg Table 1. Typical upregulated & downregulated DEPs at both time points. https://www.frontiersin.org/files/Articles/1797378/fpls-17-1797378-HTML/image_m/fpls-17-1797378-g002.jpg Figure 2. WGCNA analysis of the transcriptome: (A) Module-trait relationship analysis of different t https://www.frontiersin.org/files/Articles/1797378/fpls-17-1797378-HTML/image_m/fpls-17-1797378-g003.jpg Figure 3. Analysis of the chitinase gene family in safflower: (A) Chromosomal distribution of chitin https://www.frontiersin.org/files/Articles/1797378/fpls-17-1797378-HTML/image_m/fpls-17-1797378-g004.jpg Figure 4. Subcellular localization of CtChi19: (A) Subcellular localization prediction of CtChi19 by https://www.frontiersin.org/files/Articles/1797378/fpls-17-1797378-HTML/image_m/fpls-17-1797378-g005.jpg Figure 5. Transgenic validation of CtChi19-mediated enhanced resistance to A. alternata: (A) and (B) https://www.frontiersin.org/files/Articles/1797378/fpls-17-1797378-HTML/image_m/fpls-17-1797378-g006.jpg Figure 6. Transgenic validation of CtChi19-mediated enhanced resistance to B. cinerea: (A) and (B) I https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1753348/full https://www.frontiersin.org/files/Articles/1753348/fcimb-16-1753348-HTML/image_m/fcimb-16-1753348-g000.jpg Graphical Abstract. Created in https://BioRender.com https://www.frontiersin.org/files/Articles/1753348/fcimb-16-1753348-HTML/image_m/fcimb-16-1753348-t001.jpg Table 1. Sample cohort and demographics from healthy participants, and participants with Post-Vaccin https://www.frontiersin.org/files/Articles/1753348/fcimb-16-1753348-HTML/image_m/fcimb-16-1753348-g001.jpg Figure 1. Representative static fluorescent micrographs of platelet poor plasma (PPP) exposed to thi https://www.frontiersin.org/files/Articles/1753348/fcimb-16-1753348-HTML/image_m/fcimb-16-1753348-t002.jpg Table 2. Significant protein changes in pairwise analysis of heterogenous amyloid deposits (microclo https://www.frontiersin.org/files/Articles/1753348/fcimb-16-1753348-HTML/image_m/fcimb-16-1753348-g002.jpg Figure 2. Volcano plot illustrating the distribution of proteins for pairwise comparison (controls v https://www.frontiersin.org/files/Articles/1753348/fcimb-16-1753348-HTML/image_m/fcimb-16-1753348-g003.jpg Figure 3. Functional protein-protein interaction networks and clustering analysis for the pairwise c https://www.frontiersin.org/files/Articles/1753348/fcimb-16-1753348-HTML/image_m/fcimb-16-1753348-g004.jpg Figure 4. Comparative visualisation of proteomic profiles in PV/PIS participants and a previously pu https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1714391/full https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-t001.jpg Table 1. Single factor analysis of CSSAS score of 398 cases and related influencing factors. https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-t002.jpg Table 2. Correlation analysis between CSSAS scores and associated influencing factors in 398 cases. https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-t003.jpg Table 3. Multiple linear regression analysis of factors associated with CSSAS scores in 398 cases. https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-t004.jpg Table 4. Goodness-of-fit indicators for the multiple linear regression model of factors influencing https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-t005.jpg Table 5. Diagnostic model for CSSAS scores. https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-g001.jpg Figure 1. ROC curve for CSSAS scores <21. https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-g002.jpg Figure 2. ROC curve for CSSAS scores <7. https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-t006.jpg Table 6. Diagnostic performance of ADL scores and FSIQ for CSSAS scores <7. https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-g003.jpg Figure 3. Combined predictive model for CSSAS scores <7. https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-t007.jpg Table 7. Diagnostic performance of ADL scores and FSIQ for CSSAS scores <21. https://www.frontiersin.org/files/Articles/1714391/fpsyt-16-1714391-HTML/image_m/fpsyt-16-1714391-g004.jpg Figure 4. Combined predictive model for CSSAS scores <21. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1791759/full https://www.frontiersin.org/files/Articles/1791759/fpubh-14-1791759-HTML/image_m/fpubh-14-1791759-g001.jpg Figure 1. CONSORT flow diagram of participant recruitment, randomization, allocation, follow-up, and https://www.frontiersin.org/files/Articles/1791759/fpubh-14-1791759-HTML/image_m/fpubh-14-1791759-t001.jpg Table 1. Baseline demographic characteristics of participants. https://www.frontiersin.org/files/Articles/1791759/fpubh-14-1791759-HTML/image_m/fpubh-14-1791759-t002.jpg Table 2. Tai Chi intervention protocol and control condition description. https://www.frontiersin.org/files/Articles/1791759/fpubh-14-1791759-HTML/image_m/fpubh-14-1791759-t003.jpg Table 3. Shapiro-wilk normality test for pre-intervention scores. https://www.frontiersin.org/files/Articles/1791759/fpubh-14-1791759-HTML/image_m/fpubh-14-1791759-t004.jpg Table 4. Descriptive statistics (Mean ± SD) for pre- and post-intervention scores in Tai Chi and con https://www.frontiersin.org/files/Articles/1791759/fpubh-14-1791759-HTML/image_m/fpubh-14-1791759-t005.jpg Table 5. Repeated-measures ANOVA results for time, group, and group × time effects on psychological https://www.frontiersin.org/files/Articles/1791759/fpubh-14-1791759-HTML/image_m/fpubh-14-1791759-t006.jpg Table 6. Bonferroni-adjusted post hoc within-group comparisons (pre-post changes). https://www.frontiersin.org/files/Articles/1791759/fpubh-14-1791759-HTML/image_m/fpubh-14-1791759-g002.jpg Figure 2. Conceptual framework illustrating the potential mechanisms underlying the psychological be https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1719872/full https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g001.jpg Figure 1. The physiochemical analysis of the predicted GpWRKY proteins: (A) The size of amino acids. https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g002.jpg Figure 2. Phylogenetic analysis of the relationships between the WRKY proteins of G. pentaphyllum an https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g003.jpg Figure 3. Conserved WRKY domain of seven subfamilies in the GpWRKY proteins. (A) N-terminal WRKY dom https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g004.jpg Figure 4. Gene structure and conserved motif analysis of GpWRKY genes. (A) Gene structure. (B) Motif https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g005.jpg Figure 5. Cis-acting elements in the promoters of GpWRKY genes. https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g006.jpg Figure 6. Chromosome distribution and gene duplication relationship of GpWRKY genes. (A) Chromosome https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g007.jpg Figure 7. Expression patterns of 64 GpWRKY genes in G. pentaphyllum five tissues(R: roots, S: stems, https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g008.jpg Figure 8. Expression patterns of 64 GpWRKY genes in G. pentaphyllum seedlings across control(CK), lo https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g009.jpg Figure 9. Analysis of GpWRKY gene expression by qRT-PCR and RNA-seq. (A) Expression patterns of 10 G https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g010.jpg Figure 10. Analysis of overexpression of GpWRKY48 line in response to Cd stress in Arabidopsis. (A) https://www.frontiersin.org/files/Articles/1719872/fpls-16-1719872-HTML/image_m/fpls-16-1719872-g011.jpg Figure 11. Overexpression of GpWRKY48 enhances Cd tolerance in Arabidopsis. (A) Phenotype of control https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1782424/full https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-g001.jpg Figure 1. Mitogenome map of C. kwangsiensis (A) and C. longa (B). https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-t001.jpg Table 1. Assembly statistics for the C. kwangsiensis and C. longa mitogenome genome. https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-g002.jpg Figure 2. The PCGs composition of C. kwangsiensis and C. longa mitogenomes. The composition of PCGs https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-g003.jpg Figure 3. Relative Synonymous Codon Usage (RSCU) in the protein-coding genes of C. kwangsiensis (A) https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-g004.jpg Figure 4. Repeat sequence and simple sequence repeats in the mitogenomes of C. longa and C. kwangsie https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-g005.jpg Figure 5. Number of predicted RNA editing sites in the 39 shared protein-coding genes of Curcuma kwa https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-t002.jpg Table 2. The type of RNA editing sites in C. kwangsiensis and C. longa mitogenome genome. https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-g006.jpg Figure 6. Nucleotide diversity (Pi) of conserved protein-coding genes (A) and distribution of Ka/Ks https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-g007.jpg Figure 7. Whole-genome synteny analysis of Curcuma mitogenomes and related species. Each colored blo https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-g008.jpg Figure 8. Homologous fragments transferred between the mitochondrial and chloroplast genomes in C. k https://www.frontiersin.org/files/Articles/1782424/fpls-17-1782424-HTML/image_m/fpls-17-1782424-g009.jpg Figure 9. Phylogenetic reconstruction of Curcuma and related monocot species based on mitochondrial https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1788468/full https://www.frontiersin.org/files/Articles/1788468/fpsyg-17-1788468-HTML/image_m/fpsyg-17-1788468-t001.jpg Table 1. Correlation analysis of physical activity, mindfulness, spiritual wellbeing, and teacher bu https://www.frontiersin.org/files/Articles/1788468/fpsyg-17-1788468-HTML/image_m/fpsyg-17-1788468-g001.jpg Figure 1. The serial mediating model of mindfulness and spiritual wellbeing. *p < 0.05, **p < 0.01, https://www.frontiersin.org/files/Articles/1788468/fpsyg-17-1788468-HTML/image_m/fpsyg-17-1788468-t002.jpg Table 2. The serial mediation effect analysis. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1823452/full https://www.frontiersin.org/files/Articles/1823452/fendo-17-1823452-HTML/image_m/fendo-17-1823452-g001.jpg Figure 1. The role of signal transduction under excessive ROS on the ovary. The Keap1-Nrf2 structure https://www.frontiersin.org/files/Articles/1823452/fendo-17-1823452-HTML/image_m/fendo-17-1823452-g002.jpg Figure 2. ROS–miRNA–Hippo–GC apoptosis axis visual model. When the Hippo pathway is activated, the M https://www.frontiersin.org/files/Articles/1823452/fendo-17-1823452-HTML/image_m/fendo-17-1823452-t001.jpg Table 1. Association between hippo signaling pathway and ROS OS. https://www.frontiersin.org/files/Articles/1823452/fendo-17-1823452-HTML/image_m/fendo-17-1823452-g003.jpg Figure 3. Therapeutic roadmap (Vitamin C/melatonin/CoQ10 modulation). Image source: figdraw. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1774692/full https://www.frontiersin.org/files/Articles/1774692/fonc-16-1774692-HTML/image_m/fonc-16-1774692-g001.jpg Figure 1. (A) Patients aged >45 years exhibited lower mean Omentin concentrations compared to those https://www.frontiersin.org/files/Articles/1774692/fonc-16-1774692-HTML/image_m/fonc-16-1774692-g002.jpg Figure 2. (A) Patients with larger tumors (≥5 cm) demonstrated higher mean serum Omentin levels comp https://www.frontiersin.org/files/Articles/1774692/fonc-16-1774692-HTML/image_m/fonc-16-1774692-g003.jpg Figure 3. The mean Omentin concentration in breast cancer patients decreased post-treatment as compa https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1669405/full https://www.frontiersin.org/files/Articles/1669405/fnagi-17-1669405-HTML/image_m/fnagi-17-1669405-g001.jpg Figure 1. Structure and physiology of the BBB. BBB consists of cerebral endothelial cells, perivascu https://www.frontiersin.org/files/Articles/1669405/fnagi-17-1669405-HTML/image_m/fnagi-17-1669405-g002.jpg Figure 2. The structure of LRP1 and sLRP1. (A) LRP1 is characterized by one or more ligand-binding d https://www.frontiersin.org/files/Articles/1669405/fnagi-17-1669405-HTML/image_m/fnagi-17-1669405-g003.jpg Figure 3. Schematic diagram illustrating the role of LRP1 and sLRP1 in a three-step process controll https://www.frontiersin.org/files/Articles/1669405/fnagi-17-1669405-HTML/image_m/fnagi-17-1669405-g004.jpg Figure 4. APOE mediates Aβ-LRP1 complexes across the BBB. The gastrointestinal-brain axis (GBA) modu https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1815243/full https://www.frontiersin.org/files/Articles/1815243/frai-09-1815243-HTML/image_m/frai-09-1815243-t001.jpg Table 1. Database search strings used in the main search and sensitivity check (2 February 2026). https://www.frontiersin.org/files/Articles/1815243/frai-09-1815243-HTML/image_m/frai-09-1815243-g001.jpg Figure 1. PRISMA 2020 flow diagram for the systematic review of AI-generated news and public trust ( https://www.frontiersin.org/files/Articles/1815243/frai-09-1815243-HTML/image_m/frai-09-1815243-t002.jpg Table 2. Evidence map of included full-text studies (N = 47): exposure type × outcome domain. https://www.frontiersin.org/files/Articles/1815243/frai-09-1815243-HTML/image_m/frai-09-1815243-g002.jpg Figure 2. RQ1 (provenance/authorship): direction of effects on credibility (extractable results). https://www.frontiersin.org/files/Articles/1815243/frai-09-1815243-HTML/image_m/frai-09-1815243-g003.jpg Figure 3. RQ1 (provenance/authorship): direction of effects on trust (extractable results). https://www.frontiersin.org/files/Articles/1815243/frai-09-1815243-HTML/image_m/frai-09-1815243-t003.jpg Table 3. Disclosure cue evidence base (RQ2a-RQ2b): cue operationalisations and extractable disclosur https://www.frontiersin.org/files/Articles/1815243/frai-09-1815243-HTML/image_m/frai-09-1815243-g004.jpg Figure 4. Cue–inference–target (CIT) framework for AI provenance and disclosure effects on trust and https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1610386/full https://www.frontiersin.org/files/Articles/1610386/fcvm-12-1610386-HTML-r1/image_m/fcvm-12-1610386-g001.jpg Figure 1. Study flowchart. The study included 97 vessels from 97 patients that met the strict inclus https://www.frontiersin.org/files/Articles/1610386/fcvm-12-1610386-HTML-r1/image_m/fcvm-12-1610386-g002.jpg Figure 2. A line graph depicting the evolution of QFR values at four critical time points. QFR, quan https://www.frontiersin.org/files/Articles/1610386/fcvm-12-1610386-HTML-r1/image_m/fcvm-12-1610386-t001.jpg Table 1. Patients were divided into two groups according to follow-up QFR. https://www.frontiersin.org/files/Articles/1610386/fcvm-12-1610386-HTML-r1/image_m/fcvm-12-1610386-t002.jpg Table 2. Univariate and multivariate logistics regression analyses for predicting low QFR at follow- https://www.frontiersin.org/files/Articles/1610386/fcvm-12-1610386-HTML-r1/image_m/fcvm-12-1610386-g003.jpg Figure 3. Pretreatment QFR ROC curves for predicting the mid-term follow-up QFR. The AUCs for the ov https://www.frontiersin.org/files/Articles/1610386/fcvm-12-1610386-HTML-r1/image_m/fcvm-12-1610386-t003.jpg Table 3. Incidence of functional stenosis in the overall and small vessel groups. https://www.frontiersin.org/files/Articles/1610386/fcvm-12-1610386-HTML-r1/image_m/fcvm-12-1610386-t004.jpg Table 4. Patients were divided into two groups according to follow-up QFR. https://www.frontiersin.org/files/Articles/1610386/fcvm-12-1610386-HTML-r1/image_m/fcvm-12-1610386-g004.jpg Figure 4. Kaplan–meier curves showing probability of survival stratified by the pretreatment QFR. Th https://www.frontiersin.org/files/Articles/1610386/fcvm-12-1610386-HTML-r1/image_m/fcvm-12-1610386-g005.jpg Figure 5. Meditating effect of the DCB-treatment QFR on the pretreatment and follow-up QFR. *P < 0.0 https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1771900/full https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-t001.jpg Table 1. Nominal demographic variables of participants. https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-t002.jpg Table 2. Descriptive statistics and sex distribution for pets. https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-t003.jpg Table 3. Group descriptives across all sub-scales. https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-t004.jpg Table 4. Spearman’s correlations of research variables. https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-g001.jpg Figure 1. Comparison of mediation plots for communication behaviour (CB). https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-g002.jpg Figure 2. Comparison of mediation plots for making-up behaviour (MUB). https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-g003.jpg Figure 3. Comparison of mediation plots for social support (SS). https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-g004.jpg Figure 4. Data distribution for question: Do you use endearing words or expressions (e.g., “my baby, https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-g005.jpg Figure 5. Data distribution for the question: Do you use uncomplimentary or reproachful words or exp https://www.frontiersin.org/files/Articles/1771900/fpsyg-17-1771900-HTML-r1/image_m/fpsyg-17-1771900-t005.jpg Table 5. Contingency analysis for anthropomorphic direction. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1833070/full https://www.frontiersin.org/files/Articles/1833070/fnut-13-1833070-HTML/image_m/fnut-13-1833070-g001.jpg Figure 1. Overall mechanistic framework by which complex carbohydrates influence immune imbalance in https://www.frontiersin.org/files/Articles/1833070/fnut-13-1833070-HTML/image_m/fnut-13-1833070-g002.jpg Figure 2. Effects of complex carbohydrate structural characteristics on the spatiotemporal kinetics https://www.frontiersin.org/files/Articles/1833070/fnut-13-1833070-HTML/image_m/fnut-13-1833070-t001.jpg Table 1. Correspondence between substrate structural characteristics and fermentation outcomes. https://www.frontiersin.org/files/Articles/1833070/fnut-13-1833070-HTML/image_m/fnut-13-1833070-t002.jpg Table 2. Generation, transport, and pulmonary actions of major gut-derived metabolites. https://www.frontiersin.org/files/Articles/1833070/fnut-13-1833070-HTML/image_m/fnut-13-1833070-g003.jpg Figure 3. Regulatory patterns of gut-derived metabolites on key pulmonary pathological nodes in COPD https://www.frontiersin.org/files/Articles/1833070/fnut-13-1833070-HTML/image_m/fnut-13-1833070-t003.jpg Table 3. Relationships between key pathological nodes in COPD and regulation by gut-derived metaboli https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2025.1714577/full https://www.frontiersin.org/files/Articles/1714577/falgy-06-1714577-HTML-r1/image_m/falgy-06-1714577-g001.jpg Figure 1. Therapeutic evolution in primary chronic rhinosinusitis (CRS): from symptomatic relief to https://www.frontiersin.org/files/Articles/1714577/falgy-06-1714577-HTML-r1/image_m/falgy-06-1714577-g002.jpg Figure 2. CRS classification in clinical practice. This figure illustrates a practical approach to c https://www.frontiersin.org/files/Articles/1714577/falgy-06-1714577-HTML-r1/image_m/falgy-06-1714577-t001.jpg Table 1. Targeted therapies for CRS. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1723132/full https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g001.jpg Figure 1. Comparison between YF, ZIKV, WNV, SLEV and USUV proteins in terms of structural similarity https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g002.jpg Figure 2. Molecular docking of aurintricarboxylic acid and brequinar into the target receptor pocket https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g003.jpg Figure 3. Integrated analysis of conformational ensembles across five flavivirus membrane‐protein co https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g004.jpg Figure 4. Analysis of Temoporfin and 12-hydroxy-N-tosyl-dehydroabietylamine in the target receptor p https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g005.jpg Figure 5. Conformational and sequence‐based comparison of the flavivirus membrane protein core. (A) https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g006.jpg Figure 6. Best results for Temoporfin and 12-hydroxy-N-tosyl-dehydroabietylamine dockings against Me https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g007.jpg Figure 7. (A) Two-dimensional projection of the central conformations from six molecular dynamics si https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g008.jpg Figure 8. Best results for 18-aminoferruginol and Brefeldin A dockings against NS1 protein, where on https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g009.jpg Figure 9. Conformational diversity, core stability, and sequence conservation of NS2a across five fl https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g010.jpg Figure 10. Best results for dehydroabietic acid and dehydroabietylamine dockings against NS2a protei https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g011.jpg Figure 11. Multivariate and structural comparison of NS2b conformational ensembles across flavivirus https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g012.jpg Figure 12. Best results for Temoporfin and 12-hydroxy-N-tosyl-dehydroabietylamine dockings against N https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g013.jpg Figure 13. Comprehensive Conformational and Sequence Analysis of Flavivirus NS3 Domains (A) Two-dime https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g014.jpg Figure 14. Best results for 12-hydroxy-N-tosyl-dehydroabietylamine and obatoclax dockings against NS https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g015.jpg Figure 15. Multivariate and structural analysis of flavivirus NS5 dynamics. (A) Principal component https://www.frontiersin.org/files/Articles/1723132/fcimb-16-1723132-HTML/image_m/fcimb-16-1723132-g016.jpg Figure 16. Best results for brequinar and 18-aminoferruginol dockings against NS5 protein, where on https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1666038/full https://www.frontiersin.org/files/Articles/1666038/fnins-19-1666038-HTML/image_m/fnins-19-1666038-t001.jpg https://www.frontiersin.org/files/Articles/1666038/fnins-19-1666038-HTML/image_m/fnins-19-1666038-t002.jpg Table 1. Demographic and clinical data for all participants. https://www.frontiersin.org/files/Articles/1666038/fnins-19-1666038-HTML/image_m/fnins-19-1666038-g001.jpg Figure 1. Change in ALPS index based on DTI-ALPS analysis. (A) The mean ALPS index was different bet https://www.frontiersin.org/files/Articles/1666038/fnins-19-1666038-HTML/image_m/fnins-19-1666038-g002.jpg Figure 2. Brain regions showing altered mean susceptibility in the ROI-based analysis. They were obt https://www.frontiersin.org/files/Articles/1666038/fnins-19-1666038-HTML/image_m/fnins-19-1666038-g003.jpg Figure 3. Brain regions showing altered mean CBF in the ROI-based analysis. They were obtained by GL https://www.frontiersin.org/files/Articles/1666038/fnins-19-1666038-HTML/image_m/fnins-19-1666038-g004.jpg Figure 4. Results of correlation analyses with age, gender as covariates between clinical measuremen https://www.frontiersin.org/files/Articles/1666038/fnins-19-1666038-HTML/image_m/fnins-19-1666038-g005.jpg Figure 5. Results of mediation analysis with age, gender as covariates among mean susceptibility, me https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1717706/full https://www.frontiersin.org/files/Articles/1717706/fneur-17-1717706-HTML/image_m/fneur-17-1717706-g001.jpg Figure 1. CONSORT 2010 flow diagram. https://www.frontiersin.org/files/Articles/1717706/fneur-17-1717706-HTML/image_m/fneur-17-1717706-t001.jpg Table 1. Baseline demographic and clinical characteristics of the included participants.* https://www.frontiersin.org/files/Articles/1717706/fneur-17-1717706-HTML/image_m/fneur-17-1717706-t002.jpg Table 2. Recurrence rate of stroke. https://www.frontiersin.org/files/Articles/1717706/fneur-17-1717706-HTML/image_m/fneur-17-1717706-t003.jpg Table 3. Comparison of the 24-h ambulatory blood pressure between 2 groups and baseline.* https://www.frontiersin.org/files/Articles/1717706/fneur-17-1717706-HTML/image_m/fneur-17-1717706-t004.jpg Table 4. Comparison of the home blood pressure monitoring (mmHg) between 2 groups.* https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1744744/full https://www.frontiersin.org/files/Articles/1744744/fmed-13-1744744-HTML-r1/image_m/fmed-13-1744744-t001.jpg Table 1. Multidisciplinary bundle and graded intervention algorithm for nasal PI prevention. https://www.frontiersin.org/files/Articles/1744744/fmed-13-1744744-HTML-r1/image_m/fmed-13-1744744-g001.jpg Figure 1. Comparison of gender distribution between the historical control (HC) and intervention gro https://www.frontiersin.org/files/Articles/1744744/fmed-13-1744744-HTML-r1/image_m/fmed-13-1744744-g002.jpg Figure 2. Distribution of surgery types in historical control (HC) and intervention group (IG) patie https://www.frontiersin.org/files/Articles/1744744/fmed-13-1744744-HTML-r1/image_m/fmed-13-1744744-t002.jpg Table 2. Comparison of baseline characteristics between HC and IG groups. https://www.frontiersin.org/files/Articles/1744744/fmed-13-1744744-HTML-r1/image_m/fmed-13-1744744-t003.jpg Table 3. Clinical outcomes and effect size measures of nasal PI in HC and IG patients. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1787699/full https://www.frontiersin.org/files/Articles/1787699/fpsyg-17-1787699-HTML/image_m/fpsyg-17-1787699-g001.jpg Figure 1. Preferred reporting items for systematic reviews and meta-analyses flow diagram. Adapted f https://www.frontiersin.org/files/Articles/1787699/fpsyg-17-1787699-HTML/image_m/fpsyg-17-1787699-t001.jpg Table 1. Characteristics of studies included in the meta-analysis. https://www.frontiersin.org/files/Articles/1787699/fpsyg-17-1787699-HTML/image_m/fpsyg-17-1787699-t002.jpg Table 2. Results of the quality assessment through the CASP qualitative study checklist. https://www.frontiersin.org/files/Articles/1787699/fpsyg-17-1787699-HTML/image_m/fpsyg-17-1787699-t003.jpg Table 3. Clusters, category titles, and the number of studies that contributed meaning units to each https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1685564/full https://www.frontiersin.org/files/Articles/1685564/fnut-12-1685564-HTML/image_m/fnut-12-1685564-g001.jpg Figure 1. Flowchart outlining the participant recruitment and exclusion criteria. https://www.frontiersin.org/files/Articles/1685564/fnut-12-1685564-HTML/image_m/fnut-12-1685564-t001.jpg Table 1. The characteristics of participant cheese consumption frequency at baseline. https://www.frontiersin.org/files/Articles/1685564/fnut-12-1685564-HTML/image_m/fnut-12-1685564-t002.jpg Table 2. The characteristics of participant yogurt consumption frequency at baseline. https://www.frontiersin.org/files/Articles/1685564/fnut-12-1685564-HTML/image_m/fnut-12-1685564-t003.jpg Table 3. The associations between cheese intake and yogurt intake and sleep time at the baseline, 3- https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1804092/full https://www.frontiersin.org/files/Articles/1804092/fonc-16-1804092-HTML/image_m/fonc-16-1804092-g001.jpg Figure 1. IHC expression of MMP3. Representative images of MMP-3 in CTRL (A), BCC (B), SCC (C), and https://www.frontiersin.org/files/Articles/1804092/fonc-16-1804092-HTML/image_m/fonc-16-1804092-g002.jpg Figure 2. IHC expression of matrix MMP-9. Representative images of MMP-9 in CTRL (A), BCC (B), SCC ( https://www.frontiersin.org/files/Articles/1804092/fonc-16-1804092-HTML/image_m/fonc-16-1804092-g003.jpg Figure 3. IHC expression of MMP-14. Representative images of MMP-14 in CTRL (A), BCC (B), SCC, (C), https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1710300/full https://www.frontiersin.org/files/Articles/1710300/fpubh-13-1710300-HTML/image_m/fpubh-13-1710300-t001.jpg Table 1. Demographic characteristics in 2017 and 2020. https://www.frontiersin.org/files/Articles/1710300/fpubh-13-1710300-HTML/image_m/fpubh-13-1710300-t002.jpg Table 2. Paired comparisons of ISI total and domain scores between 2017 and 2020 among overlapping p https://www.frontiersin.org/files/Articles/1710300/fpubh-13-1710300-HTML/image_m/fpubh-13-1710300-t003.jpg Table 3. Sample composition comparisons between overlapping and single-year participants. https://www.frontiersin.org/files/Articles/1710300/fpubh-13-1710300-HTML/image_m/fpubh-13-1710300-t004.jpg Table 4. Associations between demographic characteristics, total ISI score, and domains and function https://www.frontiersin.org/files/Articles/1710300/fpubh-13-1710300-HTML/image_m/fpubh-13-1710300-t005.jpg Table 5. Associations between ISI individual items and functional status across two waves. https://www.frontiersin.org/files/Articles/1710300/fpubh-13-1710300-HTML/image_m/fpubh-13-1710300-t006.jpg Table 6. Associations between social interaction and functional status 3 years later. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1772882/full https://www.frontiersin.org/files/Articles/1772882/fpsyg-17-1772882-HTML/image_m/fpsyg-17-1772882-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/files/Articles/1772882/fpsyg-17-1772882-HTML/image_m/fpsyg-17-1772882-t002.jpg Table 2. Descriptive statistics of ES practices and EC goals across cultural groups. https://www.frontiersin.org/files/Articles/1772882/fpsyg-17-1772882-HTML/image_m/fpsyg-17-1772882-g001.jpg Figure 1. Effects of emotion competence goals on emotion socialization practices. Only significant p https://www.frontiersin.org/files/Articles/1772882/fpsyg-17-1772882-HTML/image_m/fpsyg-17-1772882-t003.jpg Table 3. Standardized path coefficients between relational/individualistic EC Goals and ES practices https://www.frontiersin.org/files/Articles/1772882/fpsyg-17-1772882-HTML/image_m/fpsyg-17-1772882-t004.jpg Table 4. Chi-square difference tests for moderation analyses. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1768977/full https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g001.jpg Figure 1. Microbial community composition in fecal samples of toddlers, adolescents, and adults. Rel https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g002.jpg Figure 2. Longitudinal development of gut microbiota diversity and composition at ASV level. (A) Sha https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g003.jpg Figure 3. Functional profile in fecal samples of toddlers, adolescents, and adults. (A) Relative abu https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g004.jpg Figure 4. Taxonomic biomarkers of the different age groups. Taxonomic differences between the three https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g005.jpg Figure 5. Functional biomarkers of the different age groups. (A) TIGRFAM subrole differences between https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g006.jpg Figure 6. Multiomics integrative analysis. (A) CIM of the multiomics molecular signature, where samp https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g007.jpg Figure 7. Temporal stability of the microbiota. (A–D) Time series analysis with complexCruncher. (A, https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g008.jpg Figure 8. Feature volatility plots for ASVs that are predictive of the time point. Longitudinal rela https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g009.jpg Figure 9. Weaning analysis. CCA at the level of (A) ASV and (B) TIGRFAM relative abundances, based o https://www.frontiersin.org/files/Articles/1768977/fmicb-17-1768977-HTML/image_m/fmicb-17-1768977-g010.jpg Figure 10. Menarche analysis. Alpha diversity of samples from female adolescents before and after me https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1707622/full https://www.frontiersin.org/files/Articles/1707622/fonc-15-1707622-HTML-r1/image_m/fonc-15-1707622-g001.jpg Figure 1. Study inclusion process. https://www.frontiersin.org/files/Articles/1707622/fonc-15-1707622-HTML-r1/image_m/fonc-15-1707622-t001.jpg Table 1. Baseline characteristics of trial patients included in the meta-analysis. https://www.frontiersin.org/files/Articles/1707622/fonc-15-1707622-HTML-r1/image_m/fonc-15-1707622-t002.jpg Table 2. Methodological quality assessment of cohort studies: the Newcastle–Ottawa scale. https://www.frontiersin.org/files/Articles/1707622/fonc-15-1707622-HTML-r1/image_m/fonc-15-1707622-g002.jpg Figure 2. The forest plot depicts the effect of the combination therapy of transarterial chemoemboli https://www.frontiersin.org/files/Articles/1707622/fonc-15-1707622-HTML-r1/image_m/fonc-15-1707622-g003.jpg Figure 3. The forest plot illustrates the impact of the combination therapy of transarterial chemoem https://www.frontiersin.org/files/Articles/1707622/fonc-15-1707622-HTML-r1/image_m/fonc-15-1707622-t003.jpg Table 3. Summary of treatment-related adverse events. https://www.frontiersin.org/files/Articles/1707622/fonc-15-1707622-HTML-r1/image_m/fonc-15-1707622-t004.jpg Table 4. Analyses of prognostic factors for survival. https://www.frontiersin.org/files/Articles/1707622/fonc-15-1707622-HTML-r1/image_m/fonc-15-1707622-g004.jpg Figure 4. For the sequential analysis of outcome measures between TACE + TKIs + ICIs and TACE + TKIs https://www.frontiersin.org/files/Articles/1707622/fonc-15-1707622-HTML-r1/image_m/fonc-15-1707622-g005.jpg Figure 5. The funnel plot with trim-and-fill for DCR (TACE + TKIs + ICIs vs. TACE + TKIs). https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1743474/full https://www.frontiersin.org/files/Articles/1743474/fbinf-06-1743474-HTML/image_m/fbinf-06-1743474-t001.jpg Table 1. Number of reads, alignment rate per sample, and number of DEGs per study, before and after https://www.frontiersin.org/files/Articles/1743474/fbinf-06-1743474-HTML/image_m/fbinf-06-1743474-g001.jpg Figure 1. Venn diagrams showing (A) the number of common DEGs shared among the individual studies an https://www.frontiersin.org/files/Articles/1743474/fbinf-06-1743474-HTML/image_m/fbinf-06-1743474-g002.jpg Figure 2. Gene Ontology (GO) enrichment analysis of Biological Processes (BP) for differentially exp https://www.frontiersin.org/files/Articles/1743474/fbinf-06-1743474-HTML/image_m/fbinf-06-1743474-g003.jpg Figure 3. PPI network of differentially expressed genes (DEGs), generated using STRING and visualize https://www.frontiersin.org/journals/urology/articles/10.3389/fruro.2026.1719894/full https://www.frontiersin.org/files/Articles/1719894/fruro-06-1719894-HTML/image_m/fruro-06-1719894-g001.jpg Figure 1. Technology development in male infertility. The evolution of AI technologies in male infer https://www.frontiersin.org/files/Articles/1719894/fruro-06-1719894-HTML/image_m/fruro-06-1719894-t001.jpg Table 1. A summary of evidence on morphological analysis using AI. https://www.frontiersin.org/files/Articles/1719894/fruro-06-1719894-HTML/image_m/fruro-06-1719894-t002.jpg Table 2. A summary of evidence on sperm DNA integrity. https://www.frontiersin.org/files/Articles/1719894/fruro-06-1719894-HTML/image_m/fruro-06-1719894-t003.jpg Table 3. A summary of evidence on sperm motility assessment. https://www.frontiersin.org/files/Articles/1719894/fruro-06-1719894-HTML/image_m/fruro-06-1719894-t004.jpg Table 4. A summary of evidence on AI-based predictive models. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1657141/full https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g001.jpg Figure 1. Process flow chart for subject inclusion. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-t001.jpg Table 1. General information comparison of women receiving assisted conception in this cycle (n=1037 https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-t002.jpg Table 2. Distribution of clinical data of infertile men in the modeling group (n = 1037). https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-t003.jpg Table 3. Distribution of laboratory data for infertile men in the model group (n = 1037). https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g002.jpg Figure 2. Lasso regression cross-curve validation diagram. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g003.jpg Figure 3. Lasso regression coefficient distribution trajectory map. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-t004.jpg Table 4. Multiple logistic regression analysis results of clinical pregnancy outcomes of ICSI-ET in https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g004.jpg Figure 4. Nomogram of whether men with infertility achieved clinical pregnancy through ICSI-ET assis https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g005.jpg Figure 5. Analysis of ROC curve of training set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g006.jpg Figure 6. ROC curve analysis of the validation set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g007.jpg Figure 7. Calibration curve for training set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g008.jpg Figure 8. Validation set calibration curve. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-t005.jpg Table 5. Detailed performance indicators of the model in each dataset. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g009.jpg Figure 9. Confusion matrix for training set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g010.jpg Figure 10. Confusion matrix for validation set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g011.jpg Figure 11. DCA curve for training set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g012.jpg Figure 12. DCA curve for validation set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g013.jpg Figure 13. ROC curve of external verification set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g014.jpg Figure 14. Calibration curves for the external validation set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g015.jpg Figure 15. Confusion matrix for external validation set. https://www.frontiersin.org/files/Articles/1657141/fendo-16-1657141-HTML/image_m/fendo-16-1657141-g016.jpg Figure 16. DCA curves for the external validation set. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1839476/full https://www.frontiersin.org/files/Articles/1839476/fendo-17-1839476-HTML/image_m/fendo-17-1839476-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1839476/fendo-17-1839476-HTML/image_m/fendo-17-1839476-g001.jpg Figure 1. Distribution of thyroid and cardiometabolic. https://www.frontiersin.org/files/Articles/1839476/fendo-17-1839476-HTML/image_m/fendo-17-1839476-g002.jpg Figure 2. Androgen distributions. https://www.frontiersin.org/files/Articles/1839476/fendo-17-1839476-HTML/image_m/fendo-17-1839476-g003.jpg Figure 3. Missingness pattern. SHBG, sex hormone-binding globulin; amh, Anti-Müllerian hormone; anti https://www.frontiersin.org/files/Articles/1839476/fendo-17-1839476-HTML/image_m/fendo-17-1839476-g004.jpg Figure 4. Forest plot of the age-adjusted Firth logistic regression model for the primary endpoint ( https://www.frontiersin.org/files/Articles/1839476/fendo-17-1839476-HTML/image_m/fendo-17-1839476-g005.jpg Figure 5. Calibration plot comparing predicted and observed probabilities for the primary endpoint ( https://www.frontiersin.org/files/Articles/1839476/fendo-17-1839476-HTML/image_m/fendo-17-1839476-g006.jpg Figure 6. Volcano plot of hormonal and metabolic group differences between TAI-positive and TAI-nega https://www.frontiersin.org/files/Articles/1839476/fendo-17-1839476-HTML/image_m/fendo-17-1839476-g007.jpg Figure 7. Ranked standardized effect sizes for hormonal and metabolic markers according to thyroid a https://www.frontiersin.org/files/Articles/1839476/fendo-17-1839476-HTML/image_m/fendo-17-1839476-g008.jpg Figure 8. Robustness analysis of the association between thyroid autoimmunity and the primary cardio https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1652519/full https://www.frontiersin.org/files/Articles/1652519/fgene-16-1652519-HTML/image_m/fgene-16-1652519-g001.jpg Figure 1. Flow diagram of the study. https://www.frontiersin.org/files/Articles/1652519/fgene-16-1652519-HTML/image_m/fgene-16-1652519-g002.jpg Figure 2. Identification and functional enrichment analysis of differentially expressed genes in two https://www.frontiersin.org/files/Articles/1652519/fgene-16-1652519-HTML/image_m/fgene-16-1652519-g003.jpg Figure 3. Analyzing the protein-protein interaction network of DEGs through MCODE and CytoHubba and https://www.frontiersin.org/files/Articles/1652519/fgene-16-1652519-HTML/image_m/fgene-16-1652519-g004.jpg Figure 4. The screening of candidate POI and RSA diagnostic genes using three machine learning algor https://www.frontiersin.org/files/Articles/1652519/fgene-16-1652519-HTML/image_m/fgene-16-1652519-g005.jpg Figure 5. Transcriptional Level Regulatory Networks and construction and validation of POI and RSA d https://www.frontiersin.org/files/Articles/1652519/fgene-16-1652519-HTML/image_m/fgene-16-1652519-g006.jpg Figure 6. Diagnostic efficacy of the target genes in the prediction of POI and RSA. (A) The violin p https://www.frontiersin.org/files/Articles/1652519/fgene-16-1652519-HTML/image_m/fgene-16-1652519-g007.jpg Figure 7. Analysis of POI and RSA immune cells. (A, B) Violin diagram indicated that the POI and RSA https://www.frontiersin.org/files/Articles/1652519/fgene-16-1652519-HTML/image_m/fgene-16-1652519-g008.jpg Figure 8. The relationship between diagnostic genes and immune cells and immune genes. (A,B) The exp https://www.frontiersin.org/files/Articles/1652519/fgene-16-1652519-HTML/image_m/fgene-16-1652519-g009.jpg Figure 9. Prediction of candidate drugs. (A) Top 12 therapeutic drug candidates generated. (B) The m https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1816603/full https://www.frontiersin.org/files/Articles/1816603/fmed-13-1816603-HTML/image_m/fmed-13-1816603-g001.jpg Figure 1. Flow chart of inclusion and exclusion criteria of participants. https://www.frontiersin.org/files/Articles/1816603/fmed-13-1816603-HTML/image_m/fmed-13-1816603-t001.jpg Table 1. The baseline characteristics of study population. https://www.frontiersin.org/files/Articles/1816603/fmed-13-1816603-HTML/image_m/fmed-13-1816603-t002.jpg Table 2. Logistic analysis between obesity and lipid indexes and the incidence of RKFD. https://www.frontiersin.org/files/Articles/1816603/fmed-13-1816603-HTML/image_m/fmed-13-1816603-t003.jpg Table 3. Logistic analysis between obesity and lipid indexes and the incidence of CKD. https://www.frontiersin.org/files/Articles/1816603/fmed-13-1816603-HTML/image_m/fmed-13-1816603-t004.jpg Table 4. ROC test of obesity and lipid indexes for RKFD. https://www.frontiersin.org/files/Articles/1816603/fmed-13-1816603-HTML/image_m/fmed-13-1816603-t005.jpg Table 5. ROC test of obesity and lipid indexes for progression to CKD. https://www.frontiersin.org/files/Articles/1816603/fmed-13-1816603-HTML/image_m/fmed-13-1816603-g002.jpg Figure 2. The predictive performance of TyG for RKFD (A) and CVAI for progression to CKD (B). https://www.frontiersin.org/files/Articles/1816603/fmed-13-1816603-HTML/image_m/fmed-13-1816603-g003.jpg Figure 3. Subgroup analysis of C-index, RFM, TyG-RFM and TyG-CVAI. (A) subgroup analysis of C-index, https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1776413/full https://www.frontiersin.org/files/Articles/1776413/fmed-13-1776413-HTML/image_m/fmed-13-1776413-g001.jpg Figure 1. The flowchart. TPA, tuberculous psoas abscesses; UPCD, ultrasound-guided percutaneous cath https://www.frontiersin.org/files/Articles/1776413/fmed-13-1776413-HTML/image_m/fmed-13-1776413-t001.jpg Table 1. Basic characteristics of the patients and observational parameters across the three groups. https://www.frontiersin.org/files/Articles/1776413/fmed-13-1776413-HTML/image_m/fmed-13-1776413-g002.jpg Figure 2. Violin plots of age, TPA volume, debridement duration and blood loss for each group. “*” i https://www.frontiersin.org/files/Articles/1776413/fmed-13-1776413-HTML/image_m/fmed-13-1776413-t002.jpg Table 2. Comparison between preoperative UPCD + surgery group and surgery-only group. https://www.frontiersin.org/files/Articles/1776413/fmed-13-1776413-HTML/image_m/fmed-13-1776413-g003.jpg Figure 3. Linear relationship analysis of debridement duration and blood loss with TPA volume. TPA, https://www.frontiersin.org/files/Articles/1776413/fmed-13-1776413-HTML/image_m/fmed-13-1776413-g004.jpg Figure 4. Kaplan–Meier analysis of the probability of recurrence or sinus tract formation in each gr https://www.frontiersin.org/files/Articles/1776413/fmed-13-1776413-HTML/image_m/fmed-13-1776413-g005.jpg Figure 5. A 34-year-old female patient with a TPA. (A) Conventional ultrasound examination revealed https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1703343/full https://www.frontiersin.org/files/Articles/1703343/fbioe-13-1703343-HTML-r1/image_m/fbioe-13-1703343-g001.jpg Figure 1. Intersection Analysis of Target Genes Associated with SLC26A9 and Triple-Negative Breast C https://www.frontiersin.org/files/Articles/1703343/fbioe-13-1703343-HTML-r1/image_m/fbioe-13-1703343-g002.jpg Figure 2. Protein–protein interaction (PPI) network of the 457 shared genes between SLC26A9 and TNBC https://www.frontiersin.org/files/Articles/1703343/fbioe-13-1703343-HTML-r1/image_m/fbioe-13-1703343-g003.jpg Figure 3. Visualization and Centrality Analysis of the Protein–Protein Interaction (PPI) Network of https://www.frontiersin.org/files/Articles/1703343/fbioe-13-1703343-HTML-r1/image_m/fbioe-13-1703343-g004.jpg Figure 4. GO Functional Enrichment Analysis of Shared Genes between SLC26A9 and Triple-Negative Brea https://www.frontiersin.org/files/Articles/1703343/fbioe-13-1703343-HTML-r1/image_m/fbioe-13-1703343-g005.jpg Figure 5. KEGG Pathway Enrichment Analysis of Shared Genes between SLC26A9 and Triple-Negative Breas https://www.frontiersin.org/files/Articles/1703343/fbioe-13-1703343-HTML-r1/image_m/fbioe-13-1703343-t001.jpg Table 1. Summary of key parameters for molecular docking and molecular dynamics. https://www.frontiersin.org/files/Articles/1703343/fbioe-13-1703343-HTML-r1/image_m/fbioe-13-1703343-g006.jpg Figure 6. Molecular Docking Models and Key Interaction Sites of S9-A13 with SLC26A9 and TP53 Protein https://www.frontiersin.org/files/Articles/1703343/fbioe-13-1703343-HTML-r1/image_m/fbioe-13-1703343-g007.jpg Figure 7. Molecular Dynamics Simulations and Energy Analysis of S9-A13 Complexes with SLC26A9 and TP https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1801837/full https://www.frontiersin.org/files/Articles/1801837/fmed-13-1801837-HTML/image_m/fmed-13-1801837-g001.jpg Figure 1. The timeline for diagnosis and treatment https://www.frontiersin.org/files/Articles/1801837/fmed-13-1801837-HTML/image_m/fmed-13-1801837-g002.jpg Figure 2. Biopsy findings (2016). (A) Hematoxylin-eosin staining (× 200): vacuolar degeneration of r https://www.frontiersin.org/files/Articles/1801837/fmed-13-1801837-HTML/image_m/fmed-13-1801837-g003.jpg Figure 3. Biopsy findings (2020). (A) Hematoxylin-eosin staining (× 200): no glomerulosclerosis; tub https://www.frontiersin.org/files/Articles/1801837/fmed-13-1801837-HTML/image_m/fmed-13-1801837-g004.jpg Figure 4. Biopsy findings. (A) Hematoxylin-eosin staining (× 400): mild mesangial cell proliferation https://www.frontiersin.org/files/Articles/1801837/fmed-13-1801837-HTML/image_m/fmed-13-1801837-t001.jpg Table 1. Summary of clinical data of cases where IMN progressed to LN. https://www.frontiersin.org/files/Articles/1801837/fmed-13-1801837-HTML/image_m/fmed-13-1801837-t002.jpg Table 2. Summary of pathology and treatment options of cases where IMN progressed to LN. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2025.1708377/full https://www.frontiersin.org/files/Articles/1708377/fspor-07-1708377-HTML/image_m/fspor-07-1708377-g001.jpg Figure 1. The traditional 0-degrees (left) and the 5-degrees oar blade (right). https://www.frontiersin.org/files/Articles/1708377/fspor-07-1708377-HTML/image_m/fspor-07-1708377-g002.jpg Figure 2. The left panels show the group level normalized muscle activation of the erector spinae mu https://www.frontiersin.org/files/Articles/1708377/fspor-07-1708377-HTML/image_m/fspor-07-1708377-t001.jpg Table 1. Presents the rowing times over 500 meters rowing for each participant during each condition https://www.frontiersin.org/files/Articles/1708377/fspor-07-1708377-HTML/image_m/fspor-07-1708377-g003.jpg Figure 3. The muscle activities of each participant at four muscle locations of the erector spinae l https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1432635/full https://www.frontiersin.org/files/Articles/1432635/fpubh-13-1432635-HTML-r1/image_m/fpubh-13-1432635-g001.jpg Figure 1. Trial planned timeline. CD community dialogue. We recommend reading this figure alongside https://www.frontiersin.org/files/Articles/1432635/fpubh-13-1432635-HTML-r1/image_m/fpubh-13-1432635-t001.jpg Table 1. Subdistrict-specific number of community clinics, maximum nearest-neighbor distance between https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1743516/full https://www.frontiersin.org/files/Articles/1743516/fpubh-14-1743516-HTML/image_m/fpubh-14-1743516-g001.jpg Figure 1. Flow chart of participants. https://www.frontiersin.org/files/Articles/1743516/fpubh-14-1743516-HTML/image_m/fpubh-14-1743516-t001.jpg Table 1. Diagnostic criteria for metabolic diseases. https://www.frontiersin.org/files/Articles/1743516/fpubh-14-1743516-HTML/image_m/fpubh-14-1743516-t002.jpg Table 2. Fitted indices for GMMs with 1–5 classes. https://www.frontiersin.org/files/Articles/1743516/fpubh-14-1743516-HTML/image_m/fpubh-14-1743516-g002.jpg Figure 2. The trajectories of metabolic diseases. https://www.frontiersin.org/files/Articles/1743516/fpubh-14-1743516-HTML/image_m/fpubh-14-1743516-t003.jpg Table 3. Distribution of characteristics by metabolic diseases trajectories groups. https://www.frontiersin.org/files/Articles/1743516/fpubh-14-1743516-HTML/image_m/fpubh-14-1743516-t004.jpg Table 4. Multivariate logistic regression analysis with metabolic diseases’ trajectory. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1763101/full https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-t001.jpg Table 1. Summarizing model performance across literature review. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g001.jpg Figure 1. Proposed architecture of age and gender verification in OTT accounts in user interface. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g002.jpg Figure 2. Architecture for Customized CNN model. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g003.jpg Figure 3. Architecture for ResNet50 model. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g004.jpg Figure 4. Architecture for VGG19 model. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g005.jpg Figure 5. Model summary of Customized CNN algorithm. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g006.jpg Figure 6. Model accuracy for Customized CNN model. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g007.jpg Figure 7. Model loss for Customized CNN model. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g008.jpg Figure 8. Model accuracy in ResNet50. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g009.jpg Figure 9. Model loss in ResNet50. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g010.jpg Figure 10. Model accuracy in VGG19. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g011.jpg Figure 11. Model loss in VGG19. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g012.jpg Figure 12. Bar chart of the final age accuracies for each model. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g013.jpg Figure 13. Performance metrics for all models. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g014.jpg Figure 14. Box plot for cross-validation comparison of gender classification accuracy’s of DL models https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g015.jpg Figure 15. Box plot for statistical distribution with accuracy’s of DL models. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-t002.jpg Table 2. Summary of all models. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g016.jpg Figure 16. Simulation results of age and gender prediction of the uploaded sample images. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g017.jpg Figure 17. User interface for age verification. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-t003.jpg Table 3. System design and performance specifications for user-interface. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g018.jpg Figure 18. Access granted for 18+ videos. https://www.frontiersin.org/files/Articles/1763101/frai-09-1763101-HTML-r1/image_m/frai-09-1763101-g019.jpg Figure 19. Access granted for 18− videos. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1812903/full https://www.frontiersin.org/files/Articles/1812903/fcvm-13-1812903-HTML-r1/image_m/fcvm-13-1812903-g001.jpg Figure 1. Covariate balance pre-/post-propensity score matching. https://www.frontiersin.org/files/Articles/1812903/fcvm-13-1812903-HTML-r1/image_m/fcvm-13-1812903-t001.jpg Table 1. Baseline characteristics. https://www.frontiersin.org/files/Articles/1812903/fcvm-13-1812903-HTML-r1/image_m/fcvm-13-1812903-t002.jpg Table 2. Valve size. https://www.frontiersin.org/files/Articles/1812903/fcvm-13-1812903-HTML-r1/image_m/fcvm-13-1812903-t003.jpg Table 3. Operative data-matched cohorts. https://www.frontiersin.org/files/Articles/1812903/fcvm-13-1812903-HTML-r1/image_m/fcvm-13-1812903-t004.jpg Table 4. Early results (≤30 days)-matched cohorts. https://www.frontiersin.org/files/Articles/1812903/fcvm-13-1812903-HTML-r1/image_m/fcvm-13-1812903-g002.jpg Figure 2. Survival probability on late events (>30 days) by surgical approach-matched cohort. https://www.frontiersin.org/files/Articles/1812903/fcvm-13-1812903-HTML-r1/image_m/fcvm-13-1812903-t005.jpg Table 5. Late results (>30 days)-matched cohorts. https://www.frontiersin.org/files/Articles/1812903/fcvm-13-1812903-HTML-r1/image_m/fcvm-13-1812903-t006.jpg Table 6. Hemodynamic data-matched cohorts. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1783855/full https://www.frontiersin.org/files/Articles/1783855/fnins-20-1783855-HTML/image_m/fnins-20-1783855-g001.jpg Figure 1. Flow diagram of the preferred reporting items for systematic reviews and meta-analyses (PR https://www.frontiersin.org/files/Articles/1783855/fnins-20-1783855-HTML/image_m/fnins-20-1783855-g002.jpg Figure 2. A set of epigenetic, hormonal, and environmental factors throughout life that can cause al https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2026.1732907/full https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-g001.jpg Figure 1. A detailed schema of possible actions compatible with an EC concept; source: (Zygmunt, 202 https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-g002.jpg Figure 2. TEAC workflow highlighting development (green), standard application (yellow), and the new https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-g003.jpg Figure 3. The examined Smulsko cluster: the satellite view (on the left) and the grid representation https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-t001.jpg Table 1. A brief overview of the Polish RSFH; source: (National Energy Conservation Agency 2012; Sta https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-g004.jpg Figure 4. Day-ahead electricity prices in Poland for 03.10 (Friday) and 04.10 (Saturday); source: (N https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-g005.jpg Figure 5. UEM maps for the Smulsko cluster energy use across scenarios: S0 (top-left), S1 (top-right https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-g006.jpg Figure 6. UEM maps of economic (top row) and environmental (bottom row) outcomes under different sce https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-t002.jpg Table 2. Energy, economic, and environmental metrics by scenario for building-oriented assessment. https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-t003.jpg Table 3. Operating-cost comparison for cluster-level management under different electricity tariffs. https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-g007.jpg Figure 7. Comparison of demand management for building-oriented and cluster-level managements for 03 https://www.frontiersin.org/files/Articles/1732907/fenrg-14-1732907-HTML/image_m/fenrg-14-1732907-g008.jpg Figure 8. Net demand and battery operation for 03.10 (top) and 04.10 (bottom). https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1739666/full https://www.frontiersin.org/files/Articles/1739666/fvets-12-1739666-HTML/image_m/fvets-12-1739666-t001.jpg Table 1. Ingredients and nutrient composition of diets. https://www.frontiersin.org/files/Articles/1739666/fvets-12-1739666-HTML/image_m/fvets-12-1739666-t002.jpg Table 2. Experimental design. https://www.frontiersin.org/files/Articles/1739666/fvets-12-1739666-HTML/image_m/fvets-12-1739666-t003.jpg Table 3. Effects of dietary compound plant essential oils on growth performance of AA broilers. https://www.frontiersin.org/files/Articles/1739666/fvets-12-1739666-HTML/image_m/fvets-12-1739666-g001.jpg Figure 1. Effects of dietary compound plant essential oils on serum biochemical. Serum biochemical a https://www.frontiersin.org/files/Articles/1739666/fvets-12-1739666-HTML/image_m/fvets-12-1739666-g002.jpg Figure 2. Histological morphology of the small intestine in AA broilers. Representative hematoxylin https://www.frontiersin.org/files/Articles/1739666/fvets-12-1739666-HTML/image_m/fvets-12-1739666-g003.jpg Figure 3. The family level abundance of cecal luminal microbiota. Treatments: (A) Control (CK), basa https://www.frontiersin.org/files/Articles/1739666/fvets-12-1739666-HTML/image_m/fvets-12-1739666-g004.jpg Figure 4. Alpha diversity index of sample. (1) Abundance-based Coverage Estimator; (2) Chao Index; ( https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1754736/full https://www.frontiersin.org/files/Articles/1754736/fmicb-17-1754736-HTML-r1/image_m/fmicb-17-1754736-t001.jpg Table 1. Experimental design. https://www.frontiersin.org/files/Articles/1754736/fmicb-17-1754736-HTML-r1/image_m/fmicb-17-1754736-g001.jpg Figure 1. pH, TVB-N, and protein of the non-fermented group and different bacterial fermentation gro https://www.frontiersin.org/files/Articles/1754736/fmicb-17-1754736-HTML-r1/image_m/fmicb-17-1754736-g002.jpg Figure 2. Partial least squares discriminant analysis (OPLS-DA) plot of non-volatile metabolite prof https://www.frontiersin.org/files/Articles/1754736/fmicb-17-1754736-HTML-r1/image_m/fmicb-17-1754736-g003.jpg Figure 3. Heatmap (a) and Venn diagram (b) of non-volatile metabolite profiles in the non-fermented https://www.frontiersin.org/files/Articles/1754736/fmicb-17-1754736-HTML-r1/image_m/fmicb-17-1754736-g004.jpg Figure 4. Bar chart of fold differences between pairs E and A, F and A (A,B), volcano plot of differ https://www.frontiersin.org/files/Articles/1754736/fmicb-17-1754736-HTML-r1/image_m/fmicb-17-1754736-g005.jpg Figure 5. Differential metabolic pathway map of the EvsA group. (A) Glycerophospholipidmetabolism; ( https://www.frontiersin.org/journals/sustainable-resource-management/articles/10.3389/fsrma.2025.1716795/full https://www.frontiersin.org/files/Articles/1716795/fsrma-04-1716795-HTML/image_m/fsrma-04-1716795-t001.jpg Table 1. Key international initiatives and frameworks relevant to battery passports and digital prod https://www.frontiersin.org/files/Articles/1716795/fsrma-04-1716795-HTML/image_m/fsrma-04-1716795-t002.jpg Table 2. Key UK readiness challenges and required enablers. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1738770/full https://www.frontiersin.org/files/Articles/1738770/frai-08-1738770-HTML-r1/image_m/frai-08-1738770-g001.jpg Figure 1. ReadyAI aims to bridge the gap between AI development and regulatory application by defini https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2025.1731284/full https://www.frontiersin.org/files/Articles/1731284/frhs-05-1731284-HTML/image_m/frhs-05-1731284-t001.jpg Table 1. Characteristics of the steering committee, the reviewers and the Delphi panel. https://www.frontiersin.org/files/Articles/1731284/frhs-05-1731284-HTML/image_m/frhs-05-1731284-g001.jpg Figure 1. The development of a paediatric oncology trigger tool, POTT. https://www.frontiersin.org/files/Articles/1731284/frhs-05-1731284-HTML/image_m/frhs-05-1731284-t002.jpg Table 2. Result of the Delphi rounds. https://www.frontiersin.org/files/Articles/1731284/frhs-05-1731284-HTML/image_m/frhs-05-1731284-t003.jpg Table 3. Result of the reviewers' survey. https://www.frontiersin.org/files/Articles/1731284/frhs-05-1731284-HTML/image_m/frhs-05-1731284-t004.jpg Table 4. The final trigger set for the paediatric oncology trigger tool. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1802526/full https://www.frontiersin.org/files/Articles/1802526/fpubh-14-1802526-HTML/image_m/fpubh-14-1802526-g001.jpg Figure 1. Revised framework for the evolution of China’s emergency management system based on Punctu https://www.frontiersin.org/files/Articles/1802526/fpubh-14-1802526-HTML/image_m/fpubh-14-1802526-g002.jpg Figure 2. Evolution of China’s emergency management system based on discontinuous. Evolution of Chin https://www.frontiersin.org/files/Articles/1802526/fpubh-14-1802526-HTML/image_m/fpubh-14-1802526-t001.jpg Table 1. Critical inflection points and their enduring consequences in post-1949 China. https://www.frontiersin.org/files/Articles/1802526/fpubh-14-1802526-HTML/image_m/fpubh-14-1802526-t002.jpg Table 2. PET variables across major punctuation events in China’s emergency management evolution. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1776878/full https://www.frontiersin.org/files/Articles/1776878/fpubh-14-1776878-HTML/image_m/fpubh-14-1776878-t001.jpg Table 1. Baseline characteristics of the study population stratified by 30-day readmission status. https://www.frontiersin.org/files/Articles/1776878/fpubh-14-1776878-HTML/image_m/fpubh-14-1776878-g001.jpg Figure 1. Receiver operating characteristic (ROC) curve of the 30-day readmission prediction model. https://www.frontiersin.org/files/Articles/1776878/fpubh-14-1776878-HTML/image_m/fpubh-14-1776878-g002.jpg Figure 2. Calibration plot comparing predicted vs. observed 30-day readmission risk. https://www.frontiersin.org/files/Articles/1776878/fpubh-14-1776878-HTML/image_m/fpubh-14-1776878-g003.jpg Figure 3. Kernel density distribution of predicted probabilities according to readmission status. https://www.frontiersin.org/files/Articles/1776878/fpubh-14-1776878-HTML/image_m/fpubh-14-1776878-g004.jpg Figure 4. Observed 30-day readmission rate across deciles of predicted risk. https://www.frontiersin.org/files/Articles/1776878/fpubh-14-1776878-HTML/image_m/fpubh-14-1776878-t002.jpg Table 2. Multivariable logistic regression for 30-day readmission. https://www.frontiersin.org/files/Articles/1776878/fpubh-14-1776878-HTML/image_m/fpubh-14-1776878-t003.jpg Table 3. Selected major DRG prefix categories with highest and lowest adjusted odds of 30-day readmi https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1720264/full https://www.frontiersin.org/files/Articles/1720264/fpubh-14-1720264-HTML/image_m/fpubh-14-1720264-g001.jpg Figure 1. Research process. https://www.frontiersin.org/files/Articles/1720264/fpubh-14-1720264-HTML/image_m/fpubh-14-1720264-t001.jpg Table 1. Indicators for social network analysis. https://www.frontiersin.org/files/Articles/1720264/fpubh-14-1720264-HTML/image_m/fpubh-14-1720264-t002.jpg Table 2. HMS stakeholders identification. https://www.frontiersin.org/files/Articles/1720264/fpubh-14-1720264-HTML/image_m/fpubh-14-1720264-t003.jpg Table 3. Network-level centralization results. https://www.frontiersin.org/files/Articles/1720264/fpubh-14-1720264-HTML/image_m/fpubh-14-1720264-t004.jpg Table 4. Standardized node centrality metrics for HMS stakeholders. https://www.frontiersin.org/files/Articles/1720264/fpubh-14-1720264-HTML/image_m/fpubh-14-1720264-g002.jpg Figure 2. Relationship network structure diagram of stakeholders in HMS. https://www.frontiersin.org/journals/allergy/articles/10.3389/falgy.2026.1819491/full https://www.frontiersin.org/files/Articles/1819491/falgy-07-1819491-HTML/image_m/falgy-07-1819491-g001.jpg Figure 1. Conceptual framework of future directions in allergic respiratory diseases toward precisio https://www.frontiersin.org/files/Articles/1819491/falgy-07-1819491-HTML/image_m/falgy-07-1819491-g002.jpg Figure 2. Literature search and study selection flow. https://www.frontiersin.org/files/Articles/1819491/falgy-07-1819491-HTML/image_m/falgy-07-1819491-g003.jpg Figure 3. Epithelial release of TSLP in response to allergens, pollutants, and viral infections init https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2026.1821211/full https://www.frontiersin.org/files/Articles/1821211/froh-07-1821211-HTML/image_m/froh-07-1821211-t001.jpg Table 1. Pico criteria for the current systematic review. https://www.frontiersin.org/files/Articles/1821211/froh-07-1821211-HTML/image_m/froh-07-1821211-g001.jpg Figure 1. Flowchart representing the process of study selection. https://www.frontiersin.org/files/Articles/1821211/froh-07-1821211-HTML/image_m/froh-07-1821211-t002.jpg Table 2. Staging systems of the OSMF in the included articles. https://www.frontiersin.org/files/Articles/1821211/froh-07-1821211-HTML/image_m/froh-07-1821211-t003.jpg Table 3. Segregation of the staging systems based on the staging criteria. https://www.frontiersin.org/files/Articles/1821211/froh-07-1821211-HTML/image_m/froh-07-1821211-t004.jpg Table 4. Quality assessment of included studies by critical appraisal skills program (CASP) checklis https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1737689/full https://www.frontiersin.org/files/Articles/1737689/fmed-13-1737689-HTML/image_m/fmed-13-1737689-g001.jpg Figure 1. Annual publication volume from 2015 to 2024. https://www.frontiersin.org/files/Articles/1737689/fmed-13-1737689-HTML/image_m/fmed-13-1737689-g002.jpg Figure 2. (A) International collaboration chord diagram illustrating inter-country linkages. (B) The https://www.frontiersin.org/files/Articles/1737689/fmed-13-1737689-HTML/image_m/fmed-13-1737689-t001.jpg Table 1. Top 10 contributing countries in psoriasis and macrophage research (2015–2024). https://www.frontiersin.org/files/Articles/1737689/fmed-13-1737689-HTML/image_m/fmed-13-1737689-g003.jpg Figure 3. (A) The top 10 journals in terms of publication volume. (B) Journal co-occurrence network https://www.frontiersin.org/files/Articles/1737689/fmed-13-1737689-HTML/image_m/fmed-13-1737689-g004.jpg Figure 4. (A) Top 10 authors by publication volume. (B) Top 10 authors and their nations by citation https://www.frontiersin.org/files/Articles/1737689/fmed-13-1737689-HTML/image_m/fmed-13-1737689-g005.jpg Figure 5. (A) The top 10 authors with the most citations by articles in this field. (B) Author co-ci https://www.frontiersin.org/files/Articles/1737689/fmed-13-1737689-HTML/image_m/fmed-13-1737689-g006.jpg Figure 6. (A) Co-citation references network of macrophage and psoriasis. (B) Reference clustering r https://www.frontiersin.org/files/Articles/1737689/fmed-13-1737689-HTML/image_m/fmed-13-1737689-g007.jpg Figure 7. (A) Top 20 keywords with the strongest citation bursts and their active time periods. (B) https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2026.1751964/full https://www.frontiersin.org/files/Articles/1751964/fsoc-11-1751964-HTML/image_m/fsoc-11-1751964-g001.jpg Figure 1. Mt Elias. Reproduced from Duri and Takawira (2026). https://www.frontiersin.org/files/Articles/1751964/fsoc-11-1751964-HTML/image_m/fsoc-11-1751964-t001.jpg Table 1. Themes and codes. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1721735/full https://www.frontiersin.org/files/Articles/1721735/fneur-17-1721735-HTML/image_m/fneur-17-1721735-t001.jpg Table 1. Socio-demographic and clinical characteristics of patients with MS and NMOSD. https://www.frontiersin.org/files/Articles/1721735/fneur-17-1721735-HTML/image_m/fneur-17-1721735-t002.jpg Table 2. MSQOL-54 results in patients with MS and NMOSD. https://www.frontiersin.org/files/Articles/1721735/fneur-17-1721735-HTML/image_m/fneur-17-1721735-t003.jpg Table 3. Analysis of the quality of life and anxiety and depression in patients with MS and NMOSD. https://www.frontiersin.org/files/Articles/1721735/fneur-17-1721735-HTML/image_m/fneur-17-1721735-t004.jpg Table 4. Correlation of disability, anxiety, and depression according to type of disease and quality https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1738772/full https://www.frontiersin.org/files/Articles/1738772/fimmu-17-1738772-HTML/image_m/fimmu-17-1738772-t001.jpg Table 1. Patients characteristics. https://www.frontiersin.org/files/Articles/1738772/fimmu-17-1738772-HTML/image_m/fimmu-17-1738772-t002.jpg Table 2. Survival and response outcome for the overall population and subgroups. https://www.frontiersin.org/files/Articles/1738772/fimmu-17-1738772-HTML/image_m/fimmu-17-1738772-g001.jpg Figure 1. (a) Kaplan-Meier curves for progression free survival in the overall cohort. Censored pati https://www.frontiersin.org/files/Articles/1738772/fimmu-17-1738772-HTML/image_m/fimmu-17-1738772-g002.jpg Figure 2. (a) Kaplan-Meier estimates for progression free survival according to number of metastatic https://www.frontiersin.org/files/Articles/1738772/fimmu-17-1738772-HTML/image_m/fimmu-17-1738772-t003.jpg Table 3. Adverse events. https://www.frontiersin.org/files/Articles/1738772/fimmu-17-1738772-HTML/image_m/fimmu-17-1738772-g003.jpg Figure 3. Oncoprint scheme representing genetic alterations, tumor mutational burden (TMB) and objec https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1789773/full https://www.frontiersin.org/files/Articles/1789773/fbioe-14-1789773-HTML/image_m/fbioe-14-1789773-g001.jpg Figure 1. GelMA hydrogel fluorescence spectra at distinct photoinitiator concentrations (n = 4–6 per https://www.frontiersin.org/files/Articles/1789773/fbioe-14-1789773-HTML/image_m/fbioe-14-1789773-g002.jpg Figure 2. Phasor analysis of GelMA hydrogel lifetime at different (A) crosslinking degrees and using https://www.frontiersin.org/files/Articles/1789773/fbioe-14-1789773-HTML/image_m/fbioe-14-1789773-g003.jpg Figure 3. (A-C) Lifetime values obtained through tri-exponential fitting of the subset (n = 24 image https://www.frontiersin.org/files/Articles/1789773/fbioe-14-1789773-HTML/image_m/fbioe-14-1789773-g004.jpg Figure 4. (A) Mean fluorescence lifetime (B) ratio of contribution of τ1 species to τ2 species (C) r https://www.frontiersin.org/files/Articles/1789773/fbioe-14-1789773-HTML/image_m/fbioe-14-1789773-g005.jpg Figure 5. (A) Mean fluorescence lifetime (B) ratio of contribution of τ1 species to τ2 species (C) r https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2025.1683318/full https://www.frontiersin.org/files/Articles/1683318/fenrg-13-1683318-HTML/image_m/fenrg-13-1683318-g007.jpg https://www.frontiersin.org/files/Articles/1683318/fenrg-13-1683318-HTML/image_m/fenrg-13-1683318-g001.jpg Figure 1. SEM of (a) Cu3N, and (b) Cu3N/MoS2(1:1). (c) and (d) EDS of Cu3N/MoS2(1:1) with Cu, Mo, N https://www.frontiersin.org/files/Articles/1683318/fenrg-13-1683318-HTML/image_m/fenrg-13-1683318-g002.jpg Figure 2. (a) XRD patterns of Cu3N, MoS2 and Cu3N/MoS2(1:1). (b) TEM of Cu3N. (c) TEM of Cu3N/MoS2(1 https://www.frontiersin.org/files/Articles/1683318/fenrg-13-1683318-HTML/image_m/fenrg-13-1683318-g003.jpg Figure 3. XPS of Cu3N, MoS2 and Cu3N/MoS2. (a) Mo 3d spectrum of MoS2. (b) Cu 2p spectrum of Cu3N. ( https://www.frontiersin.org/files/Articles/1683318/fenrg-13-1683318-HTML/image_m/fenrg-13-1683318-g004.jpg Figure 4. (a) LSV curves of Cu3N, MoS2 and Cu3N/MoS2 with different feed ratios. (b) Tafel slope of https://www.frontiersin.org/files/Articles/1683318/fenrg-13-1683318-HTML/image_m/fenrg-13-1683318-g005.jpg Figure 5. (a) EIS of Cu3N, MoS2 and Cu3N/MoS2 with different feed ratios. (b) Cdl of Cu3N and Cu3N/M https://www.frontiersin.org/files/Articles/1683318/fenrg-13-1683318-HTML/image_m/fenrg-13-1683318-g006.jpg Figure 6. Tauc plots of (a) MoS2 and (b) Cu3N. Mott-Schottky plots of (c) MoS2 and (d) Cu3N at diffe https://www.frontiersin.org/files/Articles/1683318/fenrg-13-1683318-HTML/image_m/fenrg-13-1683318-g008.jpg Scheme 1. The mechanism of Cu3N/MoS2 of electrocatalytic hydrogen evolution. https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1702572/full https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-g008.jpg GRAPHICAL ABSTRACT | Network pharmacology identified the top overlapping genes between apigenin and https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-g001.jpg Figure 1. Systematic representation depicting the study’s comprehensive flow. https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-t001.jpg Table 1. List of selected phytochemicals adhering to drug-like properties and absorption criteria (L https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-g002.jpg Figure 2. Scatter plot of phytochemicals by TPSA values- Each data point represents a phytochemical, https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-g003.jpg Figure 3. Network visualizations in Cytoscape- Compound-target protein network (A). Top 10 Hub genes https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-t002.jpg Table 2. Details of the identified clusters (score >3) from the compound target protein network. https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-g004.jpg Figure 4. Functional enrichment pathways GO Biological process (A), GO Cellular component (B), GO Mo https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-g005.jpg Figure 5. Genomic mutation map of hub genes- AKT1 (A), IL6 (B), JUN (C), NFKB1 (D), STAT3 (E), TNF ( https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-g006.jpg Figure 6. 2D and 3D interaction and binding models of the three selected genes depicting the hydroge https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-t003.jpg Table 3. Binding energies of target genes and ligands based on molecular docking analysis. https://www.frontiersin.org/files/Articles/1702572/fbinf-06-1702572-HTML/image_m/fbinf-06-1702572-g007.jpg Figure 7. MD simulation analysis of AKT1 in complex 5-Fluorouracil, Kaempferol, and Apigenin- RMSD ( https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1804329/full https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g001.jpg Figure 1. Representative dermoscopic images of the seven skin lesion classes from the HAM10000 datas https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t001.jpg Table 1. Representative prior work in skin lesion analysis. https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g002.jpg Figure 2. Workflow of the proposed morphology-guided attention network for skin cancer detection. https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g003.jpg Figure 3. The proposed system shows the complete end-to-end architecture for skin lesion classificat https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t010.jpg Algorithm 1. Training procedure of the proposed framework. https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t011.jpg Algorithm 2. Inference, explainable prediction, and uncertainty-aware triage. https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t002.jpg Table 2. Class-wise distribution of the HAM10000 skin lesion dataset. https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t003.jpg Table 3. Performance comparison with baseline architectures (5-fold cross-validation, mean ± std). https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g004.jpg Figure 4. Training and validation accuracy curves of the proposed framework over training epochs. Th https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g005.jpg Figure 5. Training and validation loss curves of the proposed framework over training epochs. The de https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t004.jpg Table 4. Training and validation performance across epochs (representative values). https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g006.jpg Figure 6. Confusion matrix of the proposed framework (5-fold average). Strong diagonal dominance ind https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t005.jpg Table 5. Detailed per-class performance metrics (5-fold average). https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g007.jpg Figure 7. One-vs-rest ROC curves for all lesion classes. The curves indicate strong separability and https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g008.jpg Figure 8. Representative explanation maps (attention/attribution overlays) produced by the proposed https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g009.jpg Figure 9. Saliency-based visualization for a representative skin lesion image is shown in this figur https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t006.jpg Table 6. Ablation study results (5-fold average). https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t007.jpg Table 7. Computational cost and efficiency metrics. https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g010.jpg Figure 10. Distribution of epistemic uncertainty for correct and incorrect predictions using the pro https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t008.jpg Table 8. Uncertainty-based rejection analysis. https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-g011.jpg Figure 11. Reliability diagram showing calibration behavior across confidence bins. The proposed fra https://www.frontiersin.org/files/Articles/1804329/fonc-16-1804329-HTML-r1/image_m/fonc-16-1804329-t009.jpg Table 9. Comparison of recent state-of-the-art methods for skin lesion analysis (2023–2025). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1692227/full https://www.frontiersin.org/files/Articles/1692227/fpsyg-16-1692227-HTML-r1/image_m/fpsyg-16-1692227-g001.jpg Figure 1. The Self-Determination Continuum Showing Types of Motivation with Their Type of Regulation https://www.frontiersin.org/files/Articles/1692227/fpsyg-16-1692227-HTML-r1/image_m/fpsyg-16-1692227-t001.jpg Table 1. Descriptive statistics and correlations for study variables. https://www.frontiersin.org/files/Articles/1692227/fpsyg-16-1692227-HTML-r1/image_m/fpsyg-16-1692227-t002.jpg Table 2. Correlations for the regulation types with the variables frequency and spread of PEB. https://www.frontiersin.org/files/Articles/1692227/fpsyg-16-1692227-HTML-r1/image_m/fpsyg-16-1692227-g002.jpg Figure 2. Correlations between the participants' regulation type and the variables frequency and spr https://www.frontiersin.org/files/Articles/1692227/fpsyg-16-1692227-HTML-r1/image_m/fpsyg-16-1692227-t003.jpg Table 3. Descriptive statistics for the everyday life PEB sectors and correlations with the variable https://www.frontiersin.org/files/Articles/1692227/fpsyg-16-1692227-HTML-r1/image_m/fpsyg-16-1692227-t004.jpg Table 4. Exemplary motivation profiles (low, medium, high) including qualitative statements on PEB w https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1656171/full https://www.frontiersin.org/files/Articles/1656171/fcimb-15-1656171-HTML/image_m/fcimb-15-1656171-g001.jpg Figure 1. Study workflow. Schematic representation of the experimental workflow used in this study. https://www.frontiersin.org/files/Articles/1656171/fcimb-15-1656171-HTML/image_m/fcimb-15-1656171-g002.jpg Figure 2. Patient recruitment overview. AA, Attikon University Hospital; AB, Thriaseio General Hospi https://www.frontiersin.org/files/Articles/1656171/fcimb-15-1656171-HTML/image_m/fcimb-15-1656171-t001.jpg Table 1. List of positive samples. https://www.frontiersin.org/files/Articles/1656171/fcimb-15-1656171-HTML/image_m/fcimb-15-1656171-g003.jpg Figure 3. UpSet plot showing the shared and unique microbial detections between SoC and PISTE diagno https://www.frontiersin.org/files/Articles/1656171/fcimb-15-1656171-HTML/image_m/fcimb-15-1656171-g004.jpg Figure 4. Diagnostic performance of PISTE compared to SoC for antimicrobial resistance detection. (A https://www.frontiersin.org/files/Articles/1656171/fcimb-15-1656171-HTML/image_m/fcimb-15-1656171-t002.jpg Table 2. Cross-tabulation of antimicrobial resistance by PISTE and SoC cultures. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1618348/full https://www.frontiersin.org/files/Articles/1618348/fneur-16-1618348-HTML/image_m/fneur-16-1618348-t001.jpg Table 1. Descriptive data of participating neurologists. https://www.frontiersin.org/files/Articles/1618348/fneur-16-1618348-HTML/image_m/fneur-16-1618348-g001.jpg Figure 1. Overview of opinions on referral standards (a), use of decision-support tools for DAT (Dev https://www.frontiersin.org/files/Articles/1618348/fneur-16-1618348-HTML/image_m/fneur-16-1618348-g002.jpg Figure 2. Factors that affect the evaluation of DAT (Device-Assisted Therapy) eligibility. Presented https://www.frontiersin.org/files/Articles/1618348/fneur-16-1618348-HTML/image_m/fneur-16-1618348-g003.jpg Figure 3. Perceived advantages of using Digital Health Technologies in Parkinson’s disease managemen https://www.frontiersin.org/files/Articles/1618348/fneur-16-1618348-HTML/image_m/fneur-16-1618348-g004.jpg Figure 4. Perceived barriers against the use of objective measurements from Digital Health Technolog https://www.frontiersin.org/files/Articles/1618348/fneur-16-1618348-HTML/image_m/fneur-16-1618348-t002.jpg Table 2. Thematic matrix showing convergence between survey and interview findings in a mixed-method https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1634739/full https://www.frontiersin.org/files/Articles/1634739/fnut-12-1634739-HTML/image_m/fnut-12-1634739-t001.jpg Table 1. The usage of nutritional supplements of Chinese pregnant women in 2018 (n = 653). https://www.frontiersin.org/files/Articles/1634739/fnut-12-1634739-HTML/image_m/fnut-12-1634739-t002.jpg Table 2. The age distribution of pregnant women across urban (n = 372) and rural (n = 281) China in https://www.frontiersin.org/files/Articles/1634739/fnut-12-1634739-HTML/image_m/fnut-12-1634739-t003.jpg Table 3. Dietary structures of pregnant women across urban (n = 372) and rural (n = 281) China in 20 https://www.frontiersin.org/files/Articles/1634739/fnut-12-1634739-HTML/image_m/fnut-12-1634739-t004.jpg Table 4. The comparative analysis of dietary recommendations and actual food intake from different c https://www.frontiersin.org/files/Articles/1634739/fnut-12-1634739-HTML/image_m/fnut-12-1634739-t005.jpg Table 5. The intake of energy and nutrients in pregnant women across urban (n = 372) and rural (n =  https://www.frontiersin.org/files/Articles/1634739/fnut-12-1634739-HTML/image_m/fnut-12-1634739-t006.jpg Table 6. The comparative analysis of the recommended values and actual intake of energy and nutrient https://www.frontiersin.org/files/Articles/1634739/fnut-12-1634739-HTML/image_m/fnut-12-1634739-t007.jpg Table 7. Energy ratios from carbohydrates, fat and proteins in pregnant women across urban (n = 372) https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1669073/full https://www.frontiersin.org/files/Articles/1669073/fimmu-16-1669073-HTML/image_m/fimmu-16-1669073-g001.jpg Figure 1. Influenza virus structure. Created with BioRender, 2025. https://www.frontiersin.org/files/Articles/1669073/fimmu-16-1669073-HTML/image_m/fimmu-16-1669073-g002.jpg Figure 2. The structure of hemagglutinin is subdivided into a globular head (HA1) containing the Rec https://www.frontiersin.org/files/Articles/1669073/fimmu-16-1669073-HTML/image_m/fimmu-16-1669073-g003.jpg Figure 3. Articles indexed in the PubMed database with the search term “Monoclonal antibodies agains https://www.frontiersin.org/files/Articles/1669073/fimmu-16-1669073-HTML/image_m/fimmu-16-1669073-t001.jpg Table 1. Clinical studies indexed in the clinical trials database were identified using the search t https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1593228/full https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-g001.jpg Figure 1. tNGS workflow diagram. https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-t001.jpg Table 1. Inclusion criteria for patient characteristics. https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-g002.jpg Figure 2. ROC curve analysis of the value of tNGS with POI diagnostic indicators (A-C). https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-g003.jpg Figure 3. Pathogen spectrum of tNGS and culture. (A) Comparison of pathogen spectra detected by tNGS https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-t002.jpg Table 2. The accuracy of tNGS in patients with POI. https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-t003.jpg Table 3. The accuracy of tNGS in patients with TB. https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-t004.jpg Table 4. The accuracy of tNGS in patients with BOI. https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-t005.jpg Table 5. The accuracy of tNGS in patients with pyogenic osteoarticular infection. https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-g004.jpg Figure 4. Consistency analysis of tNGS and culture. https://www.frontiersin.org/files/Articles/1593228/fcimb-15-1593228-HTML/image_m/fcimb-15-1593228-g005.jpg Figure 5. Time cost of tNGS vs culture. https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1708257/full https://www.frontiersin.org/files/Articles/1708257/fnhum-19-1708257-HTML/image_m/fnhum-19-1708257-g001.jpg Figure 1. Schematic of multiple-sequential-choice RSVP paradigm. (A) Experimental timeline and stimu https://www.frontiersin.org/files/Articles/1708257/fnhum-19-1708257-HTML/image_m/fnhum-19-1708257-g002.jpg Figure 2. Fixation duration before shift types. (A) Pre-shift fixation durations. Individual data ar https://www.frontiersin.org/files/Articles/1708257/fnhum-19-1708257-HTML/image_m/fnhum-19-1708257-g003.jpg Figure 3. Spatiotemporal dynamics of theta power modulations before different shifts types. Topograp https://www.frontiersin.org/files/Articles/1708257/fnhum-19-1708257-HTML/image_m/fnhum-19-1708257-g004.jpg Figure 4. Spatiotemporal dynamics of alpha power modulations before shifts across different types. T https://www.frontiersin.org/files/Articles/1708257/fnhum-19-1708257-HTML/image_m/fnhum-19-1708257-g005.jpg Figure 5. Temporal dynamics and linear trend of FMT before shifts. (A) Time course of normalized the https://www.frontiersin.org/files/Articles/1708257/fnhum-19-1708257-HTML/image_m/fnhum-19-1708257-g006.jpg Figure 6. Temporal dynamics and power of posterior alpha before shifts. (A) Time course of normalize https://www.frontiersin.org/files/Articles/1708257/fnhum-19-1708257-HTML/image_m/fnhum-19-1708257-g007.jpg Figure 7. Frontal-midline theta (FMT) slope and posterior alpha power excluding small eye movements. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1590381/full https://www.frontiersin.org/files/Articles/1590381/fneur-16-1590381-HTML/image_m/fneur-16-1590381-t001.jpg Table 1. Comparison of baseline characteristics between patients with no END and END groups. https://www.frontiersin.org/files/Articles/1590381/fneur-16-1590381-HTML/image_m/fneur-16-1590381-t002.jpg Table 2. Multivariable models showing the association between risk factors and END. https://www.frontiersin.org/files/Articles/1590381/fneur-16-1590381-HTML/image_m/fneur-16-1590381-t003.jpg Table 3. Comparison of baseline characteristics between patients with poor prognosis and good progno https://www.frontiersin.org/files/Articles/1590381/fneur-16-1590381-HTML/image_m/fneur-16-1590381-t004.jpg Table 4. Multivariable models showing the association between risk factors and good prognosis. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1738099/full https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g001.jpg Figure 1. Data screening and dataset splitting. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g002.jpg Figure 2. Resting-state data collection process. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g003.jpg Figure 3. Spatial distribution of 67 fNIRS channels in 2D and 3D views. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g004.jpg Figure 4. Data processing pipeline. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-t001.jpg Table 1. Demographic characteristics. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g005.jpg Figure 5. Model performance and feature importance on rest dataset. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g006.jpg Figure 6. Model performance and feature importance on 1-back task dataset. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g007.jpg Figure 7. Model performance and feature importance on rest and 1-back dataset. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g008.jpg Figure 8. ROC curves of the proposed models on the rest, 1-back, rest and 1back test sets. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g009.jpg Figure 9. Comparison of neural network model and MoCA. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-t002.jpg Table 2. Initial and optimized hyperparameters of neural network model. https://www.frontiersin.org/files/Articles/1738099/fneur-17-1738099-HTML/image_m/fneur-17-1738099-g010.jpg Figure 10. T-values for group differences in important features of neural networks. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1607862/full https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-t001.jpg Table 1. Manas UAV multispectral imagery and SPAD data acquisition program. https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-t002.jpg Table 2. Vegetation index and its calculation formula. https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-t003.jpg Table 3. Reflectance of spectral bands in wheat under normal irrigation (W) and drought (D) across g https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-g001.jpg Figure 1. Dynamic changes of reflectance of spectral information. HS, Heading Stage; FL, Flowering; https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-g002.jpg Figure 2. Distribution of contribution rate of vegetation index to SPAD under flood and drought trea https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-g003.jpg Figure 3. The distribution of the contribution rate of vegetation index to SPAD under flood and drou https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-g004.jpg Figure 4. The distribution map of the contribution rate of vegetation index relative to SPAD under p https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-g005.jpg Figure 5. SPAD distribution map of Zepu and Manas winter wheat at different growth stages (a) The me https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-t004.jpg Table 4. Correlation analysis between predicted values and measured values. https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-t005.jpg Table 5. Correlation analysis site comparison between predicted values and measured values. https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-t006.jpg Table 6. Correlation analysis of measured and predicted values and statistics of overlap sites. https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-g006.jpg Figure 6. Manhattan plot of 18 overlapping loci/sites. https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-t007.jpg Table 7. Candidate gene information. https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-g007.jpg Figure 7. GO enrichment analysis of candidate genes (top 30). https://www.frontiersin.org/files/Articles/1607862/fpls-16-1607862-HTML/image_m/fpls-16-1607862-g008.jpg Figure 8. KEGG enrichment analysis of candidate gene. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2026.1761656/full https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-g001.jpg Figure 1. Schematic diagram of the income-enhancement mechanism of RIII. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-g002.jpg Figure 2. Map of districts and counties in the study area. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t001.jpg Table 1. Evaluation index system for the RIII in Jiangxi Province. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t002.jpg Table 2. Variable names and variable symbols. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t003.jpg Table 3. Classification of the RIII in Jiangxi Province. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-g003.jpg Figure 3. The spatio-temporal evolution diagram of the RIII in Jiangxi Province. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t004.jpg Table 4. Regression results of the TWFE Model. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t005.jpg Table 5. The lagged impact of the RIII on farmers’ income. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t006.jpg Table 6. Regression results after excluding extreme values. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t007.jpg Table 7. Regression results of excluding samples of cities one by one. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t008.jpg Table 8. Results of robustness tests in different time periods. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t009.jpg Table 9. Regression results of the mediating effect. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t010.jpg Table 10. Results of bootstrap test for mediating effect (500 replications). https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t011.jpg Table 11. Results of full quantile regression. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t012.jpg Table 12. Terrain differences in the income–increasing effect of the RIII. https://www.frontiersin.org/files/Articles/1761656/fsufs-10-1761656-HTML/image_m/fsufs-10-1761656-t013.jpg Table 13. Differences in the income–increasing effects of the RIII in different regions. https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2026.1752451/full https://www.frontiersin.org/files/Articles/1752451/fevo-14-1752451-HTML/image_m/fevo-14-1752451-t001.jpg Table 1. Statistics of the number of regions with standard arable land coefficient from 2002 to 2023 https://www.frontiersin.org/files/Articles/1752451/fevo-14-1752451-HTML/image_m/fevo-14-1752451-g001.jpg Figure 1. Distribution map of standard cultivated land coefficient and center of gravity shift of cu https://www.frontiersin.org/files/Articles/1752451/fevo-14-1752451-HTML/image_m/fevo-14-1752451-g002.jpg Figure 2. Changes in farmland productivity of major grain producing provinces in China from 2002 to https://www.frontiersin.org/files/Articles/1752451/fevo-14-1752451-HTML/image_m/fevo-14-1752451-g003.jpg Figure 3. Temporal and spatial distribution of cropland pressure index in main grain producing areas https://www.frontiersin.org/files/Articles/1752451/fevo-14-1752451-HTML/image_m/fevo-14-1752451-t002.jpg Table 2. Statistical descriptive data of the indicator layer sample. https://www.frontiersin.org/files/Articles/1752451/fevo-14-1752451-HTML/image_m/fevo-14-1752451-t003.jpg Table 3. Estimated results (lnY is the dependent variable). https://www.frontiersin.org/files/Articles/1752451/fevo-14-1752451-HTML/image_m/fevo-14-1752451-g004.jpg Figure 4. Line plots of coupled data on arable land resources and food security in 13 grain-producin https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1763670/full https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-g001.jpg Figure 1. Research flowchart. HC, healthy control; OH, overt hypothyroidism. TSH, thyroid-stimulatin https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-t001.jpg Table 1. Demographic characteristics, clinical variables, and neuropsychological scores in patients https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-t002.jpg Table 2. For the comparison of group differences in the head motion parameters. https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-g002.jpg Figure 2. Global topological properties in OH patients and HC. (A) Global topological properties of https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-g003.jpg Figure 3. Brain regions showing significant between-group differences in nodal topological metrics w https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-t003.jpg Table 3. Brain regions showing significant differences in nodal topological properties between the O https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-g004.jpg Figure 4. Brain regions showing significant between-group differences in nodal topological metrics w https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-g005.jpg Figure 5. Comparison of SC-FC coupling at the whole-brain level and across eight subnetworks between https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-g006.jpg Figure 6. Correlation heatmap between graph metrics, subnetwork coupling, and clinical assessment sc https://www.frontiersin.org/files/Articles/1763670/fendo-17-1763670-HTML/image_m/fendo-17-1763670-t004.jpg Table 4. Significant correlations between altered nodal topological metrics and clinical/neuropsycho https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1655764/full https://www.frontiersin.org/files/Articles/1655764/fvets-12-1655764-HTML-r1/image_m/fvets-12-1655764-g001.jpg Figure 1. Left thoracic limb (LTL) amputation in a canine cadaver positioned in right lateral recumb https://www.frontiersin.org/files/Articles/1655764/fvets-12-1655764-HTML-r1/image_m/fvets-12-1655764-g002.jpg Figure 2. Preoperative skin incision marking for left pelvic limb (LPL) amputation in a canine cadav https://www.frontiersin.org/files/Articles/1655764/fvets-12-1655764-HTML-r1/image_m/fvets-12-1655764-g003.jpg Figure 3. Lateral view of the pelvis illustrating (in pink) diferent modalities of canine hemipelvec https://www.frontiersin.org/files/Articles/1655764/fvets-12-1655764-HTML-r1/image_m/fvets-12-1655764-g004.jpg Figure 4. Ventral view of the pelvis illustrating (in pink) different modalities of canine hemipelve https://www.frontiersin.org/files/Articles/1655764/fvets-12-1655764-HTML-r1/image_m/fvets-12-1655764-g005.jpg Figure 5. Total hemipelvectomy in a canine cadaver positioned in right lateral recumbency. (A) Preop https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1655874/full https://www.frontiersin.org/files/Articles/1655874/fvets-12-1655874-HTML-r1/image_m/fvets-12-1655874-g001.jpg Figure 1. Total scapulectomy in the left thoracic limb (LTL) of a canine cadaver positioned in right https://www.frontiersin.org/files/Articles/1655874/fvets-12-1655874-HTML-r1/image_m/fvets-12-1655874-g002.jpg Figure 2. Partial scapulectomy in a canine cadaver’s right thoracic limb (RTL) positioned in left la https://www.frontiersin.org/files/Articles/1655874/fvets-12-1655874-HTML-r1/image_m/fvets-12-1655874-g003.jpg Figure 3. Partial ulnectomy. (A) Radiographic image in laterolateral projection of the left radius a https://www.frontiersin.org/files/Articles/1655874/fvets-12-1655874-HTML-r1/image_m/fvets-12-1655874-g004.jpg Figure 4. Partial Ulnectomy in the Left Thoracic Limb (LTL) of a Canine Cadaver in Right Lateral Rec https://www.frontiersin.org/files/Articles/1655874/fvets-12-1655874-HTML-r1/image_m/fvets-12-1655874-g005.jpg Figure 5. Radiographic images of the right radius and ulna of a canine patient with osteosarcoma (OS https://www.frontiersin.org/files/Articles/1655874/fvets-12-1655874-HTML-r1/image_m/fvets-12-1655874-g006.jpg Figure 6. Intraoperative bone allograft treatment. (A) Liquid nitrogen application. (B) Excised tumo https://www.frontiersin.org/files/Articles/1655874/fvets-12-1655874-HTML-r1/image_m/fvets-12-1655874-g007.jpg Figure 7. (A) Osteosarcoma located in the distal third of the left radius and ulna in a canine patie https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1714036/full https://www.frontiersin.org/files/Articles/1714036/fpsyg-17-1714036-HTML/image_m/fpsyg-17-1714036-g001.jpg Figure 1. Structural model illustrating the associations between the variables (**p < 0.001). https://www.frontiersin.org/files/Articles/1714036/fpsyg-17-1714036-HTML/image_m/fpsyg-17-1714036-t001.jpg Table 1. Descriptive statistics, reliability, and correlations between the study variables. https://www.frontiersin.org/files/Articles/1714036/fpsyg-17-1714036-HTML/image_m/fpsyg-17-1714036-t002.jpg Table 2. Unstandardized coefficients for the mediation model. https://www.frontiersin.org/files/Articles/1714036/fpsyg-17-1714036-HTML/image_m/fpsyg-17-1714036-t003.jpg Table 3. Completely standardized indirect effects. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1821192/full https://www.frontiersin.org/files/Articles/1821192/fonc-16-1821192-HTML/image_m/fonc-16-1821192-g001.jpg Figure 1. Polarization and functional characteristics of TAMs. The diagram illustrates the extracell https://www.frontiersin.org/files/Articles/1821192/fonc-16-1821192-HTML/image_m/fonc-16-1821192-g002.jpg Figure 2. Metabolic phenotypes are context-dependent and may overlap in the TME. M1-like and M2-like https://www.frontiersin.org/files/Articles/1821192/fonc-16-1821192-HTML/image_m/fonc-16-1821192-g003.jpg Figure 3. Hypoxia and lactate synergistically promote TAM-mediated immunosuppression via HIF-1α/mTOR https://www.frontiersin.org/files/Articles/1821192/fonc-16-1821192-HTML/image_m/fonc-16-1821192-t001.jpg Table 1. Clinical development of lactate metabolism modulators targeting TAMs: Targets, mechanisms, https://www.frontiersin.org/files/Articles/1821192/fonc-16-1821192-HTML/image_m/fonc-16-1821192-t002.jpg Table 2. Summary of metabolic inhibitors targeting TAMs: Mechanisms, cancer types, and reprogramming https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2025.1716347/full https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-t001.jpg Table 1. The geometric parameters of booster unit of blade multiphase pump. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-t002.jpg Table 2. Design parameters of booster unit of blade multiphase pump. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g001.jpg Figure 1. Numerical model of blade multiphase pump. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g002.jpg Figure 2. The grid of computational domain. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-t003.jpg Table 3. Mesh independence verification. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g003.jpg Figure 3. Multi-objective optimization design platform. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-t004.jpg Table 4. Optimizing variable parameters and their ranges. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g004.jpg Figure 4. Geometric structure of booster unit. (a) Meridional plane, (b) 3D impeller. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g005.jpg Figure 5. The influence of blade shape parameters on efficiency and gas uniformity at the impeller o https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-t005.jpg Table 5. The contribution percentage of blade shape parameters to efficiency and gas uniformity at t https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g006.jpg Figure 6. Main effect of the influence of blade shape parameters on efficiency and gas uniformity at https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-t006.jpg Table 6. Fitting accuracy evaluation of the approximate model for efficiency and gas uniformity at t https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-t007.jpg Table 7. Comparison of the approximate model of efficiency and gas uniformity at the impeller outlet https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-t008.jpg Table 8. Optimization results of multi-objective optimization algorithm. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g007.jpg Figure 7. Optimized solution set. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g008.jpg Figure 8. Comparison of blade multiphase pump hydraulic characteristic curves before and after optim https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-t009.jpg Table 9. Hydraulic loss before and after optimization. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g009.jpg Figure 9. The distribution of gas void fraction at different blade span of the booster unit (a) befo https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g010.jpg Figure 10. Velocity and streamline patterns of the booster unit (a) before and (b) after optimizatio https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g011.jpg Figure 11. Gas uniformity at the impeller outlet (a) before and (b) after optimization. https://www.frontiersin.org/files/Articles/1716347/fmech-11-1716347-HTML/image_m/fmech-11-1716347-g012.jpg Figure 12. The Streamline patterns at different blade span of the booster unit (a) before and (b) af https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1708432/full https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-g001.jpg Figure 1. The conceptual model. https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-t001.jpg Table 1. Comparison of turnover intention scores across sociodemographic groups. https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-t002.jpg Table 2. Comparison of burnout scores across sociodemographic groups. https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-t003.jpg Table 3. Comparison of job satisfaction scores across sociodemographic groups. https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-t004.jpg Table 4. Variable coding for the multiple linear stepwise regression analysis. https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-t005.jpg Table 5. Summary of multiple linear stepwise regression results. https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-t006.jpg Table 6. Multiple linear stepwise regression analysis of factors associated with turnover intention https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-t007.jpg Table 7. The correlations among burnout, job satisfaction and turnover intention. https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-g002.jpg Figure 2. The mediating model of burnout, job satisfaction, and turnover intention. https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-t008.jpg Table 8. The path coefficients of the mediation model. https://www.frontiersin.org/files/Articles/1708432/fpubh-13-1708432-HTML/image_m/fpubh-13-1708432-t009.jpg Table 9. Decomposition of direct, indirect, and total effects of job satisfaction in the mediation m https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1766949/full https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-g001.jpg Figure 1. The proposed hypothetical model of this study. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-g002.jpg Figure 2. Flowchart of the sample selection process. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-g003.jpg Figure 3. The model of stage 1. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-g004.jpg Figure 4. The model of stage 2. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t001.jpg Table 1. Descriptive statistics of core construct (n = 671). https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t002.jpg Table 2. Assessment of the reflective measurement model. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t003.jpg Table 3. Assessment of discriminant validity using the HTMT. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t004.jpg Table 4. Assessment of formative measurement model. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t005.jpg Table 5. Results of total effects. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t006.jpg Table 6. Results of direct effects. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t007.jpg Table 7. Results of indirect effects. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t008.jpg Table 8. Assessment of effect size using the R2 and Q2. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t009.jpg Table 9. Assessment of effect size using the f2. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t010.jpg Table 10. Assessment of nonlinear effects. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t011.jpg Table 11. Assessment of endogeneity test using the Gaussian copula approach. https://www.frontiersin.org/files/Articles/1766949/fpsyg-17-1766949-HTML/image_m/fpsyg-17-1766949-t012.jpg Table 12. Fit indices for the one- to seven-segment solutions. https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2026.1800181/full https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-g001.jpg Figure 1. Methods of oil storage showing the three main techniques: above-ground tank farms, in-grou https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-g002.jpg Figure 2. Criteria used to compare the different oil storage techniques, grouped under four main cat https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-g003.jpg Figure 3. Framework used to identify and evaluate the best oil storage alternative using the hybrid https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t001.jpg Table 1. Advantage and disadvantages of underground oil storage technique. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t002.jpg Table 2. Details of expert participants. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t003.jpg Table 3. Economic sub-criteria used to compare the different oil storage techniques. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t004.jpg Table 4. Technical sub-criteria used to compare the different oil storage techniques. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t005.jpg Table 5. Environmental sub-criteria used to compare the different oil storage techniques. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t006.jpg Table 6. Social sub-criteria used to compare the different oil storage techniques. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-g004.jpg Figure 4. Fuzzy membership function. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t007.jpg Table 7. Fuzzy linguistic scale and membership function for F-AHP (Coffey and Claudio, 2021). https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-g005.jpg Figure 5. Workflow of the Fuzzy Analytical Hierarchy Process (F-AHP) used to determine the weights o https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t008.jpg Table 8. Final local and global weights of all criteria and sub-criteria, as obtained using the F-AH https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t009.jpg Table 9. Fuzzy linguistic scale and membership function for F-VIKOR (Runtuk et al., 2025). https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-g006.jpg Figure 6. Workflow of the Fuzzy VIKOR (F-VIKOR) procedure used to rank and identify the best oil sto https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t010.jpg Table 10. Expert pairwise comparisons for criteria: linguistic terms (top) and TFNs (bottom). https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t011.jpg Table 11. Defuzzified pairwise comparison matrix for criteria (crisp, reciprocal). https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t012.jpg Table 12. Consistency analysis for all pairwise comparison matrices. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t013.jpg Table 13. Experts' linguistic ratings for all sub-criteria SC1–SC16 (AG, Above-ground; IG, In-ground https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t014.jpg Table 14. SC14 (safety)—Expert linguistic ratings per alternative. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t015.jpg Table 15. SC14 (safety)—TFN mapping of expert judgments using Table 9. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t016.jpg Table 16. SC14 (safety): aggregated TFNs and centroid values dli. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t017.jpg Table 17. Ideal (fimax) and anti-ideal (fimin) values for each sub-criterion. https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-t018.jpg Table 18. F-VIKOR results: group utility Sl, individual regret Rl, compromise index Ql, and ranking https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-g007.jpg Figure 7. Sensitivity analyses under ±20% variation of F-AHP weights. (a) Economic sensitivity. (b) https://www.frontiersin.org/files/Articles/1800181/frsus-07-1800181-HTML/image_m/frsus-07-1800181-g008.jpg Figure 8. Changes in F-VIKOR ranking outcomes as a function of the compromise parameter v showing th https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1818309/full https://www.frontiersin.org/files/Articles/1818309/fimmu-17-1818309-HTML/image_m/fimmu-17-1818309-t001.jpg Table 1. PCR protocol performed for LSDV detection in samples. https://www.frontiersin.org/files/Articles/1818309/fimmu-17-1818309-HTML/image_m/fimmu-17-1818309-t002.jpg Table 2. The details of primer sequences used cytokines studies in real-time PCR. https://www.frontiersin.org/files/Articles/1818309/fimmu-17-1818309-HTML/image_m/fimmu-17-1818309-t003.jpg Table 3. Protocol in the thermal cycler for real-time PCR. https://www.frontiersin.org/files/Articles/1818309/fimmu-17-1818309-HTML/image_m/fimmu-17-1818309-g001.jpg Figure 1. Study on Mithun population and LSD seroprevalence. (A) Map illustrating the Mithun populat https://www.frontiersin.org/files/Articles/1818309/fimmu-17-1818309-HTML/image_m/fimmu-17-1818309-g002.jpg Figure 2. Immune response of the goatpox vaccine in Mithun. (A) Dot plot depicting the kappa value w https://www.frontiersin.org/files/Articles/1818309/fimmu-17-1818309-HTML/image_m/fimmu-17-1818309-g003.jpg Figure 3. (A) Bar chart comparing cytokines associated with Th1 and Th2 responses. (B) Line chart sh https://www.frontiersin.org/files/Articles/1818309/fimmu-17-1818309-HTML/image_m/fimmu-17-1818309-t004.jpg Table 4. Comparing ΔCt values between cytokine groups in vaccinated Mithun. https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1770473/full https://www.frontiersin.org/files/Articles/1770473/fspor-08-1770473-HTML/image_m/fspor-08-1770473-t001.jpg Table 1. Sample of theme generation https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2026.1769269/full https://www.frontiersin.org/files/Articles/1769269/fspor-08-1769269-HTML/image_m/fspor-08-1769269-g001.jpg Figure 1. Framework of the keys to success in Mixed Ability rugby. From da-Silva (22). https://www.frontiersin.org/files/Articles/1769269/fspor-08-1769269-HTML/image_m/fspor-08-1769269-t001.jpg Table 1. Sociodemographic characteristics of participants in MA rugby and basketball. https://www.frontiersin.org/files/Articles/1769269/fspor-08-1769269-HTML/image_m/fspor-08-1769269-t002.jpg Table 2. Descriptive statistics by thematic blocks of mixed ability basketball and rugby. https://www.frontiersin.org/files/Articles/1769269/fspor-08-1769269-HTML/image_m/fspor-08-1769269-t003.jpg Table 3. Mann–Whitney U test results between basketball and rugby for the different thematic blocks. https://www.frontiersin.org/files/Articles/1769269/fspor-08-1769269-HTML/image_m/fspor-08-1769269-t004.jpg Table 4. Mann–Whitney U test results between basketball and rugby on how society can improve the liv https://www.frontiersin.org/files/Articles/1769269/fspor-08-1769269-HTML/image_m/fspor-08-1769269-t005.jpg Table 5. Measures of association in contingency tables between variables and years of experience in https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2026.1819788/full https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-g001.jpg Figure 1. Immunohistochemical staining for RACK1 in a section of moderately differentiated OSCC. Pos https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-g002.jpg Figure 2. Immunohistochemical staining of RPS6 in section of poorly differentiated OSCC. Tumor islan https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-t001.jpg Table 1. Clinico-pathologic characteristics of study population (N = 100). https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-t002.jpg Table 2. Association of RACK-1 and RPS6 expression with clinicopathological parameters in patients w https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-t003.jpg Table 3. Association of RACK1 and RPS6 expression with overall survival (OS), disease-free survival https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-g003.jpg Figure 3. Kaplan–meier survival analysis based on RACK1 expression in OSCC patients kaplan–meier plo https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-g004.jpg Figure 4. Kaplan–meier survival analysis based on RPS6 expression in OSCC patients kaplan–meier plot https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-t004.jpg Table 4. Multivariate Cox proportional hazards regression models of RACK1 and RPS6 expression with O https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-g005.jpg Figure 5. Linear regression analysis showing the relationship between RACK1 and RPS6 expression leve https://www.frontiersin.org/files/Articles/1819788/froh-07-1819788-HTML/image_m/froh-07-1819788-g006.jpg Figure 6. Receiver operating characteristic (ROC) curves for RACK1 and RPS6 expression in predicting https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1702153/full https://www.frontiersin.org/files/Articles/1702153/fpsyg-17-1702153-HTML/image_m/fpsyg-17-1702153-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/files/Articles/1702153/fpsyg-17-1702153-HTML/image_m/fpsyg-17-1702153-t002.jpg Table 2. Fit indices from latent profile analysis (N = 201). https://www.frontiersin.org/files/Articles/1702153/fpsyg-17-1702153-HTML/image_m/fpsyg-17-1702153-t003.jpg Table 3. Probabilities of belonging to two latent classes. https://www.frontiersin.org/files/Articles/1702153/fpsyg-17-1702153-HTML/image_m/fpsyg-17-1702153-g001.jpg Figure 1. Kinesiophobia scores across two latent profiles in patients undergoing coronary artery byp https://www.frontiersin.org/files/Articles/1702153/fpsyg-17-1702153-HTML/image_m/fpsyg-17-1702153-t004.jpg Table 4. Binary logistic regression results predicting profile membership. https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2026.1755424/full https://www.frontiersin.org/files/Articles/1755424/fresc-07-1755424-HTML-r1/image_m/fresc-07-1755424-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/files/Articles/1755424/fresc-07-1755424-HTML-r1/image_m/fresc-07-1755424-t002.jpg Table 2. Model fit indices for latent profile models with different numbers of classes (N = 296). https://www.frontiersin.org/files/Articles/1755424/fresc-07-1755424-HTML-r1/image_m/fresc-07-1755424-g001.jpg Figure 1. The scores of the three potential categories of patients with coronary heart disease. Fear https://www.frontiersin.org/files/Articles/1755424/fresc-07-1755424-HTML-r1/image_m/fresc-07-1755424-g002.jpg Figure 2. Graphs of profiles based on Tampa Scale for Kinesiophobia Heart scale four domains. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1672198/full https://www.frontiersin.org/files/Articles/1672198/fmicb-16-1672198-HTML-r1/image_m/fmicb-16-1672198-g001.jpg Figure 1. (A) Intestinal pathological changes and symptoms induced by pathogenic bacterial infection https://www.frontiersin.org/files/Articles/1672198/fmicb-16-1672198-HTML-r1/image_m/fmicb-16-1672198-t001.jpg Table 1. Case reports of phage therapy for diarrheal diseases. https://www.frontiersin.org/files/Articles/1672198/fmicb-16-1672198-HTML-r1/image_m/fmicb-16-1672198-t002.jpg Table 2. Case reports on the application of phage therapy against Cholera. https://www.frontiersin.org/files/Articles/1672198/fmicb-16-1672198-HTML-r1/image_m/fmicb-16-1672198-t003.jpg Table 3. Case studies on phage-based treatment of typhoid fever. https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2026.1811399/full https://www.frontiersin.org/files/Articles/1811399/fnimg-05-1811399-HTML/image_m/fnimg-05-1811399-t001.jpg Table 1. Demographics and baseline clinical features of the participants. https://www.frontiersin.org/files/Articles/1811399/fnimg-05-1811399-HTML/image_m/fnimg-05-1811399-t002.jpg Table 2. Clinical symptom severity before and after ECT. https://www.frontiersin.org/files/Articles/1811399/fnimg-05-1811399-HTML/image_m/fnimg-05-1811399-t003.jpg Table 3. Changed FC in brain regions between MDDs and HCs (seed 1: R-STG). https://www.frontiersin.org/files/Articles/1811399/fnimg-05-1811399-HTML/image_m/fnimg-05-1811399-g001.jpg Figure 1. (a, b) Decreased FC between L-SFG and R-ACG in MDDs at baseline compared with HCs. https://www.frontiersin.org/files/Articles/1811399/fnimg-05-1811399-HTML/image_m/fnimg-05-1811399-t004.jpg Table 4. Changed FC in MDDs after ECT (seed 1: R-STG). https://www.frontiersin.org/files/Articles/1811399/fnimg-05-1811399-HTML/image_m/fnimg-05-1811399-g002.jpg Figure 2. (a, b) Decreased FC between R-STG and R-MTG in MDDs after ECT. https://www.frontiersin.org/files/Articles/1811399/fnimg-05-1811399-HTML/image_m/fnimg-05-1811399-t005.jpg Table 5. Changed FC in MDDs after ECT (seed 2: L-SFG). https://www.frontiersin.org/files/Articles/1811399/fnimg-05-1811399-HTML/image_m/fnimg-05-1811399-g003.jpg Figure 3. (a) Decreased FC between L-SFG and R-SOG, between L-SFG and R-SFG, increased FC between L- https://www.frontiersin.org/files/Articles/1811399/fnimg-05-1811399-HTML/image_m/fnimg-05-1811399-g004.jpg Figure 4. The negative correlation between HAMD scores after ECT and FC in R-STG and L-IOG. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1724644/full https://www.frontiersin.org/files/Articles/1724644/fneur-16-1724644-HTML-r1/image_m/fneur-16-1724644-g001.jpg Figure 1. Flow chart of patient cohorts. SP: Sepsis; SAE: Sepsis-associated encephalopathy. https://www.frontiersin.org/files/Articles/1724644/fneur-16-1724644-HTML-r1/image_m/fneur-16-1724644-g002.jpg Figure 2. Alterations in the structure of the intestinal microbiota in patients with SP and SAE. Fec https://www.frontiersin.org/files/Articles/1724644/fneur-16-1724644-HTML-r1/image_m/fneur-16-1724644-g003.jpg Figure 3. Functional predictions of microbial species associated with differentially abundant miRNAs https://www.frontiersin.org/files/Articles/1724644/fneur-16-1724644-HTML-r1/image_m/fneur-16-1724644-g004.jpg Figure 4. Changes in fecal miRNA expression in patients with sepsis and SAE. (A) PCA. (B) Volcano pl https://www.frontiersin.org/files/Articles/1724644/fneur-16-1724644-HTML-r1/image_m/fneur-16-1724644-g005.jpg Figure 5. The abundance of microbial species enriched in SAE samples was significantly correlated wi https://www.frontiersin.org/files/Articles/1724644/fneur-16-1724644-HTML-r1/image_m/fneur-16-1724644-g006.jpg Figure 6. Machine learning-based identification of biomarkers distinguishing SP from SAE. (A) LASSO https://www.frontiersin.org/files/Articles/1724644/fneur-16-1724644-HTML-r1/image_m/fneur-16-1724644-g007.jpg Figure 7. Diagnostic potential of hsa-miR-30e-3p and hsa-miR-223-5p for and mechanistic roles of the https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1762372/full https://www.frontiersin.org/files/Articles/1762372/fped-14-1762372-HTML/image_m/fped-14-1762372-g001.jpg Figure 1. (A–D) Scattered large dark red patches on the trunk and extremities on hospital day 1. https://www.frontiersin.org/files/Articles/1762372/fped-14-1762372-HTML/image_m/fped-14-1762372-t001.jpg Table 1. Clinical laboratory report. https://www.frontiersin.org/files/Articles/1762372/fped-14-1762372-HTML/image_m/fped-14-1762372-g002.jpg Figure 2. (a, b) Epidermal necrosis, superficial crusting, a large number of necrotic keratin-formin https://www.frontiersin.org/files/Articles/1762372/fped-14-1762372-HTML/image_m/fped-14-1762372-g003.jpg Figure 3. Body temperature. https://www.frontiersin.org/files/Articles/1762372/fped-14-1762372-HTML/image_m/fped-14-1762372-g004.jpg Figure 4. Darkening erythematous lesions, reduced exudation, formation of dry crusts after 2 weeks. https://www.frontiersin.org/files/Articles/1762372/fped-14-1762372-HTML/image_m/fped-14-1762372-g005.jpg Figure 5. Nearly healed ulcerations, postinflammatory hyperpigmentation after 6 weeks. https://www.frontiersin.org/files/Articles/1762372/fped-14-1762372-HTML/image_m/fped-14-1762372-t002.jpg Table 2. Summary of clinical features and therapies in preschool-aged children of febrile ulceronecr https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1671899/full https://www.frontiersin.org/files/Articles/1671899/fendo-16-1671899-HTML/image_m/fendo-16-1671899-g001.jpg Figure 1. The flowchart of the study. RSA, recurrent spontaneous abortion; PGT, preimplantation gene https://www.frontiersin.org/files/Articles/1671899/fendo-16-1671899-HTML/image_m/fendo-16-1671899-t001.jpg Table 1. Baseline characteristics of RIF women undergoing FET in the IR and non-IR group. https://www.frontiersin.org/files/Articles/1671899/fendo-16-1671899-HTML/image_m/fendo-16-1671899-t002.jpg Table 2. Pregnancy and neonatal outcomes of RIF women undergoing FET in the IR and non-IR group. https://www.frontiersin.org/files/Articles/1671899/fendo-16-1671899-HTML/image_m/fendo-16-1671899-t003.jpg Table 3. Univariate and multivariate generalized estimating equations analyses results of the associ https://www.frontiersin.org/files/Articles/1671899/fendo-16-1671899-HTML/image_m/fendo-16-1671899-t004.jpg Table 4. Baseline characteristics of RIF women undergoing FET in the metformin and non-metformin gro https://www.frontiersin.org/files/Articles/1671899/fendo-16-1671899-HTML/image_m/fendo-16-1671899-t005.jpg Table 5. Pregnancy and neonatal outcomes of RIF women undergoing FET in the metformin and non-metfor https://www.frontiersin.org/files/Articles/1671899/fendo-16-1671899-HTML/image_m/fendo-16-1671899-t006.jpg Table 6. Univariate and multivariate generalized estimating equations analyses results of the associ https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1750572/full https://www.frontiersin.org/files/Articles/1750572/fpls-17-1750572-HTML/image_m/fpls-17-1750572-g001.jpg Figure 1. Phytoene synthase active site motif analysis of tobacco and typical PSY members. https://www.frontiersin.org/files/Articles/1750572/fpls-17-1750572-HTML/image_m/fpls-17-1750572-g002.jpg Figure 2. Phylogenetic analysis of tobacco and typical PSY members. The tobacco PSY members together https://www.frontiersin.org/files/Articles/1750572/fpls-17-1750572-HTML/image_m/fpls-17-1750572-g003.jpg Figure 3. Synteny analysis of PSY genes between tobacco and tomato. The gray line in the background https://www.frontiersin.org/files/Articles/1750572/fpls-17-1750572-HTML/image_m/fpls-17-1750572-g004.jpg Figure 4. Distribution of tobacco PSY genes on chromosomes and segmental duplication events. All of https://www.frontiersin.org/files/Articles/1750572/fpls-17-1750572-HTML/image_m/fpls-17-1750572-g005.jpg Figure 5. The regulatory cis-elements in the promoter regions of tobacco PSY genes were predicted by https://www.frontiersin.org/files/Articles/1750572/fpls-17-1750572-HTML/image_m/fpls-17-1750572-g006.jpg Figure 6. The expression profiles of the NtPSY genes in nine different tissues were examined by usin https://www.frontiersin.org/files/Articles/1750572/fpls-17-1750572-HTML/image_m/fpls-17-1750572-g007.jpg Figure 7. The expression profiles of the NtPSY genes under cold treatment were examined by using tra https://www.frontiersin.org/files/Articles/1750572/fpls-17-1750572-HTML/image_m/fpls-17-1750572-g008.jpg Figure 8. The NtPSY1 functions in conferring carotenoid flux and cold tolerance. (A) Phenotypes of o https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1632684/full https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g001.jpg Figure 1. The overall workflow of this study. The research is structured into four core stages: (A) https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g002.jpg Figure 2. Some examples of the self-constructed dataset. https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-t001.jpg Table 1. Instance details of the self-constructed dataset. https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g003.jpg Figure 3. Distribution of instances in the Fine24 dataset by Normalized Size. In the figure, instanc https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-t002.jpg Table 2. The instance distribution quantified by category and size in the Fine24 dataset, including https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g004.jpg Figure 4. The overall network structure of CPD-WeedNet. https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g005.jpg Figure 5. Structural diagram of the CSP-MUIB module, where e represents the scaling factor and E rep https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g006.jpg Figure 6. Structural diagram of PFA. https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g007.jpg Figure 7. Structural diagram of DFS. https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-t003.jpg Table 3. Experimental environment configuration. https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g008.jpg Figure 8. Changes in training and validation results and loss function of CPD-WeedNet on the self-co https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-t004.jpg Table 4. Performance comparison with other models on the self-constructed dataset. https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-t005.jpg Table 5. Performance comparison with other models on the Fine24 dataset. https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g009.jpg Figure 9. Precision-Recall curve graphs. (A) Obtained from the prediction on the test set of the sel https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-t006.jpg Table 6. Results of the ablation experiment. https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g010.jpg Figure 10. Feature map visualization comparing the proposed CSP-MUIB module with the baseline C3K2 m https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g011.jpg Figure 11. Qualitative comparison of neck network feature attention heatmaps between our proposed mo https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g012.jpg Figure 12. Qualitative comparison of segmentation results on the self-constructed dataset, highlight https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g013.jpg Figure 13. Qualitative results for large-sized targets on the Fine24 dataset. The rows display the o https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g014.jpg Figure 14. Qualitative segmentation results for medium-sized targets on the Fine24 dataset. The rows https://www.frontiersin.org/files/Articles/1632684/fpls-16-1632684-HTML/image_m/fpls-16-1632684-g015.jpg Figure 15. Qualitative segmentation results for small-sized targets on the Fine24 dataset, demonstra https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1763258/full https://www.frontiersin.org/files/Articles/1763258/fimmu-17-1763258-HTML/image_m/fimmu-17-1763258-g001.jpg Figure 1. Experimental Design. (A) Workflow of experimental. (B) BVDV detection by RT-PCR. Yellow an https://www.frontiersin.org/files/Articles/1763258/fimmu-17-1763258-HTML/image_m/fimmu-17-1763258-g002.jpg Figure 2. Transcriptome differential expression analyses. (A) Volcano plot of differential expressio https://www.frontiersin.org/files/Articles/1763258/fimmu-17-1763258-HTML/image_m/fimmu-17-1763258-g003.jpg Figure 3. GSVA and single-cell deconvolution analysis. (A) Venn diagram of significantly differentia https://www.frontiersin.org/files/Articles/1763258/fimmu-17-1763258-HTML/image_m/fimmu-17-1763258-g004.jpg Figure 4. Methylation analyses of calf groups. (A) Volcano plot of differentially methylated sites. https://www.frontiersin.org/files/Articles/1763258/fimmu-17-1763258-HTML/image_m/fimmu-17-1763258-g005.jpg Figure 5. Methylation analyses of dam groups. (A) Volcano plot of differentially methylated sites. ( https://www.frontiersin.org/files/Articles/1763258/fimmu-17-1763258-HTML/image_m/fimmu-17-1763258-g006.jpg Figure 6. PI calf-specific DNA methylation markers. (A) Distribution of significantly differentially https://www.frontiersin.org/files/Articles/1763258/fimmu-17-1763258-HTML/image_m/fimmu-17-1763258-t001.jpg Table 1. Functions of key genes identified in this study. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1775356/full https://www.frontiersin.org/files/Articles/1775356/fmed-13-1775356-HTML/image_m/fmed-13-1775356-t001.jpg Table 1. Baseline characteristics of hospitalized elderly patients with CHF. https://www.frontiersin.org/files/Articles/1775356/fmed-13-1775356-HTML/image_m/fmed-13-1775356-t002.jpg Table 2. Univariable analysis of oral frailty among hospitalized elderly patients with CHF. https://www.frontiersin.org/files/Articles/1775356/fmed-13-1775356-HTML/image_m/fmed-13-1775356-t003.jpg Table 3. Assignment of variables in the multivariable logistic regression analysis. https://www.frontiersin.org/files/Articles/1775356/fmed-13-1775356-HTML/image_m/fmed-13-1775356-t004.jpg Table 4. Multivariable logistic regression analysis of oral frailty in elderly patients with CHF. https://www.frontiersin.org/files/Articles/1775356/fmed-13-1775356-HTML/image_m/fmed-13-1775356-g001.jpg Figure 1. Nomogram for predicting oral frailty in elderly patients with CHF. https://www.frontiersin.org/files/Articles/1775356/fmed-13-1775356-HTML/image_m/fmed-13-1775356-g002.jpg Figure 2. Receiver operating characteristic (ROC) curve of the risk assessment model. https://www.frontiersin.org/files/Articles/1775356/fmed-13-1775356-HTML/image_m/fmed-13-1775356-g003.jpg Figure 3. Calibration curve of the prediction model. https://www.frontiersin.org/files/Articles/1775356/fmed-13-1775356-HTML/image_m/fmed-13-1775356-g004.jpg Figure 4. Decision curve analysis (DCA) of the prediction model. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1662735/full https://www.frontiersin.org/files/Articles/1662735/fmed-12-1662735-HTML/image_m/fmed-12-1662735-t001.jpg Table 1. Comparison of BCVA and CRT between the two groups before and after treatment. https://www.frontiersin.org/files/Articles/1662735/fmed-12-1662735-HTML/image_m/fmed-12-1662735-g001.jpg Figure 1. Comparison of best-corrected visual acuity between the reference aflibercept and biosimila https://www.frontiersin.org/files/Articles/1662735/fmed-12-1662735-HTML/image_m/fmed-12-1662735-g002.jpg Figure 2. Macular central retinal thickness measurements in the reference aflibercept and biosimilar https://www.frontiersin.org/files/Articles/1662735/fmed-12-1662735-HTML/image_m/fmed-12-1662735-t002.jpg Table 2. Comparison of FAZ area, SVD, and DVD between the reference aflibercept and biosimilar QL120 https://www.frontiersin.org/files/Articles/1662735/fmed-12-1662735-HTML/image_m/fmed-12-1662735-g003.jpg Figure 3. Foveal avascular zone area in the reference aflibercept and biosimilar QL1207 groups at ba https://www.frontiersin.org/files/Articles/1662735/fmed-12-1662735-HTML/image_m/fmed-12-1662735-g004.jpg Figure 4. Superficial vascular density in the reference aflibercept and biosimilar QL1207 groups at https://www.frontiersin.org/files/Articles/1662735/fmed-12-1662735-HTML/image_m/fmed-12-1662735-g005.jpg Figure 5. Deep vascular density in the reference aflibercept and biosimilar QL1207 groups at baselin https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1612605/full https://www.frontiersin.org/files/Articles/1612605/fonc-15-1612605-HTML-r1/image_m/fonc-15-1612605-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1612605/fonc-15-1612605-HTML-r1/image_m/fonc-15-1612605-g001.jpg Figure 1. Major potential risk factors of lung carcinogenesis. Smoking includes active smoking and s https://www.frontiersin.org/files/Articles/1612605/fonc-15-1612605-HTML-r1/image_m/fonc-15-1612605-g002.jpg Figure 2. Genome structure of the Merkel cell polyomavirus. (A) MCPyV, a double-stranded DNA virus c https://www.frontiersin.org/files/Articles/1612605/fonc-15-1612605-HTML-r1/image_m/fonc-15-1612605-t001.jpg Table 1. Studies on MCPyV detection from LC specimens around the globe (2009-2024). https://www.frontiersin.org/files/Articles/1612605/fonc-15-1612605-HTML-r1/image_m/fonc-15-1612605-g003.jpg Figure 3. Potential distant metastasis of MCPyV-positive MCC to lung. Integration of multiple detect https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1752555/full https://www.frontiersin.org/files/Articles/1752555/fpsyt-17-1752555-HTML/image_m/fpsyt-17-1752555-g001.jpg Figure 1. Study design flow chart. ADL, Activities of Daily Living; HAMA, Hamilton Anxiety Rating Sc https://www.frontiersin.org/files/Articles/1752555/fpsyt-17-1752555-HTML/image_m/fpsyt-17-1752555-t001.jpg Table 1. Baseline characteristics of the two groups. https://www.frontiersin.org/files/Articles/1752555/fpsyt-17-1752555-HTML/image_m/fpsyt-17-1752555-t002.jpg Table 2. Outcome measures at baseline and after 4-week intervention for both groups. https://www.frontiersin.org/files/Articles/1752555/fpsyt-17-1752555-HTML/image_m/fpsyt-17-1752555-g002.jpg Figure 2. Comparison of visual analogue scale (VAS) scores between the two groups. (A) Estimated mar https://www.frontiersin.org/files/Articles/1752555/fpsyt-17-1752555-HTML/image_m/fpsyt-17-1752555-g003.jpg Figure 3. Comparison of Hamilton anxiety rating scale (HAMA) scores between the two groups. (A) Esti https://www.frontiersin.org/files/Articles/1752555/fpsyt-17-1752555-HTML/image_m/fpsyt-17-1752555-g004.jpg Figure 4. Comparison of Hamilton depression rating scale(HAMD) scores between the two groups. (A) Es https://www.frontiersin.org/files/Articles/1752555/fpsyt-17-1752555-HTML/image_m/fpsyt-17-1752555-g005.jpg Figure 5. Comparison of Pittsburgh sleep quality index (PSQI) scores between the two groups. (A) Est https://www.frontiersin.org/files/Articles/1752555/fpsyt-17-1752555-HTML/image_m/fpsyt-17-1752555-g006.jpg Figure 6. Comparison of activities of daily living (ADL) scores between the two groups. (A) Estimate https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1753050/full https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-g001.jpg Figure 1. As shown in the figure, the proposed heatmap prediction framework for pertussis regions is https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-g002.jpg Figure 2. Example diagram of 3DCNN model architecture. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-g003.jpg Figure 3. This figure illustrates the overall structure of the Spatial Propagation-Aware Prediction https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-g004.jpg Figure 4. This figure illustrates the structure of the Spatial-Temporal Mixed State Output Head, inc https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-g005.jpg Figure 5. The dataset used in this paper is constructed. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-t001.jpg Table 1. Hyperparameters and runtime environment configuration. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-t002.jpg Table 2. Performance comparison of different methods on the pertussis heatmap prediction task. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-t003.jpg Table 3. Ablation study results of different model components. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-g006.jpg Figure 6. Statistical analysis of experimental results. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-g007.jpg Figure 7. Density scatter plot experiment results of predicted and true values. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-g008.jpg Figure 8. Qualitative visualization of experimental results. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-g009.jpg Figure 9. This histogram depicts the stability characteristics of different models in terms of error https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-t004.jpg Table 4. Performance under different data missing rates. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-t005.jpg Table 5. Input window sensitivity results under different T. https://www.frontiersin.org/files/Articles/1753050/fpubh-14-1753050-HTML/image_m/fpubh-14-1753050-t006.jpg Table 6. Experimental results of sensitivity to different architectures. https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1792877/full https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-t001.jpg Table 1. Overview of RNA sequencing runs retrieved from the NCBI platform, representing run accessio https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-g001.jpg Figure 1. Volcano plot representing differentially expressed genes identified using GEO2R. Red dots https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-g002.jpg Figure 2. Venn diagram generated using Venny representing common genes between Lean vs. T2DM and Lea https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-g003.jpg Figure 3. GO enrichment analysis of differentially expressed genes: (A) Biological Processes (BP), ( https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-t002.jpg Table 2. Representation of binding affinity, ADME and toxicity of the retrieved ligands. https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-t003.jpg Table 3. Representation of Molecular docking results of different ligands and control with their ID https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-g004.jpg Figure 4. A 2D interaction diagram from the results of molecular docking-representing how different https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-g005.jpg Figure 5. Molecular dynamics simulation analysis of the protein-ligand complex showing the conformat https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-t004.jpg Table 4. Statistical summary of molecular dynamics parameters including RMSD, RMSF, radius of gyrati https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-g006.jpg Figure 6. (A–C) 2D and 3D free energy landscape (FEL) plots of IL-6–ligand complexes constructed usi https://www.frontiersin.org/files/Articles/1792877/fbinf-06-1792877-HTML/image_m/fbinf-06-1792877-g007.jpg Figure 7. Dynamic Cross-Correlation Matrix Plots of IL-6 showing residue-wise correlated motions for https://www.frontiersin.org/journals/environmental-archaeology/articles/10.3389/fearc.2026.1738188/full https://www.frontiersin.org/files/Articles/1738188/fearc-05-1738188-HTML/image_m/fearc-05-1738188-g001.jpg Figure 1. Images showing moments of the manufacturing of the experimental lithic tools. (A) MB knapp https://www.frontiersin.org/files/Articles/1738188/fearc-05-1738188-HTML/image_m/fearc-05-1738188-g002.jpg Figure 2. (A) Diagram illustrating the cutting actions performed on the bone surface, categorized ba https://www.frontiersin.org/files/Articles/1738188/fearc-05-1738188-HTML/image_m/fearc-05-1738188-t001.jpg Table 1. Sample of cut marks selected for the microtopography analysis. https://www.frontiersin.org/files/Articles/1738188/fearc-05-1738188-HTML/image_m/fearc-05-1738188-t002.jpg Table 2. Descriptions of the qualitative criteria used to evaluate tool control during the cutting a https://www.frontiersin.org/files/Articles/1738188/fearc-05-1738188-HTML/image_m/fearc-05-1738188-g003.jpg Figure 3. Control ratings of cutting actions across different variables. (A) Boxplots showing contro https://www.frontiersin.org/files/Articles/1738188/fearc-05-1738188-HTML/image_m/fearc-05-1738188-g004.jpg Figure 4. Boxplots comparing three metric variables of cut marks produced by tools made from dacite, https://www.frontiersin.org/files/Articles/1738188/fearc-05-1738188-HTML/image_m/fearc-05-1738188-t003.jpg Table 3. Principal component analysis (PCA) results showing the percentage of variance explained by https://www.frontiersin.org/files/Articles/1738188/fearc-05-1738188-HTML/image_m/fearc-05-1738188-g005.jpg Figure 5. PCA scatter biplots of the metric variables characterizing morphometric patterns in the in https://www.frontiersin.org/files/Articles/1738188/fearc-05-1738188-HTML/image_m/fearc-05-1738188-g006.jpg Figure 6. PCA scatter plots based on Elliptic Fourier coefficients representing the form of cut mark https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1788062/full https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-t001.jpg Table 1. Summary of mean square estimates for GCA and SCA based on the NCII design. https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-t002.jpg Table 2. Superior parental lines and hybrids identified based on general and specific combining abil https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-t003.jpg Table 3. Genetic variance components for traits evaluated under the North Carolina Design II. https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-t004.jpg Table 4. Heterosis (h, %) of the 20 experimental hybrids (H1–H20) evaluated for ten agronomic and fr https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-t005.jpg Table 5. Heterobeltiosis (Hb, %) of the 20 experimental hybrids (H1–H20) for agronomic and fruit qua https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-t006.jpg Table 6. Mean squares from ANOVA for plant architecture, fruit quality, and yield-related traits in https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-g001.jpg Figure 1. Visual representation of the parental lines evaluated in the study, highlighting the morph https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-g002.jpg Figure 2. Dunnett’s test for (A) leaf length (LL), (B) leaf width (LW), (C) internode length (IL), a https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-g003.jpg Figure 3. Dunnett’s test for (A) pericarp firmness (PF), (B) fruit shape (FS), and (C) soluble solid https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-g004.jpg Figure 4. Dunnett’s test for (A) number of fruits per plant (NFP), (B) yield per plant (YPP), and (C https://www.frontiersin.org/files/Articles/1788062/fpls-17-1788062-HTML/image_m/fpls-17-1788062-g005.jpg Figure 5. (A) Ranking of genotypes in ascending order based on the multi-trait stability index (MGID https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1803589/full https://www.frontiersin.org/files/Articles/1803589/fpubh-14-1803589-HTML/image_m/fpubh-14-1803589-t001.jpg Table 1. Respondents' general demographic characteristics and knowledge levels regarding environment https://www.frontiersin.org/files/Articles/1803589/fpubh-14-1803589-HTML/image_m/fpubh-14-1803589-t002.jpg Table 2. Accuracy of each question item in objectively measuring actual knowledge levels among pharm https://www.frontiersin.org/files/Articles/1803589/fpubh-14-1803589-HTML/image_m/fpubh-14-1803589-g001.jpg Figure 1. Current concerns and practices of pharmacy students regarding environmental risks of PECs https://www.frontiersin.org/files/Articles/1803589/fpubh-14-1803589-HTML/image_m/fpubh-14-1803589-t003.jpg Table 3. Association between practices and knowledge levels regarding environmental risks of PECs an https://www.frontiersin.org/files/Articles/1803589/fpubh-14-1803589-HTML/image_m/fpubh-14-1803589-t004.jpg Table 4. Attitudes of pharmacy students toward environmental risks of PECs and their source control https://www.frontiersin.org/files/Articles/1803589/fpubh-14-1803589-HTML/image_m/fpubh-14-1803589-t005.jpg Table 5. Pearson's correlation coefficients between KAP dimensions of environmental risks of PECs an https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2026.1748446/full https://www.frontiersin.org/files/Articles/1748446/fevo-14-1748446-HTML/image_m/fevo-14-1748446-t001.jpg Table 1. The 17 studied populations of the Asellus aquaticus species complex and their sample sizes. https://www.frontiersin.org/files/Articles/1748446/fevo-14-1748446-HTML/image_m/fevo-14-1748446-g001.jpg Figure 1. Map showing the location of sampling sites, modified after Herczeg et al., 2023. Abbreviat https://www.frontiersin.org/files/Articles/1748446/fevo-14-1748446-HTML/image_m/fevo-14-1748446-g002.jpg Figure 2. Measured morphological traits of Asellus aquaticus and their function. https://www.frontiersin.org/files/Articles/1748446/fevo-14-1748446-HTML/image_m/fevo-14-1748446-t002.jpg Table 2. Results of the univariate Bayesian double hierarchical generalized linear models (DHGLMs). https://www.frontiersin.org/files/Articles/1748446/fevo-14-1748446-HTML/image_m/fevo-14-1748446-g003.jpg Figure 3. Forest plot of within-population variance. Squares represent the effect sizes (variance co https://www.frontiersin.org/files/Articles/1748446/fevo-14-1748446-HTML/image_m/fevo-14-1748446-g004.jpg Figure 4. Forest plot of among-population variance. Squares represent the effect sizes (variance com https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1769834/full https://www.frontiersin.org/files/Articles/1769834/fnut-13-1769834-HTML/image_m/fnut-13-1769834-t001.jpg Table 1. UPLC elution gradient program for positive and negative ion modes. https://www.frontiersin.org/files/Articles/1769834/fnut-13-1769834-HTML/image_m/fnut-13-1769834-g001.jpg Figure 1. Scatter plots of QC injections from the LC-MS metabolomics workflow, used to assess analyt https://www.frontiersin.org/files/Articles/1769834/fnut-13-1769834-HTML/image_m/fnut-13-1769834-g002.jpg Figure 2. Principal component analysis (PCA) score plots of sample-level non-volatile metabolite pro https://www.frontiersin.org/files/Articles/1769834/fnut-13-1769834-HTML/image_m/fnut-13-1769834-g003.jpg Figure 3. OPLS-DA score plots and permutation-test validation of metabolite profiles for G2R and the https://www.frontiersin.org/files/Articles/1769834/fnut-13-1769834-HTML/image_m/fnut-13-1769834-g004.jpg Figure 4. KEGG pathway enrichment bubble plots based on differential metabolites between G2R and the https://www.frontiersin.org/files/Articles/1769834/fnut-13-1769834-HTML/image_m/fnut-13-1769834-t002.jpg Table 2. Parameters of the calibration curves for the antioxidant assays. https://www.frontiersin.org/files/Articles/1769834/fnut-13-1769834-HTML/image_m/fnut-13-1769834-g005.jpg Figure 5. Comparison of free radical scavenging activities (ABTS•+, •OH, and DPPH•) of black tea ext https://www.frontiersin.org/files/Articles/1769834/fnut-13-1769834-HTML/image_m/fnut-13-1769834-t003.jpg Table 3. Contents of total phenolics, total flavonoids and total antioxidant capacity (FRAP) in blac https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1792896/full https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-g000.jpg Graphical Abstract. https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-t001.jpg Table 1. Primary and secondary antibodies employed for immunofluorescence staining. https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-t002.jpg Table 2. Cytokine analyzed with multiplex bead-based immunoassay. https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-t003.jpg Table 3. Genes and forward/reverse sequences of primers used for qRT-PCR. https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-g001.jpg Figure 1. SH-SY5Y and U138 MG cell differentiation into neuron-like (NLCs) and astrocyte-like cells https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-g002.jpg Figure 2. Reactive transcriptional shift of ALCs and HMC-3 cells after pro-inflammatory stimulation. https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-g003.jpg Figure 3. Formation and morphological assessment of bi-hNSPHs. (A) Representative bright-field micro https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-g004.jpg Figure 4. Generation and morphometrical evaluation of tri-hNSPHs. (A) Representative bright-field mi https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-g005.jpg Figure 5. Internal composition of tri-hNSPHs. (A) Representative immunofluorescence images of tri-hN https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-g006.jpg Figure 6. Inflammatory induction and anti-inflammatory treatments of tri-hNSPHs. Quantification of i https://www.frontiersin.org/files/Articles/1792896/fimmu-17-1792896-HTML-r1/image_m/fimmu-17-1792896-g007.jpg Figure 7. Characterization of the hypoxia-induction treatments in tri-hNSPHs. (A) Representative HIF https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1641865/full https://www.frontiersin.org/files/Articles/1641865/fimmu-16-1641865-HTML/image_m/fimmu-16-1641865-g001.jpg Figure 1. Pivotal role of ERK5 in metastasis. The epithelial-to-mesenchymal transition (EMT) is a cr https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1760658/full https://www.frontiersin.org/files/Articles/1760658/fmars-13-1760658-HTML-r1/image_m/fmars-13-1760658-g000.jpg Graphical Abstract. Jellyfish biomass valorization pipeline within the blue bioeconomy. https://www.frontiersin.org/files/Articles/1760658/fmars-13-1760658-HTML-r1/image_m/fmars-13-1760658-t001.jpg Table 1. Jellyfish-derived biomolecules and their functional uses in biomedical sciences. https://www.frontiersin.org/files/Articles/1760658/fmars-13-1760658-HTML-r1/image_m/fmars-13-1760658-t002.jpg Table 2. A high-level comparison of major extraction routes for jellyfish-derived biomaterials and b https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1763782/full https://www.frontiersin.org/files/Articles/1763782/fphar-17-1763782-HTML/image_m/fphar-17-1763782-g001.jpg Figure 1. Mechanism of drug or environment-induced hepatorenal toxicity. https://www.frontiersin.org/files/Articles/1763782/fphar-17-1763782-HTML/image_m/fphar-17-1763782-t001.jpg Table 1. Comparison of structure and function of hepatorenal metabolic barriers. https://www.frontiersin.org/files/Articles/1763782/fphar-17-1763782-HTML/image_m/fphar-17-1763782-g002.jpg Figure 2. Structure and function of hepatic metabolic barrier. https://www.frontiersin.org/files/Articles/1763782/fphar-17-1763782-HTML/image_m/fphar-17-1763782-g003.jpg Figure 3. Structure and function of renal metabolic barrier. https://www.frontiersin.org/files/Articles/1763782/fphar-17-1763782-HTML/image_m/fphar-17-1763782-t002.jpg Table 2. Comparison of damage mechanism of drugs and environmental toxicants on hepatorenal metaboli https://www.frontiersin.org/files/Articles/1763782/fphar-17-1763782-HTML/image_m/fphar-17-1763782-g004.jpg Figure 4. Intervention strategies and mechanisms of natural products for hepatorenal metabolic barri https://www.frontiersin.org/files/Articles/1763782/fphar-17-1763782-HTML/image_m/fphar-17-1763782-t003.jpg Table 3. Intervention of herbal components on hepatorenal metabolic barriers injury. https://www.frontiersin.org/files/Articles/1763782/fphar-17-1763782-HTML/image_m/fphar-17-1763782-t004.jpg Table 4. Intervention of single Chinese medicinal herbs on hepatorenal metabolic barriers injury. https://www.frontiersin.org/files/Articles/1763782/fphar-17-1763782-HTML/image_m/fphar-17-1763782-t005.jpg Table 5. Intervention of Chinese medicinal prescriptions on hepatorenal metabolic barriers injury. https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2026.1802923/full https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-t001.jpg Table 1. Experimental grouping and exposure dose (n = 10). https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-t002.jpg Table 2. In vitro experimental grouping. https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-t003.jpg Table 3. Apoptosis was detected by Annexin V-FITC/PI double staining. https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-g001.jpg Figure 1. Behavioral performance in AlCl3 exposed aging mouse and effects of salidroside treatment a https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-t004.jpg Table 4. Evasion incubation period of positioning navigation experiment (x¯ s n = 10). https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-t005.jpg Table 5. Space exploration experiment mouse looking for platform experiment results (x¯± s n = 10). https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-t006.jpg Table 6. Effect of SAL on neurotransmitter - acetylcholinesterase activity (x¯± s). https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-g002.jpg Figure 2. Mitochondrial biomarkers measured in mitochondrial samples isolated from mouse brain neuro https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-g003.jpg Figure 3. Quantification of Aβ1-42 and β-CTF in mitochondrial samples, and sAPPα, α-/β-/γ-Secretase, https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-g004.jpg Figure 4. Network and enrichment analyses of putative salidroside targets. (A,B) PPI network showing https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-g005.jpg Figure 5. Effects of salidroside on cell viability, oxidative stress, and apoptosis in OX-induced HT https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-t007.jpg Table 7. Determination of mitochondrial function in HT-22 cells (x¯± s). https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-g006.jpg Figure 6. Protective effects of salidroside on mitochondrial protein aggregation: co-localization of https://www.frontiersin.org/files/Articles/1802923/fnbeh-20-1802923-HTML/image_m/fnbeh-20-1802923-g007.jpg Figure 7. Mechanistic diagram of salidroside’s effects on mitochondrial protein aggregation and mito https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2026.1768135/full https://www.frontiersin.org/files/Articles/1768135/frsc-08-1768135-HTML/image_m/frsc-08-1768135-g001.jpg Figure 1. System architecture and key design parameters of the solar-powered, off-grid air quality s https://www.frontiersin.org/files/Articles/1768135/frsc-08-1768135-HTML/image_m/frsc-08-1768135-g002.jpg Figure 2. Considerations for designing and deploying a solar-powered lower cost air quality sensor. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1694066/full https://www.frontiersin.org/files/Articles/1694066/fimmu-16-1694066-HTML/image_m/fimmu-16-1694066-t001.jpg Table 1. Prominent activation pathways and interaction of keratinocytes with other immune cell types https://www.frontiersin.org/files/Articles/1694066/fimmu-16-1694066-HTML/image_m/fimmu-16-1694066-t002.jpg Table 2. Selected examples of stress cytokeratins implicated in inflammatory responses. https://www.frontiersin.org/files/Articles/1694066/fimmu-16-1694066-HTML/image_m/fimmu-16-1694066-g001.jpg Figure 1. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram o https://www.frontiersin.org/files/Articles/1694066/fimmu-16-1694066-HTML/image_m/fimmu-16-1694066-g002.jpg Figure 2. Overview of keratinocyte stress and immune feedback loop in inflammatory skin disease. Sch https://www.frontiersin.org/files/Articles/1694066/fimmu-16-1694066-HTML/image_m/fimmu-16-1694066-g003.jpg Figure 3. Disease-specific keratinocyte activation patterns and cytokeratin expression across inflam https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2026.1800761/full https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g001.jpg Figure 1. Schematic representation of the preparation of maghnite-H+ and maghnite-Na+ catalysts. https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-t001.jpg Table 1. Effect of maghnite-H+ and maghnite-Na+ catalyst loading on the synthesis yields of NBMP mon https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g002.jpg Figure 2. Schematic illustration of the synthesis of 1,4-bis (methacryloyl) piperazine (NBMP) monome https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g003.jpg Figure 3. Physicochemical characterization of raw and modified maghnite (Mag-Na+ and Mag-H+). (A) FT https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g004.jpg Figure 4. Effect of maghnite-H+ and maghnite-Na+ catalyst concentration on the yields of NBMP monome https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g005.jpg Figure 5. FTIR spectra of NBMP and poly (NBMP), highlighting key bond changes during polymerization. https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-t002.jpg Table 2. 1H NMR chemical shifts of NBMP (monomer) and poly (NBMP). https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-t003.jpg Table 3. 13C NMR chemical shifts of NBMP (monomer) and poly (NBMP). https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g006.jpg Figure 6. Poly (NBMP) characterization: (A) Representative SEM image showing well-organized crystall https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-t004.jpg Table 4. Antibacterial activity of poly (NBMP) at different concentrations against Gram-positive and https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-t005.jpg Table 5. Predicted physicochemical, pharmacokinetic, and drug-likeness properties of the NBMP Monome https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g007.jpg Figure 7. Antibacterial activity of poly (NBMP) at different concentrations (625, 1,250, 2,500, and https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g008.jpg Figure 8. Bioavailability and BBB permeability analysis of the NBMP monomer: (A) The oral bioavailab https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g009.jpg Figure 9. Toxicity prediction analysis of the NBMP monomer; (A) NBMP molecular structure; (B) Potent https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-t006.jpg Table 6. Binding affinities, interactions, and redocking validation of NBMP and co-crystallized liga https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g010.jpg Figure 10. Surface views of protein-ligand complexes, active sites, and 3D bonds of NBMP with target https://www.frontiersin.org/files/Articles/1800761/fchem-14-1800761-HTML-r1/image_m/fchem-14-1800761-g011.jpg Figure 11. The molecular dynamics simulation analyses showing (A) RMSD (nm), (B) RMSF (nm), and (C) https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1757532/full https://www.frontiersin.org/files/Articles/1757532/fimmu-17-1757532-HTML/image_m/fimmu-17-1757532-t001.jpg Table 1. Demographic and disease characteristics of the general population compared to patients who https://www.frontiersin.org/files/Articles/1757532/fimmu-17-1757532-HTML/image_m/fimmu-17-1757532-t002.jpg Table 2. Baseline thyroid function parameters and characteristics of thyroid immune-related adverse https://www.frontiersin.org/files/Articles/1757532/fimmu-17-1757532-HTML/image_m/fimmu-17-1757532-t003.jpg Table 3. Crude (Univariable analysis) and adjusted (Multivariable analysis) associations of clinical https://www.frontiersin.org/files/Articles/1757532/fimmu-17-1757532-HTML/image_m/fimmu-17-1757532-g001.jpg Figure 1. Progression-free survival (PFS) according to the development of immunotherapy-related thyr https://www.frontiersin.org/files/Articles/1757532/fimmu-17-1757532-HTML/image_m/fimmu-17-1757532-g002.jpg Figure 2. Overall survival (OS) according to the development of immunotherapy-related thyroid toxici https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1743252/full https://www.frontiersin.org/files/Articles/1743252/fimmu-17-1743252-HTML/image_m/fimmu-17-1743252-t001.jpg Table 1. Demographics of the subjects analysed in this study. https://www.frontiersin.org/files/Articles/1743252/fimmu-17-1743252-HTML/image_m/fimmu-17-1743252-g001.jpg Figure 1. NK1R was expressed on epithelial cells in the oesophageal mucosa. (A) Representative image https://www.frontiersin.org/files/Articles/1743252/fimmu-17-1743252-HTML/image_m/fimmu-17-1743252-g002.jpg Figure 2. Expression of NK1R on T lymphocytes and mast cells in the oesophageal mucosa. (A) In the o https://www.frontiersin.org/files/Articles/1743252/fimmu-17-1743252-HTML/image_m/fimmu-17-1743252-g003.jpg Figure 3. Substance P induces dose-dependent phosphorylation of NF-κB in NE-1 cells. After 30 min of https://www.frontiersin.org/files/Articles/1743252/fimmu-17-1743252-HTML/image_m/fimmu-17-1743252-g004.jpg Figure 4. Substance P exposure induces dose-dependent release of IL-8 and IL-6 from NE-1 cells. Afte https://www.frontiersin.org/files/Articles/1743252/fimmu-17-1743252-HTML/image_m/fimmu-17-1743252-g005.jpg Figure 5. MRGPRX2+ mast cells are present in the oesophageal mucosa. (A) Representative images showi https://www.frontiersin.org/files/Articles/1743252/fimmu-17-1743252-HTML/image_m/fimmu-17-1743252-t002.jpg Table 2. The distance from substance P+ nerve fibres and the closest mast cell in healthy and GORD s https://www.frontiersin.org/files/Articles/1743252/fimmu-17-1743252-HTML/image_m/fimmu-17-1743252-g006.jpg Figure 6. Representative images showing mast cells closely apposed to substance P+ nerve fibres in t https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1754264/full https://www.frontiersin.org/files/Articles/1754264/fpsyg-17-1754264-HTML/image_m/fpsyg-17-1754264-t001.jpg Table 1. Means (and standard deviations) of participant age, education level, socioeconomic index, a https://www.frontiersin.org/files/Articles/1754264/fpsyg-17-1754264-HTML/image_m/fpsyg-17-1754264-t002.jpg Table 2. Example of items in the affirmative form for each form of humor for the appreciation dimens https://www.frontiersin.org/files/Articles/1754264/fpsyg-17-1754264-HTML/image_m/fpsyg-17-1754264-g001.jpg Figure 1. Average scores for appreciation by type of humor among young adults, middle-aged adults, a https://www.frontiersin.org/files/Articles/1754264/fpsyg-17-1754264-HTML/image_m/fpsyg-17-1754264-g002.jpg Figure 2. Average scores for production by type of humor among young adults, middle-aged adults, and https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1770957/full https://www.frontiersin.org/files/Articles/1770957/fimmu-17-1770957-HTML/image_m/fimmu-17-1770957-g001.jpg Figure 1. In vitro growth-inhibitory effect of JNJ. 4T1 cells were treated with 10 µM JNJ or 0.1% DM https://www.frontiersin.org/files/Articles/1770957/fimmu-17-1770957-HTML/image_m/fimmu-17-1770957-g002.jpg Figure 2. JNJ-induced alterations in 4T1 cells phenotype. (A) Cells were treated with 10 µM JNJ or 0 https://www.frontiersin.org/files/Articles/1770957/fimmu-17-1770957-HTML/image_m/fimmu-17-1770957-g003.jpg Figure 3. JNJ modulation of the ERK signaling pathway. (A) Cells were treated with 10 µM JNJ or 0.1% https://www.frontiersin.org/files/Articles/1770957/fimmu-17-1770957-HTML/image_m/fimmu-17-1770957-g004.jpg Figure 4. Influence of ERK signaling pathway on JNJ effects. (A) Cells were treated with 10 µM JNJ p https://www.frontiersin.org/files/Articles/1770957/fimmu-17-1770957-HTML/image_m/fimmu-17-1770957-g005.jpg Figure 5. JNJ in vivo effect. (A) 4T1 cells were inoculated with 10 µM JNJ or vehicle (Ct), and tumo https://www.frontiersin.org/files/Articles/1770957/fimmu-17-1770957-HTML/image_m/fimmu-17-1770957-g006.jpg Figure 6. Cytokines secretion in tumor- and lymph node-isolated cells. Samples from control (Ct) and https://www.frontiersin.org/files/Articles/1770957/fimmu-17-1770957-HTML/image_m/fimmu-17-1770957-g007.jpg Figure 7. Tumors ex vivo studies. Total [(A), n = 20], CD4+ [(B), n = 20], and CD8+ [(C), n = 20] ly https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1747362/full https://www.frontiersin.org/files/Articles/1747362/fpubh-14-1747362-HTML/image_m/fpubh-14-1747362-g001.jpg Figure 1. A flowchart describing the general framework of the study. https://www.frontiersin.org/files/Articles/1747362/fpubh-14-1747362-HTML/image_m/fpubh-14-1747362-g002.jpg Figure 2. Mean accuracy, precision, recall, F1-score, and ROC-AUC of the XGB model across different https://www.frontiersin.org/files/Articles/1747362/fpubh-14-1747362-HTML/image_m/fpubh-14-1747362-g003.jpg Figure 3. Analysis of the Feature Importance of Students’ AI Learning and Usage Behavior. (The detai https://www.frontiersin.org/files/Articles/1747362/fpubh-14-1747362-HTML/image_m/fpubh-14-1747362-g004.jpg Figure 4. System for promoting students’ AI learning and usage behavior. (A) System homepage. (B) In https://www.frontiersin.org/files/Articles/1747362/fpubh-14-1747362-HTML/image_m/fpubh-14-1747362-g005.jpg Figure 5. The distribution of respondents’ choices across the options. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1811317/full https://www.frontiersin.org/files/Articles/1811317/fimmu-17-1811317-HTML/image_m/fimmu-17-1811317-g001.jpg Figure 1. Study flow chart. The study flow chart and the main machine learning methodological steps https://www.frontiersin.org/files/Articles/1811317/fimmu-17-1811317-HTML/image_m/fimmu-17-1811317-t001.jpg Table 1. Descriptive characteristics of assessed patients evaluated in the preimputation dataset ana https://www.frontiersin.org/files/Articles/1811317/fimmu-17-1811317-HTML/image_m/fimmu-17-1811317-g002.jpg Figure 2. The occurrence of MAS is associated with poor prognosis. Here, the overall survival analys https://www.frontiersin.org/files/Articles/1811317/fimmu-17-1811317-HTML/image_m/fimmu-17-1811317-t002.jpg Table 2. HR Cox model estimation evaluating predictors of mortality in assessed patients with Still’ https://www.frontiersin.org/files/Articles/1811317/fimmu-17-1811317-HTML/image_m/fimmu-17-1811317-t003.jpg Table 3. OR logistic regression model estimation evaluating predictors of MAS in assessed patients w https://www.frontiersin.org/files/Articles/1811317/fimmu-17-1811317-HTML/image_m/fimmu-17-1811317-t004.jpg Table 4. OR logistic regression model estimation evaluating dichotomized clinical predictors of MAS https://www.frontiersin.org/files/Articles/1811317/fimmu-17-1811317-HTML/image_m/fimmu-17-1811317-t005.jpg Table 5. Probability of MAS according to selected clinical variables and different possible patient https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1787278/full https://www.frontiersin.org/files/Articles/1787278/fonc-16-1787278-HTML/image_m/fonc-16-1787278-t001.jpg Table 1. Participant characteristics. https://www.frontiersin.org/files/Articles/1787278/fonc-16-1787278-HTML/image_m/fonc-16-1787278-g001.jpg Figure 1. Self-reported race/ethnicity and genetic similarity for study participants. Top: Distribut https://www.frontiersin.org/files/Articles/1787278/fonc-16-1787278-HTML/image_m/fonc-16-1787278-t002.jpg Table 2. Crude (Unadjusted) associations with VUS results. https://www.frontiersin.org/files/Articles/1787278/fonc-16-1787278-HTML/image_m/fonc-16-1787278-g002.jpg Figure 2. Adjusted odds of having ≥1 variant of uncertain significance (VUS) by self-reported race/e https://www.frontiersin.org/files/Articles/1787278/fonc-16-1787278-HTML/image_m/fonc-16-1787278-g003.jpg Figure 3. Distribution of Indigenous American genetic similarity in our cohort (n=597) and in the su https://www.frontiersin.org/files/Articles/1787278/fonc-16-1787278-HTML/image_m/fonc-16-1787278-g004.jpg Figure 4. Adjusted odds of VUS by Indigenous American genetic similarity. The forest plot displays t https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1665968/full https://www.frontiersin.org/files/Articles/1665968/fonc-15-1665968-HTML/image_m/fonc-15-1665968-g001.jpg Figure 1. CT scan examination. The first section documents numerous metastatic localizations. (A) sh https://www.frontiersin.org/files/Articles/1665968/fonc-15-1665968-HTML/image_m/fonc-15-1665968-g002.jpg Figure 2. Methods Immunophenotype analysis. The tumor cells were stained with a BV421-conjugated mon https://www.frontiersin.org/files/Articles/1665968/fonc-15-1665968-HTML/image_m/fonc-15-1665968-t001.jpg Table 1. The characteristics of patients reported in the literature, including our case affected by https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1681890/full https://www.frontiersin.org/files/Articles/1681890/fphar-16-1681890-HTML-r1/image_m/fphar-16-1681890-g001.jpg Figure 1. The natural product 6-MF shows the inhibitory effect on the proliferation of melanoma cell https://www.frontiersin.org/files/Articles/1681890/fphar-16-1681890-HTML-r1/image_m/fphar-16-1681890-g002.jpg Figure 2. RNA-seq analysis and western blot demonstrate that 6-MF suppresses PI3K-AKT signaling path https://www.frontiersin.org/files/Articles/1681890/fphar-16-1681890-HTML-r1/image_m/fphar-16-1681890-g003.jpg Figure 3. The combination of 6-MF and IFN-γ inhibits the SLC7A11/GPX4 axis via increasing STAT1 phos https://www.frontiersin.org/files/Articles/1681890/fphar-16-1681890-HTML-r1/image_m/fphar-16-1681890-g004.jpg Figure 4. Six-MF blocks circPIAS1 biogenesis by reducing the expression levels of PTBP1. (A) CircPIA https://www.frontiersin.org/files/Articles/1681890/fphar-16-1681890-HTML-r1/image_m/fphar-16-1681890-g005.jpg Figure 5. The natural metabolite 6-MF enhances the therapeutic effect of PD-1 inhibitors on melanoma https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1749330/full https://www.frontiersin.org/files/Articles/1749330/fpubh-14-1749330-HTML/image_m/fpubh-14-1749330-g001.jpg Figure 1. Timeline for antiracism curriculum implementation. https://www.frontiersin.org/files/Articles/1749330/fpubh-14-1749330-HTML/image_m/fpubh-14-1749330-t001.jpg Table 1. Articles reviewed and recommended by the diversity, equity, and inclusion committee to scho https://www.frontiersin.org/files/Articles/1749330/fpubh-14-1749330-HTML/image_m/fpubh-14-1749330-t002.jpg Table 2. Antiracism lesson overview by core course. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1762535/full https://www.frontiersin.org/files/Articles/1762535/fimmu-17-1762535-HTML-r1/image_m/fimmu-17-1762535-g001.jpg Figure 1. Patient selection and treatment allocation. NMOSD, neuromyelitis optica spectrum disorder; https://www.frontiersin.org/files/Articles/1762535/fimmu-17-1762535-HTML-r1/image_m/fimmu-17-1762535-t001.jpg Table 1. Demographics and clinical characteristics of the 10 RTX-resistant NMOSD patients. https://www.frontiersin.org/files/Articles/1762535/fimmu-17-1762535-HTML-r1/image_m/fimmu-17-1762535-t002.jpg Table 2. Clinical timeline of the 10 RTX-resistant NMOSD patients. https://www.frontiersin.org/files/Articles/1762535/fimmu-17-1762535-HTML-r1/image_m/fimmu-17-1762535-t003.jpg Table 3. EDSS scores and ARR before and after therapy switch in 10 RTX-resistant NMOSD patients. https://www.frontiersin.org/files/Articles/1762535/fimmu-17-1762535-HTML-r1/image_m/fimmu-17-1762535-g002.jpg Figure 2. Treatment timeline and relapse events in 10 RTX-resistant NMOSD patients. Time zero (0) re https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1804626/full https://www.frontiersin.org/files/Articles/1804626/fnut-13-1804626-HTML/image_m/fnut-13-1804626-g001.jpg Figure 1. (A) UV–Vis absorption spectra of AgNCs@PVP and PVP; (B) Emission spectra of AgNCs@PVP at d https://www.frontiersin.org/files/Articles/1804626/fnut-13-1804626-HTML/image_m/fnut-13-1804626-g002.jpg Figure 2. (A) HRTEM (20 nm) of AgNCs@PVP; (B) HRTEM diagram (50 nm) of AgNCs@PVP; (C) FT-IR spectrum https://www.frontiersin.org/files/Articles/1804626/fnut-13-1804626-HTML/image_m/fnut-13-1804626-g003.jpg Figure 3. (A) Effect of different pH values on the fluorescence intensity of AgNCs@PVP; (B) Effect o https://www.frontiersin.org/files/Articles/1804626/fnut-13-1804626-HTML/image_m/fnut-13-1804626-g004.jpg Figure 4. (A) Fluorescence emission spectrum of AgNCs@PVP with different concentrations of iodide; ( https://www.frontiersin.org/files/Articles/1804626/fnut-13-1804626-HTML/image_m/fnut-13-1804626-g005.jpg Figure 5. The fluorescence intensity changes of AgNCs@PVP mixed with different interfering species. https://www.frontiersin.org/files/Articles/1804626/fnut-13-1804626-HTML/image_m/fnut-13-1804626-g006.jpg Figure 6. (A) UV–Vis absorption spectra of iodide, AgNCs@PVP, and AgNCs@PVP-iodide system, excitatio https://www.frontiersin.org/files/Articles/1804626/fnut-13-1804626-HTML/image_m/fnut-13-1804626-t001.jpg Table 1. Application of the fluorescence method and ICP-MS for the determination of iodide in seawee https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1729880/full https://www.frontiersin.org/files/Articles/1729880/fneur-16-1729880-HTML-r1/image_m/fneur-16-1729880-g001.jpg Figure 1. Flow chart of patient inclusion. https://www.frontiersin.org/files/Articles/1729880/fneur-16-1729880-HTML-r1/image_m/fneur-16-1729880-t001.jpg Table 1. Baseline characteristics of the study population stratified by 90-day functional outcome. https://www.frontiersin.org/files/Articles/1729880/fneur-16-1729880-HTML-r1/image_m/fneur-16-1729880-g002.jpg Figure 2. Cross-validation and LASSO regression for predictor selection. (A) Cross-validation proces https://www.frontiersin.org/files/Articles/1729880/fneur-16-1729880-HTML-r1/image_m/fneur-16-1729880-g003.jpg Figure 3. Feature importance ranking and correlation analysis of LASSO-selected variables. (A) Featu https://www.frontiersin.org/files/Articles/1729880/fneur-16-1729880-HTML-r1/image_m/fneur-16-1729880-t002.jpg Table 2. Multifactor logistic regression results for predictors of poor prognosis in AIS-LVO patient https://www.frontiersin.org/files/Articles/1729880/fneur-16-1729880-HTML-r1/image_m/fneur-16-1729880-g004.jpg Figure 4. Nomogram for predicting poor prognosis in AIS-LVO patients. https://www.frontiersin.org/files/Articles/1729880/fneur-16-1729880-HTML-r1/image_m/fneur-16-1729880-g005.jpg Figure 5. ROC curves (A,B), calibration curves (C), and DCA curve (D) of the nomogram. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1688250/full https://www.frontiersin.org/files/Articles/1688250/fimmu-16-1688250-HTML-r1/image_m/fimmu-16-1688250-g001.jpg Figure 1. Structural and domain organization of major heat shock protein (HSP) families. (A) Represe https://www.frontiersin.org/files/Articles/1688250/fimmu-16-1688250-HTML-r1/image_m/fimmu-16-1688250-t001.jpg Table 1. Summary of the structural class, molecular characteristics, primary functions, client prote https://www.frontiersin.org/files/Articles/1688250/fimmu-16-1688250-HTML-r1/image_m/fimmu-16-1688250-g002.jpg Figure 2. Role of HSP27 in osteoarthritis pathogenesis and chondrocyte homeostasis. Pro-inflammatory https://www.frontiersin.org/files/Articles/1688250/fimmu-16-1688250-HTML-r1/image_m/fimmu-16-1688250-g003.jpg Figure 3. The role of HSP60 in chondrocyte homeostasis and osteoarthritis pathogenesis. HSP60 expres https://www.frontiersin.org/files/Articles/1688250/fimmu-16-1688250-HTML-r1/image_m/fimmu-16-1688250-g004.jpg Figure 4. Dual role of HSP70 in osteoarthritis: protective intracellular effects versus pro-inflamma https://www.frontiersin.org/files/Articles/1688250/fimmu-16-1688250-HTML-r1/image_m/fimmu-16-1688250-g005.jpg Figure 5. GRP78-mediated ER stress response in osteoarthritis. GRP78 is upregulated in chondrocytes https://www.frontiersin.org/files/Articles/1688250/fimmu-16-1688250-HTML-r1/image_m/fimmu-16-1688250-g006.jpg Figure 6. Role of HSP90 in chondrocyte stress responses, apoptosis, and fibrosis in osteoarthritis. https://www.frontiersin.org/files/Articles/1688250/fimmu-16-1688250-HTML-r1/image_m/fimmu-16-1688250-g007.jpg Figure 7. Overview of treatment options targeting HSPs in OA treatment. https://www.frontiersin.org/files/Articles/1688250/fimmu-16-1688250-HTML-r1/image_m/fimmu-16-1688250-t002.jpg Table 2. Context-dependent protective versus pathogenic roles of major HSP families in osteoarthriti https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1608796/full https://www.frontiersin.org/files/Articles/1608796/fpsyg-17-1608796-HTML/image_m/fpsyg-17-1608796-g001.jpg Figure 1. Flowchart of the Maturo software process. https://www.frontiersin.org/files/Articles/1608796/fpsyg-17-1608796-HTML/image_m/fpsyg-17-1608796-g002.jpg Figure 2. The workflow of GAM monitoring: (1) Question input; (2) pictures capture; and the profile https://www.frontiersin.org/files/Articles/1608796/fpsyg-17-1608796-HTML/image_m/fpsyg-17-1608796-g003.jpg Figure 3. Visual report of individual and team example from Maturo. (a) Person general page; (b) per https://www.frontiersin.org/files/Articles/1608796/fpsyg-17-1608796-HTML/image_m/fpsyg-17-1608796-g004.jpg Figure 4. Detailed information and suggestion of individual and team examples from Maturo. (a) Perso https://www.frontiersin.org/files/Articles/1608796/fpsyg-17-1608796-HTML/image_m/fpsyg-17-1608796-t001.jpg Table 1. Data collection, result presentation and labor requied. https://www.frontiersin.org/files/Articles/1608796/fpsyg-17-1608796-HTML/image_m/fpsyg-17-1608796-t002.jpg Table 2. Comparison of methods for estimating biological age against the expert method for the youth https://www.frontiersin.org/files/Articles/1608796/fpsyg-17-1608796-HTML/image_m/fpsyg-17-1608796-g005.jpg Figure 5. Intraclass correlations and scatterplots for estimates of PHV speed derived from the Matur https://www.frontiersin.org/files/Articles/1608796/fpsyg-17-1608796-HTML/image_m/fpsyg-17-1608796-g006.jpg Figure 6. Intraclass correlations and scatterplots for estimates of PHV age derived from the Maturo https://www.frontiersin.org/files/Articles/1608796/fpsyg-17-1608796-HTML/image_m/fpsyg-17-1608796-t003.jpg Table 3. The crosstabulation of maturation status between expert method and Maturo software. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1611709/full https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-g001.jpg Figure 1. Model development and validation flowchart. PCAD, premature coronary artery disease; PIV, https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-t001.jpg Table 1. General characteristics of patients before and after PSM. https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-g002.jpg Figure 2. Multiple machine learning results. (A) Gradient Boosting Machine (GBM): The top five featu https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-t002.jpg Table 2. Logistic regression. https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-g003.jpg Figure 3. Decision tree. The accuracy of the model is 0.88 and the confidence level is 0.86. WBC, wh https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-g004.jpg Figure 4. An ensemble model for the prognosis of PCAD. The StepCox[forward]-RSF ensemble model exhib https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-g005.jpg Figure 5. Random survival forest. (A) Show that the error rate of the model stabilizes when the numb https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-t003.jpg Table 3. Univariate regression analysis of PCAD. https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-g006.jpg Figure 6. ROC curve. (A) The optimal threshold for the PIV index predicted by the PCAD prognostic mo https://www.frontiersin.org/files/Articles/1611709/fcvm-12-1611709-HTML-r1/image_m/fcvm-12-1611709-g007.jpg Figure 7. Kaplan–Meier survival analysis. (A) The follow-up of patients with different PIV index gro https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2026.1791536/full https://www.frontiersin.org/files/Articles/1791536/fmolb-13-1791536-HTML/image_m/fmolb-13-1791536-t001.jpg Table 1. Members of the HSP70 family. HSP70s may be stress inducible or not inducible and reside in https://www.frontiersin.org/files/Articles/1791536/fmolb-13-1791536-HTML/image_m/fmolb-13-1791536-g001.jpg Figure 1. Molecular structure of HSP70 and its cycle. The structure of HSP70 (on the right) includes https://www.frontiersin.org/files/Articles/1791536/fmolb-13-1791536-HTML/image_m/fmolb-13-1791536-g002.jpg Figure 2. HSF1/HSP70 axis. At the center, cellular stress signalization by pathogen-, damage-, and m https://www.frontiersin.org/files/Articles/1791536/fmolb-13-1791536-HTML/image_m/fmolb-13-1791536-g003.jpg Figure 3. HSP70 family members. At the center, HSP72 (HSPA1A, inducible HSP70) and HSP73 (HSC70/HSPA https://www.frontiersin.org/files/Articles/1791536/fmolb-13-1791536-HTML/image_m/fmolb-13-1791536-g004.jpg Figure 4. HSP70 interactome. HSP70 molecules (at the center from the left) include a nucleotide-bind https://www.frontiersin.org/files/Articles/1791536/fmolb-13-1791536-HTML/image_m/fmolb-13-1791536-t002.jpg Table 2. Co-chaperones of HSP70 family. The degree of expression of the effects of various cellular https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1776111/full https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g001.jpg Figure 1. Roadmap of article. This study explores the evolutionary conservation of structural featur https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g002.jpg Figure 2. PubMed’s decision tree diagram. This figure describes in full detail the searching strateg https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g003.jpg Figure 3. PRISMA flowchart. This flowchart shows the selection and screening process used to obtain https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g004.jpg Figure 4. Metabolic pathways of Dsb proteins. The reaction mechanisms are shown in a graphic way. A https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g005.jpg Figure 5. Mechanistic representation of the oxidative folding pathway mediated by PDI and ERO1p. Sch https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-t001.jpg Table 1. Generalities of the 11 selected PDIs. https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g006.jpg Figure 6. Phylogenetic tree of 29 PDI proteins from various organisms. Boxes indicate proteins with https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-t002.jpg Table 2. Consensus of each conserved block based on physicochemical properties in the primary struct https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g007.jpg Figure 7. Alignment of conserved blocks identified by GBlocks. Hydrophobic amino acids are shown in https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g008.jpg Figure 8. Conserved secondary structures in PDI and Dsb proteins. The PDI secondary structure consen https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g009.jpg Figure 9. Structural similarity matrix of PDI proteins. The labels are the same as those used in Tab https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g010.jpg Figure 10. Conservation of sequence and tertiary structure in the selected PDI proteins. On the bott https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g011.jpg Figure 11. Visualization of conserved blocks at the tertiary structure level. Conserved regions in t https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-g012.jpg Figure 12. Comparison of human PDIA1 (gray), DsbC (purple), and DsbG (green) from Escherichia coli. https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-t003.jpg Table 3. Surface of PDI Active Site. Km values have been determined. https://www.frontiersin.org/files/Articles/1776111/fbinf-06-1776111-HTML/image_m/fbinf-06-1776111-t004.jpg Table 4. Effects of extracellular PDI on several diseases. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1632828/full https://www.frontiersin.org/files/Articles/1632828/fpubh-13-1632828-HTML/image_m/fpubh-13-1632828-t001.jpg Table 1. Participants across the cancer care and ALC studies. https://www.frontiersin.org/files/Articles/1632828/fpubh-13-1632828-HTML/image_m/fpubh-13-1632828-t002.jpg Table 2. Examples of focus group and interview questions. https://www.frontiersin.org/files/Articles/1632828/fpubh-13-1632828-HTML/image_m/fpubh-13-1632828-t003.jpg Table 3. Summary of findings with extended quotes. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1674434/full https://www.frontiersin.org/files/Articles/1674434/fpubh-13-1674434-HTML/image_m/fpubh-13-1674434-t001.jpg Table 1. Age-standardized rates and estimated annual percentage change for rheumatic heart disease b https://www.frontiersin.org/files/Articles/1674434/fpubh-13-1674434-HTML/image_m/fpubh-13-1674434-g001.jpg Figure 1. Age and sex distribution of rheumatic heart disease burden globally in 2021. (A) Incident https://www.frontiersin.org/files/Articles/1674434/fpubh-13-1674434-HTML/image_m/fpubh-13-1674434-g002.jpg Figure 2. Global trends in rheumatic heart disease burden from 1990 to 2021 by sex. (A) Incidence, ( https://www.frontiersin.org/files/Articles/1674434/fpubh-13-1674434-HTML/image_m/fpubh-13-1674434-g003.jpg Figure 3. National distribution of rheumatic heart disease burden in 2021 by Socio-Demographic Index https://www.frontiersin.org/files/Articles/1674434/fpubh-13-1674434-HTML/image_m/fpubh-13-1674434-g004.jpg Figure 4. Age-standardized burden of rheumatic heart disease by region and Socio-demographic Index ( https://www.frontiersin.org/files/Articles/1674434/fpubh-13-1674434-HTML/image_m/fpubh-13-1674434-g005.jpg Figure 5. Global distribution of RHD Burden (2021) and temporal trends (1990–2021). (A) Age-standard https://www.frontiersin.org/files/Articles/1674434/fpubh-13-1674434-HTML/image_m/fpubh-13-1674434-g006.jpg Figure 6. Inequality in Rheumatic Heart Disease Burden by SDI Using CI and SII Metrics (1990–2021). https://www.frontiersin.org/files/Articles/1674434/fpubh-13-1674434-HTML/image_m/fpubh-13-1674434-g007.jpg Figure 7. Rheumatic heart disease burden between 1990 and 2021 by SDI quintile and contributing fact https://www.frontiersin.org/files/Articles/1674434/fpubh-13-1674434-HTML/image_m/fpubh-13-1674434-g008.jpg Figure 8. Projected age-standardized rates for RHD metrics by SDI quintile from 1990 to 2050. (A) Ag https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1702430/full https://www.frontiersin.org/files/Articles/1702430/fcimb-15-1702430-HTML/image_m/fcimb-15-1702430-g004.jpg Figure 4. (A–F) Photomicrographs of testicular sections of animals from CG and IG stained with H.E. https://www.frontiersin.org/files/Articles/1702430/fcimb-15-1702430-HTML/image_m/fcimb-15-1702430-t001.jpg Table 1. Primary and secondary antibodies. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1740919/full https://www.frontiersin.org/files/Articles/1740919/fphys-16-1740919-HTML/image_m/fphys-16-1740919-g001.jpg Figure 1. From established evidence to proposed hypothesis: a homologous framework for cold adaptati https://www.frontiersin.org/files/Articles/1740919/fphys-16-1740919-HTML/image_m/fphys-16-1740919-g002.jpg Figure 2. Cold Exposure Signaling cascades: The mechanistic foundations of dual cardiovascular outco https://www.frontiersin.org/files/Articles/1740919/fphys-16-1740919-HTML/image_m/fphys-16-1740919-g003.jpg Figure 3. Divergent Adaptive Strategies of Cardiac and Skeletal Muscle to Cold Exposure. This is a c https://www.frontiersin.org/files/Articles/1740919/fphys-16-1740919-HTML/image_m/fphys-16-1740919-t001.jpg Table 1. Cardiovascular effects and potential applications of different cold exposure modalities. https://www.frontiersin.org/files/Articles/1740919/fphys-16-1740919-HTML/image_m/fphys-16-1740919-g004.jpg Figure 4. Biphasic Regulatory Network of Cold Exposure on the Cardiovascular System. Intermittent bu https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1655523/full https://www.frontiersin.org/files/Articles/1655523/fneur-16-1655523-HTML/image_m/fneur-16-1655523-g001.jpg Figure 1. Flow chart of case collection. https://www.frontiersin.org/files/Articles/1655523/fneur-16-1655523-HTML/image_m/fneur-16-1655523-t001.jpg Table 1. Comparison of clinical data between modeling and validation group. https://www.frontiersin.org/files/Articles/1655523/fneur-16-1655523-HTML/image_m/fneur-16-1655523-t002.jpg Table 2. Comparison of clinical data between the sleep disordered and non-sleep disordered groups. https://www.frontiersin.org/files/Articles/1655523/fneur-16-1655523-HTML/image_m/fneur-16-1655523-t003.jpg Table 3. Independent variable assignment methods. https://www.frontiersin.org/files/Articles/1655523/fneur-16-1655523-HTML/image_m/fneur-16-1655523-t004.jpg Table 4. Analysis of factors affecting postoperative sleep disorders in elderly patients undergoing https://www.frontiersin.org/files/Articles/1655523/fneur-16-1655523-HTML/image_m/fneur-16-1655523-g002.jpg Figure 2. Nomogram modeling of postoperative sleep disorders in elderly patients undergoing general https://www.frontiersin.org/files/Articles/1655523/fneur-16-1655523-HTML/image_m/fneur-16-1655523-g003.jpg Figure 3. Nomogram Model for postoperative sleep disorders in elderly patients undergoing general an https://www.frontiersin.org/files/Articles/1655523/fneur-16-1655523-HTML/image_m/fneur-16-1655523-g004.jpg Figure 4. Nomogram model for postoperative sleep disorders in elderly patients undergoing general an https://www.frontiersin.org/files/Articles/1655523/fneur-16-1655523-HTML/image_m/fneur-16-1655523-g005.jpg Figure 5. DCA curve for the nomogram. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1772106/full https://www.frontiersin.org/files/Articles/1772106/fendo-17-1772106-HTML/image_m/fendo-17-1772106-t001.jpg Table 1. Baseline characteristics of the overall cohort and comparison between the training and vali https://www.frontiersin.org/files/Articles/1772106/fendo-17-1772106-HTML/image_m/fendo-17-1772106-g001.jpg Figure 1. Feature selection using the LASSO regression model. (A) LASSO Regression Model Factor Sele https://www.frontiersin.org/files/Articles/1772106/fendo-17-1772106-HTML/image_m/fendo-17-1772106-t002.jpg Table 2. Predictive performance of seven models in the validation set. https://www.frontiersin.org/files/Articles/1772106/fendo-17-1772106-HTML/image_m/fendo-17-1772106-g002.jpg Figure 2. Performance evaluation of seven machine learning models in the training and validation set https://www.frontiersin.org/files/Articles/1772106/fendo-17-1772106-HTML/image_m/fendo-17-1772106-g003.jpg Figure 3. Confusion matrix heatmaps of machine learning models in the validation set. (A) LightGBM; https://www.frontiersin.org/files/Articles/1772106/fendo-17-1772106-HTML/image_m/fendo-17-1772106-g004.jpg Figure 4. LightGBM model explanation by the SHAP method. (A) Bar chart of the all features. (B) Bees https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1718034/full https://www.frontiersin.org/files/Articles/1718034/fpsyg-16-1718034-HTML/image_m/fpsyg-16-1718034-t001.jpg Table 1. General information of interview subjects. https://www.frontiersin.org/files/Articles/1718034/fpsyg-16-1718034-HTML/image_m/fpsyg-16-1718034-t002.jpg Table 2. Themes and sub-themes. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1820594/full https://www.frontiersin.org/files/Articles/1820594/fpls-17-1820594-HTML/image_m/fpls-17-1820594-g001.jpg Figure 1. OsIDD9 characterization and phylogenetic analysis. (A) Grey and blue blocks represented th https://www.frontiersin.org/files/Articles/1820594/fpls-17-1820594-HTML/image_m/fpls-17-1820594-g002.jpg Figure 2. Generation of OsIDD9 knockout mutants. (A) Diagram of OsIDD9 gene structure. Yellow and gr https://www.frontiersin.org/files/Articles/1820594/fpls-17-1820594-HTML/image_m/fpls-17-1820594-g003.jpg Figure 3. Generation of OsIDD9 over-expression lines. (A) Detection of OsIDD9 expression levels in W https://www.frontiersin.org/files/Articles/1820594/fpls-17-1820594-HTML/image_m/fpls-17-1820594-g004.jpg Figure 4. The expression of crown root development-related genes is regulated by OsIDD9. (A)Transcri https://www.frontiersin.org/files/Articles/1820594/fpls-17-1820594-HTML/image_m/fpls-17-1820594-g005.jpg Figure 5. uORFIDD9-mediated reporter gene translation. (A) Sketch of OsIDD9 structure. Yellow boxes https://www.frontiersin.org/files/Articles/1820594/fpls-17-1820594-HTML/image_m/fpls-17-1820594-g006.jpg Figure 6. Function of OsIDD9 in rice crown root development. (A) Crown root phenotypes of WT and idd https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1737121/full https://www.frontiersin.org/files/Articles/1737121/fmed-12-1737121-HTML/image_m/fmed-12-1737121-gr0001.jpg Graphical Abstract. DEX, dexmedetomidine; NS, not significant; CPB, cardiopulmonary bypass; ICU, int https://www.frontiersin.org/files/Articles/1737121/fmed-12-1737121-HTML/image_m/fmed-12-1737121-g001.jpg Figure 1. PRISMA flow diagram of included and excluded studies. PRISMA, Preferred Reporting Items fo https://www.frontiersin.org/files/Articles/1737121/fmed-12-1737121-HTML/image_m/fmed-12-1737121-g002.jpg Figure 2. Publication trends and leading contributors on DEX and AKI in cardiac surgery (from Web of https://www.frontiersin.org/files/Articles/1737121/fmed-12-1737121-HTML/image_m/fmed-12-1737121-g003.jpg Figure 3. Research trends in DEX on AKI in patients undergoing cardiac surgery (from Web of Science https://www.frontiersin.org/files/Articles/1737121/fmed-12-1737121-HTML/image_m/fmed-12-1737121-t001.jpg Table 1. Demographic information of included studies. https://www.frontiersin.org/files/Articles/1737121/fmed-12-1737121-HTML/image_m/fmed-12-1737121-t002.jpg Table 2. Summary of the included studies. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1689275/full https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g001.jpg Figure 1. Single-cell transcriptomic analysis of LUAD. t-SNE visualization of cell clusters, annotat https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g002.jpg Figure 2. Identification of angiogenesis-related hub genes in LUAD epithelial cells and MR analysis. https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g003.jpg Figure 3. Validation of ASPH and PTTG1 expression. (A) t-SNE plots overlaid with expression densitie https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g004.jpg Figure 4. The impact of ASPH knockdown on the biological behavior of lung adenocarcinoma cells in vi https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g005.jpg Figure 5. Validation and development of a prognostic model based on key genes. (A) Forest plot gener https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g006.jpg Figure 6. Correlation between risk score and clinical characteristics. (A–D) Violin plots illustrati https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g007.jpg Figure 7. Development and assessment of the prognostic nomogram. (A) Forest plot from univariate Cox https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g008.jpg Figure 8. Relationship between characteristics and Risk Score of the tumor microenvironment. (A-C) V https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g009.jpg Figure 9. Immune therapy markers and pathway enrichment linked to Risk Score. (A-C) Violin plots exa https://www.frontiersin.org/files/Articles/1689275/fimmu-16-1689275-HTML/image_m/fimmu-16-1689275-g010.jpg Figure 10. Projected drug sensitivity across risk categories. Boxplots contrasting the estimated hal https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1677901/full https://www.frontiersin.org/files/Articles/1677901/fimmu-16-1677901-HTML/image_m/fimmu-16-1677901-g001.jpg Figure 1. Mechanisms by which neutrophils regulate other immune cells. Neutrophils can act on Treg c https://www.frontiersin.org/files/Articles/1677901/fimmu-16-1677901-HTML/image_m/fimmu-16-1677901-g002.jpg Figure 2. Pathways of neutrophil extracellular trap (NET) formation. Neutrophils release NETs throug https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1706472/full https://www.frontiersin.org/files/Articles/1706472/fimmu-16-1706472-HTML-r1/image_m/fimmu-16-1706472-g001.jpg Figure 1. Gut microbiota metabolites maintain intestinal homeostasis by regulating the host immune s https://www.frontiersin.org/files/Articles/1706472/fimmu-16-1706472-HTML-r1/image_m/fimmu-16-1706472-g002.jpg Figure 2. Coordinated regulation of inflammatory signaling by microbial metabolites. Gut microbiota- https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1750743/full https://www.frontiersin.org/files/Articles/1750743/fimmu-16-1750743-HTML/image_m/fimmu-16-1750743-g001.jpg Figure 1. Neutrophils orchestrate a complex immunoregulatory network with T cells, B cells, and T re https://www.frontiersin.org/files/Articles/1750743/fimmu-16-1750743-HTML/image_m/fimmu-16-1750743-g002.jpg Figure 2. The dual roles and functional balance of neutrophils in inflammatory bowel disease (IBD). https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1780865/full https://www.frontiersin.org/files/Articles/1780865/fimmu-17-1780865-HTML/image_m/fimmu-17-1780865-t001.jpg Table 1. Immunomodulatory roles of major short-chain fatty acids (SCFAs) through G protein-coupled r https://www.frontiersin.org/files/Articles/1780865/fimmu-17-1780865-HTML/image_m/fimmu-17-1780865-g001.jpg Figure 1. Integrated mechanisms by which gut microbiota-derived metabolites regulate Treg/Th17 balan https://www.frontiersin.org/files/Articles/1780865/fimmu-17-1780865-HTML/image_m/fimmu-17-1780865-g002.jpg Figure 2. Dysregulation of the microbiota-metabolite network drives Treg/Th17 imbalance in inflammat https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1698537/full https://www.frontiersin.org/files/Articles/1698537/fpsyt-16-1698537-HTML-r1/image_m/fpsyt-16-1698537-t001.jpg Table 1. Fit indices for latent class models of NSSI and SBs. https://www.frontiersin.org/files/Articles/1698537/fpsyt-16-1698537-HTML-r1/image_m/fpsyt-16-1698537-g001.jpg Figure 1. Plots of three latent classes for the NSSI and SBs. https://www.frontiersin.org/files/Articles/1698537/fpsyt-16-1698537-HTML-r1/image_m/fpsyt-16-1698537-t002.jpg Table 2. General characteristics of three classes of NSSI and SBs among the study population (N = 5, https://www.frontiersin.org/files/Articles/1698537/fpsyt-16-1698537-HTML-r1/image_m/fpsyt-16-1698537-t003.jpg Table 3. Prevalence of ACE types between males and females. https://www.frontiersin.org/files/Articles/1698537/fpsyt-16-1698537-HTML-r1/image_m/fpsyt-16-1698537-t004.jpg Table 4. Associations between ACEs types and three classes of NSSI and SBs. https://www.frontiersin.org/files/Articles/1698537/fpsyt-16-1698537-HTML-r1/image_m/fpsyt-16-1698537-g002.jpg Figure 2. (A) Gender differences in the associations between ACEs types and suicidal ideation class; https://www.frontiersin.org/journals/blockchain/articles/10.3389/fbloc.2026.1766232/full https://www.frontiersin.org/files/Articles/1766232/fbloc-09-1766232-HTML/image_m/fbloc-09-1766232-t001.jpg Table 1. Benchmarking traditional systems against the proposed smart agriculture framework. https://www.frontiersin.org/files/Articles/1766232/fbloc-09-1766232-HTML/image_m/fbloc-09-1766232-t002.jpg Table 2. Technology interactions. https://www.frontiersin.org/files/Articles/1766232/fbloc-09-1766232-HTML/image_m/fbloc-09-1766232-t003.jpg Table 3. Model validation: comparison of actual and predicted crop damage costs. https://www.frontiersin.org/files/Articles/1766232/fbloc-09-1766232-HTML/image_m/fbloc-09-1766232-g001.jpg Figure 1. Smart agriculture 5.0 pipeline. https://www.frontiersin.org/files/Articles/1766232/fbloc-09-1766232-HTML/image_m/fbloc-09-1766232-g002.jpg Figure 2. Neutrosophic-regression-RL coupling flowchart. https://www.frontiersin.org/files/Articles/1766232/fbloc-09-1766232-HTML/image_m/fbloc-09-1766232-g003.jpg Figure 3. Multiple crops optimize hyperparameters conditions with reinforcement learning. https://www.frontiersin.org/files/Articles/1766232/fbloc-09-1766232-HTML/image_m/fbloc-09-1766232-g004.jpg Figure 4. Crop agriculture framework process using blockchain. https://www.frontiersin.org/files/Articles/1766232/fbloc-09-1766232-HTML/image_m/fbloc-09-1766232-g005.jpg Figure 5. Multicropping RL optimization. https://www.frontiersin.org/files/Articles/1766232/fbloc-09-1766232-HTML/image_m/fbloc-09-1766232-t004.jpg Table 4. Dataset of crop damage, crop weight, investment, and receipts for various crops. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1763558/full https://www.frontiersin.org/files/Articles/1763558/fimmu-17-1763558-HTML/image_m/fimmu-17-1763558-g001.jpg Figure 1. Flow chart of literature screening. https://www.frontiersin.org/files/Articles/1763558/fimmu-17-1763558-HTML/image_m/fimmu-17-1763558-t001.jpg Table 1. Summary of network meta-analysis effect sizes. https://www.frontiersin.org/files/Articles/1763558/fimmu-17-1763558-HTML/image_m/fimmu-17-1763558-g002.jpg Figure 2. Network geometry of exercise interventions for chronic inflammatory markers in postmenopau https://www.frontiersin.org/files/Articles/1763558/fimmu-17-1763558-HTML/image_m/fimmu-17-1763558-t002.jpg Table 2. Probability-based ranking results (SUCRA) across exercise interventions for immunometabolic https://www.frontiersin.org/files/Articles/1763558/fimmu-17-1763558-HTML/image_m/fimmu-17-1763558-g003.jpg Figure 3. Probability ranking of different outcome measures for each intervention. https://www.frontiersin.org/files/Articles/1763558/fimmu-17-1763558-HTML/image_m/fimmu-17-1763558-g004.jpg Figure 4. Comparison-adjusted funnel plots. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1688777/full https://www.frontiersin.org/files/Articles/1688777/fphar-16-1688777-HTML-r1/image_m/fphar-16-1688777-t001.jpg Table 1. Primer sequences used for quantitative real-time PCR analysis. https://www.frontiersin.org/files/Articles/1688777/fphar-16-1688777-HTML-r1/image_m/fphar-16-1688777-t002.jpg Table 2. The effect of LHBGRA43 on morphological parameters. https://www.frontiersin.org/files/Articles/1688777/fphar-16-1688777-HTML-r1/image_m/fphar-16-1688777-t003.jpg Table 3. The effect of LHBGRA43 on biochemical parameters. https://www.frontiersin.org/files/Articles/1688777/fphar-16-1688777-HTML-r1/image_m/fphar-16-1688777-g001.jpg Figure 1. Histological analysis of the liver and the level of markers of hepatic lipid metabolism. R https://www.frontiersin.org/files/Articles/1688777/fphar-16-1688777-HTML-r1/image_m/fphar-16-1688777-g002.jpg Figure 2. The level of proinflammatory mediators, NLRP3 inflammasome and subcellular distribution of https://www.frontiersin.org/files/Articles/1688777/fphar-16-1688777-HTML-r1/image_m/fphar-16-1688777-g003.jpg Figure 3. Histological analysis, integrity and permeability of the small intestine and the level of https://www.frontiersin.org/files/Articles/1688777/fphar-16-1688777-HTML-r1/image_m/fphar-16-1688777-g004.jpg Figure 4. Gut microbiota composition and alpha and beta diversity. Bar chart of relative abundance o https://www.frontiersin.org/files/Articles/1688777/fphar-16-1688777-HTML-r1/image_m/fphar-16-1688777-g005.jpg Figure 5. Short-chain fatty acids (SCFA) profile. The concentration of acetic acid (A), propionic ac https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1661867/full https://www.frontiersin.org/files/Articles/1661867/fmed-12-1661867-HTML/image_m/fmed-12-1661867-g001.jpg Figure 1. (A) CT image of Patient 1 with interstitial lung disease. Absent left breast, consistent w https://www.frontiersin.org/files/Articles/1661867/fmed-12-1661867-HTML/image_m/fmed-12-1661867-g002.jpg Figure 2. Dose distribution of Patient 2 treated with radiotherapy. Palliative radiotherapy was admi https://www.frontiersin.org/files/Articles/1661867/fmed-12-1661867-HTML/image_m/fmed-12-1661867-g003.jpg Figure 3. CT image of Patient 2 with interstitial lung disease. Postoperative changes after left bre https://www.frontiersin.org/files/Articles/1661867/fmed-12-1661867-HTML/image_m/fmed-12-1661867-g004.jpg Figure 4. Timeline of diagnosis, interventions and outcomes. (A) The timeline of diagnosis, interven https://www.frontiersin.org/files/Articles/1661867/fmed-12-1661867-HTML/image_m/fmed-12-1661867-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1661867/fmed-12-1661867-HTML/image_m/fmed-12-1661867-t002.jpg Table 2. Laboratory findings of 2 cases at ILD onset. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1748279/full https://www.frontiersin.org/files/Articles/1748279/fonc-16-1748279-HTML-r1/image_m/fonc-16-1748279-g001.jpg Figure 1. Contrast-enhanced CT images of the GHA patient. (A-I) The tumor is located in segments VI- https://www.frontiersin.org/files/Articles/1748279/fonc-16-1748279-HTML-r1/image_m/fonc-16-1748279-g002.jpg Figure 2. Contrast-enhanced MRI images of the GHA patient. (A-C) On T1-weighted imaging, hepatic met https://www.frontiersin.org/files/Articles/1748279/fonc-16-1748279-HTML-r1/image_m/fonc-16-1748279-g003.jpg Figure 3. Intraoperative images in TAE obtained via DSA. (A) Obvious contrast extravasation (local i https://www.frontiersin.org/files/Articles/1748279/fonc-16-1748279-HTML-r1/image_m/fonc-16-1748279-g004.jpg Figure 4. Images of gastroscopy, HE staining, and immunohistochemical staining. (A, B) Gastroscopic https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1693000/full https://www.frontiersin.org/files/Articles/1693000/fphar-17-1693000-HTML/image_m/fphar-17-1693000-g001.jpg Figure 1. Effect of rhein on D-gal-treated NRK-52E cells’ viability and inhibition on D-gal-induced https://www.frontiersin.org/files/Articles/1693000/fphar-17-1693000-HTML/image_m/fphar-17-1693000-g002.jpg Figure 2. Rhein reduced autophagy through mTOR signaling in NRK-52E cells treated with D-gal. (A) WB https://www.frontiersin.org/files/Articles/1693000/fphar-17-1693000-HTML/image_m/fphar-17-1693000-g003.jpg Figure 3. Rhein inhibited necroptosis in D-gal-treated NRK-52E cells. (A) WB analysis of RIPK1, RIPK https://www.frontiersin.org/files/Articles/1693000/fphar-17-1693000-HTML/image_m/fphar-17-1693000-g004.jpg Figure 4. Investigative findings on the role of rhein in aging via network pharmacology. (A) A Venn https://www.frontiersin.org/files/Articles/1693000/fphar-17-1693000-HTML/image_m/fphar-17-1693000-g005.jpg Figure 5. Rhein regulated the TNF-α-mediated necroptosis crosstalk in D-gal-treated NRK-52E cells. ( https://www.frontiersin.org/files/Articles/1693000/fphar-17-1693000-HTML/image_m/fphar-17-1693000-g006.jpg Figure 6. Rhein attenuated D-gal-induced renal aging and fibrotic injury in rats. (A) Body weight of https://www.frontiersin.org/files/Articles/1693000/fphar-17-1693000-HTML/image_m/fphar-17-1693000-g007.jpg Figure 7. Regulation of serum metabolic profiles by rhein and pathway enrichment analysis. (A) PCA o https://www.frontiersin.org/files/Articles/1693000/fphar-17-1693000-HTML/image_m/fphar-17-1693000-g008.jpg Figure 8. Rhein alleviated D-gal-induced renal aging and fibrotic injury by TNF-α-mediated necroptos https://www.frontiersin.org/files/Articles/1693000/fphar-17-1693000-HTML/image_m/fphar-17-1693000-g009.jpg Figure 9. The schematic diagram of the mechanism in rhein targeting TNF-α-mediated autophagy and nec https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2026.1814009/full https://www.frontiersin.org/files/Articles/1814009/fmars-13-1814009-HTML/image_m/fmars-13-1814009-t001.jpg Table 1. Individual sandbar shark catches for fishery independent surveys (i.e., offshore BLL, coast https://www.frontiersin.org/files/Articles/1814009/fmars-13-1814009-HTML/image_m/fmars-13-1814009-g001.jpg Figure 1. Maps showing catch per unit effort (CPUE) for YOY sandbar shark along the Texas coast by g https://www.frontiersin.org/files/Articles/1814009/fmars-13-1814009-HTML/image_m/fmars-13-1814009-g002.jpg Figure 2. Temporal trends in predicted GAM effects on log(CPUE) for (A) the estuarine gillnet data ( https://www.frontiersin.org/files/Articles/1814009/fmars-13-1814009-HTML/image_m/fmars-13-1814009-g003.jpg Figure 3. Lengths of sandbar sharks caught in shore-based (A), estuarine (B), coastal (C) and offsho https://www.frontiersin.org/files/Articles/1814009/fmars-13-1814009-HTML/image_m/fmars-13-1814009-g004.jpg Figure 4. Fork length size composition of sandbar sharks for each data series: shore-based (A), estu https://www.frontiersin.org/files/Articles/1814009/fmars-13-1814009-HTML/image_m/fmars-13-1814009-g005.jpg Figure 5. Annual mean CPUE of young-of-the-year (YOY) sandbar sharks (<71 cm FL) from the TPWD gilln https://www.frontiersin.org/files/Articles/1814009/fmars-13-1814009-HTML/image_m/fmars-13-1814009-g006.jpg Figure 6. Mean CPUE trends for sandbar sharks caught in the shore-based TSR. Young-of-the-year (YOY) https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1738166/full https://www.frontiersin.org/files/Articles/1738166/fimmu-16-1738166-HTML/image_m/fimmu-16-1738166-g001.jpg Figure 1. Representative flow cytometry analysis of CD71+ erythroid cells (CECs) in peripheral blood https://www.frontiersin.org/files/Articles/1738166/fimmu-16-1738166-HTML/image_m/fimmu-16-1738166-t001.jpg Table 1. Baseline characteristics of preterm infants by hsPDA status. https://www.frontiersin.org/files/Articles/1738166/fimmu-16-1738166-HTML/image_m/fimmu-16-1738166-g002.jpg Figure 2. Scatter plot and linear regression illustrating the inverse relationship between gestation https://www.frontiersin.org/files/Articles/1738166/fimmu-16-1738166-HTML/image_m/fimmu-16-1738166-t002.jpg Table 2. Comparison of hematologic and clinical parameters by hsPDA status in preterm infants. https://www.frontiersin.org/files/Articles/1738166/fimmu-16-1738166-HTML/image_m/fimmu-16-1738166-g003.jpg Figure 3. Comparison of CEC levels between preterm infants with and without hsPDA. Infants diagnosed https://www.frontiersin.org/files/Articles/1738166/fimmu-16-1738166-HTML/image_m/fimmu-16-1738166-t003.jpg Table 3. Logistic regression analysis identifying risk factors for hsPDA in preterm infants. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1711809/full https://www.frontiersin.org/files/Articles/1711809/fmed-12-1711809-HTML-r1/image_m/fmed-12-1711809-g001.jpg Figure 1. Clinical timeline of the patient with metastatic breast carcinoma and CAS. The diagram sum https://www.frontiersin.org/files/Articles/1711809/fmed-12-1711809-HTML-r1/image_m/fmed-12-1711809-t001.jpg Table 1. Evolution of hematologic and serologic findings during the patient’s admission, nadir and d https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1786157/full https://www.frontiersin.org/files/Articles/1786157/fpsyg-17-1786157-HTML/image_m/fpsyg-17-1786157-g001.jpg Figure 1. Research hypothesis model. https://www.frontiersin.org/files/Articles/1786157/fpsyg-17-1786157-HTML/image_m/fpsyg-17-1786157-t001.jpg Table 1. Demographic statistical analysis. https://www.frontiersin.org/files/Articles/1786157/fpsyg-17-1786157-HTML/image_m/fpsyg-17-1786157-t002.jpg Table 2. Gender differences between different variables. https://www.frontiersin.org/files/Articles/1786157/fpsyg-17-1786157-HTML/image_m/fpsyg-17-1786157-t003.jpg Table 3. Grade differences between different variables. https://www.frontiersin.org/files/Articles/1786157/fpsyg-17-1786157-HTML/image_m/fpsyg-17-1786157-t004.jpg Table 4. Correlation analysis of the four variables. https://www.frontiersin.org/files/Articles/1786157/fpsyg-17-1786157-HTML/image_m/fpsyg-17-1786157-t005.jpg Table 5. Regression analysis of the relationship between variables in the model. https://www.frontiersin.org/files/Articles/1786157/fpsyg-17-1786157-HTML/image_m/fpsyg-17-1786157-t006.jpg Table 6. Bootstrap mediating effect analysis. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1797096/full https://www.frontiersin.org/files/Articles/1797096/fimmu-17-1797096-HTML/image_m/fimmu-17-1797096-g001.jpg Figure 1. Microbial biofilm-driven immunopathological pathways in chronic rhinosinusitis. (a) Biofil https://www.frontiersin.org/files/Articles/1797096/fimmu-17-1797096-HTML/image_m/fimmu-17-1797096-t001.jpg Table 1. Representative studies of biofilm detection methods and associated findings in CRS. https://www.frontiersin.org/files/Articles/1797096/fimmu-17-1797096-HTML/image_m/fimmu-17-1797096-t002.jpg Table 2. Comparison of biofilm detection technologies. https://www.frontiersin.org/files/Articles/1797096/fimmu-17-1797096-HTML/image_m/fimmu-17-1797096-t003.jpg Table 3. Clinical translation status of emerging anti-biofilm therapies for CRS. https://www.frontiersin.org/files/Articles/1797096/fimmu-17-1797096-HTML/image_m/fimmu-17-1797096-g002.jpg Figure 2. A translational roadmap for biofilm management in CRS. This figure categorizes therapeutic https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1719385/full https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t001.jpg Table 1. Clinical information of patients. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-g001.jpg Figure 1. (A–H) were indole fermentation tube, lactose fermentation tube, esculin fermentation tube, https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t002.jpg Table 2. Biochemical identification result. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t003.jpg Table 3. The minimum inhibitory concentration of F.n standard strain and F.n clinical strain (μg/mL) https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-g002.jpg Figure 2. (A) MIC assay of F.n standard strain. (B) MIC assay of F.n clinical strain. Red circles in https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t004.jpg Table 4. Antibacterial ring diameter of F.n standard strain and F.n clinical strain (mm, x¯ ± s). https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t005.jpg Table 5. The adhesion (%, x̄ ± s) and invasion ability (‰, x̄ ± s) to KB and bEnd.3 cells. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-g003.jpg Figure 3. (A, B) Expression of Claudin-5. * means p < 0.05. (C, D) Expression of ZO-1. * means p < 0 https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t006.jpg Table 6. Number of proteins and peptides identified. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t007.jpg Table 7. The number of differentially expressed proteins. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t008.jpg Table 8. Subdivision of differential proteins. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-g004.jpg Figure 4. (A) GO enrichment analysis results of upregulated differentially expressed proteins. (B) G https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t009.jpg Table 9. Up-regulated differential proteins in the F.n clinical strain. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t010.jpg Table 10. Down-regulated differential proteins in the F.n clinical strain. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t011.jpg Table 11. Differentially expressed proteins associated with immune escape and antibiotic resistance. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t012.jpg Table 12. Differential proteins associated with the ribosomal 50S subunit in F.n clinical strains. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-t013.jpg Table 13. Differential expression of outer membrane channel-related proteins. https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-g005.jpg Figure 5. (A) KEGG pathway enrichment analysis results of upregulated proteins. (B) STRING protein-p https://www.frontiersin.org/files/Articles/1719385/fcimb-16-1719385-HTML/image_m/fcimb-16-1719385-g006.jpg Figure 6. (A) KEGG pathway enrichment analysis results of downregulated proteins. (B) STRING protein https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1780907/full https://www.frontiersin.org/files/Articles/1780907/fpsyt-17-1780907-HTML/image_m/fpsyt-17-1780907-g001.jpg Figure 1. Schematic diagram of neuromodulation strategies and common targets for post-stroke cogniti https://www.frontiersin.org/files/Articles/1780907/fpsyt-17-1780907-HTML/image_m/fpsyt-17-1780907-g002.jpg Figure 2. Mechanisms, brain function detection approaches, and challenges underlying neuromodulation https://www.frontiersin.org/files/Articles/1780907/fpsyt-17-1780907-HTML/image_m/fpsyt-17-1780907-t001.jpg Table 1. Application of TMS on PSCI patients in preclinical and clinical studies in the last 3 years https://www.frontiersin.org/files/Articles/1780907/fpsyt-17-1780907-HTML/image_m/fpsyt-17-1780907-t002.jpg Table 2. Application of TES on PSCI patients in preclinical and clinical studies in the last 5 years https://www.frontiersin.org/files/Articles/1780907/fpsyt-17-1780907-HTML/image_m/fpsyt-17-1780907-t003.jpg Table 3. Application of VNS on PSCI patients in preclinical and clinical studies in the last 10 year https://www.frontiersin.org/files/Articles/1780907/fpsyt-17-1780907-HTML/image_m/fpsyt-17-1780907-t004.jpg Table 4. Application of PBM on PSCI patients in preclinical and clinical studies in the last 10 year https://www.frontiersin.org/files/Articles/1780907/fpsyt-17-1780907-HTML/image_m/fpsyt-17-1780907-t005.jpg Table 5. Application of BCI on PSCI patients in clinical studies in the last 5 years. https://www.frontiersin.org/files/Articles/1780907/fpsyt-17-1780907-HTML/image_m/fpsyt-17-1780907-g003.jpg Figure 3. Future prospects of neuromodulation for stroke from the perspective of cognitive-motor int https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1723428/full https://www.frontiersin.org/files/Articles/1723428/fendo-16-1723428-HTML/image_m/fendo-16-1723428-t001.jpg Table 1. Gradual biochemical changes during the DKA treatment. https://www.frontiersin.org/files/Articles/1723428/fendo-16-1723428-HTML/image_m/fendo-16-1723428-g001.jpg Figure 1. Cardiac arrest in the 7th hour of severe DKA treatment, changing from asystole to monomorp https://www.frontiersin.org/files/Articles/1723428/fendo-16-1723428-HTML/image_m/fendo-16-1723428-g002.jpg Figure 2. Post-resuscitation ECG revealed ST depression in the inferior and lateral leads, which can https://www.frontiersin.org/files/Articles/1723428/fendo-16-1723428-HTML/image_m/fendo-16-1723428-g003.jpg Figure 3. Troponin trends during in-hospital stay. https://www.frontiersin.org/files/Articles/1723428/fendo-16-1723428-HTML/image_m/fendo-16-1723428-g004.jpg Figure 4. Echocardiography of our patient after resuscitation revealed left ventricular systolic imp https://www.frontiersin.org/files/Articles/1723428/fendo-16-1723428-HTML/image_m/fendo-16-1723428-g005.jpg Figure 5. Gradual recovery of the systolic function of the left ventricle. EF, ejection fraction; ED https://www.frontiersin.org/files/Articles/1723428/fendo-16-1723428-HTML/image_m/fendo-16-1723428-t002.jpg Table 2. Literature review of cardiovascular manifestations in patients with diabetic ketoacidosis. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1807075/full https://www.frontiersin.org/files/Articles/1807075/fmed-13-1807075-HTML/image_m/fmed-13-1807075-g001.jpg Figure 1. Flowchart of the study. https://www.frontiersin.org/files/Articles/1807075/fmed-13-1807075-HTML/image_m/fmed-13-1807075-t001.jpg Table 1. Characteristics of the studies included in the meta-analysis. https://www.frontiersin.org/files/Articles/1807075/fmed-13-1807075-HTML/image_m/fmed-13-1807075-g002.jpg Figure 2. Forest plot for the levels of (A) saliva MDA; (B) serum MDA in patients with OLK compared https://www.frontiersin.org/files/Articles/1807075/fmed-13-1807075-HTML/image_m/fmed-13-1807075-g003.jpg Figure 3. Forest plot for the levels of (A) serum CAT; (B) serum GPx; (C) serum SOD; (D) serum GSH i https://www.frontiersin.org/files/Articles/1807075/fmed-13-1807075-HTML/image_m/fmed-13-1807075-t002.jpg Table 2. Quality score of the included studies. https://www.frontiersin.org/files/Articles/1807075/fmed-13-1807075-HTML/image_m/fmed-13-1807075-g004.jpg Figure 4. Mechanism of antioxidant enzymes in the development and progression of OLK. In a healthy s https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1719784/full https://www.frontiersin.org/files/Articles/1719784/fendo-17-1719784-HTML/image_m/fendo-17-1719784-t001.jpg Table 1. Eligibility criteria structured according to the PICOS framework. https://www.frontiersin.org/files/Articles/1719784/fendo-17-1719784-HTML/image_m/fendo-17-1719784-t002.jpg Table 2. Characteristics of the studies included in the systematic review. https://www.frontiersin.org/files/Articles/1719784/fendo-17-1719784-HTML/image_m/fendo-17-1719784-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1719784/fendo-17-1719784-HTML/image_m/fendo-17-1719784-g002.jpg Figure 2. Risk of bias heatmap for observational studies using the ROBINS-I tool. https://www.frontiersin.org/files/Articles/1719784/fendo-17-1719784-HTML/image_m/fendo-17-1719784-t003.jpg Table 3. Risk of bias assessment for the included studies. https://www.frontiersin.org/files/Articles/1719784/fendo-17-1719784-HTML/image_m/fendo-17-1719784-g003.jpg Figure 3. Risk of bias assessment for the randomized controlled trial using the RoB 2 tool. https://www.frontiersin.org/files/Articles/1719784/fendo-17-1719784-HTML/image_m/fendo-17-1719784-g004.jpg Figure 4. Pathogenetic model linking exposure to EDCs with EC through oxidative stress and DNA damag https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1628781/full https://www.frontiersin.org/files/Articles/1628781/fmed-12-1628781-HTML/image_m/fmed-12-1628781-g001.jpg Figure 1. Study flow diagram of participants in the randomized trial. https://www.frontiersin.org/files/Articles/1628781/fmed-12-1628781-HTML/image_m/fmed-12-1628781-t001.jpg Table 1. Characteristics of the patients. https://www.frontiersin.org/files/Articles/1628781/fmed-12-1628781-HTML/image_m/fmed-12-1628781-t002.jpg Table 2. Perioperative data. https://www.frontiersin.org/files/Articles/1628781/fmed-12-1628781-HTML/image_m/fmed-12-1628781-g002.jpg Figure 2. Liver function variables at baseline, POD1, and POD2. (A) ALT levels; (B) AST levels; (C) https://www.frontiersin.org/files/Articles/1628781/fmed-12-1628781-HTML/image_m/fmed-12-1628781-t003.jpg Table 3. Biochemical markers of liver function at each time point. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1662818/full https://www.frontiersin.org/files/Articles/1662818/fphar-16-1662818-HTML/image_m/fphar-16-1662818-t001.jpg Table 1. OFA strategies in gastrointestinal surgery. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1702185/full https://www.frontiersin.org/files/Articles/1702185/fimmu-16-1702185-HTML-r1/image_m/fimmu-16-1702185-t001.jpg Table 1. Anti-inflammatory mechanism of action of taVNS and key pathways. https://www.frontiersin.org/files/Articles/1702185/fimmu-16-1702185-HTML-r1/image_m/fimmu-16-1702185-g001.jpg Figure 1. Distribution of clinical applications of transcutaneous auricular vagus nerve stimulation https://www.frontiersin.org/files/Articles/1702185/fimmu-16-1702185-HTML-r1/image_m/fimmu-16-1702185-g002.jpg Figure 2. Schematic diagram of the CAP activated by taVNS. https://www.frontiersin.org/files/Articles/1702185/fimmu-16-1702185-HTML-r1/image_m/fimmu-16-1702185-t002.jpg Table 2. Evidence of clinical use of taVNS in inflammatory diseases. https://www.frontiersin.org/files/Articles/1702185/fimmu-16-1702185-HTML-r1/image_m/fimmu-16-1702185-t003.jpg Table 3. Effects of taVNS stimulation parameter optimization on anti-inflammatory outcomes. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1731301/full https://www.frontiersin.org/files/Articles/1731301/fmed-13-1731301-HTML/image_m/fmed-13-1731301-t001.jpg Table 1. The key comparative features of the TAPB and QLB. https://www.frontiersin.org/files/Articles/1731301/fmed-13-1731301-HTML/image_m/fmed-13-1731301-t002.jpg Table 2. Summary of key RCTs evaluating liposomal bupivacaine in fascial plane blocks. https://www.frontiersin.org/files/Articles/1731301/fmed-13-1731301-HTML/image_m/fmed-13-1731301-t003.jpg Table 3. Proposed clinical framework: matching regional block selection to laparoscopic gynecologica https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1744375/full https://www.frontiersin.org/files/Articles/1744375/fimmu-17-1744375-HTML/image_m/fimmu-17-1744375-g001.jpg Figure 1. Histone H3 crotonylation is significantly increased in AD mouse models. (A, B) Representat https://www.frontiersin.org/files/Articles/1744375/fimmu-17-1744375-HTML/image_m/fimmu-17-1744375-g002.jpg Figure 2. Lateral ventricle injection of crotonic acid increases histone crotonylation and induces c https://www.frontiersin.org/files/Articles/1744375/fimmu-17-1744375-HTML/image_m/fimmu-17-1744375-g003.jpg Figure 3. Lateral ventricle injection of crotonic acid reduces synapse-associated proteins and impai https://www.frontiersin.org/files/Articles/1744375/fimmu-17-1744375-HTML/image_m/fimmu-17-1744375-g004.jpg Figure 4. Elevated H3K18cr promotes microglia activation and the release of pro-inflammatory factors https://www.frontiersin.org/files/Articles/1744375/fimmu-17-1744375-HTML/image_m/fimmu-17-1744375-g005.jpg Figure 5. Histone crotonylation significantly activates inflammation-related pathways. (A) A total o https://www.frontiersin.org/files/Articles/1744375/fimmu-17-1744375-HTML/image_m/fimmu-17-1744375-g006.jpg Figure 6. An enhanced H3K18cr upregulates microglia expression of pro-inflammation-related transcrip https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1791457/full https://www.frontiersin.org/files/Articles/1791457/fsurg-13-1791457-HTML/image_m/fsurg-13-1791457-t001.jpg Table 1. Summary of nerve blocks applied in breast surgery. https://www.frontiersin.org/files/Articles/1791457/fsurg-13-1791457-HTML/image_m/fsurg-13-1791457-t002.jpg Table 2. Summary of key comparative evidence on nerve block techniques in breast surgery. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1665061/full https://www.frontiersin.org/files/Articles/1665061/fvets-13-1665061-HTML/image_m/fvets-13-1665061-t001.jpg Table 1. Baseline characteristics of dogs with chronic kidney disease (CKD) in the Beraprost therapy https://www.frontiersin.org/files/Articles/1665061/fvets-13-1665061-HTML/image_m/fvets-13-1665061-g001.jpg Figure 1. Kaplan–Meier survival curves for dogs with chronic kidney disease (CKD). The survival prob https://www.frontiersin.org/files/Articles/1665061/fvets-13-1665061-HTML/image_m/fvets-13-1665061-t002.jpg Table 2. Median event-free survival for secondary endpoints. https://www.frontiersin.org/files/Articles/1665061/fvets-13-1665061-HTML/image_m/fvets-13-1665061-t003.jpg Table 3. Multivariable Cox proportional hazards analysis for all-cause mortality in dogs with chroni https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1754405/full https://www.frontiersin.org/files/Articles/1754405/fimmu-17-1754405-HTML/image_m/fimmu-17-1754405-t001.jpg Table 1. Demographic and clinical characteristics of patient cohorts. https://www.frontiersin.org/files/Articles/1754405/fimmu-17-1754405-HTML/image_m/fimmu-17-1754405-g001.jpg Figure 1. Directed acyclic graph representing assumed causal relations between exposure, covariates, https://www.frontiersin.org/files/Articles/1754405/fimmu-17-1754405-HTML/image_m/fimmu-17-1754405-g002.jpg Figure 2. Visualization of raw ECP and eosinophil count data. Combined box-and-beeswarm plots of (A) https://www.frontiersin.org/files/Articles/1754405/fimmu-17-1754405-HTML/image_m/fimmu-17-1754405-g003.jpg Figure 3. Eosinophilic cationic protein levels are increased in hereditary angioedema. Combined tabu https://www.frontiersin.org/files/Articles/1754405/fimmu-17-1754405-HTML/image_m/fimmu-17-1754405-g004.jpg Figure 4. Cell counts of eosinophil granulocytes are not increased in hereditary angioedema. Combine https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1813283/full https://www.frontiersin.org/files/Articles/1813283/fvets-13-1813283-HTML/image_m/fvets-13-1813283-g001.jpg Figure 1. Experimental design. After a week of handling and daily weighing, the training phase start https://www.frontiersin.org/files/Articles/1813283/fvets-13-1813283-HTML/image_m/fvets-13-1813283-g002.jpg Figure 2. Apparatuses for cognitive judgement bias testing. (A) TS task: Touchscreen chamber with “t https://www.frontiersin.org/files/Articles/1813283/fvets-13-1813283-HTML/image_m/fvets-13-1813283-g003.jpg Figure 3. Cognitive judgement bias (CJB) training and test. (A) Training durations. Data are shown a https://www.frontiersin.org/files/Articles/1813283/fvets-13-1813283-HTML/image_m/fvets-13-1813283-g004.jpg Figure 4. Results of the elevated plus maze (EPM) and faecal corticosterone metabolites (FCMs). (A) https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1775949/full https://www.frontiersin.org/files/Articles/1775949/fpsyt-17-1775949-HTML-r1/image_m/fpsyt-17-1775949-g001.jpg Figure 1. Hypothetical model of the associations between SI and IA among adolescents with SI. H repr https://www.frontiersin.org/files/Articles/1775949/fpsyt-17-1775949-HTML-r1/image_m/fpsyt-17-1775949-t001.jpg Table 1. Demographic and psychosocial characteristics of the participants (N = 462). https://www.frontiersin.org/files/Articles/1775949/fpsyt-17-1775949-HTML-r1/image_m/fpsyt-17-1775949-t002.jpg Table 2. Correlation matrix of key variables. https://www.frontiersin.org/files/Articles/1775949/fpsyt-17-1775949-HTML-r1/image_m/fpsyt-17-1775949-t003.jpg Table 3. Average variance extracted (AVE) and discriminant validity analysis. https://www.frontiersin.org/files/Articles/1775949/fpsyt-17-1775949-HTML-r1/image_m/fpsyt-17-1775949-g002.jpg Figure 2. Mediation model of the association between suicidal ideation and internet addiction with s https://www.frontiersin.org/files/Articles/1775949/fpsyt-17-1775949-HTML-r1/image_m/fpsyt-17-1775949-t004.jpg Table 4. Pathway estimates and bootstrapped confidence intervals for the association between suicida https://www.frontiersin.org/files/Articles/1775949/fpsyt-17-1775949-HTML-r1/image_m/fpsyt-17-1775949-t005.jpg Table 5. Regression analysis of the moderation model for self-esteem and internet addiction. https://www.frontiersin.org/files/Articles/1775949/fpsyt-17-1775949-HTML-r1/image_m/fpsyt-17-1775949-g003.jpg Figure 3. Interaction between suicidal ideation (SI) and school connectedness (SC) in relation to se https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2026.1748749/full https://www.frontiersin.org/files/Articles/1748749/frsc-08-1748749-HTML/image_m/frsc-08-1748749-g001.jpg Figure 1. Methodological framework and analytical workflow of the study. https://www.frontiersin.org/files/Articles/1748749/frsc-08-1748749-HTML/image_m/frsc-08-1748749-t001.jpg Table 1. Summary of respondent socio-demographic characteristics and travel behaviors across the thr https://www.frontiersin.org/files/Articles/1748749/frsc-08-1748749-HTML/image_m/frsc-08-1748749-t002.jpg Table 2. Comparison of respondents’ perceptions regarding automation and mode. https://www.frontiersin.org/files/Articles/1748749/frsc-08-1748749-HTML/image_m/frsc-08-1748749-t003.jpg Table 3. Results for group differences in perceived crime risk and unwillingness to use autonomous p https://www.frontiersin.org/files/Articles/1748749/frsc-08-1748749-HTML/image_m/frsc-08-1748749-t004.jpg Table 4. Model estimation results for perceived risk of crime occurrence, perceived risk of being vi https://www.frontiersin.org/files/Articles/1748749/frsc-08-1748749-HTML/image_m/frsc-08-1748749-g002.jpg Figure 2. Perceived risks and unwillingness by gender. https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2025.1677376/full https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-g001.jpg Figure 1. Pixis Robobus, an autonomous shuttle used in the real-world simulated user study (left) an https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-g002.jpg Figure 2. Screen examples shown in the online survey. From upper left to lower right: normal screen, https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-g003.jpg Figure 3. Screens shown inside the shuttle during the real-world simulated testing. From upper left https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-g004.jpg Figure 4. Test circuit in Spain with the different scenario trajectories. https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-g005.jpg Figure 5. Distribution of Likert-scale ratings for different aggression reporting tools. https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-t001.jpg Table 1. Gender differences in aggression reporting (emergency) HMI preferences with Mean, Median, M https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-g006.jpg Figure 6. Responses by gender on the information about other passengers during reservation (top) and https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-t002.jpg Table 2. Descriptive and inferential statistics for security and privacy concerns (N = 66) with Mean https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-g007.jpg Figure 7. Real-world testing user feedback on safety-related questions. https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-g008.jpg Figure 8. Real-world testing user feedback on emergency stop screens. https://www.frontiersin.org/files/Articles/1677376/fmech-11-1677376-HTML/image_m/fmech-11-1677376-g009.jpg Figure 9. Responses related to security-related events such as aggressions and devices for reporting https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1811163/full https://www.frontiersin.org/files/Articles/1811163/fimmu-17-1811163-HTML/image_m/fimmu-17-1811163-g001.jpg Figure 1. Baseline CT images showing the left renal primary tumor, a largely necrotic 9 × 7 cm mass, https://www.frontiersin.org/files/Articles/1811163/fimmu-17-1811163-HTML/image_m/fimmu-17-1811163-g002.jpg Figure 2. Previous renal core needle biopsy showing clear cell renal cell carcinoma (H.E, 2x, top; 1 https://www.frontiersin.org/files/Articles/1811163/fimmu-17-1811163-HTML/image_m/fimmu-17-1811163-g003.jpg Figure 3. CT scan after four cycles of therapy showing reduction of the left renal lesion to 37 × 35 https://www.frontiersin.org/files/Articles/1811163/fimmu-17-1811163-HTML/image_m/fimmu-17-1811163-g004.jpg Figure 4. Histological images from the radical nephrectomy specimen. At low magnification, (A) (CD10 https://www.frontiersin.org/files/Articles/1811163/fimmu-17-1811163-HTML/image_m/fimmu-17-1811163-t001.jpg Table 1. Overview of the patient’s therapy, key clinical events, imaging findings and treatment-rela https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1660903/full https://www.frontiersin.org/files/Articles/1660903/fendo-16-1660903-HTML-r2/image_m/fendo-16-1660903-g001.jpg Figure 1. Research flow chart. https://www.frontiersin.org/files/Articles/1660903/fendo-16-1660903-HTML-r2/image_m/fendo-16-1660903-t001.jpg Table 1. Comparison of baseline data between training and validation sets. https://www.frontiersin.org/files/Articles/1660903/fendo-16-1660903-HTML-r2/image_m/fendo-16-1660903-g002.jpg Figure 2. Best match factor screening by lasso regression. (A) The Lasso regression path diagram; (B https://www.frontiersin.org/files/Articles/1660903/fendo-16-1660903-HTML-r2/image_m/fendo-16-1660903-g003.jpg Figure 3. The ROC curve, calibration curve, and the decision curve analysis of three models in the t https://www.frontiersin.org/files/Articles/1660903/fendo-16-1660903-HTML-r2/image_m/fendo-16-1660903-g004.jpg Figure 4. Nomogram of the prediction model for early nephropathy in elderly living with T2DM. https://www.frontiersin.org/files/Articles/1660903/fendo-16-1660903-HTML-r2/image_m/fendo-16-1660903-g005.jpg Figure 5. Performance of seven machine learning models between training and validation sets. (A, B) https://www.frontiersin.org/files/Articles/1660903/fendo-16-1660903-HTML-r2/image_m/fendo-16-1660903-t002.jpg Table 2. Performance of seven machine learning models between training and validation sets. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1765852/full https://www.frontiersin.org/files/Articles/1765852/fpsyg-17-1765852-HTML/image_m/fpsyg-17-1765852-t001.jpg Table 1. Instructional design of the case-based inclusive education coursework. https://www.frontiersin.org/files/Articles/1765852/fpsyg-17-1765852-HTML/image_m/fpsyg-17-1765852-t002.jpg Table 2. Pre- and post-intervention changes in SPTs' efficacy across dimensions and the total scale https://www.frontiersin.org/files/Articles/1765852/fpsyg-17-1765852-HTML/image_m/fpsyg-17-1765852-t003.jpg Table 3. Mixed-design ANOVA examining the effects of time and disability-related contact experiences https://www.frontiersin.org/files/Articles/1765852/fpsyg-17-1765852-HTML/image_m/fpsyg-17-1765852-t004.jpg Table 4. Model fit statistics for the pre- and post-intervention latent profile analyses. https://www.frontiersin.org/files/Articles/1765852/fpsyg-17-1765852-HTML/image_m/fpsyg-17-1765852-g001.jpg Figure 1. Latent profile shapes for the selected three-class solution of SPTs' efficacy for inclusiv https://www.frontiersin.org/files/Articles/1765852/fpsyg-17-1765852-HTML/image_m/fpsyg-17-1765852-g002.jpg Figure 2. Latent profile probabilities and transition probabilities from pre- to post-intervention. https://www.frontiersin.org/files/Articles/1765852/fpsyg-17-1765852-HTML/image_m/fpsyg-17-1765852-t005.jpg Table 5. Multinomial logistic regression of the effects of two types of contact experiences on laten https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1717234/full https://www.frontiersin.org/files/Articles/1717234/fpubh-14-1717234-HTML/image_m/fpubh-14-1717234-t001.jpg Table 1. Demographic and study-related features by latent profile membership (n = 408). https://www.frontiersin.org/files/Articles/1717234/fpubh-14-1717234-HTML/image_m/fpubh-14-1717234-t002.jpg Table 2. The fitting results of the latent profile model for digital literacy in undergraduate nursi https://www.frontiersin.org/files/Articles/1717234/fpubh-14-1717234-HTML/image_m/fpubh-14-1717234-t003.jpg Table 3. The probability of attribution of three potential profiles (n = 408). https://www.frontiersin.org/files/Articles/1717234/fpubh-14-1717234-HTML/image_m/fpubh-14-1717234-g001.jpg Figure 1. Potential profile category characteristics of digital literacy among undergraduate nursing https://www.frontiersin.org/files/Articles/1717234/fpubh-14-1717234-HTML/image_m/fpubh-14-1717234-t004.jpg Table 4. Variable assignment status. https://www.frontiersin.org/files/Articles/1717234/fpubh-14-1717234-HTML/image_m/fpubh-14-1717234-t005.jpg Table 5. Logistic regression analysis of factors associated with potential profiles of latent digita https://www.frontiersin.org/files/Articles/1717234/fpubh-14-1717234-HTML/image_m/fpubh-14-1717234-t006.jpg Table 6. One-way ANOVA of innovative behavior among nursing students across different digital litera https://www.frontiersin.org/journals/genome-editing/articles/10.3389/fgeed.2025.1663352/full https://www.frontiersin.org/files/Articles/1663352/fgeed-07-1663352-HTML/image_m/fgeed-07-1663352-g001.jpg Figure 1. Chronological overview of major genome editing innovations. From SpCas9, high-fidelity var https://www.frontiersin.org/files/Articles/1663352/fgeed-07-1663352-HTML/image_m/fgeed-07-1663352-g002.jpg Figure 2. Schematic representations of SpCas9, sgRNA and the SpCas9/sgRNA/DNA complex. (a) Diagram o https://www.frontiersin.org/files/Articles/1663352/fgeed-07-1663352-HTML/image_m/fgeed-07-1663352-t001.jpg Table 1. Summary of key improvements in the specificity and efficiency of CRISPR/Cas9 editing. See m https://www.frontiersin.org/files/Articles/1663352/fgeed-07-1663352-HTML/image_m/fgeed-07-1663352-g003.jpg Figure 3. Schematic representation of SpCas9, Cytosine Base editor (CBE) and Prime editor (PE). (a) https://www.frontiersin.org/files/Articles/1663352/fgeed-07-1663352-HTML/image_m/fgeed-07-1663352-t002.jpg Table 2. Summary of SpCas9 orthologs used in plants and their main characteristics. https://www.frontiersin.org/files/Articles/1663352/fgeed-07-1663352-HTML/image_m/fgeed-07-1663352-t003.jpg Table 3. Summary of major advancements in prime editing specificity and efficiency. See main text fo https://www.frontiersin.org/files/Articles/1663352/fgeed-07-1663352-HTML/image_m/fgeed-07-1663352-g004.jpg Figure 4. Schematic representation of insertion via GRAND. (a) Following nicking and PBS annealing, https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1726208/full https://www.frontiersin.org/files/Articles/1726208/fmed-12-1726208-HTML/image_m/fmed-12-1726208-g001.jpg Figure 1. (A) Family pedigree. The square represents males and while the circle represents females. https://www.frontiersin.org/files/Articles/1726208/fmed-12-1726208-HTML/image_m/fmed-12-1726208-t001.jpg Table 1. Whole-exome sequencing detail of the patient. https://www.frontiersin.org/files/Articles/1726208/fmed-12-1726208-HTML/image_m/fmed-12-1726208-g002.jpg Figure 2. The chromatograms of the DNA sequencing. The arrows highlight the altered location of the https://www.frontiersin.org/files/Articles/1726208/fmed-12-1726208-HTML/image_m/fmed-12-1726208-g003.jpg Figure 3. Three-dimensional (3D) rendering and structural analysis of both the wild-type and mutant https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1664012/full https://www.frontiersin.org/files/Articles/1664012/fpls-16-1664012-HTML/image_m/fpls-16-1664012-t001.jpg Table 1. Description of marker sets including whole-genome sequencing (WGS) data and LD-pruned marke https://www.frontiersin.org/files/Articles/1664012/fpls-16-1664012-HTML/image_m/fpls-16-1664012-g001.jpg Figure 1. Linkage disequilibrium (LD) decay in the mango gene pool. The X-axis shows the physical di https://www.frontiersin.org/files/Articles/1664012/fpls-16-1664012-HTML/image_m/fpls-16-1664012-t002.jpg Table 2. Genomic predictive abilities for fruit blush color (FBC), average fruit weight (AFW), fruit https://www.frontiersin.org/files/Articles/1664012/fpls-16-1664012-HTML/image_m/fpls-16-1664012-t003.jpg Table 3. Significant marker-trait associations for average fruit weight (AFW), fruit blush color (FB https://www.frontiersin.org/files/Articles/1664012/fpls-16-1664012-HTML/image_m/fpls-16-1664012-t004.jpg Table 4. Candidate genes identified near significant SNP markers associated with fruit blush color ( https://www.frontiersin.org/files/Articles/1664012/fpls-16-1664012-HTML/image_m/fpls-16-1664012-g002.jpg Figure 2. Predictive ability of breeding population parent phenotypes: (A) without accounting for po https://www.frontiersin.org/files/Articles/1664012/fpls-16-1664012-HTML/image_m/fpls-16-1664012-g003.jpg Figure 3. Predictive ability of gene pool individuals under 5-fold cross-validation: (A) without acc https://www.frontiersin.org/files/Articles/1664012/fpls-16-1664012-HTML/image_m/fpls-16-1664012-g004.jpg Figure 4. Predictive ability of breeding population parent phenotypes: (A) without population struct https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1729642/full https://www.frontiersin.org/files/Articles/1729642/fmed-13-1729642-HTML/image_m/fmed-13-1729642-g001.jpg Figure 1. Comprehensive bioinformatics workflow for IBD subtype classification using multidataset in https://www.frontiersin.org/files/Articles/1729642/fmed-13-1729642-HTML/image_m/fmed-13-1729642-t001.jpg Table 1. Characteristics of GEO datasets used in this study. https://www.frontiersin.org/files/Articles/1729642/fmed-13-1729642-HTML/image_m/fmed-13-1729642-g002.jpg Figure 2. Single-cell analysis reveals distinct cellular and molecular signatures in IBD subtypes us https://www.frontiersin.org/files/Articles/1729642/fmed-13-1729642-HTML/image_m/fmed-13-1729642-g003.jpg Figure 3. Differential gene expression analysis and functional network characterization in IBD. (A) https://www.frontiersin.org/files/Articles/1729642/fmed-13-1729642-HTML/image_m/fmed-13-1729642-g004.jpg Figure 4. Development and validation of an IBD diagnostic model based on hub gene expression. (A) Cr https://www.frontiersin.org/files/Articles/1729642/fmed-13-1729642-HTML/image_m/fmed-13-1729642-g005.jpg Figure 5. Immune infiltration-based risk stratification reveals distinct immune cell composition pat https://www.frontiersin.org/files/Articles/1729642/fmed-13-1729642-HTML/image_m/fmed-13-1729642-g006.jpg Figure 6. Comprehensive analysis of the 18-gene signature reveals network interactions, individual d https://www.frontiersin.org/files/Articles/1729642/fmed-13-1729642-HTML/image_m/fmed-13-1729642-g007.jpg Figure 7. Immunohistochemical validation of key biomarkers in IBD tissues. Representative IHC staini https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1783385/full https://www.frontiersin.org/files/Articles/1783385/fmicb-17-1783385-HTML/image_m/fmicb-17-1783385-g001.jpg Figure 1. (A) Experimental design; (B) body weight; (C) FBG levels before treatment started (at 5 we https://www.frontiersin.org/files/Articles/1783385/fmicb-17-1783385-HTML/image_m/fmicb-17-1783385-t001.jpg Table 1. Scoring criteria for histopathology. https://www.frontiersin.org/files/Articles/1783385/fmicb-17-1783385-HTML/image_m/fmicb-17-1783385-g002.jpg Figure 2. Effects of dorzagliatin and TTP399 on glucose tolerance and insulin tolerance in HFD-fed m https://www.frontiersin.org/files/Articles/1783385/fmicb-17-1783385-HTML/image_m/fmicb-17-1783385-g003.jpg Figure 3. Effects of dorzagliatin on the composition of the gut microbiota in HFD-fed mice. (A) Alph https://www.frontiersin.org/files/Articles/1783385/fmicb-17-1783385-HTML/image_m/fmicb-17-1783385-g004.jpg Figure 4. Effects of TTP399 on the composition of the gut microbiota in HFD-fed mice. (A) Alpha dive https://www.frontiersin.org/files/Articles/1783385/fmicb-17-1783385-HTML/image_m/fmicb-17-1783385-g005.jpg Figure 5. Dorzagliatin and TTP399 have no significant effect on intestinal barrier integrity or infl https://www.frontiersin.org/files/Articles/1783385/fmicb-17-1783385-HTML/image_m/fmicb-17-1783385-g006.jpg Figure 6. Correlation analysis. (A) Correlation analysis between biochemical indices, inflammatory m https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2025.1726295/full https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-g001.jpg Figure 1. Non-traditional and traditional risk factors of CVD in humans. Icons were sourced from htt https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-g002.jpg Figure 2. Flowchart of eligibility and exclusion criteria for participant selection. https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-g003.jpg Figure 3. Flowchart of the study design investigating endospore-forming B. subtilis isolated from th https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-t001.jpg Table 1. Demographic summary of 40 men, including patients with CVD (n = 20) and men without CVD (n  https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-g004.jpg Figure 4. (A,B) Optical microscopy images of Gram-positive B. subtilis and malachite green endospore https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-g005.jpg Figure 5. Number of B. subtilis isolates recovered from third molar exudate sites at the time of ext https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-g006.jpg Figure 6. (A) Biofilm-forming B. subtilis colonies on CRA showing black pigmentation. (B) Non-biofil https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-g007.jpg Figure 7. Correlation between B. subtilis isolates from CVD patients and control participants. https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-g008.jpg Figure 8. Association between B. subtilis isolation and blood pressure. https://www.frontiersin.org/files/Articles/1726295/froh-06-1726295-HTML/image_m/froh-06-1726295-g009.jpg Figure 9. Imaginary mechanistic pathways linking oral B. subtilis to CVD. CRP refers to C-reactive p https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1809851/full https://www.frontiersin.org/files/Articles/1809851/fimmu-17-1809851-HTML/image_m/fimmu-17-1809851-g001.jpg Figure 1. Key pathogenic mechanisms driving inflammation and fibrosis in renal fibrosis. (a) Inflamm https://www.frontiersin.org/files/Articles/1809851/fimmu-17-1809851-HTML/image_m/fimmu-17-1809851-g002.jpg Figure 2. The cGAS-STING signaling pathway mediates macrophage polarization. https://www.frontiersin.org/files/Articles/1809851/fimmu-17-1809851-HTML/image_m/fimmu-17-1809851-t001.jpg Table 1. A summary table of drugs, targets and functions related to the cGAS-STING pathway. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1722846/full https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-t001.jpg Table 1. Demographic data of patients. https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-t002.jpg Table 2. Outcomes after bedside pleural fixation in the FS group and 50%GS group. https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-g001.jpg Figure 1. Comparison of efficacy between 50%GS and FS. https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-g002.jpg Figure 2. Number of interventions required for air leak resolution. https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-g003.jpg Figure 3. Comparison of the number of interventions required between FS and 50%GS. https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-g004.jpg Figure 4. Kaplan-Meier curve for prolonged air leak risk. https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-g005.jpg Figure 5. Spectrum and frequency of complications by treatment group. https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-g006.jpg Figure 6. Univariable logistic regression of factors influencing first intervention efficacy in the https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-g007.jpg Figure 7. Multivariable logistic regression of factors influencing first intervention efficacy in th https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-g008.jpg Figure 8. Univariable Cox regression analysis of factors influencing time to chest tubve removal. https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-t003.jpg Table 3. Multivariable logistic regression analysis of factors associated with success after the fir https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-t004.jpg Table 4. Multivariable cox regression analysis of factors associated with time to chest tube removal https://www.frontiersin.org/files/Articles/1722846/fsurg-12-1722846-HTML/image_m/fsurg-12-1722846-g009.jpg Figure 9. Multivariable Cox regression analysis of factors influencing time to chest tube removal. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1658592/full https://www.frontiersin.org/files/Articles/1658592/fendo-16-1658592-HTML/image_m/fendo-16-1658592-g001.jpg Figure 1. Comparative Diagram of HPG Axis Signaling in Normal vs. Menopausal Women: HPG axis signali https://www.frontiersin.org/files/Articles/1658592/fendo-16-1658592-HTML/image_m/fendo-16-1658592-g002.jpg Figure 2. Influence of Intracellular Chloride on GABA-A Receptor Function in GnRH Neurons: This figu https://www.frontiersin.org/files/Articles/1658592/fendo-16-1658592-HTML/image_m/fendo-16-1658592-g003.jpg Figure 3. Distinct hypothalamic kisspeptin neuron populations mediate estrogen feedback regulation o https://www.frontiersin.org/files/Articles/1658592/fendo-16-1658592-HTML/image_m/fendo-16-1658592-t001.jpg Table 1. Comparison of estrogen types used in menopause hormone therapy (MHT), highlighting potency, https://www.frontiersin.org/files/Articles/1658592/fendo-16-1658592-HTML/image_m/fendo-16-1658592-g004.jpg Figure 4. Neurokinin B (NKB), produced by KNDy neurons in the hypothalamus, activates GnRH neurons, https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1710612/full https://www.frontiersin.org/files/Articles/1710612/fendo-16-1710612-HTML/image_m/fendo-16-1710612-g001.jpg Figure 1. Incision and Trocar positions as well as flap dissection ranges in GTET and GITET. (A) Inc https://www.frontiersin.org/files/Articles/1710612/fendo-16-1710612-HTML/image_m/fendo-16-1710612-g002.jpg Figure 2. The CUSUM learning curve. Scatter plot of operative time plotted chronologically according https://www.frontiersin.org/files/Articles/1710612/fendo-16-1710612-HTML/image_m/fendo-16-1710612-g003.jpg Figure 3. Based on the critical values, GTET and GITET were divided into GTET (learning phase), GTET https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1681649/full https://www.frontiersin.org/files/Articles/1681649/fped-13-1681649-HTML-r1/image_m/fped-13-1681649-t001.jpg Table 1. Original studies on confocal laser endomicroscopy in pediatric gastrointestinal diseases. https://www.frontiersin.org/files/Articles/1681649/fped-13-1681649-HTML-r1/image_m/fped-13-1681649-g001.jpg Figure 1. Confocal Laser endomicroscopy vs. conventional histology magnification. (A) Confocal image https://www.frontiersin.org/files/Articles/1681649/fped-13-1681649-HTML-r1/image_m/fped-13-1681649-g002.jpg Figure 2. Pros and cons of confocal Laser endomicroscopy and its application in pediatric gastroente https://www.frontiersin.org/files/Articles/1681649/fped-13-1681649-HTML-r1/image_m/fped-13-1681649-g003.jpg Figure 3. Explanatory pictures of the main applications of confocal Laser endomicroscopy in pediatri https://www.frontiersin.org/files/Articles/1681649/fped-13-1681649-HTML-r1/image_m/fped-13-1681649-g004.jpg Figure 4. Explanatory pictures of the main applications of confocal Laser endomicroscopy in pediatri https://www.frontiersin.org/files/Articles/1681649/fped-13-1681649-HTML-r1/image_m/fped-13-1681649-t002.jpg Table 2. Potential applications of confocal laser endomicroscopy (CLE) in pediatric gastrointestinal https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1716668/full https://www.frontiersin.org/files/Articles/1716668/fped-13-1716668-HTML/image_m/fped-13-1716668-g001.jpg Figure 1. Confocal laser endomicroscopy vs. conventional histology magnification. (A) Confocal image https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1600798/full https://www.frontiersin.org/files/Articles/1600798/fnagi-17-1600798-HTML/image_m/fnagi-17-1600798-t001.jpg Table 1. Patients’ clinical phenotype. https://www.frontiersin.org/files/Articles/1600798/fnagi-17-1600798-HTML/image_m/fnagi-17-1600798-t002.jpg Table 2. Patients’ clinical phenotype as for cognitive status (CDR 0 vs. 0.5–4) and as for sex. https://www.frontiersin.org/files/Articles/1600798/fnagi-17-1600798-HTML/image_m/fnagi-17-1600798-t003.jpg Table 3. Multivariate ordinal regression model with CDR score (0 vs. 0.5 vs. 1–4) as the dependent v https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1745421/full https://www.frontiersin.org/files/Articles/1745421/fnut-13-1745421-HTML-r2/image_m/fnut-13-1745421-t001.jpg Table 1. Baseline characteristics of patients. https://www.frontiersin.org/files/Articles/1745421/fnut-13-1745421-HTML-r2/image_m/fnut-13-1745421-t002.jpg Table 2. Comparison of serum nutrient markers according to tumor grade. https://www.frontiersin.org/files/Articles/1745421/fnut-13-1745421-HTML-r2/image_m/fnut-13-1745421-g001.jpg Figure 1. Multivariate logistic regression predictors of high tumor grade in breast cancer patients. https://www.frontiersin.org/files/Articles/1745421/fnut-13-1745421-HTML-r2/image_m/fnut-13-1745421-t003.jpg Table 3. Factor loadings for major dietary patterns. https://www.frontiersin.org/files/Articles/1745421/fnut-13-1745421-HTML-r2/image_m/fnut-13-1745421-t004.jpg Table 4. Multivariable regression analyses for dietary patterns and serum biomarkers in relation to https://www.frontiersin.org/files/Articles/1745421/fnut-13-1745421-HTML-r2/image_m/fnut-13-1745421-t005.jpg Table 5. Correlation matrix of serum nutrients, oxidative stress markers, and dietary patterns in br https://www.frontiersin.org/files/Articles/1745421/fnut-13-1745421-HTML-r2/image_m/fnut-13-1745421-g002.jpg Figure 2. Heatmap showing correlations between serum nutrients, oxidative stress markers, and dietar https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1678352/full https://www.frontiersin.org/files/Articles/1678352/fmed-12-1678352-HTML/image_m/fmed-12-1678352-g001.jpg Figure 1. PRISMA flow diagram of the study selection process. https://www.frontiersin.org/files/Articles/1678352/fmed-12-1678352-HTML/image_m/fmed-12-1678352-g002.jpg Figure 2. Risk of bias graph. https://www.frontiersin.org/files/Articles/1678352/fmed-12-1678352-HTML/image_m/fmed-12-1678352-g003.jpg Figure 3. Network evidence plot. (A) FEV1%, (B) 6MWT, (C) CAT. CT conventional treatment. https://www.frontiersin.org/files/Articles/1678352/fmed-12-1678352-HTML/image_m/fmed-12-1678352-g004.jpg Figure 4. Forest plots. (A) FEV1%, (B) 6MWT, (C) CAT. CT conventional treatment. https://www.frontiersin.org/files/Articles/1678352/fmed-12-1678352-HTML/image_m/fmed-12-1678352-t001.jpg Table 1. Ranking table of SUCRA values. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1760794/full https://www.frontiersin.org/files/Articles/1760794/fpubh-14-1760794-HTML/image_m/fpubh-14-1760794-g001.jpg Figure 1. Participant flow. https://www.frontiersin.org/files/Articles/1760794/fpubh-14-1760794-HTML/image_m/fpubh-14-1760794-t001.jpg Table 1. Analysis of participants' definitions of social prescribing. https://www.frontiersin.org/files/Articles/1760794/fpubh-14-1760794-HTML/image_m/fpubh-14-1760794-t002.jpg Table 2. Participant selection of potential domains of support through SP from a multiple-choice que https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1813663/full https://www.frontiersin.org/files/Articles/1813663/fpsyg-17-1813663-HTML/image_m/fpsyg-17-1813663-g001.jpg Figure 1. Screen capture of activities in Quizizz. https://www.frontiersin.org/files/Articles/1813663/fpsyg-17-1813663-HTML/image_m/fpsyg-17-1813663-t001.jpg Table 1. Intervention operation details. https://www.frontiersin.org/files/Articles/1813663/fpsyg-17-1813663-HTML/image_m/fpsyg-17-1813663-g002.jpg Figure 2. Screen capture of learning tasks in Quizizz. https://www.frontiersin.org/files/Articles/1813663/fpsyg-17-1813663-HTML/image_m/fpsyg-17-1813663-t002.jpg Table 2. Descriptive statistics of translation pre- and post-test scores. https://www.frontiersin.org/files/Articles/1813663/fpsyg-17-1813663-HTML/image_m/fpsyg-17-1813663-t003.jpg Table 3. Results of ANCOVA of translation post-test scores. https://www.frontiersin.org/files/Articles/1813663/fpsyg-17-1813663-HTML/image_m/fpsyg-17-1813663-t004.jpg Table 4. Descriptive statistics of engagement pre- and post- survey scores. https://www.frontiersin.org/files/Articles/1813663/fpsyg-17-1813663-HTML/image_m/fpsyg-17-1813663-t005.jpg Table 5. Results of ANCOVA of pre- and post-survey scores. https://www.frontiersin.org/files/Articles/1813663/fpsyg-17-1813663-HTML/image_m/fpsyg-17-1813663-t006.jpg Table 6. Paired-sample t-test for engagement. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1739206/full https://www.frontiersin.org/files/Articles/1739206/fonc-15-1739206-HTML-r1/image_m/fonc-15-1739206-g001.jpg Figure 1. CT and MRI imaging of PRNRP. https://www.frontiersin.org/files/Articles/1739206/fonc-15-1739206-HTML-r1/image_m/fonc-15-1739206-g002.jpg Figure 2. CT and MRI imaging of RCCC. https://www.frontiersin.org/files/Articles/1739206/fonc-15-1739206-HTML-r1/image_m/fonc-15-1739206-g003.jpg Figure 3. The pathological manifestations of PRNRP. https://www.frontiersin.org/files/Articles/1739206/fonc-15-1739206-HTML-r1/image_m/fonc-15-1739206-g004.jpg Figure 4. The pathological manifestations of RCCC. https://www.frontiersin.org/journals/communications-and-networks/articles/10.3389/frcmn.2026.1771504/full https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t001.jpg Table 1. QoS/QoE Metrics used in performance evaluation. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t002.jpg Table 2. Specifications of devices and equipment used in network design. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g001.jpg Figure 1. The topology of the baseline ITPC network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t003.jpg Table 3. Floor-by-Floor distribution of departments and subscribers in the institution. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g002.jpg Figure 2. The topology of the baseline ministry network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t004.jpg Table 4. Floor-by-floor distribution of departments and subscribers in the ministry. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t005.jpg Table 5. Comparative Summary of the baseline networks. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t006.jpg Table 6. Baseline network simulation requirements. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t007.jpg Table 7. The applications and profiles used in the network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t008.jpg Table 8. Baseline network simulation requirements. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t009.jpg Table 9. The applications and profiles used in the network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g003.jpg Figure 3. Ethernet technology using switches and hubs. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t010.jpg Table 10. Types of gigabit ethernet. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g004.jpg Figure 4. Ethernet technology using switches only. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g005.jpg Figure 5. Ethernet technology using regrouping devices. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g006.jpg Figure 6. Ethernet technology by using regrouping devices and external server. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g007.jpg Figure 7. Application-layer end-to-end delay in the ethernet-based service-enabled network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g008.jpg Figure 8. Application-layer end-to-end delay in the baseline wireless LAN. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g009.jpg Figure 9. Wireless LAN load in the baseline network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g010.jpg Figure 10. Wireless LAN throughput in the baseline network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g011.jpg Figure 11. Traffic received at the main switch in the baseline network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g012.jpg Figure 12. Traffic received at floor-level switches in the baseline network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g013.jpg Figure 13. Average application-layer end-to-end delay for service-enabled scenarios. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g014.jpg Figure 14. Traffic received at the main switch across scenarios. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g015.jpg Figure 15. Traffic received at the main server in service-enabled Scenarios 3 and 4. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g016.jpg Figure 16. Average load on the main server in service-enabled Scenarios 3 and 4. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g017.jpg Figure 17. Traffic received by the firewall across scenarios. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t011.jpg Table 11. Classification of ITPC simulation scenarios. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-t012.jpg Table 12. Comparative cost and feasibility indicators for ITPC scenarios. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g018.jpg Figure 18. Ethernet technology by using an ethernet 1000BaseX cable for the ministry network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g019.jpg Figure 19. Ethernet technology by using an Ethernet 10 Gbps Link for the ministry network. https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g020.jpg Figure 20. Ethernet technology using an Ethernet 10 Gbps Link and using devices regrouping for the m https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g021.jpg Figure 21. Traffic received for all the application (bytes/s) in the baseline network and all scenar https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g022.jpg Figure 22. Upload/Download response time for the email application(s) in the baseline network and ac https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g023.jpg Figure 23. Object response time for the HTTP application (s) in the baseline network and all scenari https://www.frontiersin.org/files/Articles/1771504/frcmn-07-1771504-HTML-r1/image_m/frcmn-07-1771504-g024.jpg Figure 24. Application-layer end-to-end delay (s) in the baseline network and all scenarios. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1806164/full https://www.frontiersin.org/files/Articles/1806164/fnins-20-1806164-HTML/image_m/fnins-20-1806164-g001.jpg Figure 1. Flowchart of the search strategy in the study. https://www.frontiersin.org/files/Articles/1806164/fnins-20-1806164-HTML/image_m/fnins-20-1806164-g002.jpg Figure 2. Overall publication trends and literature distribution visualization. (A) Temporal evoluti https://www.frontiersin.org/files/Articles/1806164/fnins-20-1806164-HTML/image_m/fnins-20-1806164-g003.jpg Figure 3. Countries/regions and institutions of publications in the field of NODDI. (A) Countries/re https://www.frontiersin.org/files/Articles/1806164/fnins-20-1806164-HTML/image_m/fnins-20-1806164-g004.jpg Figure 4. Visualization of the authors analysis. (A) The number of publications and average citation https://www.frontiersin.org/files/Articles/1806164/fnins-20-1806164-HTML/image_m/fnins-20-1806164-t001.jpg Table 1. List of the top 10 most cited publications. https://www.frontiersin.org/files/Articles/1806164/fnins-20-1806164-HTML/image_m/fnins-20-1806164-g005.jpg Figure 5. Timeline view of the cited references. The cluster tag on the right shows the topic. The l https://www.frontiersin.org/files/Articles/1806164/fnins-20-1806164-HTML/image_m/fnins-20-1806164-g006.jpg Figure 6. Visual analysis of keywords and topics. (A) Keywords co-occurrence network diagram. Nodes https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1783138/full https://www.frontiersin.org/files/Articles/1783138/fnhum-20-1783138-HTML/image_m/fnhum-20-1783138-g001.jpg Figure 1. The proposed causal chain from self-referential processing to cognitive mode. The mechanis https://www.frontiersin.org/files/Articles/1783138/fnhum-20-1783138-HTML/image_m/fnhum-20-1783138-t001.jpg Table 1. Hierarchical organization of information processing elements in the human brain with their https://www.frontiersin.org/files/Articles/1783138/fnhum-20-1783138-HTML/image_m/fnhum-20-1783138-t002.jpg Table 2. Summary of testable predictions generated by the framework. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1669007/full https://www.frontiersin.org/files/Articles/1669007/fgene-16-1669007-HTML/image_m/fgene-16-1669007-g001.jpg Figure 1. Structural domains of human phenylalanine hydroxylase (PAH) and predicted 3D model. (A) Sc https://www.frontiersin.org/files/Articles/1669007/fgene-16-1669007-HTML/image_m/fgene-16-1669007-g002.jpg Figure 2. Evidence of tandem duplication of exon 2 in the PAH gene. (A) Long-read Nanopore sequencin https://www.frontiersin.org/files/Articles/1669007/fgene-16-1669007-HTML/image_m/fgene-16-1669007-g003.jpg Figure 3. Genomic confirmation of tandem exon 2 duplication. (A) Schematic representation of the dup https://www.frontiersin.org/files/Articles/1669007/fgene-16-1669007-HTML/image_m/fgene-16-1669007-g004.jpg Figure 4. In silico predicted tridimensional model of PAH harboring a tandem duplication of residues https://www.frontiersin.org/journals/nuclear-medicine/articles/10.3389/fnume.2026.1777541/full https://www.frontiersin.org/files/Articles/1777541/fnume-06-1777541-HTML/image_m/fnume-06-1777541-t001.jpg Table 1. Patients characteristics. https://www.frontiersin.org/files/Articles/1777541/fnume-06-1777541-HTML/image_m/fnume-06-1777541-g001.jpg Figure 1. Maximum-intensity projection (MIP), PET and fused images of [18F]FDG and [68Ga]Ga-FAPI-04 https://www.frontiersin.org/files/Articles/1777541/fnume-06-1777541-HTML/image_m/fnume-06-1777541-g002.jpg Figure 2. Correlation plots of immunoreactive score (IRS) with tumor volume (TV) and total lesion up https://www.frontiersin.org/files/Articles/1777541/fnume-06-1777541-HTML/image_m/fnume-06-1777541-t002.jpg Table 2. Correlation of immunoreactive score of fibroblast activation protein (FAP) expression with https://www.frontiersin.org/files/Articles/1777541/fnume-06-1777541-HTML/image_m/fnume-06-1777541-g003.jpg Figure 3. Immunoreactive score of fibroblast activation protein (FAP) expression depending on the pr https://www.frontiersin.org/files/Articles/1777541/fnume-06-1777541-HTML/image_m/fnume-06-1777541-g004.jpg Figure 4. Kaplan–Meier curves for progression-free survival (PFS) with and without FDG+/FAPI- lesion https://www.frontiersin.org/files/Articles/1777541/fnume-06-1777541-HTML/image_m/fnume-06-1777541-t003.jpg Table 3. Impact of imaging-derived and clinical parameters on progression-free-survival. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1785399/full https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-g001.jpg Figure 1. Weekly distribution of detected influenza viruses and SARS-CoV-2 during the 2024–2025 seas https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-g002.jpg Figure 2. Positivity rates of respiratory viruses, detected in patients from different age groups (y https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-t001.jpg Table 1. Comparison of socio-demographic and clinical characteristics according to influenza virus s https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-t002.jpg Table 2. Number (%) of detected respiratory viruses among outpatients and inpatients. https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-g003.jpg Figure 3. Proportions of respiratory viruses detected in patients with tracheobronchitis, bronchioli https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-t003.jpg Table 3. Number of single infections (in bold) and co-infections with participation of individual re https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-g004.jpg Figure 4. Proportions of single, dual, triple, and quadruple infections with participation of respir https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-g005.jpg Figure 5. Phylogenetic analysis of the HA nucleotide sequences from influenza A(H1N1)pdm09 viruses c https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-g006.jpg Figure 6. Phylogenetic analysis of the HA nucleotide sequences from influenza A(H3N2) viruses circul https://www.frontiersin.org/files/Articles/1785399/fmicb-17-1785399-HTML-r2/image_m/fmicb-17-1785399-g007.jpg Figure 7. Phylogenetic analysis of the HA nucleotide sequences from influenza B/Victoria lineage vir https://www.frontiersin.org/journals/lab-on-a-chip-technologies/articles/10.3389/frlct.2026.1761794/full https://www.frontiersin.org/files/Articles/1761794/frlct-05-1761794-HTML/image_m/frlct-05-1761794-g001.jpg Figure 1. (a,b) POCT System illustrating the use of a simple microfluidic chip and partitioning reag https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1677640/full https://www.frontiersin.org/files/Articles/1677640/fvets-12-1677640-HTML-r2/image_m/fvets-12-1677640-t001.jpg Table 1. Susceptibility of mcr-carrying E. coli isolates from poultry farms in Uganda to 15 antibiot https://www.frontiersin.org/files/Articles/1677640/fvets-12-1677640-HTML-r2/image_m/fvets-12-1677640-g001.jpg Figure 1. Phylogenetic relationship of the genomes of colistin-resistant E. coli isolates recovered https://www.frontiersin.org/files/Articles/1677640/fvets-12-1677640-HTML-r2/image_m/fvets-12-1677640-g002.jpg Figure 2. Pulsed-field gel electrophoresis (PFGE) image showing bands at the expected ~63 kb region, https://www.frontiersin.org/files/Articles/1677640/fvets-12-1677640-HTML-r2/image_m/fvets-12-1677640-g003.jpg Figure 3. A linear, true to scale illustration of the circular mcr-carrying IncI2(Delta) plasmid fro https://www.frontiersin.org/files/Articles/1677640/fvets-12-1677640-HTML-r2/image_m/fvets-12-1677640-t002.jpg Table 2. Representative data of susceptibility testing of mcr-carrying 23-MO00583-5 donor, recipient https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1804224/full https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-g001.jpg Figure 1. PRISMA flow diagram. https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-g002.jpg Figure 2. Distribution of ROB 2 domain-level and overall risk-of-bias judgements. This figure shows https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-g003.jpg Figure 3. CINeMA six-domain judgements and overall confidence for comparisons versus control. Heatma https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-g004.jpg Figure 4. Evidence network, SUCRA ranking, and rankograms across exercise modalities. (a) Evidence n https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-g005.jpg Figure 5. Forest plot of pooled effects of exercise interventions versus control. Forest plot of poo https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-t001.jpg Table 1. Pairwise and network meta-analysis results. https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-g006.jpg Figure 6. Overall dose–response relationship between exercise dose. Overall dose–response relationsh https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-g007.jpg Figure 7. Modality-specific dose–response relationships. Dose–response curves are shown separately f https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-g008.jpg Figure 8. Overall effects of different exercise modalities on balance and illustrative recommended d https://www.frontiersin.org/files/Articles/1804224/fneur-17-1804224-HTML/image_m/fneur-17-1804224-g009.jpg Figure 9. Dose-adjusted contour-enhanced funnel plot. The x-axis shows the dose-adjusted residual ef https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1670985/full https://www.frontiersin.org/files/Articles/1670985/fpubh-13-1670985-HTML-r2/image_m/fpubh-13-1670985-t001.jpg Table 1. Socio-demographic characteristics. https://www.frontiersin.org/files/Articles/1670985/fpubh-13-1670985-HTML-r2/image_m/fpubh-13-1670985-g001.jpg Figure 1. Caregiver’s first response to child’s diarrhea. https://www.frontiersin.org/files/Articles/1670985/fpubh-13-1670985-HTML-r2/image_m/fpubh-13-1670985-t002.jpg Table 2. Association between socio-demographic characteristics and healthcare-seeking practices. https://www.frontiersin.org/files/Articles/1670985/fpubh-13-1670985-HTML-r2/image_m/fpubh-13-1670985-t003.jpg Table 3. Association between health system factors and healthcare-seeking practices. https://www.frontiersin.org/files/Articles/1670985/fpubh-13-1670985-HTML-r2/image_m/fpubh-13-1670985-t004.jpg Table 4. Association between knowledge, attitudes, perceptions, and healthcare-seeking practices. https://www.frontiersin.org/files/Articles/1670985/fpubh-13-1670985-HTML-r2/image_m/fpubh-13-1670985-t005.jpg Table 5. Association between knowledge, attitudes, perceptions, and healthcare-seeking practices. https://www.frontiersin.org/files/Articles/1670985/fpubh-13-1670985-HTML-r2/image_m/fpubh-13-1670985-t006.jpg Table 6. Multivariate logistic regression model (AOR) (p < 0.2). https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1701264/full https://www.frontiersin.org/files/Articles/1701264/fmed-13-1701264-HTML-r1/image_m/fmed-13-1701264-t001.jpg Table 1. Previously reported cases of constitutional indocyanine green excretory defect. https://www.frontiersin.org/files/Articles/1701264/fmed-13-1701264-HTML-r1/image_m/fmed-13-1701264-g001.jpg Figure 1. Gd-EOB-DTPA-enhanced hepatobiliary MRI. (A) Arterial phase, (B) portal venous phase, (C) d https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1812198/full https://www.frontiersin.org/files/Articles/1812198/fpsyt-17-1812198-HTML/image_m/fpsyt-17-1812198-t001.jpg Table 1. Demographic data of patients with schizophrenia. https://www.frontiersin.org/files/Articles/1812198/fpsyt-17-1812198-HTML/image_m/fpsyt-17-1812198-g001.jpg Figure 1. Analysis workflow. ADHD, attention-deficit/hyperactivity disorder; ARS, antipsychotic resp https://www.frontiersin.org/files/Articles/1812198/fpsyt-17-1812198-HTML/image_m/fpsyt-17-1812198-g002.jpg Figure 2. Correlation analyses between PRSs and DIBS. Scatter plots show a correlation between (A) A https://www.frontiersin.org/files/Articles/1812198/fpsyt-17-1812198-HTML/image_m/fpsyt-17-1812198-g003.jpg Figure 3. Correlation analyses between PRSs and ARS. Scatter plots show a correlation between (A) AD https://www.frontiersin.org/files/Articles/1812198/fpsyt-17-1812198-HTML/image_m/fpsyt-17-1812198-g004.jpg Figure 4. Gene expression analyses between subgroups. (A) The violin plot shows the distribution of https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1646996/full https://www.frontiersin.org/files/Articles/1646996/fimmu-16-1646996-HTML/image_m/fimmu-16-1646996-g001.jpg Figure 1. Identification of compound heterozygous USP18 C230X/G317S mutations in patients with sever https://www.frontiersin.org/files/Articles/1646996/fimmu-16-1646996-HTML/image_m/fimmu-16-1646996-g002.jpg Figure 2. Hyperactivation of type I IFN signaling and heightened sensitivity to IFNα of patient PBMC https://www.frontiersin.org/files/Articles/1646996/fimmu-16-1646996-HTML/image_m/fimmu-16-1646996-g003.jpg Figure 3. The G317S mutant USP18 compromised negative regulation of type I IFN signaling through its https://www.frontiersin.org/files/Articles/1646996/fimmu-16-1646996-HTML/image_m/fimmu-16-1646996-g004.jpg Figure 4. Clinical improvement with ruxolitinib treatment and schematic model of G317S mutant USP18 https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1752001/full https://www.frontiersin.org/files/Articles/1752001/fimmu-17-1752001-HTML/image_m/fimmu-17-1752001-g001.jpg Figure 1. Study workflow and analytical pipeline. https://www.frontiersin.org/files/Articles/1752001/fimmu-17-1752001-HTML/image_m/fimmu-17-1752001-g002.jpg Figure 2. Genetic and epigenetic alterations of TRP channel regulators across human cancers. (A) Onc https://www.frontiersin.org/files/Articles/1752001/fimmu-17-1752001-HTML/image_m/fimmu-17-1752001-g003.jpg Figure 3. Predictive value of 22 TRP channel regulatory factors and TRP subtypes for clinical progno https://www.frontiersin.org/files/Articles/1752001/fimmu-17-1752001-HTML/image_m/fimmu-17-1752001-g004.jpg Figure 4. Correlation analysis between GC TRP subtypes and TME as well as tumor immune function. (A) https://www.frontiersin.org/files/Articles/1752001/fimmu-17-1752001-HTML/image_m/fimmu-17-1752001-g005.jpg Figure 5. Functional analysis of TRP subtype in GC. (A) The correlation between the expression level https://www.frontiersin.org/files/Articles/1752001/fimmu-17-1752001-HTML/image_m/fimmu-17-1752001-g006.jpg Figure 6. Construction of WGCNA and TRPscore for GC. (A) The cluster dendrogram was generated based https://www.frontiersin.org/files/Articles/1752001/fimmu-17-1752001-HTML/image_m/fimmu-17-1752001-g007.jpg Figure 7. Evaluation of immune therapy response based on TRPscore. (A) The heatmap displays the rela https://www.frontiersin.org/files/Articles/1752001/fimmu-17-1752001-HTML/image_m/fimmu-17-1752001-g008.jpg Figure 8. scRNA-seq analysis and functional characterization of TRPV2 in GC. (A) t-distributed stoch https://www.frontiersin.org/files/Articles/1752001/fimmu-17-1752001-HTML/image_m/fimmu-17-1752001-g009.jpg Figure 9. The impact of TRPscore and key signaling pathways on GC occurrence and survival. (A, B) Un https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1661639/full https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-t001.jpg Table 1. Component herbs of MDD. https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g001.jpg Figure 1. MDD can reduce mortality in mice with SI-ALI and alleviate inflammation and respiratory fu https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g002.jpg Figure 2. Total ion chromatogram of Modified Dachengqi Decoction (MDD) in negative ion mode of UPLC- https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-t002.jpg Table 2. Results of UPLC-MS analysis of MDD. https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g003.jpg Figure 3. MDD reduced inflammatory infiltration in the lungs and intestine, and alleviated endotheli https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g004.jpg Figure 4. MDD improved the dysbiosis of the gut microbiota. (A) The Chao1 and (B) PD whole tree curv https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g005.jpg Figure 5. MDD regulates bile acid metabolism. (A) PCA plots of samples from each group(n=5-6). (B) H https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g006.jpg Figure 6. Correlation analysis between phylum and genus level of gut microbiota and bile acid metabo https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g007.jpg Figure 7. MDD alleviated endothelial dysfunction by promoting the reduction of NETs. (A, B) The co-e https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g008.jpg Figure 8. Network pharmacological analysis of MDD and sepsis. (A) Intersection targets of sepsis and https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g009.jpg Figure 9. MDD primarily acted on the FXR/TLR4/MYD88 signaling pathway. (A) FXR expression in lung ti https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g010.jpg Figure 10. The potential mechanism of MDD in alleviating SI-ALI partially through influencing bile a https://www.frontiersin.org/files/Articles/1661639/fcimb-16-1661639-HTML/image_m/fcimb-16-1661639-g011.jpg Figure 11. Methodology of this study. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1740334/full https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-i001.jpg Graphical Abstract. Graphical abstract of the meta-analysis. https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-g001.jpg Figure 1. The flowchart of the literature screening process. https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-t001.jpg Table 1. The baseline characteristics of the included studies. https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-g002.jpg Figure 2. Assessments of risk bias. https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-g003.jpg Figure 3. Forest plots for meta-analysis. (A) Clinical effective rate; (B) ECG effective rate; (C) f https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-t002.jpg Table 2A. Active components of guanxinshutong capsule. Active components of GXST identified from TCM https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-t004.jpg Table 2B. Active components of GXST identified from BATMAN-TCM. https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-g004.jpg Figure 4. The results of network pharmacology. (A) Correspondence of GXST ingredients and targets; ( https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-t003.jpg Table 3. Binding affinities of molecular docking. https://www.frontiersin.org/files/Articles/1740334/fcvm-13-1740334-HTML/image_m/fcvm-13-1740334-g005.jpg Figure 5. Binding models of key targets and ingredients. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1665623/full https://www.frontiersin.org/files/Articles/1665623/fgene-16-1665623-HTML/image_m/fgene-16-1665623-t001.jpg Table 1. Clinical and paraclinical characteristics before and after treatment with metformin. https://www.frontiersin.org/files/Articles/1665623/fgene-16-1665623-HTML/image_m/fgene-16-1665623-t002.jpg Table 2. Clinical and genetic characteristics of patients in published studies with NUS1 variants an https://www.frontiersin.org/files/Articles/1665623/fgene-16-1665623-HTML/image_m/fgene-16-1665623-g001.jpg Figure 1. Neuroimaging and electrophysiological findings over time. (A1.1) EEG (2012, bipolar montag https://www.frontiersin.org/files/Articles/1665623/fgene-16-1665623-HTML/image_m/fgene-16-1665623-g002.jpg Figure 2. NUS1 gene structure and location of reported variants associated with PME. This figure ill https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1725123/full https://www.frontiersin.org/files/Articles/1725123/fpubh-13-1725123-HTML-r1/image_m/fpubh-13-1725123-t001.jpg Table 1. Basic information and clinical characteristics of EUD patients. https://www.frontiersin.org/files/Articles/1725123/fpubh-13-1725123-HTML-r1/image_m/fpubh-13-1725123-t002.jpg Table 2. Comparison of differences in clinical features between T0 and T1. https://www.frontiersin.org/files/Articles/1725123/fpubh-13-1725123-HTML-r1/image_m/fpubh-13-1725123-g001.jpg Figure 1. The network structure of symptoms at two time points. Nodes represent 10 core symptoms, an https://www.frontiersin.org/files/Articles/1725123/fpubh-13-1725123-HTML-r1/image_m/fpubh-13-1725123-g002.jpg Figure 2. The standardized centrality index of each node in the symptom network at two time points. https://www.frontiersin.org/files/Articles/1725123/fpubh-13-1725123-HTML-r1/image_m/fpubh-13-1725123-g003.jpg Figure 3. Two subplots illustrate network stability analysis, correlation stability coefficient (CS- https://www.frontiersin.org/files/Articles/1725123/fpubh-13-1725123-HTML-r1/image_m/fpubh-13-1725123-g004.jpg Figure 4. Cross-lagged panel network of core symptoms. Nodes indicate symptoms in the network, and e https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1709767/full https://www.frontiersin.org/files/Articles/1709767/feduc-10-1709767-HTML-r1/image_m/feduc-10-1709767-t001.jpg Table 1. Illustrates the distinctions and connections among the four approaches. https://www.frontiersin.org/files/Articles/1709767/feduc-10-1709767-HTML-r1/image_m/feduc-10-1709767-g001.jpg Figure 1. Evolution of a student design project: (A) initial concept sketch, (B) testing prototype, https://www.frontiersin.org/files/Articles/1709767/feduc-10-1709767-HTML-r1/image_m/feduc-10-1709767-t002.jpg Table 2. List of questions included in the pre- and post- Likert survey. https://www.frontiersin.org/files/Articles/1709767/feduc-10-1709767-HTML-r1/image_m/feduc-10-1709767-g002.jpg Figure 2. Pre–post comparison of mean Likert scores (1–5) across 12 survey items in ENGR128. https://www.frontiersin.org/files/Articles/1709767/feduc-10-1709767-HTML-r1/image_m/feduc-10-1709767-t003.jpg Table 3. Summary of statistical analysis obtained from pre- and post-surveys. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1749819/full https://www.frontiersin.org/files/Articles/1749819/fvets-13-1749819-HTML-r1/image_m/fvets-13-1749819-t001.jpg Table 1. Predicted recombination events in complete PDCoV genomes. https://www.frontiersin.org/files/Articles/1749819/fvets-13-1749819-HTML-r1/image_m/fvets-13-1749819-g001.jpg Figure 1. Molecular and metagenomic identification of pathogens associated with swine diarrhea. (A) https://www.frontiersin.org/files/Articles/1749819/fvets-13-1749819-HTML-r1/image_m/fvets-13-1749819-g002.jpg Figure 2. Phylogenetic analysis of the novel PDCoV-ZJHZ2024 strain. (A) Maximum likelihood phylogene https://www.frontiersin.org/files/Articles/1749819/fvets-13-1749819-HTML-r1/image_m/fvets-13-1749819-g003.jpg Figure 3. Codon usage bias analysis of the PDCoV complete genome and spike (S) gene. (A) Relative sy https://www.frontiersin.org/files/Articles/1749819/fvets-13-1749819-HTML-r1/image_m/fvets-13-1749819-t002.jpg Table 2. Confidence analysis of recombination events in PDCoV whole genome and S gene. https://www.frontiersin.org/files/Articles/1749819/fvets-13-1749819-HTML-r1/image_m/fvets-13-1749819-g004.jpg Figure 4. Cross-validation of a recombination event in PDCoV-ZJHZ2024 using different analytical met https://www.frontiersin.org/files/Articles/1749819/fvets-13-1749819-HTML-r1/image_m/fvets-13-1749819-g005.jpg Figure 5. Phylogenetic analyses of PDCoV based on non-recombinant and recombinant genomic regions. ( https://www.frontiersin.org/journals/medical-technology/articles/10.3389/fmedt.2025.1685088/full https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-g001.jpg Figure 1. The overall flowchart of the research. https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-t001.jpg Table 1. Detailed information about the packages used in machine learning models. https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-t002.jpg Table 2. The detailed demographic information of the patients with thyroid cancer. https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-g002.jpg Figure 2. Correlation heatmaps of patients’ characteristics features in experimental group (A), cont https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-t003.jpg Table 3. Univariate analysis of clinical and biochemical factors associated with occult lung metasta https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-t004.jpg Table 4. Multivariate analysis of variables related to lung metastasis. . https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-g003.jpg Figure 3. Receiver operating characteristic (ROC) curves comparing the classification performance of https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-t005.jpg Table 5. Performance of various prediction models predicting lung metastasis thyroid cancer using a https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-g004.jpg Figure 4. The shapley additive exPlanations values of the better prediction model, LR. (A) Average i https://www.frontiersin.org/files/Articles/1685088/fmedt-07-1685088-HTML/image_m/fmedt-07-1685088-g005.jpg Figure 5. Decision curve analysis for multiple models. x-axis: the threshold probabilities, which in https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1734790/full https://www.frontiersin.org/files/Articles/1734790/fpubh-14-1734790-HTML-r1/image_m/fpubh-14-1734790-g001.jpg Figure 1. The central focus of women-centered family planning policies compared to conventional outc https://www.frontiersin.org/files/Articles/1734790/fpubh-14-1734790-HTML-r1/image_m/fpubh-14-1734790-g002.jpg Figure 2. A schematic representation of the defining factors that shape women’s fertility decision. https://www.frontiersin.org/files/Articles/1734790/fpubh-14-1734790-HTML-r1/image_m/fpubh-14-1734790-g003.jpg Figure 3. The interplay between women’s abilities (i.e., financial capabilities and physical readine https://www.frontiersin.org/files/Articles/1734790/fpubh-14-1734790-HTML-r1/image_m/fpubh-14-1734790-g004.jpg Figure 4. A framework for understanding and developing women-centered policies for quality birth. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1658362/full https://www.frontiersin.org/files/Articles/1658362/fendo-16-1658362-HTML/image_m/fendo-16-1658362-g001.jpg Figure 1. Breast MRI demonstrating therapy-related changes in a patient with 17α-hydroxylase/17,20-l https://www.frontiersin.org/files/Articles/1658362/fendo-16-1658362-HTML/image_m/fendo-16-1658362-t001.jpg Table 1. Laboratory data before and six months after therapy for the patient with 17-OHD. https://www.frontiersin.org/files/Articles/1658362/fendo-16-1658362-HTML/image_m/fendo-16-1658362-g002.jpg Figure 2. Non-contrast CT scan showing therapy-related adrenal changes in the patient with 17-OHD. ( https://www.frontiersin.org/files/Articles/1658362/fendo-16-1658362-HTML/image_m/fendo-16-1658362-t002.jpg Table 2. Clinical and demographic characteristics of patient with 17-OHD advanced breast development https://www.frontiersin.org/files/Articles/1658362/fendo-16-1658362-HTML/image_m/fendo-16-1658362-t003.jpg Table 3. Hormonal profiles of patients with 17-OHD with advanced breast development. https://www.frontiersin.org/files/Articles/1658362/fendo-16-1658362-HTML/image_m/fendo-16-1658362-g003.jpg Figure 3. Distribution of CYP17A1 (NM_000102) variants in patients with 17-OHD with advanced breast https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1663092/full https://www.frontiersin.org/files/Articles/1663092/fbioe-13-1663092-HTML-r1/image_m/fbioe-13-1663092-g001.jpg Figure 1. Overview of Red Blood Cells as Drug Delivery Vehicles (A) The drug loading method of RBCs https://www.frontiersin.org/files/Articles/1663092/fbioe-13-1663092-HTML-r1/image_m/fbioe-13-1663092-t001.jpg Table 1. Overview of current RBCs-based drug delivery systems. https://www.frontiersin.org/files/Articles/1663092/fbioe-13-1663092-HTML-r1/image_m/fbioe-13-1663092-t002.jpg Table 2. The main differences between RBC-EVs and RBCM. https://www.frontiersin.org/files/Articles/1663092/fbioe-13-1663092-HTML-r1/image_m/fbioe-13-1663092-g002.jpg Figure 2. Applications of RBCs as carriers for pulmonary drug delivery. (A) Schematic illustration o https://www.frontiersin.org/files/Articles/1663092/fbioe-13-1663092-HTML-r1/image_m/fbioe-13-1663092-g003.jpg Figure 3. IA injection of RH-NCs enables enhanced drug delivery to downstream brain tissue. (A) Sche https://www.frontiersin.org/files/Articles/1663092/fbioe-13-1663092-HTML-r1/image_m/fbioe-13-1663092-g004.jpg Figure 4. Application of RBCs-based systems for liver-targeted drug delivery. (A) RBC-EVs exhibit pr https://www.frontiersin.org/files/Articles/1663092/fbioe-13-1663092-HTML-r1/image_m/fbioe-13-1663092-g005.jpg Figure 5. Application of RBCs-based systemsfor spleen targeted drug delivery. (A,B) Schematic illust https://www.frontiersin.org/files/Articles/1663092/fbioe-13-1663092-HTML-r1/image_m/fbioe-13-1663092-g006.jpg Figure 6. Application of RBCs-based systems for systemic drug delivery. Schematic illustration of co https://www.frontiersin.org/files/Articles/1663092/fbioe-13-1663092-HTML-r1/image_m/fbioe-13-1663092-t003.jpg Table 3. RBC-mediated organ-selective drug delivery: Target tissues, strategies, and therapeutic app https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1789515/full https://www.frontiersin.org/files/Articles/1789515/fpsyg-17-1789515-HTML/image_m/fpsyg-17-1789515-g001.jpg Figure 1. The overall research model with summarized empirical results. *p < 0.05; **p < 0.01. https://www.frontiersin.org/files/Articles/1789515/fpsyg-17-1789515-HTML/image_m/fpsyg-17-1789515-t001.jpg Table 1. Confirmatory factor analysis results of construct discriminant validity. https://www.frontiersin.org/files/Articles/1789515/fpsyg-17-1789515-HTML/image_m/fpsyg-17-1789515-t002.jpg Table 2. Means, standard deviations, and correlations among the study variables. https://www.frontiersin.org/files/Articles/1789515/fpsyg-17-1789515-HTML/image_m/fpsyg-17-1789515-t003.jpg Table 3. Estimated effects and bias-corrected confidence intervals. https://www.frontiersin.org/files/Articles/1789515/fpsyg-17-1789515-HTML/image_m/fpsyg-17-1789515-t004.jpg Table 4. Moderated mediation effect of mindfulness on creativity at various values of performance fe https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2026.1752849/full https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g001.jpg Figure 1. Location of study area. https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g002.jpg Figure 2. Workflow of data processing. https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g003.jpg Figure 3. Temporal and perpendicular baseline of Sentinel-1 data used. Red dots indicate the SAR acq https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g004.jpg Figure 4. Map of deformation rate in Nansha (Negative values represent subsidence): (A) Subsidence r https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g005.jpg Figure 5. Distribution of major construction sites in Nansha: (A,B) Spatial distribution of construc https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g006.jpg Figure 6. Distribution of aquaculture areas in Nansha (2019–2023): (A) Spatial distribution of aquac https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g007.jpg Figure 7. Relationship between soft soil and subsidence: (A) Soft soil thickness; (B) Distribution o https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g008.jpg Figure 8. Time series of subsidence in construction areas: (A) Subsidence rate at point P1; (B) Subs https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g009.jpg Figure 9. Subsidence rates and field survey results of field survey points: (A–C) located in Zhujian https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g010.jpg Figure 10. Time series of subsidence in construction areas: (A) Subsidence rate at point P3; (B) Tim https://www.frontiersin.org/files/Articles/1752849/feart-14-1752849-HTML/image_m/feart-14-1752849-g011.jpg Figure 11. Machine learning modeling results: (A) Variable importance ranking; (B) InSAR-derived sub https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1782867/full https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-g001.jpg Figure 1. Schematic diagram of the three-dimensional geometric model reconstruction of the portal ve https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-g002.jpg Figure 2. Three-dimensional models of the portal vein branch of the patient before and after balloon https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-g003.jpg Figure 3. Mesh generation of the portal vein branch model before and after balloon angioplasty. https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-t001.jpg Table 1. The number of mesh generated with different mesh sizes and the corresponding hemodynamic pa https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-g004.jpg Figure 4. An illustration of the boundary conditions of the portal vein system in the computational https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-t002.jpg Table 2. Parameters of resistance and capacitance in the Three-Element Windkessel coupled model of t https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-g005.jpg Figure 5. The portal vein blood flow velocity distribution of the patient before and after balloon a https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-g006.jpg Figure 6. The portal vein flow velocity and blood flow in each branch before and after balloon angio https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-g007.jpg Figure 7. Changes in WSS within the portal vein branch before and after balloon angioplasty: (a) Fol https://www.frontiersin.org/files/Articles/1782867/fbioe-14-1782867-HTML/image_m/fbioe-14-1782867-g008.jpg Figure 8. The portal vein pressure distribution of the patient before and after balloon angioplasty. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1774142/full https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-g001.jpg Figure 1. Schematic diagrams of the in vitro evaluation system using particle emission monitoring sy https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-t001.jpg Table 1. Inhalation flow parameters and particle emission signal indices under four different inhala https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-t002.jpg Table 2. Inhalation performance for all inhalation patterns (n = 3, mean ± S.D.). https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-g002.jpg Figure 2. Drug deposition at each stage of ACI in four inhalation patterns (n = 3, mean ± S.D.) The https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-g003.jpg Figure 3. Influence of inhalation pattern on inhalation performance (n = 3, mean ± S.D.). Each panel https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-g004.jpg Figure 4. Scanning electron micrographs of particles collected from Symbicort® Turbuhaler® device an https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-g005.jpg Figure 5. Geometric particle size distribution of particles deposited on ACI Stage 3 after inhalatio https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-g006.jpg Figure 6. Regression models describing the relationship between inhalation performance and inhalatio https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-t003.jpg Table 3. Optimal univariate regression models for predicting inhalation performance parameters. https://www.frontiersin.org/files/Articles/1774142/fphar-17-1774142-HTML/image_m/fphar-17-1774142-t004.jpg Table 4. Model fitting statistics, including R2, AIC, RMSE, and MAE, for each explanatory and outcom https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1656037/full https://www.frontiersin.org/files/Articles/1656037/fmicb-16-1656037-HTML/image_m/fmicb-16-1656037-g001.jpg Figure 1. Phylogenetic tree based on 16S rRNA gene sequence showing the position of strain G02 and c https://www.frontiersin.org/files/Articles/1656037/fmicb-16-1656037-HTML/image_m/fmicb-16-1656037-t001.jpg Table 1. Growth-promoting characteristic index. https://www.frontiersin.org/files/Articles/1656037/fmicb-16-1656037-HTML/image_m/fmicb-16-1656037-g002.jpg Figure 2. The dissolution of selenium by strain G02 and the pH changes. (a) Solubilization of Se-ric https://www.frontiersin.org/files/Articles/1656037/fmicb-16-1656037-HTML/image_m/fmicb-16-1656037-g003.jpg Figure 3. Metabolomic changes associated with Se(0) solubilization by strain G02. (a) The Venn diagr https://www.frontiersin.org/files/Articles/1656037/fmicb-16-1656037-HTML/image_m/fmicb-16-1656037-g004.jpg Figure 4. Biomass and Se concentration of lettuce. (a) Dry weight of shoot; (b) Dry weight of root; https://www.frontiersin.org/files/Articles/1656037/fmicb-16-1656037-HTML/image_m/fmicb-16-1656037-t002.jpg Table 2. Effects of inoculation with strain G02 on Se translocation and bioconcentration factor of l https://www.frontiersin.org/files/Articles/1656037/fmicb-16-1656037-HTML/image_m/fmicb-16-1656037-t003.jpg Table 3. Changes of enzyme activity in rhizosphere soil of lettuce under different treatments. https://www.frontiersin.org/files/Articles/1656037/fmicb-16-1656037-HTML/image_m/fmicb-16-1656037-g005.jpg Figure 5. Analysis of microbial diversity in lettuce rhizosphere soil. (a) Venn diagram of species a https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1817560/full https://www.frontiersin.org/files/Articles/1817560/fcell-14-1817560-HTML/image_m/fcell-14-1817560-g001.jpg Figure 1. Tumor heterogeneity organized along intratumoral, intertumoral, and temporal axes and oper https://www.frontiersin.org/files/Articles/1817560/fcell-14-1817560-HTML/image_m/fcell-14-1817560-g002.jpg Figure 2. Patient derived organoid biobanks translate interpatient molecular diversity into function https://www.frontiersin.org/files/Articles/1817560/fcell-14-1817560-HTML/image_m/fcell-14-1817560-g003.jpg Figure 3. Organoid based framework for interrogating temporal heterogeneity. (A) Simplified patient https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1741833/full https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t001.jpg Table 1. Narrative synthesis of environmental dimensions linking ESG and mental health. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t002.jpg Table 2. Narrative synthesis of social dimensions linking ESG and mental health. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t003.jpg Table 3. Narrative synthesis of governance dimensions linking ESG and mental health. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-g001.jpg Figure 1. Descriptive overview of ESG pillars and their contextual associations with mental well-bei https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t004.jpg Table 4. Descriptive statistics of mental health prevalence across 31 countries (2010–2022). https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-g002.jpg Figure 2. Cross-national trends in mental health disorder prevalence (2010–2022). The dual-panel gra https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t005.jpg Table 5. Definition of environmental (E) and mental health variables used in the analysis. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t006.jpg Table 6. Panel regression results: environmental (E) ESG indicators and mental health prevalence. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t007.jpg Table 7. Comparative performance of clustering algorithms based on normalized validity indices. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t008.jpg Table 8. Hierarchical clustering results and standardized cluster centers for environmental–mental h https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t009.jpg Table 9. Cluster centers for standardized ESG and mental health indicators. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-g003.jpg Figure 3. Model selection and cluster validation using elbow method and t-SNE visualization. The fig https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t010.jpg Table 10. Key socioeconomic and demographic indicators relevant to mental health and social sustaina https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t011.jpg Table 11. Fixed and random effects panel regression: social determinants and mental health prevalenc https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t012.jpg Table 12. Cluster validity indices across algorithms. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t013.jpg Table 13. Hierarchical clustering results: cluster size, heterogeneity, and cohesion metrics. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t014.jpg Table 14. Hierarchical clustering results: cluster size, heterogeneity, and cohesion metrics. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-g004.jpg Figure 4. Hierarchical clustering selection and validation using elbow criteria and t-SNE visualizat https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t015.jpg Table 15. Summary of key governance, economic, and innovation indicators. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t016.jpg Table 16. Panel regression results: effects of GDP growth, R&D expenditure, and rule of law on menta https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t017.jpg Table 17. Comparative performance of clustering algorithms based on internal validity metrics. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t018.jpg Table 18. Hierarchical clustering structure: cluster size, heterogeneity, and cohesion. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t019.jpg Table 19. Cluster characteristics by mental health and governance indicators. https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-g005.jpg Figure 5. Hierarchical clustering solution: structure, selection, and validation. This figure integr https://www.frontiersin.org/files/Articles/1741833/fpubh-14-1741833-HTML-r1/image_m/fpubh-14-1741833-t020.jpg Table 20. Integrated summary of ESG dimensions and mental health evidence. https://www.frontiersin.org/journals/urology/articles/10.3389/fruro.2025.1662692/full https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g001.jpg Figure 1. Schematic workflow of the integrated multi-omics analysis in this study. https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g002.jpg Figure 2. Volcano plot displaying differentially expressed genes (DEGs) between prostate cancer samp https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g003.jpg Figure 3. Comparative analysis of tumor immune infiltration landscapes between DOCK3-high and DOCK3- https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g004.jpg Figure 4. Single-sample Gene Set Enrichment Analysis (ssGSEA) showcasing the enrichment of immune-re https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g005.jpg Figure 5. Evaluation of immune and stromal cell enrichment scores in DOCK3-high and DOCK3-low groups https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g006.jpg Figure 6. Tumor Mutational Burden (TMB) landscape. https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g007.jpg Figure 7. Weighted Gene Co-expression Network Analysis (WGCNA). https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g008.jpg Figure 8. Differential expression of DOCK3 between prostate tumor tissues and normal adjacent tissue https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g009.jpg Figure 9. Association between CDKN3 expression levels and key clinical pathological features: (A) T https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g010.jpg Figure 10. Heatmap of unsupervised clustering of tumor samples based on DOCK3 and CDKN3 expression p https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g011.jpg Figure 11. Immune cell infiltration profiles (via CIBERSORT) across distinct clusters defined by DOC https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g012.jpg Figure 12. Validation of the association between gene expression (DOCK3) and clinical features in th https://www.frontiersin.org/files/Articles/1662692/fruro-05-1662692-HTML/image_m/fruro-05-1662692-g013.jpg Figure 13. Dimensionality reduction plots (t-SNE and UMAP) showing single-cell clustering by sample https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1752827/full https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-g000.jpg Graphical Abstract. In the physiological condition (left panel), the lncRNA ADAMTS9-AS2 sequesters D https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-t001.jpg Table 1. The primer sequences for siRNA. https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-g001.jpg Figure 1. Functional characterization of ADAMTS9-AS2 in ESCC cells. (A) RT-qPCR analysis of ADAMTS9- https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-g002.jpg Figure 2. CADM2 overexpression rescues ADAMTS9-AS2 knockdown-induced oncogenic phenotypes in TE-1 ce https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-t002.jpg Table 2. Site-specific methylation levels of 17 CpG sites within the cg03455765 region in ESCC tumor https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-g003.jpg Figure 3. Identification of the CADM2 methylation and its regulation by ADAMTS9-AS2. (A) Genome-wide https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-g004.jpg Figure 4. ADAMTS9-AS2 constrains DNMT3B to prevent CADM2 epigenetic silencing. (A) mRNA microarray h https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-t003.jpg Table 3. Differentially expressed genes related to the ADAMTS9-AS2/DNMT3B/CADM2 axis in ESCC tumors https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-g005.jpg Figure 5. DNMT3B protein expression in ESCC tissues by IHC. (A) Representative IHC images of DNMT3B https://www.frontiersin.org/files/Articles/1752827/fimmu-17-1752827-HTML-r1/image_m/fimmu-17-1752827-g006.jpg Figure 6. Correlation of the ADAMTS9-AS2/CADM2 axis with immune infiltration in ESCC. (A) Scatter pl https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1676761/full https://www.frontiersin.org/files/Articles/1676761/fsurg-12-1676761-HTML/image_m/fsurg-12-1676761-t001.jpg Table 1. Comparison of general clinical data between two groups of patients. https://www.frontiersin.org/files/Articles/1676761/fsurg-12-1676761-HTML/image_m/fsurg-12-1676761-t002.jpg Table 2. Comparison of the perioperative general condition and incisional complications between the https://www.frontiersin.org/files/Articles/1676761/fsurg-12-1676761-HTML/image_m/fsurg-12-1676761-g001.jpg Figure 1. (A) Lateral and harris view radiographs of the right displaced intra-articular calcaneus f https://www.frontiersin.org/files/Articles/1676761/fsurg-12-1676761-HTML/image_m/fsurg-12-1676761-g002.jpg Figure 2. Change trends of postoperative VAS scores in two groups. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1784858/full https://www.frontiersin.org/files/Articles/1784858/fmed-13-1784858-HTML-r1/image_m/fmed-13-1784858-g001.jpg Figure 1. Digital ulcers involving the index and middle fingers of the right hand (arrows). The ulce https://www.frontiersin.org/files/Articles/1784858/fmed-13-1784858-HTML-r1/image_m/fmed-13-1784858-t001.jpg Table 1. Timeline table. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1641392/full https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-g001.jpg Figure 1. The development procedure of the cardiac rehabilitation scale. https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-t001.jpg Table 1. The search strategy of pubMed. https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-t002.jpg Table 2. Sociodemographic characteristics of participants (N = 509). https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-g002.jpg Figure 2. Flow diagram of literature review. https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-t003.jpg Table 3. Item analysis of the scale. https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-g003.jpg Figure 3. The scree plot of exploratory factor analysis. https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-t004.jpg Table 4. Pattern matrix of the scale after the factor analysis. https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-g004.jpg Figure 4. The five-factor model of cardiac rehabilitation scale (A1-E5 represent each scale item, wh https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-t005.jpg Table 5. Goodness-of-fit statistics of the scale. https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-t006.jpg Table 6. The AVE values and CR values of the scale. https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-t007.jpg Table 7. The correlation coefficient and sqrt (AVE) of the scale. https://www.frontiersin.org/files/Articles/1641392/fcvm-12-1641392-HTML/image_m/fcvm-12-1641392-t008.jpg Table 8. Reliability tests: total scale and dimensions. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1645778/full https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-t001.jpg Table 1. F. multiflora ARF gene primer sequence. https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g001.jpg Figure 1. Growth and development changes and osmotic regulator contents in different tissues of F. m https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g002.jpg Figure 2. (A) Determination of chlorophyll content in F. multiflora under the white light (W), red l https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-t002.jpg Table 2. Chromaticity values of the upper surface of F. multiflora leaves. https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-t003.jpg Table 3. Secondary metabolites of F. multiflora. https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-t004.jpg Table 4. Physicochemical properties of F. multiflora ARF protein family. https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g003.jpg Figure 3. The phylogenetic tree of the ARF (Auxin Response Factor) gene family in F. multiflora. Ora https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g004.jpg Figure 4. (A) Conserved motifs in FmARF proteins; (B) Exon-intron structure; (C) Amino acid composit https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g005.jpg Figure 5. Analysis of cis-acting element of promoter (A) and gene structure (B) of F. muliflora ARF. https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g006.jpg Figure 6. Distribution of F. multiflora ARF gene family on chromosomes. https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-t005.jpg Table 5. Collinear relationship among Fm ARF genes within species. https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g007.jpg Figure 7. Collinearity analysis. (A) Collinearity of the F. multiflora ARF gene family in chromosome https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g008.jpg Figure 8. Heatmap of ARF gene family expression levels in F. multiflora under different light qualit https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g009.jpg Figure 9. Correlation prediction analysis network heatmap of leaf growth, physiological and biochemi https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g010.jpg Figure 10. Correlation prediction analysis network heatmap of stem growth, secondary metabolites, an https://www.frontiersin.org/files/Articles/1645778/fpls-16-1645778-HTML/image_m/fpls-16-1645778-g011.jpg Figure 11. Correlation prediction analysis network heatmap of root growth, secondary metabolites, an https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1677560/full https://www.frontiersin.org/files/Articles/1677560/fnut-12-1677560-HTML/image_m/fnut-12-1677560-g001.jpg Figure 1. Influence of AOE on serum uric acid level, body weight, and organ coefficients in HUA mice https://www.frontiersin.org/files/Articles/1677560/fnut-12-1677560-HTML/image_m/fnut-12-1677560-g002.jpg Figure 2. Improvement of purine metabolism in HUA mice by AOE treatment. (A) Liver XOD activity; (B) https://www.frontiersin.org/files/Articles/1677560/fnut-12-1677560-HTML/image_m/fnut-12-1677560-g003.jpg Figure 3. Improvement of kidney injury in HUA mice by AOE treatment. (A) Serum creatinine level; (B) https://www.frontiersin.org/files/Articles/1677560/fnut-12-1677560-HTML/image_m/fnut-12-1677560-g004.jpg Figure 4. AOE ameliorated the inflammatory response in HUA mice. Serum (A) IL-6, (B) IL-1β, and (C) https://www.frontiersin.org/files/Articles/1677560/fnut-12-1677560-HTML/image_m/fnut-12-1677560-g005.jpg Figure 5. AOE ameliorated oxidative stress in HUA mice. Serum (A) SOD, (B) CAT, and (C) GSH-Px activ https://www.frontiersin.org/files/Articles/1677560/fnut-12-1677560-HTML/image_m/fnut-12-1677560-g006.jpg Figure 6. AOE treatment restored the transcript profiling in HUA mice. (A) Volcano plot of the DEGs https://www.frontiersin.org/files/Articles/1677560/fnut-12-1677560-HTML/image_m/fnut-12-1677560-g007.jpg Figure 7. GO (A) and KEGG (B) pathway enrichment of DEGs; (C) Gene set enrichment analysis (GSEA) of https://www.frontiersin.org/files/Articles/1677560/fnut-12-1677560-HTML/image_m/fnut-12-1677560-g008.jpg Figure 8. The transcriptome-revealed FPKM values of the DEGs. (A) p65/RelA; (B) NF-κB1/p50; (C) PPAR https://www.frontiersin.org/files/Articles/1677560/fnut-12-1677560-HTML/image_m/fnut-12-1677560-g009.jpg Figure 9. The potential mechanism underlying the protective effects of AOE against hyperuricemia and https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1726695/full https://www.frontiersin.org/files/Articles/1726695/fonc-15-1726695-HTML/image_m/fonc-15-1726695-g001.jpg Figure 1. Chest CT (lung window) performed at initial diagnosis. (A) A spiculated nodule (arrow) is https://www.frontiersin.org/files/Articles/1726695/fonc-15-1726695-HTML/image_m/fonc-15-1726695-g002.jpg Figure 2. Core biopsies showing the tumor with glandular architecture (H&E stain, magnification (A) https://www.frontiersin.org/files/Articles/1726695/fonc-15-1726695-HTML/image_m/fonc-15-1726695-g003.jpg Figure 3. Therapeutic timeline of the patient. https://www.frontiersin.org/files/Articles/1726695/fonc-15-1726695-HTML/image_m/fonc-15-1726695-t001.jpg Table 1. Immunohistochemical marker expression in PEAC, MCRC, and LUAD. https://www.frontiersin.org/files/Articles/1726695/fonc-15-1726695-HTML/image_m/fonc-15-1726695-g004.jpg Figure 4. Diagnostic workflow for suspected PEAC. https://www.frontiersin.org/files/Articles/1726695/fonc-15-1726695-HTML/image_m/fonc-15-1726695-t002.jpg Table 2. Summary of published cases of pulmonary enteric. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1787798/full https://www.frontiersin.org/files/Articles/1787798/fphar-17-1787798-HTML-r1/image_m/fphar-17-1787798-g008.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1787798/fphar-17-1787798-HTML-r1/image_m/fphar-17-1787798-g001.jpg Figure 1. Network pharmacology analysis predicts SBG targets and pathways affecting UM. (A) Venn dia https://www.frontiersin.org/files/Articles/1787798/fphar-17-1787798-HTML-r1/image_m/fphar-17-1787798-g002.jpg Figure 2. BAI inhibited the proliferation of CM cell lines and induced cell cycle S arrest. (A) Micr https://www.frontiersin.org/files/Articles/1787798/fphar-17-1787798-HTML-r1/image_m/fphar-17-1787798-g003.jpg Figure 3. BAI triggers mitochondria-mediated apoptosis in CM cells. (A) Pictures of Hoechst33342 sta https://www.frontiersin.org/files/Articles/1787798/fphar-17-1787798-HTML-r1/image_m/fphar-17-1787798-g004.jpg Figure 4. BAI suppressed the migration and EMT of CM cells. (A–D) Scratch assay to detect the migrat https://www.frontiersin.org/files/Articles/1787798/fphar-17-1787798-HTML-r1/image_m/fphar-17-1787798-g005.jpg Figure 5. Transcriptomic sequencing and Western blotting analysis of BAI Treatment on CM cells. (A) https://www.frontiersin.org/files/Articles/1787798/fphar-17-1787798-HTML-r1/image_m/fphar-17-1787798-g006.jpg Figure 6. BAI suppressed oncogenic properties of CM cells by blocking the PI3K/AKT/mTOR axis. (A) Cl https://www.frontiersin.org/files/Articles/1787798/fphar-17-1787798-HTML-r1/image_m/fphar-17-1787798-g007.jpg Figure 7. BAI inhibits choroidal melanoma growth in vivo. (A–C) Nude mice were executed 17 days afte https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1658494/full https://www.frontiersin.org/files/Articles/1658494/fonc-16-1658494-HTML/image_m/fonc-16-1658494-g001.jpg Figure 1. (A, B) Pre-treatment imaging prior to glucocorticoid pulse therapy. (C, D) Post-treatment https://www.frontiersin.org/files/Articles/1658494/fonc-16-1658494-HTML/image_m/fonc-16-1658494-g002.jpg Figure 2. Post-glucocorticoid pulse therapy imaging at our hospital. On sagittal T2 (A, B) and T1 en https://www.frontiersin.org/files/Articles/1658494/fonc-16-1658494-HTML/image_m/fonc-16-1658494-g003.jpg Figure 3. (A) Tumor cells show loss of H3K27me3 expression, indicating H3K27me3 mutation, with posit https://www.frontiersin.org/files/Articles/1658494/fonc-16-1658494-HTML/image_m/fonc-16-1658494-g004.jpg Figure 4. Timeline of clinical symptoms, imaging findings, diagnostic workup, treatment intervention https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1678663/full https://www.frontiersin.org/files/Articles/1678663/fphar-16-1678663-HTML-r1/image_m/fphar-16-1678663-t001.jpg Table 1. Case 1 eye signs and grading. https://www.frontiersin.org/files/Articles/1678663/fphar-16-1678663-HTML-r1/image_m/fphar-16-1678663-g001.jpg Figure 1. (Case1) (A,B) Fundus photographs of OD and OS: showing bilateral optic disc edema with ind https://www.frontiersin.org/files/Articles/1678663/fphar-16-1678663-HTML-r1/image_m/fphar-16-1678663-t002.jpg Table 2. Case 2 eye signs and grading. https://www.frontiersin.org/files/Articles/1678663/fphar-16-1678663-HTML-r1/image_m/fphar-16-1678663-g002.jpg Figure 2. (Case2) (A,B) Fundus photographs of OD and OS: showing bilateral optic discs with a reddis https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1633209/full https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-g001.jpg Figure 1. The proposed DMFF-Net architecture primarily consists of two main parts: the encoder and t https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-g002.jpg Figure 2. The structure diagram of the MDSDC module features diamonds with “C” to represent concaten https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-g003.jpg Figure 3. The structure diagram of the GGCAM shows circles with “+” to represent addition operations https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-g004.jpg Figure 4. The structure diagram of the MHLFF illustrates the integration of features from different https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-g005.jpg Figure 5. Example images of skin lesions and their corresponding label images from the ISIC dataset. https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-t001.jpg Table 1. Skin lesion segmentation performances of different networks on ISIC 2016. https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-g006.jpg Figure 6. Visual comparison with different methods on ISIC 2016 (a) and 2017 (b) datasets. https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-t002.jpg Table 2. Skin lesion segmentation performances of different networks on ISIC 2017. https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-t003.jpg Table 3. Skin lesion segmentation performances of different networks on ISIC 2018. https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-g007.jpg Figure 7. (a) Visual comparison with different methods on the ISIC 2018 dataset. (b) Cross-validatio https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-g008.jpg Figure 8. Comparison of different attention maps extracted at various stages: (a) input image, (b) G https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-t004.jpg Table 4. Comparison of ablation experiments for the main module in the DMFF-Net, where Params is the https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-g009.jpg Figure 9. (a) Visualization comparison of ablation experiments on the ISIC 2017 dataset. (b) Qualita https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-t005.jpg Table 5. Single-module ablation study on ISIC dataset. https://www.frontiersin.org/files/Articles/1633209/fmed-12-1633209-HTML/image_m/fmed-12-1633209-t006.jpg Table 6. Performance comparison between DMFF-Net and TransUNet on the ISIC 2017 dataset. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1625748/full https://www.frontiersin.org/files/Articles/1625748/fimmu-16-1625748-HTML/image_m/fimmu-16-1625748-g001.jpg Figure 1. Transmission of Mycobacterium tuberculosis (M. tb) infection (Created with BioRender.com). https://www.frontiersin.org/files/Articles/1625748/fimmu-16-1625748-HTML/image_m/fimmu-16-1625748-g002.jpg Figure 2. Summary of the cell-mediated immune response of M. tb infection (Created with BioRender.co https://www.frontiersin.org/files/Articles/1625748/fimmu-16-1625748-HTML/image_m/fimmu-16-1625748-g003.jpg Figure 3. Summary of the factors influencing the development of autoimmune disease (Created with Bio https://www.frontiersin.org/files/Articles/1625748/fimmu-16-1625748-HTML/image_m/fimmu-16-1625748-g004.jpg Figure 4. Summary of the immune responses involved in SLE (Created with BioRender.com). https://www.frontiersin.org/files/Articles/1625748/fimmu-16-1625748-HTML/image_m/fimmu-16-1625748-t001.jpg Table 1. Overview of the cytokine profile in patients with SLE and TB. https://www.frontiersin.org/files/Articles/1625748/fimmu-16-1625748-HTML/image_m/fimmu-16-1625748-t002.jpg Table 2. IGRA and TST performance in immunocompetent and immunocompromised SLE patients. https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1620818/full https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g001.jpg Figure 1. Tectonic maps with major igneous rocks in the study area (modified after Zhou et al., 2017 https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g002.jpg Figure 2. 1:2500000 geological map in the study area. https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g003.jpg Figure 3. Topographic map in the study area [The terrain data was downloaded from (https://www.gsclo https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g004.jpg Figure 4. Map of the Bouguer Gravity (a) and Aeromagnetic (b) anomalies in the study area. https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g005.jpg Figure 5. Map of Aeromagnetic anomalies with Upward Continuation to 5 km (a), 10 km (b) and 20 km (c https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g006.jpg Figure 6. Map of the Bouguer Gravity (a) and Aeromagnetic (b) anomalies with Upward Continuation to https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g007.jpg Figure 7. Map showing the magnetic data of Ningwu area Deposits: ①Meishan, ②Washan, ③Gaocun, ④Heshan https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g008.jpg Figure 8. Map showing the magnetic data of Luzong area Deposits: ①Longqiao, ②Luohe, ③Nihe. https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g009.jpg Figure 9. Map showing the magnetic data of Edongnan area Deposits: ①Chengchao, ②Tieshan, ③Jinshandia https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g010.jpg Figure 10. Map showing the background (a) and separated local (b) magnetic data in Ningwu area. https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g011.jpg Figure 11. Map showing the background (a) and local (b) magnetic data separated in Luzong area. https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-t001.jpg Table 1. Density and magnetic properties of rocks in the study area. https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g012.jpg Figure 12. Map showing the background (a) and local (b) magnetic data separated in Edongnan area. https://www.frontiersin.org/files/Articles/1620818/feart-13-1620818-HTML/image_m/feart-13-1620818-g013.jpg Figure 13. Map showing the Curie depths (a) and geological interpretation section seismic profile (b https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1828329/full https://www.frontiersin.org/files/Articles/1828329/fcvm-13-1828329-HTML/image_m/fcvm-13-1828329-t001.jpg Table 1. Baseline characteristics for differentiation between T2MI and T1MI. https://www.frontiersin.org/files/Articles/1828329/fcvm-13-1828329-HTML/image_m/fcvm-13-1828329-t002.jpg Table 2. Comparison of medication at presentation. https://www.frontiersin.org/files/Articles/1828329/fcvm-13-1828329-HTML/image_m/fcvm-13-1828329-g001.jpg Figure 1. Kaplan–meier curve. (A Cumulative Incidence of 5-year mortality; B Cumulative Incidence of https://www.frontiersin.org/files/Articles/1828329/fcvm-13-1828329-HTML/image_m/fcvm-13-1828329-g002.jpg Figure 2. Comparison of future readmission and ICU readmission. https://www.frontiersin.org/files/Articles/1828329/fcvm-13-1828329-HTML/image_m/fcvm-13-1828329-g003.jpg Figure 3. Comparison of future recurrent myocardial infarction. https://www.frontiersin.org/files/Articles/1828329/fcvm-13-1828329-HTML/image_m/fcvm-13-1828329-g004.jpg Figure 4. All-cause mortality stratified by T2MI phenotype. (A) Subgroup analyses of 5-year mortalit https://www.frontiersin.org/files/Articles/1828329/fcvm-13-1828329-HTML/image_m/fcvm-13-1828329-g006.jpg . https://www.frontiersin.org/files/Articles/1828329/fcvm-13-1828329-HTML/image_m/fcvm-13-1828329-g005.jpg Figure 5. (A) Subgroup analyses of 5-year mortality. (B) Subgroup analyses of 6-month mortality. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1811341/full https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-g001.jpg Figure 1. The detailed participant selection flowchart of this study. https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-g002.jpg Figure 2. The distribution of ZJU index. https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-t001.jpg Table 1. Baseline demographic and clinical characteristics of the study cohort, stratified by ZJU in https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-t002.jpg Table 2. Baseline demographic and clinical characteristics of the study cohort, stratified by glycem https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-g003.jpg Figure 3. The incidence rate for reversion to normoglycemia/progression to DM stratified by ZJU inde https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-t003.jpg Table 3. ​Relationship of ZJU index with glucose status transition in an IFG population assessed by https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-t004.jpg Table 4. ​Relationship of ZJU index with glycemic status transition in an IFG population assessed by https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-g004.jpg Figure 4. Kaplan-Meier curves for glycemic status transition stratified by the ZJU index quartiles. https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-g005.jpg Figure 5. The relationship between ZJU index and reversion to normoglycemia/progression to DM in an https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-t005.jpg Table 5. Threshold effect analysis of ZJU index on progression to DM in an IFG population. https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-g006.jpg Figure 6. Forest plot of subgroup analysis for the association of the ZJU index with reversion to no https://www.frontiersin.org/files/Articles/1811341/fendo-17-1811341-HTML-r1/image_m/fendo-17-1811341-g007.jpg Figure 7. Forest plot of subgroup analysis for the association of the ZJU index with progression to https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1693346/full https://www.frontiersin.org/files/Articles/1693346/fcell-13-1693346-HTML/image_m/fcell-13-1693346-t001.jpg Table 1. Primers used for RT-PCR in this study. https://www.frontiersin.org/files/Articles/1693346/fcell-13-1693346-HTML/image_m/fcell-13-1693346-g001.jpg Figure 1. Maternal Embryonic Leucine Zipper Kinase (MELK) is significantly upregulated in pulmonary https://www.frontiersin.org/files/Articles/1693346/fcell-13-1693346-HTML/image_m/fcell-13-1693346-g002.jpg Figure 2. OTS167 inhibits MELK expression, suppressing proliferation and migration of HPASMCs. HPASM https://www.frontiersin.org/files/Articles/1693346/fcell-13-1693346-HTML/image_m/fcell-13-1693346-g003.jpg Figure 3. MELK overexpression promotes proliferation and migration of HPASMCs. HPASMCs were infected https://www.frontiersin.org/files/Articles/1693346/fcell-13-1693346-HTML/image_m/fcell-13-1693346-g004.jpg Figure 4. Inhibition of MELK delays the phenotypic switch of HPASMCs from a contractile to a synthet https://www.frontiersin.org/files/Articles/1693346/fcell-13-1693346-HTML/image_m/fcell-13-1693346-g005.jpg Figure 5. MELK promotes activation of the Hippo–YAP/TAZ signaling pathway in HPASMCs. (A) KEGG enric https://www.frontiersin.org/files/Articles/1693346/fcell-13-1693346-HTML/image_m/fcell-13-1693346-g006.jpg Figure 6. Hippo–YAP/TAZ signaling mediates MELK-induced proliferation and migration of PASMCs. (A,B) https://www.frontiersin.org/files/Articles/1693346/fcell-13-1693346-HTML/image_m/fcell-13-1693346-g007.jpg Figure 7. Effects of MELK inhibition by OTS-167 on PAH and vascular remodeling in Sugen/hypoxia (Su/ https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1687242/full https://www.frontiersin.org/files/Articles/1687242/fmed-12-1687242-HTML/image_m/fmed-12-1687242-t001.jpg Table 1. Baseline patient characteristics. https://www.frontiersin.org/files/Articles/1687242/fmed-12-1687242-HTML/image_m/fmed-12-1687242-t002.jpg Table 2. Evaluation of efficacy. https://www.frontiersin.org/files/Articles/1687242/fmed-12-1687242-HTML/image_m/fmed-12-1687242-g001.jpg Figure 1. Disease-free survival (DFS) in the blinatumomab group (n = 11) and the conventional chemot https://www.frontiersin.org/files/Articles/1687242/fmed-12-1687242-HTML/image_m/fmed-12-1687242-g002.jpg Figure 2. Cumulative incidence curves for relapse comparing the blinatumomab group (n = 11) and the https://www.frontiersin.org/files/Articles/1687242/fmed-12-1687242-HTML/image_m/fmed-12-1687242-g003.jpg Figure 3. OS in the blinatumomab group (n = 11) versus the conventional chemotherapy group (n = 13). https://www.frontiersin.org/files/Articles/1687242/fmed-12-1687242-HTML/image_m/fmed-12-1687242-g004.jpg Figure 4. Treatment response comparison between the blinatumomab-based regimen and the conventional https://www.frontiersin.org/files/Articles/1687242/fmed-12-1687242-HTML/image_m/fmed-12-1687242-t003.jpg Table 3. Comparison of safety profiles between the blinatumomab group and the conventional chemother https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1713768/full https://www.frontiersin.org/files/Articles/1713768/fpubh-13-1713768-HTML-r1/image_m/fpubh-13-1713768-t001.jpg Table 1. Sample characteristics (N = 11,576). https://www.frontiersin.org/files/Articles/1713768/fpubh-13-1713768-HTML-r1/image_m/fpubh-13-1713768-g001.jpg Figure 1. Prevalence rates of insomnia symptoms at the three assessment points. T1 = Time 1; T2 = Ti https://www.frontiersin.org/files/Articles/1713768/fpubh-13-1713768-HTML-r1/image_m/fpubh-13-1713768-g002.jpg Figure 2. Change patterns of insomnia symptoms. The diagram illustrates the longitudinal flow of ins https://www.frontiersin.org/files/Articles/1713768/fpubh-13-1713768-HTML-r1/image_m/fpubh-13-1713768-g003.jpg Figure 3. Trajectory of insomnia symptoms. T1 = Time 1; T2 = Time 2; T3 = Time 3. https://www.frontiersin.org/files/Articles/1713768/fpubh-13-1713768-HTML-r1/image_m/fpubh-13-1713768-g004.jpg Figure 4. Risk and protective factors associated with membership in the non-resistance group (vs. re https://www.frontiersin.org/files/Articles/1713768/fpubh-13-1713768-HTML-r1/image_m/fpubh-13-1713768-g005.jpg Figure 5. Risk and protective factors associated with membership in the recovery group (vs. chronic https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1752079/full https://www.frontiersin.org/files/Articles/1752079/fnut-13-1752079-HTML-r1/image_m/fnut-13-1752079-t001.jpg Table 1. Baseline characteristics of study participants. https://www.frontiersin.org/files/Articles/1752079/fnut-13-1752079-HTML-r1/image_m/fnut-13-1752079-g001.jpg Figure 1. Prevalence trend of carotid atherosclerosis by age and sex groups among study participants https://www.frontiersin.org/files/Articles/1752079/fnut-13-1752079-HTML-r1/image_m/fnut-13-1752079-t002.jpg Table 2. Logistic regression analyses of associations between serum uric acid and carotid atheroscle https://www.frontiersin.org/files/Articles/1752079/fnut-13-1752079-HTML-r1/image_m/fnut-13-1752079-g002.jpg Figure 2. Dose–response association between serum uric acid and carotid atherosclerosis. Adjusted fo https://www.frontiersin.org/files/Articles/1752079/fnut-13-1752079-HTML-r1/image_m/fnut-13-1752079-g003.jpg Figure 3. Subgroup analysis of the association between serum uric acid and carotid atherosclerosis b https://www.frontiersin.org/files/Articles/1752079/fnut-13-1752079-HTML-r1/image_m/fnut-13-1752079-g004.jpg Figure 4. Subgroup analysis of the dose–response association between serum uric acid and carotid ath https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1785260/full https://www.frontiersin.org/files/Articles/1785260/fendo-17-1785260-HTML/image_m/fendo-17-1785260-t001.jpg Table 1. Clinical evidence regarding DM and IDD. https://www.frontiersin.org/files/Articles/1785260/fendo-17-1785260-HTML/image_m/fendo-17-1785260-t002.jpg Table 2. Major studies on the induction of IDD by HG through inflammation. https://www.frontiersin.org/files/Articles/1785260/fendo-17-1785260-HTML/image_m/fendo-17-1785260-g001.jpg Figure 1. HG triggers pro-inflammatory cytokines like TNF-α, IL-1β, and IL-6 in DM, causing IVD degr https://www.frontiersin.org/files/Articles/1785260/fendo-17-1785260-HTML/image_m/fendo-17-1785260-g002.jpg Figure 2. HG triggers IDD through oxidative stress and complex signaling, promoting ECM degradation https://www.frontiersin.org/files/Articles/1785260/fendo-17-1785260-HTML/image_m/fendo-17-1785260-t003.jpg Table 3. Major studies on the induction of IDD by HG through oxidative stress. https://www.frontiersin.org/files/Articles/1785260/fendo-17-1785260-HTML/image_m/fendo-17-1785260-t004.jpg Table 4. Major studies on the induction of IDD by HG through ECM degradation. https://www.frontiersin.org/files/Articles/1785260/fendo-17-1785260-HTML/image_m/fendo-17-1785260-g003.jpg Figure 3. HG disrupts ECM balance, causing IDD. MMPs and ADAMTSs degrade ECM, regulated by factors l https://www.frontiersin.org/files/Articles/1785260/fendo-17-1785260-HTML/image_m/fendo-17-1785260-g004.jpg Figure 4. HG triggers IDD through multiple pathways, including JNK, p38 MAPK, Sirt1/P53, ChREBP/p300 https://www.frontiersin.org/files/Articles/1785260/fendo-17-1785260-HTML/image_m/fendo-17-1785260-t005.jpg Table 5. Major studies on the induction of IDD by HG through apoptosis. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1763973/full https://www.frontiersin.org/files/Articles/1763973/fendo-17-1763973-HTML-r1/image_m/fendo-17-1763973-g001.jpg Figure 1. Flowchart of the study. https://www.frontiersin.org/files/Articles/1763973/fendo-17-1763973-HTML-r1/image_m/fendo-17-1763973-g002.jpg Figure 2. The distribution of ePWV. https://www.frontiersin.org/files/Articles/1763973/fendo-17-1763973-HTML-r1/image_m/fendo-17-1763973-g003.jpg Figure 3. Stacked column chart of IFG incidence. The horizontal axis displays the ePWV quartiles (Q1 https://www.frontiersin.org/files/Articles/1763973/fendo-17-1763973-HTML-r1/image_m/fendo-17-1763973-t001.jpg Table 1. Baseline demographic and clinical characteristics of the study cohort, stratified by ePWV q https://www.frontiersin.org/files/Articles/1763973/fendo-17-1763973-HTML-r1/image_m/fendo-17-1763973-g004.jpg Figure 4. K-M curves illustrate IFG incidence by ePWV quartiles (Log-rank test P < 0.0001). https://www.frontiersin.org/files/Articles/1763973/fendo-17-1763973-HTML-r1/image_m/fendo-17-1763973-t002.jpg Table 2. The association between ePWV and the risk of IFG among the study population in different mo https://www.frontiersin.org/files/Articles/1763973/fendo-17-1763973-HTML-r1/image_m/fendo-17-1763973-g005.jpg Figure 5. The nonlinear relationship between ePWV and the risk of IFG. We adjusted variables includi https://www.frontiersin.org/files/Articles/1763973/fendo-17-1763973-HTML-r1/image_m/fendo-17-1763973-t003.jpg Table 3. Threshold effect of ePWV on the incidence of IFG. https://www.frontiersin.org/files/Articles/1763973/fendo-17-1763973-HTML-r1/image_m/fendo-17-1763973-g006.jpg Figure 6. Forest plot of subgroup analysis of the association between ePWV and the risk of IFG. https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1751851/full https://www.frontiersin.org/files/Articles/1751851/fcell-13-1751851-HTML/image_m/fcell-13-1751851-g001.jpg Figure 1. Application of AI in IVD repair biomaterials design (A) Schematic illustration of a single https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1730683/full https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-t001.jpg Table 1. The common diseases and pests of pakchoi and key characteristics. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g001.jpg Figure 1. Image examples of the data set. (a) is the Diamondback Moth, (b) is the Leaf Miner, (c) is https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g002.jpg Figure 2. Data enhancement. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g003.jpg Figure 3. YOLOv8 model network structure. Conv is the convolution module, C2f is the cross-stage par https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-t002.jpg Table 2. Performance results of the YOLOv8 series version. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g004.jpg Figure 4. Network structure of the improved YOLOv8n model. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g005.jpg Figure 5. Network structure diagram of EMA attention mechanism. X denotes the input feature map, C, https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g006.jpg Figure 6. Structure diagram of the partial convolution module. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g007.jpg Figure 7. Structure diagram of C2f-PE module. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g008.jpg Figure 8. FPN, PANeT and BiFPN structure diagram. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g009.jpg Figure 9. Wise-IoU loss function. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-t003.jpg Table 3. Training environment and hardware platform parameters table. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-t004.jpg Table 4. Some key parameters set during model training. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g010.jpg Figure 10. Loss value curves of YOLOv8-DBW. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-t005.jpg Table 5. Comparison of the effects between different attention mechanisms. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-t006.jpg Table 6. Results of ablation experiment. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-t007.jpg Table 7. Performance comparison of different IoU loss. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-t008.jpg Table 8. Performance comparison of mainstream models. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g011.jpg Figure 11. Radar chart of the comprehensive performance comparison of the mainstream. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-t009.jpg Table 9. Detailed recognition performance for the seven categories. https://www.frontiersin.org/files/Articles/1730683/fpls-16-1730683-HTML/image_m/fpls-16-1730683-g012.jpg Figure 12. Recognition performance of different models for pakchoi diseases and pests. In the figure https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1671022/full https://www.frontiersin.org/files/Articles/1671022/fimmu-16-1671022-HTML/image_m/fimmu-16-1671022-t001.jpg Table 1. Linear equations, LOD and spike recoveries of food additives. https://www.frontiersin.org/files/Articles/1671022/fimmu-16-1671022-HTML/image_m/fimmu-16-1671022-g001.jpg Figure 1. The metabolic profile of the serum was contributed to the relationship between the level o https://www.frontiersin.org/files/Articles/1671022/fimmu-16-1671022-HTML/image_m/fimmu-16-1671022-t002.jpg Table 2. Chi-square test on the association between food additives and childhood asthma. https://www.frontiersin.org/files/Articles/1671022/fimmu-16-1671022-HTML/image_m/fimmu-16-1671022-g002.jpg Figure 2. The flowchart of animal experimentation and the morphology of the lung tissues by H&E stai https://www.frontiersin.org/files/Articles/1671022/fimmu-16-1671022-HTML/image_m/fimmu-16-1671022-g003.jpg Figure 3. Inflammatory characterization in the BALF and serum.The proportion of eosinophil granulocy https://www.frontiersin.org/files/Articles/1671022/fimmu-16-1671022-HTML/image_m/fimmu-16-1671022-g004.jpg Figure 4. Immune cell profiling in murine lung tissue analyzed by flow cytometry Murine lung tissue https://www.frontiersin.org/files/Articles/1671022/fimmu-16-1671022-HTML/image_m/fimmu-16-1671022-g005.jpg Figure 5. Immune cell profiling in murine MLN tissue analyzed by flow cytometryMurine MLN tissue was https://www.frontiersin.org/files/Articles/1671022/fimmu-16-1671022-HTML/image_m/fimmu-16-1671022-g006.jpg Figure 6. Heatmap visualization of differentially metabolites of murine CD4+ cell affected by food a https://www.frontiersin.org/files/Articles/1671022/fimmu-16-1671022-HTML/image_m/fimmu-16-1671022-g007.jpg Figure 7. Pathway enrichment analysis of differential metabolites of murine CD4+ cell in MLN affecte https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1667855/full https://www.frontiersin.org/files/Articles/1667855/fneur-16-1667855-HTML/image_m/fneur-16-1667855-g001.jpg Figure 1. Study flowchart. https://www.frontiersin.org/files/Articles/1667855/fneur-16-1667855-HTML/image_m/fneur-16-1667855-t001.jpg Table 1. Baseline characteristics of patients with or without history of AF. https://www.frontiersin.org/files/Articles/1667855/fneur-16-1667855-HTML/image_m/fneur-16-1667855-t002.jpg Table 2. Rate of in-hospital mortality and complications in IV t-PA treated AIS patients with VS. wi https://www.frontiersin.org/files/Articles/1667855/fneur-16-1667855-HTML/image_m/fneur-16-1667855-g002.jpg Figure 2. Subgroup analysis. NIHSS, National Institute of Health Stroke Scale; IVT, intravenous thro https://www.frontiersin.org/files/Articles/1667855/fneur-16-1667855-HTML/image_m/fneur-16-1667855-g003.jpg Figure 3. ROC curve of atrial fibrillation history for predicting in-hospital mortality in acute isc https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1696430/full https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-t001.jpg Table 1. General information. https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g001.jpg Figure 1. Surgical steps of the transfer of the iliac flap utilizing the ascending branch of the lat https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g002.jpg Figure 2. Schematic diagram of iliac flap implantation. https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g003.jpg Figure 3. Imaging evaluation protocol for PVIBGT in the treatment of ONFH. https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g004.jpg Figure 4. Comparison of Harris scores before surgery and at the last follow-up. The results indicate https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g005.jpg Figure 5. Contour distribution map of Harris scores before and after surgery at the last follow-up. https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g006.jpg Figure 6. Anteroposterior and frog-leg x-ray images of the right hip joint in a typical patient. Pre https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g007.jpg Figure 7. Follow-up CT and 3D reconstruction images. Preoperative CT cross-section (A); CT cross-sec https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g008.jpg Figure 8. Contour distribution map of the maximum length and diameter of the implanted iliac bone gr https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g009.jpg Figure 9. Preoperative and 6-month postoperative DCE-MRI images of the right femoral head. (A,B) Pre https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g010.jpg Figure 10. Comparison of hemodynamic parameters of the right femoral head necrosis area and edema ar https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g011.jpg Figure 11. Postoperative CT perfusion (CTP) results of bilateral hip joints. (A) Blood flow (BF, mL/ https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-g012.jpg Figure 12. Comparison of hemodynamic parameters of bilateral femoral heads in postoperative patients https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-t002.jpg Table 2. Multivariate logistic regression analysis of factors influencing surgical success after PVI https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-t003.jpg Table 3. Summary of representative clinical studies on hip-preserving surgical techniques for osteon https://www.frontiersin.org/files/Articles/1696430/fsurg-12-1696430-HTML/image_m/fsurg-12-1696430-t004.jpg Table 4. Causes of hip preservation failure in patients undergoing PVIBGT. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1661969/full https://www.frontiersin.org/files/Articles/1661969/fmed-12-1661969-HTML/image_m/fmed-12-1661969-g001.jpg Figure 1. Description of the flowchart illustrating the process of selecting the study population. E https://www.frontiersin.org/files/Articles/1661969/fmed-12-1661969-HTML/image_m/fmed-12-1661969-t001.jpg Table 1. Age and sex distribution of patients tested for EBV antibody profile and EBV-DNA. https://www.frontiersin.org/files/Articles/1661969/fmed-12-1661969-HTML/image_m/fmed-12-1661969-t002.jpg Table 2. Age and sex distribution of patients tested for anti-T. gondii IgM/IgG, anti-CMV IgM/IgG, a https://www.frontiersin.org/files/Articles/1661969/fmed-12-1661969-HTML/image_m/fmed-12-1661969-t003.jpg Table 3. Relationship between different EBV antibody status and different virus antibody positive ra https://www.frontiersin.org/files/Articles/1661969/fmed-12-1661969-HTML/image_m/fmed-12-1661969-t004.jpg Table 4. Relationship between different EBV infection status and different virus IgM antibody positi https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1749526/full https://www.frontiersin.org/files/Articles/1749526/fimmu-17-1749526-HTML/image_m/fimmu-17-1749526-g001.jpg Figure 1. Phenotypic, transcriptional, and chromatin accessibility profiles during naïve CD8+ T cell https://www.frontiersin.org/files/Articles/1749526/fimmu-17-1749526-HTML/image_m/fimmu-17-1749526-g002.jpg Figure 2. Enriched biological processes of differentially regulated genes in naïve CD8+ T cells duri https://www.frontiersin.org/files/Articles/1749526/fimmu-17-1749526-HTML/image_m/fimmu-17-1749526-g003.jpg Figure 3. Identification of master TFs and their DNA binding footprints. (A–C) Regulatory networks s https://www.frontiersin.org/files/Articles/1749526/fimmu-17-1749526-HTML/image_m/fimmu-17-1749526-g004.jpg Figure 4. Chromatin accessibility and transcriptional dynamics of memory versus effector-associated https://www.frontiersin.org/files/Articles/1749526/fimmu-17-1749526-HTML/image_m/fimmu-17-1749526-g005.jpg Figure 5. Chromatin accessible patterns and transcriptional regulation of T cell exhaustion- and met https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1619123/full https://www.frontiersin.org/files/Articles/1619123/fonc-15-1619123-HTML-r1/image_m/fonc-15-1619123-g001.jpg Figure 1. CCDC58 is highly expressed in LUAD tissues and cell lines. (A) Pan-cancer analysis of CCDC https://www.frontiersin.org/files/Articles/1619123/fonc-15-1619123-HTML-r1/image_m/fonc-15-1619123-g002.jpg Figure 2. Survival analysis and clinicopathological correlation of CCDC58. (A) Survival analysis of https://www.frontiersin.org/files/Articles/1619123/fonc-15-1619123-HTML-r1/image_m/fonc-15-1619123-t001.jpg Table 1. Correlation between CCDC58 expression and clinicopathological characteristics. https://www.frontiersin.org/files/Articles/1619123/fonc-15-1619123-HTML-r1/image_m/fonc-15-1619123-g003.jpg Figure 3. Functional enrichment and TME analysis. (A, B) KEGG and GSEA enrichment analyses; (C–E) TM https://www.frontiersin.org/files/Articles/1619123/fonc-15-1619123-HTML-r1/image_m/fonc-15-1619123-g004.jpg Figure 4. CCDC58 knockdown suppresses cell proliferation. (A–D) Knockdown efficiency of CCDC58 asses https://www.frontiersin.org/files/Articles/1619123/fonc-15-1619123-HTML-r1/image_m/fonc-15-1619123-g005.jpg Figure 5. Effect of CCDC58 on cell migration. (A–D) Wound healing and cell migration assays for asse https://www.frontiersin.org/files/Articles/1619123/fonc-15-1619123-HTML-r1/image_m/fonc-15-1619123-g006.jpg Figure 6. Effect of CCDC58 on cell cycle and apoptosis. (A, B) CCDC58 knockdown influences cell cycl https://www.frontiersin.org/files/Articles/1619123/fonc-15-1619123-HTML-r1/image_m/fonc-15-1619123-g007.jpg Figure 7. CCDC58 influences the PI3K/AKT signaling pathway. (A, B) CCDC58 knockdown reduced p-PI3K a https://www.frontiersin.org/files/Articles/1619123/fonc-15-1619123-HTML-r1/image_m/fonc-15-1619123-g008.jpg Figure 8. Effect of CCDC58 on tumor growth. (A) Scheme of the experimental design; (B) Images of the https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1675572/full https://www.frontiersin.org/files/Articles/1675572/fimmu-16-1675572-HTML/image_m/fimmu-16-1675572-g001.jpg Figure 1. Genomic organization of TGEV and host innate immune responses. TGEV is an enveloped, posit https://www.frontiersin.org/files/Articles/1675572/fimmu-16-1675572-HTML/image_m/fimmu-16-1675572-g002.jpg Figure 2. Schematic representation of NLRP3 inflammasome and cGAS-STING pathway activation. This fig https://www.frontiersin.org/files/Articles/1675572/fimmu-16-1675572-HTML/image_m/fimmu-16-1675572-g003.jpg Figure 3. Mechanisms by which TGEV evades host PRRs-mediated innate immune responses. In uninfected https://www.frontiersin.org/files/Articles/1675572/fimmu-16-1675572-HTML/image_m/fimmu-16-1675572-g004.jpg Figure 4. TGEV-induced disruption of intestinal barrier and modulation of Notch signaling. TGEV infe https://www.frontiersin.org/files/Articles/1675572/fimmu-16-1675572-HTML/image_m/fimmu-16-1675572-t001.jpg Table 1. Autophagy-related molecules and their functions during TGEV infection. https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2026.1774851/full https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-g008.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-t001.jpg Table 1. Summary of physicochemical characterization of G-AgNPs synthesized using Gazania rigens lea https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-g001.jpg Figure 1. UV-Vis spectrum of G-AgNPs. https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-g002.jpg Figure 2. Elemental and morphological characterization of biogenic silver nanoparticles (G-AgNPs). ( https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-g003.jpg Figure 3. TEM images of G-AgNPs reveal predominantly spherical nanoparticles with smaller core sizes https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-g004.jpg Figure 4. Dynamic light scattering (DLS) analysis of biogenic G-AgNPs indicating average hydrodynami https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-g005.jpg Figure 5. FTIR spectra of (a) G. rigens extract and (b) biogenic silver nanoparticles (G-AgNPs). The https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-g006.jpg Figure 6. G-AgNPs, ampicillin, and ampicillin-associated G-AgNPs (ampicillin + G-AgNPs) were tested https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-t002.jpg Table 2. MICs and MBCs for the G-AgNPs. https://www.frontiersin.org/files/Articles/1774851/fchem-14-1774851-HTML/image_m/fchem-14-1774851-g007.jpg Figure 7. G-AgNPs cytotoxic effect on breast cancer cell line (MDA-MB-231) and a non-cancerous breas https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2026.1798640/full https://www.frontiersin.org/files/Articles/1798640/fendo-17-1798640-HTML/image_m/fendo-17-1798640-t001.jpg Table 1. Clinical characteristics of the DN patients included in this study (N = 26). https://www.frontiersin.org/files/Articles/1798640/fendo-17-1798640-HTML/image_m/fendo-17-1798640-g001.jpg Figure 1. Overview of the identified variants in diabetic nephropathy compared to the control sample https://www.frontiersin.org/files/Articles/1798640/fendo-17-1798640-HTML/image_m/fendo-17-1798640-g002.jpg Figure 2. Integrative analysis of gene association strength and WES variant consequence in DN. (A) G https://www.frontiersin.org/files/Articles/1798640/fendo-17-1798640-HTML/image_m/fendo-17-1798640-t002.jpg Table 2. Biological processes and pathways enriched by high- and moderate-impact variants. https://www.frontiersin.org/files/Articles/1798640/fendo-17-1798640-HTML/image_m/fendo-17-1798640-t003.jpg Table 3. Prioritized novel missense variants identified in DN cases. https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2026.1741854/full https://www.frontiersin.org/files/Articles/1741854/fnmol-19-1741854-HTML/image_m/fnmol-19-1741854-t001.jpg Table 1. Demographic characteristics and self-reported symptom frequencies of the study participants https://www.frontiersin.org/files/Articles/1741854/fnmol-19-1741854-HTML/image_m/fnmol-19-1741854-t002.jpg Table 2. Summary of symptom impact questionnaire (SIQR) results of the study participants. https://www.frontiersin.org/files/Articles/1741854/fnmol-19-1741854-HTML/image_m/fnmol-19-1741854-t003.jpg Table 3. Summary of fatigue assessment scale results of the study participants. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1746002/full https://www.frontiersin.org/files/Articles/1746002/fneur-17-1746002-HTML-r1/image_m/fneur-17-1746002-g001.jpg Figure 1. Molecular mechanisms leading to FMR1-PM-associated conditions. Three nonexclusive models a https://www.frontiersin.org/files/Articles/1746002/fneur-17-1746002-HTML-r1/image_m/fneur-17-1746002-t001.jpg Table 1. Diagnostic criteria for FXTAS (14, 28, 29). https://www.frontiersin.org/files/Articles/1746002/fneur-17-1746002-HTML-r1/image_m/fneur-17-1746002-t002.jpg Table 2. Stages of FXTAS progression. https://www.frontiersin.org/files/Articles/1746002/fneur-17-1746002-HTML-r1/image_m/fneur-17-1746002-g002.jpg Figure 2. MRI of hyperintensities of the middle cerebellar peduncles (MCP). https://www.frontiersin.org/files/Articles/1746002/fneur-17-1746002-HTML-r1/image_m/fneur-17-1746002-t003.jpg Table 3. Recommendations for dysphagia management. https://www.frontiersin.org/files/Articles/1746002/fneur-17-1746002-HTML-r1/image_m/fneur-17-1746002-t004.jpg Table 4. Resources for support of FXTAS patients. https://www.frontiersin.org/files/Articles/1746002/fneur-17-1746002-HTML-r1/image_m/fneur-17-1746002-t005.jpg Table 5. Clinical practice summary: managing fragile X-associated tremor/ataxia syndrome (FXTAS). https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1798375/full https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-t001.jpg Table 1. Formulation and proximate composition of experimental diets. https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-g001.jpg Figure 1. Overview of the experimental design. After a 2-week acclimatation period, European seabass https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-t002.jpg Table 2. Sequences of the primer pairs used in the qPCR. https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-t003.jpg Table 3. Key performance indicators of European seabass fed a commercial-like diet (Control) and two https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-g002.jpg Figure 2. Innate and acquired immune responses of European seabass fed β-glucan (BG) supplemented di https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-g003.jpg Figure 3. Relative gene expression in the skin of European seabass fed β-glucan (BG) supplemented di https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-g004.jpg Figure 4. Relative gene expression in the skin of European seabass fed β-glucan (BG) supplemented di https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-g005.jpg Figure 5. Relative gene expression in the intestine of European seabass fed β-glucan (BG) supplement https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-g006.jpg Figure 6. Relative gene expression in the intestine of European seabass fed β-glucan (BG) supplement https://www.frontiersin.org/files/Articles/1798375/fimmu-17-1798375-HTML/image_m/fimmu-17-1798375-g007.jpg Figure 7. Canonical discriminant analysis of gene expression biomarkers in challenged European seaba https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1773311/full https://www.frontiersin.org/files/Articles/1773311/fcimb-16-1773311-HTML/image_m/fcimb-16-1773311-g001.jpg Figure 1. Biosynthesis of Pal-CoA. Pal-CoA, the lipid donor for protein S-palmitoylation, is primari https://www.frontiersin.org/files/Articles/1773311/fcimb-16-1773311-HTML/image_m/fcimb-16-1773311-g002.jpg Figure 2. Protein S-palmitoylation/depalmitoylation cycle. Palmitate from Pal-CoA is first linked vi https://www.frontiersin.org/files/Articles/1773311/fcimb-16-1773311-HTML/image_m/fcimb-16-1773311-t001.jpg Table 1. S-palmitoylation of viral proteins and their functional roles in the virus life cycle. https://www.frontiersin.org/files/Articles/1773311/fcimb-16-1773311-HTML/image_m/fcimb-16-1773311-t002.jpg Table 2. Comparative overview of S-palmitoylation and depalmitoylation strategies across virus famil https://www.frontiersin.org/files/Articles/1773311/fcimb-16-1773311-HTML/image_m/fcimb-16-1773311-t003.jpg Table 3. Context-dependent functions of host depalmitoylases in virus-host interactions. https://www.frontiersin.org/files/Articles/1773311/fcimb-16-1773311-HTML/image_m/fcimb-16-1773311-g003.jpg Figure 3. The role of S-Palmitoylation and depalmitoylation in antiviral innate immune signaling. Ce https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1687361/full https://www.frontiersin.org/files/Articles/1687361/fcvm-12-1687361-HTML/image_m/fcvm-12-1687361-g001.jpg Figure 1. Flowchart of study population. hsPDA, hemodynamically significant patent ductus arteriosus https://www.frontiersin.org/files/Articles/1687361/fcvm-12-1687361-HTML/image_m/fcvm-12-1687361-t001.jpg Table 1. General characteristics of premature twins in hsPDA group and non-hsPDA group. https://www.frontiersin.org/files/Articles/1687361/fcvm-12-1687361-HTML/image_m/fcvm-12-1687361-t002.jpg Table 2. Univariate and multivariate analysis of risk factors for hsPDA in premature twins. https://www.frontiersin.org/files/Articles/1687361/fcvm-12-1687361-HTML/image_m/fcvm-12-1687361-t003.jpg Table 3. General characteristics of premature twins with successful and failed ibuprofen treatment f https://www.frontiersin.org/files/Articles/1687361/fcvm-12-1687361-HTML/image_m/fcvm-12-1687361-t004.jpg Table 4. Univariate and multivariate analysis of ibuprofen treatment failure in premature twins with https://www.frontiersin.org/files/Articles/1687361/fcvm-12-1687361-HTML/image_m/fcvm-12-1687361-t005.jpg Table 5. Comparison of medication usage among different types of twins. https://www.frontiersin.org/files/Articles/1687361/fcvm-12-1687361-HTML/image_m/fcvm-12-1687361-t006.jpg Table 6. Analysis of biological indicators among sIUGR group, non-sIUGR group, and sIUGR + hsPDA gro https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1735895/full https://www.frontiersin.org/files/Articles/1735895/fphar-17-1735895-HTML-r1/image_m/fphar-17-1735895-g006.jpg GRAPHICAL ABSTRACT | https://www.frontiersin.org/files/Articles/1735895/fphar-17-1735895-HTML-r1/image_m/fphar-17-1735895-g001.jpg Figure 1. Hypothesis of the pathogenesis of depression. https://www.frontiersin.org/files/Articles/1735895/fphar-17-1735895-HTML-r1/image_m/fphar-17-1735895-t001.jpg Table 1. Pharmacological effects of ZSS. https://www.frontiersin.org/files/Articles/1735895/fphar-17-1735895-HTML-r1/image_m/fphar-17-1735895-g002.jpg Figure 2. Origin and function of ZSS. https://www.frontiersin.org/files/Articles/1735895/fphar-17-1735895-HTML-r1/image_m/fphar-17-1735895-g003.jpg Figure 3. The main active metabolites in ZSS. https://www.frontiersin.org/files/Articles/1735895/fphar-17-1735895-HTML-r1/image_m/fphar-17-1735895-t002.jpg Table 2. The main chemical metabolites of ZSS. https://www.frontiersin.org/files/Articles/1735895/fphar-17-1735895-HTML-r1/image_m/fphar-17-1735895-g004.jpg Figure 4. Diagram of the antidepressant mechanism of ZSS. https://www.frontiersin.org/files/Articles/1735895/fphar-17-1735895-HTML-r1/image_m/fphar-17-1735895-g005.jpg Figure 5. Application of Chinese medicinal material ZSS, which has the same origin as food and medic https://www.frontiersin.org/journals/environmental-economics/articles/10.3389/frevc.2025.1552502/full https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-t001.jpg Table 1. Summary table of variables. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-g001.jpg Figure 1. Country-specific heterogeneity-energy poverty determinants. Source: WDI data and author's https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-g002.jpg Figure 2. Temporal trends in energy poverty determinants. Source: WDI data and author's own calculat https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-t002.jpg Table 2. Diagnostic tests. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-t003.jpg Table 3. Regression coefficients: access to clean fuels & technologies as dependent variable. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-t004.jpg Table 4. Instrumental variable regression coefficients. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-t005.jpg Table 5. Regression coefficients: access to electricity as dependent variable. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-t006.jpg Table 6. Quantile regression results: access to clean fuel as dependent variable. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-g003.jpg Figure 3. Quantile regression result-energy availability. Source: Author's own calculations. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-g004.jpg Figure 4. Quantile regression result-energy efficiency. Source: Author's own calculations. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-g005.jpg Figure 5. Quantile regression result-energy affordability. Source: Author's own calculations. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-t007.jpg Table 7. Regression coefficients- access to clean fuel as dependent variable. https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-g006.jpg Figure 6. Rural-urban divide: influence of financial capacity on energy poverty. Source: Author's ow https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-g007.jpg Figure 7. Rural-urban divide: influence of energy availability on Energy Poverty. Source: Author's o https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-g008.jpg Figure 8. Rural-urban divide: influence of energy efficiency on energy poverty. Source: Author's own https://www.frontiersin.org/files/Articles/1552502/frevc-04-1552502-HTML/image_m/frevc-04-1552502-g009.jpg Figure 9. Rural- urban divide: influence of energy composition on energy poverty. Source: Author's o https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1797570/full https://www.frontiersin.org/files/Articles/1797570/fpubh-14-1797570-HTML/image_m/fpubh-14-1797570-g001.jpg Figure 1. PVST flow chart in Fujian Province, China. https://www.frontiersin.org/files/Articles/1797570/fpubh-14-1797570-HTML/image_m/fpubh-14-1797570-t001.jpg Table 1. Follow-up table for children exposed to HBV (n = 75,907). https://www.frontiersin.org/files/Articles/1797570/fpubh-14-1797570-HTML/image_m/fpubh-14-1797570-t002.jpg Table 2. Characteristics of follow-up and lost-to-follow-up groups (n = 75,744). https://www.frontiersin.org/files/Articles/1797570/fpubh-14-1797570-HTML/image_m/fpubh-14-1797570-t003.jpg Table 3. PVST status for 2021–2023 (n = 75,014). https://www.frontiersin.org/files/Articles/1797570/fpubh-14-1797570-HTML/image_m/fpubh-14-1797570-t004.jpg Table 4. Risk factors associated with PVST not being completed of highly exposed children (n = 12,69 https://www.frontiersin.org/files/Articles/1797570/fpubh-14-1797570-HTML/image_m/fpubh-14-1797570-t005.jpg Table 5. Risk factors associated with PVST not being completed of commonly exposed children (n = 62, https://www.frontiersin.org/files/Articles/1797570/fpubh-14-1797570-HTML/image_m/fpubh-14-1797570-g002.jpg Figure 2. Multivariable logistic regression results for PVST not being completed of highly exposed c https://www.frontiersin.org/files/Articles/1797570/fpubh-14-1797570-HTML/image_m/fpubh-14-1797570-g003.jpg Figure 3. Multivariable logistic regression results for PVST not being completed of commonly exposed https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1695704/full https://www.frontiersin.org/files/Articles/1695704/fpsyg-16-1695704-HTML/image_m/fpsyg-16-1695704-g001.jpg Figure 1. Conceptual model of chain mediation and moderation. AIT, AI-Assisted Training; TSL; Techni https://www.frontiersin.org/files/Articles/1695704/fpsyg-16-1695704-HTML/image_m/fpsyg-16-1695704-t001.jpg Table 1. Model fit indices. https://www.frontiersin.org/files/Articles/1695704/fpsyg-16-1695704-HTML/image_m/fpsyg-16-1695704-t002.jpg Table 2. Confirmatory factor analysis (CFA) results. https://www.frontiersin.org/files/Articles/1695704/fpsyg-16-1695704-HTML/image_m/fpsyg-16-1695704-t003.jpg Table 3. Correlations, descriptive statistics, and square roots of AVEs. https://www.frontiersin.org/files/Articles/1695704/fpsyg-16-1695704-HTML/image_m/fpsyg-16-1695704-g002.jpg Figure 2. Chain mediation path model with standardized path coefficients. https://www.frontiersin.org/files/Articles/1695704/fpsyg-16-1695704-HTML/image_m/fpsyg-16-1695704-t004.jpg Table 4. Chain mediation effect analysis based on bootstrap estimates. https://www.frontiersin.org/files/Articles/1695704/fpsyg-16-1695704-HTML/image_m/fpsyg-16-1695704-t005.jpg Table 5. Moderation effect analysis. https://www.frontiersin.org/files/Articles/1695704/fpsyg-16-1695704-HTML/image_m/fpsyg-16-1695704-t006.jpg Table 6. Comparison of moderating effects at high and low levels of psychological adaptability. https://www.frontiersin.org/files/Articles/1695704/fpsyg-16-1695704-HTML/image_m/fpsyg-16-1695704-g003.jpg Figure 3. Simple slopes of AIT predicting SP at levels of PA. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1735163/full https://www.frontiersin.org/files/Articles/1735163/fbioe-13-1735163-HTML-r1/image_m/fbioe-13-1735163-t001.jpg Table 1. Details of characteristic between KOA group and HC group. https://www.frontiersin.org/files/Articles/1735163/fbioe-13-1735163-HTML-r1/image_m/fbioe-13-1735163-g001.jpg Figure 1. Correlation between quadriceps surface sEMG and JPR absolute error angle. Plot (a): Stair_ https://www.frontiersin.org/files/Articles/1735163/fbioe-13-1735163-HTML-r1/image_m/fbioe-13-1735163-g002.jpg Figure 2. Correlation between quadriceps surface sEMG in walking and walking up and down steps. Each https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1774233/full https://www.frontiersin.org/files/Articles/1774233/fpsyg-17-1774233-HTML-r1/image_m/fpsyg-17-1774233-g001.jpg Figure 1. Cognitive style automated classification model. https://www.frontiersin.org/files/Articles/1774233/fpsyg-17-1774233-HTML-r1/image_m/fpsyg-17-1774233-g002.jpg Figure 2. Flow chart of the experiment. https://www.frontiersin.org/files/Articles/1774233/fpsyg-17-1774233-HTML-r1/image_m/fpsyg-17-1774233-t001.jpg Table 1. Normalized eye movement indices. https://www.frontiersin.org/files/Articles/1774233/fpsyg-17-1774233-HTML-r1/image_m/fpsyg-17-1774233-g003.jpg Figure 3. Performance metrics for each model. https://www.frontiersin.org/files/Articles/1774233/fpsyg-17-1774233-HTML-r1/image_m/fpsyg-17-1774233-g004.jpg Figure 4. Heat map of eye movements of learners with verbal cognitive style. https://www.frontiersin.org/files/Articles/1774233/fpsyg-17-1774233-HTML-r1/image_m/fpsyg-17-1774233-g005.jpg Figure 5. Heat map of eye movements of learners with representational cognitive style. https://www.frontiersin.org/files/Articles/1774233/fpsyg-17-1774233-HTML-r1/image_m/fpsyg-17-1774233-t002.jpg Table 2. Correlation of indicators with cognitive style. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1743986/full https://www.frontiersin.org/files/Articles/1743986/fmed-13-1743986-HTML/image_m/fmed-13-1743986-t001.jpg Table 1. Balance test between training set and validation set. https://www.frontiersin.org/files/Articles/1743986/fmed-13-1743986-HTML/image_m/fmed-13-1743986-t002.jpg Table 2. Baseline characteristics and comparative analysis. https://www.frontiersin.org/files/Articles/1743986/fmed-13-1743986-HTML/image_m/fmed-13-1743986-g001.jpg Figure 1. LASSO-based feature selection and optimization of the regularization parameter. Panel (a) https://www.frontiersin.org/files/Articles/1743986/fmed-13-1743986-HTML/image_m/fmed-13-1743986-g002.jpg Figure 2. Performance comparison of machine-learning models on the training and test sets. (a) Train https://www.frontiersin.org/files/Articles/1743986/fmed-13-1743986-HTML/image_m/fmed-13-1743986-g003.jpg Figure 3. ROC comparison of the GBDT model and APACHE II score. The GBDT model (red) shows better di https://www.frontiersin.org/files/Articles/1743986/fmed-13-1743986-HTML/image_m/fmed-13-1743986-g004.jpg Figure 4. Calibration and decision-curve analysis of the GBDT model. (a) Calibration curve of the ca https://www.frontiersin.org/files/Articles/1743986/fmed-13-1743986-HTML/image_m/fmed-13-1743986-g005.jpg Figure 5. SHAP interpretation of the GBDT model. (a) Global feature importance ranked by mean absolu https://www.frontiersin.org/files/Articles/1743986/fmed-13-1743986-HTML/image_m/fmed-13-1743986-g006.jpg Figure 6. Nomogram for predicting mortality risk using the top six predictors. Points are assigned f https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1834833/full https://www.frontiersin.org/files/Articles/1834833/fonc-16-1834833-HTML-r1/image_m/fonc-16-1834833-g001.jpg Figure 1. Notch signaling signatures and Let-7b expression deviations in Osimertinib resistant cells https://www.frontiersin.org/files/Articles/1834833/fonc-16-1834833-HTML-r1/image_m/fonc-16-1834833-g002.jpg Figure 2. Notch signaling activation dependent stem cells renewal was responsible for Osimertinib re https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1798926/full https://www.frontiersin.org/files/Articles/1798926/fmed-13-1798926-HTML/image_m/fmed-13-1798926-t001.jpg Table 1. Baseline characteristics of the included population. https://www.frontiersin.org/files/Articles/1798926/fmed-13-1798926-HTML/image_m/fmed-13-1798926-t002.jpg Table 2. Risk factors for the occurrence of psoriasis in patients. https://www.frontiersin.org/files/Articles/1798926/fmed-13-1798926-HTML/image_m/fmed-13-1798926-g001.jpg Figure 1. LASSO regression and Boruta algorithm. (A) Cross-validation curve for model fitting. (B) T https://www.frontiersin.org/files/Articles/1798926/fmed-13-1798926-HTML/image_m/fmed-13-1798926-g002.jpg Figure 2. The RCS diagram showing the relationship between key variables and psoriasis. (A) MONO. (B https://www.frontiersin.org/files/Articles/1798926/fmed-13-1798926-HTML/image_m/fmed-13-1798926-g003.jpg Figure 3. Multivariate logistic regression analysis of independent risk factors for psoriasis. https://www.frontiersin.org/files/Articles/1798926/fmed-13-1798926-HTML/image_m/fmed-13-1798926-g004.jpg Figure 4. Risk prediction for the onset of psoriasis. (A) Nomogram. (B) The calibration curve. (C) T https://www.frontiersin.org/files/Articles/1798926/fmed-13-1798926-HTML/image_m/fmed-13-1798926-g005.jpg Figure 5. The expression patterns of MONO, ORM2, ORM1, and AGP in different severities of psoriasis. https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1765309/full https://www.frontiersin.org/files/Articles/1765309/fped-14-1765309-HTML/image_m/fped-14-1765309-t001.jpg Table 1. Age characteristics of participants. https://www.frontiersin.org/files/Articles/1765309/fped-14-1765309-HTML/image_m/fped-14-1765309-g001.jpg Figure 1. Flowchart of participant recruitment. https://www.frontiersin.org/files/Articles/1765309/fped-14-1765309-HTML/image_m/fped-14-1765309-t002.jpg Table 2. Blood marker information of participants. https://www.frontiersin.org/files/Articles/1765309/fped-14-1765309-HTML/image_m/fped-14-1765309-g002.jpg Figure 2. ROC curve of the combined indicators. https://www.frontiersin.org/files/Articles/1765309/fped-14-1765309-HTML/image_m/fped-14-1765309-t003.jpg Table 3. Results of logistic regression analysis. https://www.frontiersin.org/files/Articles/1765309/fped-14-1765309-HTML/image_m/fped-14-1765309-t004.jpg Table 4. Simplified logistic regression results. https://www.frontiersin.org/files/Articles/1765309/fped-14-1765309-HTML/image_m/fped-14-1765309-t005.jpg Table 5. Blood marker information of SCP by Age group. https://www.frontiersin.org/files/Articles/1765309/fped-14-1765309-HTML/image_m/fped-14-1765309-t006.jpg Table 6. Correlation analysis of blood markers and Age. https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2025.1687669/full https://www.frontiersin.org/files/Articles/1687669/fsoc-10-1687669-HTML/image_m/fsoc-10-1687669-t001.jpg Table 1. Sample distribution of logistical personnel – 26th Ecuadorian Antarctic expedition. https://www.frontiersin.org/files/Articles/1687669/fsoc-10-1687669-HTML/image_m/fsoc-10-1687669-t002.jpg Table 2. Sample characteristics. https://www.frontiersin.org/files/Articles/1687669/fsoc-10-1687669-HTML/image_m/fsoc-10-1687669-t003.jpg Table 3. Perceived sentiment analysis on wellbeing and the job environment. https://www.frontiersin.org/files/Articles/1687669/fsoc-10-1687669-HTML/image_m/fsoc-10-1687669-t004.jpg https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1778984/full https://www.frontiersin.org/files/Articles/1778984/fmicb-17-1778984-HTML/image_m/fmicb-17-1778984-g001.jpg Figure 1. Morphological classes of A. baumannii bacteriophages (Fortaleza et al., 2025; Ganeshan and https://www.frontiersin.org/files/Articles/1778984/fmicb-17-1778984-HTML/image_m/fmicb-17-1778984-g002.jpg Figure 2. Taxonomic and morphological diversity of bacteriophages infecting A. baumannii (Veesler an https://www.frontiersin.org/files/Articles/1778984/fmicb-17-1778984-HTML/image_m/fmicb-17-1778984-g003.jpg Figure 3. Schematic overview of bacteriophage-based therapeutic approaches (Cha et al., 2018; Fortal https://www.frontiersin.org/files/Articles/1778984/fmicb-17-1778984-HTML/image_m/fmicb-17-1778984-t001.jpg Table 1. Representative of bacteriophages targeting multidrug-resistant A. baumannii. https://www.frontiersin.org/files/Articles/1778984/fmicb-17-1778984-HTML/image_m/fmicb-17-1778984-g004.jpg Figure 4. The mechanisms of bacterial resistance to phage attachment. (1) Attachment of phage to its https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1677762/full https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g001.jpg Figure 1. Schematic structure of the Monkeypox virus (MPXV) particle. The virion showcases the centr https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-t001.jpg Table 1. Key MPXV immunomodulatory proteins targeting cytokine and chemokine responses. https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g002.jpg Figure 2. Modulatory effects of IFN-ω, IFN-β, and IFN-γ on MPXV neutralizing antibody titers in a do https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g003.jpg Figure 3. Expression levels of key ISGs (STAT1, STAT2, ISG15, ISG56, PKR, IDO) in MPXV-infected cell https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g004.jpg Figure 4. The replication cycle of Monkeypox virus (MPXV) within a host cell. The cycle begins with https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g005.jpg Figure 5. The diverse transmission mechanisms of MPXV include zoonotic transfer via infected animals https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g006.jpg Figure 6. A chronological review of Mpox highlights critical discoveries and global responses over t https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g007.jpg Figure 7. Number of Mpox cases per month reported to October 2024 (Modified Source: WHO): (https://w https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g008.jpg Figure 8. (A) Clade-specific genomic variations of monkeypox virus (MPXV), highlighting temporal eme https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-t002.jpg Table 2. Current MPV cases trends: aggregated data from 2022 to 2024, providing insights into the gl https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g009.jpg Figure 9. Percentage of symptoms in Mpox cases reported in 2023- Dec 2024 (https://worldhealthorg.sh https://www.frontiersin.org/files/Articles/1677762/fcimb-15-1677762-HTML/image_m/fcimb-15-1677762-g010.jpg Figure 10. The clinical spectrum and progression of Mpox disease. The diagram outlines the four typi https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1803212/full https://www.frontiersin.org/files/Articles/1803212/fpls-17-1803212-HTML/image_m/fpls-17-1803212-g001.jpg Figure 1. EA-30X UAV. https://www.frontiersin.org/files/Articles/1803212/fpls-17-1803212-HTML/image_m/fpls-17-1803212-t001.jpg Table 1. Experiment treatment setup. https://www.frontiersin.org/files/Articles/1803212/fpls-17-1803212-HTML/image_m/fpls-17-1803212-g002.jpg Figure 2. Schematic diagram of flight patterns and droplet sampling: (a) intra-row, intra-row-high-s https://www.frontiersin.org/files/Articles/1803212/fpls-17-1803212-HTML/image_m/fpls-17-1803212-t002.jpg Table 2. Summary of meteorological data for each treatment. https://www.frontiersin.org/files/Articles/1803212/fpls-17-1803212-HTML/image_m/fpls-17-1803212-g003.jpg Figure 3. (a) Droplet coverage and (b) droplet density in the inner and outer zones of the tree cano https://www.frontiersin.org/files/Articles/1803212/fpls-17-1803212-HTML/image_m/fpls-17-1803212-g004.jpg Figure 4. Distribution of (a) droplet coverage and (b) droplet density in the vertical direction of https://www.frontiersin.org/files/Articles/1803212/fpls-17-1803212-HTML/image_m/fpls-17-1803212-t003.jpg Table 3. Summary of droplet deposition evaluation indexes for all treatment. https://www.frontiersin.org/files/Articles/1803212/fpls-17-1803212-HTML/image_m/fpls-17-1803212-g005.jpg Figure 5. Statistics of droplet distribution ranges at sampling points for (a) T1; (b) T2; (c) T3; ( https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1806606/full https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g001.jpg Figure 1. Morphological characteristics of G. farreri (a) Photograph of G. farreri plants. (b, c) Le https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g002.jpg Figure 2. Leaf chlorophyll, soluble sugar, and soluble protein contents. (a) Chlorophyll a content. https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g003.jpg Figure 3. Leaf proline content, malondialdehyde content, and antioxidant enzyme activities. (a) Prol https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g004.jpg Figure 4. Concentrations of bioactive compounds in leaves of G. farreri. (a) Loganic acid; (b) Swert https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g005.jpg Figure 5. Differentially expressed genes and KEGG pathway enrichment analysis. (a, d) Volcano plot o https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g006.jpg Figure 6. Weighted gene co-expression network analysis. (a) WGCNA module classification. (b) Heatmap https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g007.jpg Figure 7. Differentially expressed genes involved in the plant–pathogen interaction pathway. T15, T2 https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g008.jpg Figure 8. Metabolomic analysis. (a) Principal component analysis score plot of samples. (b) Hierarch https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g009.jpg Figure 9. SDMs and DEGs involved in flavonoid biosynthesis. 4CL, 4-coumarate:CoA ligase; CHS, chalco https://www.frontiersin.org/files/Articles/1806606/fpls-17-1806606-HTML-r1/image_m/fpls-17-1806606-g010.jpg Figure 10. Schematic model illustrating the regulation of antioxidant systems and flavonoid biosynth https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1629001/full https://www.frontiersin.org/files/Articles/1629001/fimmu-16-1629001-HTML/image_m/fimmu-16-1629001-g001.jpg Figure 1. Conventional clustering methods using 182 DEGs between responders and non-responders in th https://www.frontiersin.org/files/Articles/1629001/fimmu-16-1629001-HTML/image_m/fimmu-16-1629001-g002.jpg Figure 2. PCA plot of SHAP values for 182 DEGs highlights potential patient subclasses with distinct https://www.frontiersin.org/files/Articles/1629001/fimmu-16-1629001-HTML/image_m/fimmu-16-1629001-g003.jpg Figure 3. Potential patient subclasses exhibited distinct characteristics with clinical significance https://www.frontiersin.org/files/Articles/1629001/fimmu-16-1629001-HTML/image_m/fimmu-16-1629001-g004.jpg Figure 4. A further DEG analysis between the patient subclasses identified potential genes associate https://www.frontiersin.org/files/Articles/1629001/fimmu-16-1629001-HTML/image_m/fimmu-16-1629001-g005.jpg Figure 5. Mutation profiles stratified by each cluster. (A) An oncoprint illustrating gene mutations https://www.frontiersin.org/files/Articles/1629001/fimmu-16-1629001-HTML/image_m/fimmu-16-1629001-t001.jpg Table 1. Characteristics of each cluster. https://www.frontiersin.org/files/Articles/1629001/fimmu-16-1629001-HTML/image_m/fimmu-16-1629001-g006.jpg Figure 6. Patient classification based on selected immunogenic parameters. (A) A histogram of compos https://www.frontiersin.org/files/Articles/1629001/fimmu-16-1629001-HTML/image_m/fimmu-16-1629001-g007.jpg Figure 7. PCA plot of SHAP values for immunotherapy response associated genes highlights potential p https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1772164/full https://www.frontiersin.org/files/Articles/1772164/fpubh-14-1772164-HTML-r3/image_m/fpubh-14-1772164-t001.jpg Table 1. Normality assessment using Kolmogorov–Smirnov test. https://www.frontiersin.org/files/Articles/1772164/fpubh-14-1772164-HTML-r3/image_m/fpubh-14-1772164-t002.jpg Table 2. Internal consistency reliability of study instruments. https://www.frontiersin.org/files/Articles/1772164/fpubh-14-1772164-HTML-r3/image_m/fpubh-14-1772164-t003.jpg Table 3. Participants’ socio-demographic characteristics. https://www.frontiersin.org/files/Articles/1772164/fpubh-14-1772164-HTML-r3/image_m/fpubh-14-1772164-t004.jpg Table 4. Correlations between psychosocial variables and psychological distress. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1711755/full https://www.frontiersin.org/files/Articles/1711755/feduc-11-1711755-HTML/image_m/feduc-11-1711755-t001.jpg Table 1. Crisis coil model educational objectives. https://www.frontiersin.org/files/Articles/1711755/feduc-11-1711755-HTML/image_m/feduc-11-1711755-t002.jpg Table 2. Participating institutions and student numbers by country and year (2022–2024). https://www.frontiersin.org/files/Articles/1711755/feduc-11-1711755-HTML/image_m/feduc-11-1711755-t003.jpg Table 3. Alignment between claims, processes, and evidence. https://www.frontiersin.org/files/Articles/1711755/feduc-11-1711755-HTML/image_m/feduc-11-1711755-t004.jpg Table 4. Evidence map linking themes to student and faculty data. https://www.frontiersin.org/files/Articles/1711755/feduc-11-1711755-HTML/image_m/feduc-11-1711755-t005.jpg Table 5. Key domains, takeaways, and exemplary quotes. https://www.frontiersin.org/files/Articles/1711755/feduc-11-1711755-HTML/image_m/feduc-11-1711755-t006.jpg Table 6. Key takeaways from the CRIISIS COIL model. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1659077/full https://www.frontiersin.org/files/Articles/1659077/fimmu-16-1659077-HTML/image_m/fimmu-16-1659077-g001.jpg Figure 1. Pseudotime analysis of CD8+T cells infiltrating ESCC tissues. (A) UMAP plot depicting the https://www.frontiersin.org/files/Articles/1659077/fimmu-16-1659077-HTML/image_m/fimmu-16-1659077-g002.jpg Figure 2. Distribution of Tpex infiltration in ESCC and adjacent normal tissues. (A) mIHC panoramic https://www.frontiersin.org/files/Articles/1659077/fimmu-16-1659077-HTML/image_m/fimmu-16-1659077-t001.jpg Table 1. The correlation between ratio of infiltrating CD8+T cells, TCF1+CD8+T cells, PD-1+CD8+T cel https://www.frontiersin.org/files/Articles/1659077/fimmu-16-1659077-HTML/image_m/fimmu-16-1659077-t002.jpg Table 2. Univariate analysis and multivariate analysis of factors affecting survival of ESCC patient https://www.frontiersin.org/files/Articles/1659077/fimmu-16-1659077-HTML/image_m/fimmu-16-1659077-g003.jpg Figure 3. Prognostic analysis of Tpex infiltration in ESCC tissues at different stages. (A) Panorami https://www.frontiersin.org/files/Articles/1659077/fimmu-16-1659077-HTML/image_m/fimmu-16-1659077-g004.jpg Figure 4. Single-cell transcriptomic profiling of CD8+T-cell states and differentiation dynamics in https://www.frontiersin.org/files/Articles/1659077/fimmu-16-1659077-HTML/image_m/fimmu-16-1659077-g005.jpg Figure 5. Cell–cell communication landscape between Tpex and other immune subsets. (A) Total number https://www.frontiersin.org/files/Articles/1659077/fimmu-16-1659077-HTML/image_m/fimmu-16-1659077-g006.jpg Figure 6. Functional interpretation of Tpex signaling by ligand–receptor and pathway enrichment anal https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1717501/full https://www.frontiersin.org/files/Articles/1717501/fpls-16-1717501-HTML/image_m/fpls-16-1717501-g001.jpg Figure 1. The chemical and biochemical properties of soil in different aged poplar plantations. NO3– https://www.frontiersin.org/files/Articles/1717501/fpls-16-1717501-HTML/image_m/fpls-16-1717501-t001.jpg Table 1. Soil microbial diversity in different aged poplar plantations. https://www.frontiersin.org/files/Articles/1717501/fpls-16-1717501-HTML/image_m/fpls-16-1717501-g002.jpg Figure 2. Relative abundance of the dominant phyla, classes and genera of bacteria (A) and fungi (B) https://www.frontiersin.org/files/Articles/1717501/fpls-16-1717501-HTML/image_m/fpls-16-1717501-g003.jpg Figure 3. Nonmetric multidimensional scaling (NMDS) representation of soil bacterial community (A) a https://www.frontiersin.org/files/Articles/1717501/fpls-16-1717501-HTML/image_m/fpls-16-1717501-g004.jpg Figure 4. The co-occurrence networks of soil bacterial (A) and fungal (B) communities; parameters ch https://www.frontiersin.org/files/Articles/1717501/fpls-16-1717501-HTML/image_m/fpls-16-1717501-t002.jpg Table 2. Correlations between soil microbial diversity and soil chemical and biochemical properties https://www.frontiersin.org/files/Articles/1717501/fpls-16-1717501-HTML/image_m/fpls-16-1717501-t003.jpg Table 3. RDA results of effects of soil chemical and biochemical properties on soil microbial divers https://www.frontiersin.org/files/Articles/1717501/fpls-16-1717501-HTML/image_m/fpls-16-1717501-g005.jpg Figure 5. Response ratio (RR) of soil microbial diversity, network parameters and dominant phyla, cl https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1781423/full https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g001.jpg Figure 1. The Kano model. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g002.jpg Figure 2. A flow chart of age-friendly RV design. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g003.jpg Figure 3. On-site interviews. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-t001.jpg Table 1. User needs for older adult RV design. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-t002.jpg Table 2. Kano classification for positive/negative questions. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-t003.jpg Table 3. Kano attributes and Better-Worse coefficients of RV user needs. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g004.jpg Figure 4. A hierarchical structure model of design needs for age-friendly RVs. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-t004.jpg Table 4. Scale and explanation of the judgment matrix. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-t005.jpg Table 5. AHP-based weight values of user needs indicators for age-friendly RVs. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-t006.jpg Table 6. Consistency test result. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g005.jpg Figure 5. QFD quality house. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-t007.jpg Table 7. QFD correlation matrix between user needs and quality characteristics. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g006.jpg Figure 6. Design opportunity points. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g007.jpg Figure 7. Sketch plan 1. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g008.jpg Figure 8. Sketch plan 2. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g009.jpg Figure 9. Sketch plan 3. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-t008.jpg Table 8. Evaluator satisfaction with the three age-friendly RV design schemes. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g010.jpg Figure 10. The rendering of the optimized design. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g011.jpg Figure 11. The rendering of the final scheme. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g012.jpg Figure 12. Size data. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g013.jpg Figure 13. Functional zoning (a. Interior layout plan; b. Kitchen; c. Bathroom; d. Lounge (L); e. Lo https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g014.jpg Figure 14. Information architecture of “RoamEase”. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g015.jpg Figure 15. Examples of high-fidelity prototypes. https://www.frontiersin.org/files/Articles/1781423/fpubh-14-1781423-HTML/image_m/fpubh-14-1781423-g016.jpg Figure 16. A selected display of the interior interactive interface. https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2026.1806067/full https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g001.jpg Figure 1. (A) puncture operation; (B) puncture lateral DR; (C) puncture anteroposterior DR; (D) cann https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g002.jpg Figure 2. (A) Extra-endoscopic visualized reamer and (B) TESSYS reamer. https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g003.jpg Figure 3. Visualized reamer group (right paracentral lumbar disc herniation): (A) preoperative lumba https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g004.jpg Figure 4. TESSYS technique group (left paracentral lumbar disc herniation); (A) preoperative lumbar https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-t001.jpg Table 1. Comparison of baseline characteristics and perioperative outcomes between the Two groups. https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-t002.jpg Table 2. Comparison of postoperative VAS, ODI scores, and modified MacNab criteria between the two g https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-t003.jpg Table 3. Tests for interaction in prespecified subgroups. https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g005.jpg Figure 5. Forest plot of subgroup analysis for back VAS scores at 12 months postoperatively. https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g006.jpg Figure 6. Forest plot of subgroup analysis for leg VAS scores at 12 months postoperatively. https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g007.jpg Figure 7. Forest plot of subgroup analysis for ODI at 12 months postoperatively. https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g008.jpg Figure 8. Forest plot of subgroup analysis for change in back VAS scores (Δ). https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g009.jpg Figure 9. Forest plot of subgroup analysis for change in leg VAS scores (Δ). https://www.frontiersin.org/files/Articles/1806067/fsurg-13-1806067-HTML/image_m/fsurg-13-1806067-g010.jpg Figure 10. Forest plot of subgroup analysis for change in ODI (Δ). https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1815276/full https://www.frontiersin.org/files/Articles/1815276/fpsyg-17-1815276-HTML-r1/image_m/fpsyg-17-1815276-t001.jpg Table 1. Coding results of taboo motifs in heterogeneous wife narratives. https://www.frontiersin.org/files/Articles/1815276/fpsyg-17-1815276-HTML-r1/image_m/fpsyg-17-1815276-t002.jpg Table 2. Coding results of taboo motifs in heterogeneous husband narratives. https://www.frontiersin.org/files/Articles/1815276/fpsyg-17-1815276-HTML-r1/image_m/fpsyg-17-1815276-g001.jpg Figure 1. A three-stage psychological model of human-non-human boundary perception. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1782621/full https://www.frontiersin.org/files/Articles/1782621/fimmu-17-1782621-HTML/image_m/fimmu-17-1782621-t001.jpg Table 1. Clinical phenotypes of donors participating in the study. https://www.frontiersin.org/files/Articles/1782621/fimmu-17-1782621-HTML/image_m/fimmu-17-1782621-g001.jpg Figure 1. (A) Visualisation of cell metaclusters generated using CD45+ cell data including both the https://www.frontiersin.org/files/Articles/1782621/fimmu-17-1782621-HTML/image_m/fimmu-17-1782621-g002.jpg Figure 2. (A) Flow cytometry gating strategies applied to CD15+CD16+ neutrophils identified using th https://www.frontiersin.org/files/Articles/1782621/fimmu-17-1782621-HTML/image_m/fimmu-17-1782621-t002.jpg Table 2. Outcomes of linear regression model for blood CD10low neutrophil cluster frequencies as the https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1671056/full https://www.frontiersin.org/files/Articles/1671056/feduc-10-1671056-HTML/image_m/feduc-10-1671056-g001.jpg Figure 1. Entrepreneurial school leadership: a conceptual model considering context and educational https://www.frontiersin.org/files/Articles/1671056/feduc-10-1671056-HTML/image_m/feduc-10-1671056-t001.jpg Table 1. Summary of findings from the document analysis. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1787235/full https://www.frontiersin.org/files/Articles/1787235/fvets-13-1787235-HTML/image_m/fvets-13-1787235-g001.jpg Figure 1. Surgical instrumentation for the lateral full-endoscopic lumbosacral foraminotomy procedur https://www.frontiersin.org/files/Articles/1787235/fvets-13-1787235-HTML/image_m/fvets-13-1787235-g002.jpg Figure 2. Docking the spinal endoscope cannula. A 1.1 mm k-wire is inserted through the incision, at https://www.frontiersin.org/files/Articles/1787235/fvets-13-1787235-HTML/image_m/fvets-13-1787235-g003.jpg Figure 3. Determination of anatomic landmarks (cranial is to the left of all images). Following init https://www.frontiersin.org/files/Articles/1787235/fvets-13-1787235-HTML/image_m/fvets-13-1787235-g004.jpg Figure 4. Endoscopic bone resection. The foraminotomy can be started with a 3 mm diameter fluted bar https://www.frontiersin.org/files/Articles/1787235/fvets-13-1787235-HTML/image_m/fvets-13-1787235-g005.jpg Figure 5. Completion of foraminotomy and determination of procedure end-point. The endoscope is rota https://www.frontiersin.org/files/Articles/1787235/fvets-13-1787235-HTML/image_m/fvets-13-1787235-t001.jpg Table 1. Signalment, pre- and post-operative clinical assessments for clinical cases. https://www.frontiersin.org/files/Articles/1787235/fvets-13-1787235-HTML/image_m/fvets-13-1787235-g006.jpg Figure 6. Clinical case 1. An intermittent non-weight bearing right pelvic limb lameness was present https://www.frontiersin.org/files/Articles/1787235/fvets-13-1787235-HTML/image_m/fvets-13-1787235-t002.jpg Table 2. Intra- and post-operative assessments for all cases. https://www.frontiersin.org/files/Articles/1787235/fvets-13-1787235-HTML/image_m/fvets-13-1787235-g007.jpg Figure 7. Neuroforaminal enlargement. Pre- vs. post-operative neuroforaminal volumes (mm3) of cadave https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1733164/full https://www.frontiersin.org/files/Articles/1733164/fimmu-16-1733164-HTML/image_m/fimmu-16-1733164-g001.jpg Figure 1. Animal model and intravital imaging system. (A) The ‘Window Chamber’ intravital imaging sy https://www.frontiersin.org/files/Articles/1733164/fimmu-16-1733164-HTML/image_m/fimmu-16-1733164-g002.jpg Figure 2. In vivo real-time imaging of tumor growth, angiogenesis, and salmonella distribution in th https://www.frontiersin.org/files/Articles/1733164/fimmu-16-1733164-HTML/image_m/fimmu-16-1733164-g003.jpg Figure 3. In vitro and in vivo effects of YB1 on endothelial cells. (A) Tube formation in HUVEC cult https://www.frontiersin.org/files/Articles/1733164/fimmu-16-1733164-HTML/image_m/fimmu-16-1733164-g004.jpg Figure 4. Mechanism diagram of YB1-induced intratumoral vascular thrombosis and intratumoral coloniz https://www.frontiersin.org/files/Articles/1733164/fimmu-16-1733164-HTML/image_m/fimmu-16-1733164-g005.jpg Figure 5. Time-lapse tracking of Salmonella in tumor related vessel. (A) Time-lapse tracking of Salm https://www.frontiersin.org/files/Articles/1733164/fimmu-16-1733164-HTML/image_m/fimmu-16-1733164-g006.jpg Figure 6. Late-phase of YB1-induced tumor vascular disruption. (A) Within 120 minutes after treatmen https://www.frontiersin.org/files/Articles/1733164/fimmu-16-1733164-HTML/image_m/fimmu-16-1733164-g007.jpg Figure 7. Late-phase dynamics of YB1-induced intratumoral colonization. (A, B) EGFP-labeled YB1 capt https://www.frontiersin.org/files/Articles/1733164/fimmu-16-1733164-HTML/image_m/fimmu-16-1733164-g008.jpg Figure 8. YB1 inhibitory effect on early-stage tumor. (A) Images of the tumor and associated blood v https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1588607/full https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-t001.jpg Table 1. Global incidence, prevalence, mortality, and DALYs of 5 drug use disorders from 1990 to 202 https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g001.jpg Figure 1. The EAPC of ASIR for amphetamine, cannabis and cocaine use disorders in global and 21 regi https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g002.jpg Figure 2. The EAPC of ASIR for opioid and other drug use disorders in global and 21 regions. ASIR ag https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-t002.jpg Table 2. Regional incidence and ASIR of the 5 drug use disorders in 2021. https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g003.jpg Figure 3. Global incidence of amphetamine, cannabis and cocaine use disorders in 204 countries or te https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g004.jpg Figure 4. Global incidence of opioid and other drug use disorders in 204 countries or territories in https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g005.jpg Figure 5. ASIR of amphetamine, cannabis and cocaine use disorders for 204 countries and territories https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g006.jpg Figure 6. ASIR of opioid and other drug use disorders for 204 countries and territories by SDI. The https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g007.jpg Figure 7. Global incidence of amphetamine, cannabis and cocaine use disorders by age and sex in 2021 https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g008.jpg Figure 8. Global incidence of opioid and other drug use disorders by age and sex in 2021. https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-t003.jpg Table 3. Percentage of 5 drug use disorders deaths and DALYs attributed to risk factors in 1990 and https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g009.jpg Figure 9. Trends and forecast rates of 5 drug use disorders ASIR worldwide change from 2021 to 2035. https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g010.jpg Figure 10. Change in incidence of 5 drug use disorders decomposed by three population-level determin https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g011.jpg Figure 11. Health inequality regression curves for the incidence. https://www.frontiersin.org/files/Articles/1588607/fpubh-13-1588607-HTML/image_m/fpubh-13-1588607-g012.jpg Figure 12. Health concentration curves for the incidence. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1697253/full https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-g001.jpg Figure 1. Theoretical analysis framework. https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-g002.jpg Figure 2. Study area. https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-t001.jpg Table 1. Descriptive statistics for each variable. https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-t002.jpg Table 2. Empowerment results of social networks, ecological perceptions and place attachments. https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-t003.jpg Table 3. Analysis of differences in the means of the variables. https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-t004.jpg Table 4. Regression results of relational networks on rural habitat improvement behavior. https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-t005.jpg Table 5. Results of endogeneity test of relational networks on rural habitat improvement behavior. https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-t006.jpg Table 6. Results of the analysis of the heterogeneity of the rural population in terms of gender, ho https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-t007.jpg Table 7. Results of the mediating effect test for ecological perceptions and place attachment. https://www.frontiersin.org/files/Articles/1697253/fpsyg-16-1697253-HTML/image_m/fpsyg-16-1697253-t008.jpg Table 8. Results of the analysis of the differences in the specific behaviors of neighbor and cadre https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1687329/full https://www.frontiersin.org/files/Articles/1687329/fimmu-16-1687329-HTML/image_m/fimmu-16-1687329-g001.jpg Figure 1. Trial profile. https://www.frontiersin.org/files/Articles/1687329/fimmu-16-1687329-HTML/image_m/fimmu-16-1687329-t001.jpg Table 1. Patient characteristics. https://www.frontiersin.org/files/Articles/1687329/fimmu-16-1687329-HTML/image_m/fimmu-16-1687329-t002.jpg Table 2. Patient-based analyses according to pCR. https://www.frontiersin.org/files/Articles/1687329/fimmu-16-1687329-HTML/image_m/fimmu-16-1687329-g002.jpg Figure 2. (A–D) [68Ga]Ga-FAPI-04 PET parameters in different T staging. All the data were compared w https://www.frontiersin.org/files/Articles/1687329/fimmu-16-1687329-HTML/image_m/fimmu-16-1687329-g003.jpg Figure 3. ROC curves of the ability of [68Ga]Ga-FAPI-04 PET and CT or MR enhanced scan to predict pC https://www.frontiersin.org/files/Articles/1687329/fimmu-16-1687329-HTML/image_m/fimmu-16-1687329-t003.jpg Table 3. Predictive performance of visual and quantitative PET assessment for pCR. https://www.frontiersin.org/files/Articles/1687329/fimmu-16-1687329-HTML/image_m/fimmu-16-1687329-t004.jpg Table 4. Patient-based diagnostic performance for pCR. https://www.frontiersin.org/files/Articles/1687329/fimmu-16-1687329-HTML/image_m/fimmu-16-1687329-g004.jpg Figure 4. (A) A patient in her 50s presented with a 2-month history of hematochezia, which led to th https://www.frontiersin.org/files/Articles/1687329/fimmu-16-1687329-HTML/image_m/fimmu-16-1687329-g005.jpg Figure 5. A 59-year-old male patient presented with black stool, anemia, and elevated levels of CEA https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1621684/full https://www.frontiersin.org/files/Articles/1621684/fpls-16-1621684-HTML-r1/image_m/fpls-16-1621684-g001.jpg Figure 1. Flowchart of the plant balance model, consisting of the source-limited yield calculation ( https://www.frontiersin.org/files/Articles/1621684/fpls-16-1621684-HTML-r1/image_m/fpls-16-1621684-g002.jpg Figure 2. Measured (blue bars) and modelled (yellow bar) lettuce yields in VF setups. (see Supplemen https://www.frontiersin.org/files/Articles/1621684/fpls-16-1621684-HTML-r1/image_m/fpls-16-1621684-g003.jpg Figure 3. Effect of PPFD, LUE and temperature on (actual) yield in lettuce and tomato under current https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1659096/full https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-t001.jpg Table 1. Composition of Thyme Essential Oil (TEO) assessed by GC–MS (Gas Chromatography-Mass Spectro https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g001.jpg Figure 1. Phylogenetic clustering of P. aeruginosa strains based on ERIC-PCR (Enterobacterial Repeti https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g002.jpg Figure 2. Characterization of P. aeruginosa strains (n = 10) biofilms. The average biofilm biomass a https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g003.jpg Figure 3. Visual representation of the data distribution for biofilm features of separate P. aerugin https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-t002.jpg Table 2. Summarized statistical differences in biofilm features between particular P. aeruginosa str https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-t003.jpg Table 3. Summarized statistical differences in biofilm CFU/mL (Colony-Forming Unit) number between p https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g004.jpg Figure 4. Antimicrobial activity of the tested compounds against P. aeruginosa strains assessed usin https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g005.jpg Figure 5. Antimicrobial activity of the tested compounds against P. aeruginosa (n = 10) strains asse https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-t004.jpg Table 4. Summarized statistical differences in susceptibility to the tested compounds between partic https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-t005.jpg Table 5. Summarized statistical differences in susceptibility to the tested compounds between partic https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g006.jpg Figure 6. Antibiofilm activity of the tested compounds against P. aeruginosa strains (n = 10) expres https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-t006.jpg Table 6. Summarized statistical differences in susceptibility to the tested compounds between partic https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g007.jpg Figure 7. Microscopic visualizations of the P. aeruginosa biofilm (covering the surface of a 24-well https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g008.jpg Figure 8. Comparison of biofilm features between genetically distinct groups of P. aeruginosa strain https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g009.jpg Figure 9. Visual representation of the data distribution for biofilm features across genetically dis https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g010.jpg Figure 10. Comparison of antimicrobial and antibiofilm activity of the tested compounds against gene https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g011.jpg Figure 11. Visual representation of the data distribution for antimicrobial and antibiofilm activity https://www.frontiersin.org/files/Articles/1659096/fphar-16-1659096-HTML-r1/image_m/fphar-16-1659096-g012.jpg Figure 12. Summary of P. aeruginosa strains’ biofilm features and pseudomonal resistance to Thyme Es https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1668594/full https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g001.jpg Figure 1. Biofilm mass of S. aureus strains (n = 26) cultured on polystyrene (PS) surface and in dif https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g002.jpg Figure 2. Biofilm metabolic activity of S. aureus strains (n = 26) cultured on different surfaces: p https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g003.jpg Figure 3. Biofilm viable cell number of S. aureus strains (n = 10) cultured on different surfaces: p https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-t001.jpg Table 1. Summarized statistical differences in biofilm metabolic activity of S. aureus strains (n =  https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-t002.jpg Table 2. Summarized statistical differences in biofilm viable cell number of S. aureus strains (n =  https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g004.jpg Figure 4. Scatter plots of correlations of S. aureus strains’ (n = 26) biofilm mass and biofilm meta https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g005.jpg Figure 5. Scatter plots of correlations of S. aureus strains’ (n = 10) biofilm mass and biofilm viab https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g006.jpg Figure 6. Scatter plots of correlations of S. aureus strains’ (n = 10) biofilm metabolic activity an https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g007.jpg Figure 7. Antibiofilm activity of rosemary essential oil (REO) or thyme essential oil (TEO) against https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g008.jpg Figure 8. Antibiofilm activity of rosemary essential oil (REO) or thyme essential oil (TEO) against https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-t003.jpg Table 3. Summarized statistical differences in antibiofilm activity of rosemary essential oil (REO) https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-t004.jpg Table 4. Summarized statistical differences in antibiofilm activity of thyme essential oil (TEO) aga https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-t005.jpg Table 5. Summarized statistical differences in antibiofilm activity of rosemary essential oil (REO) https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-t006.jpg Table 6. Summarized statistical differences in antibiofilm activity of rosemary essential oil (REO) https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-t007.jpg Table 7. Summarized statistical differences in antibiofilm activity of rosemary essential oil (REO) https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g009.jpg Figure 9. Microscopic visualization of S. aureus biofilms (n = 2) grown on polystyrene in tryptic so https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g010.jpg Figure 10. Mass spectra presenting protein profiles of S. aureus S3 strain cultured on biocellulose https://www.frontiersin.org/files/Articles/1668594/fmicb-16-1668594-HTML/image_m/fmicb-16-1668594-g011.jpg Figure 11. Mass spectra presenting protein profiles of S. aureus S4 strain cultured on biocellulose https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1724247/full https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-g001.jpg Figure 1. Flow chart depicting the systematic review process utilized in this study. https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-t001.jpg Table 1. Chart of 57 patients with fluid overload-associated large B-cell lymphoma. https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-t002.jpg Table 2. Characteristics of 57 patients with FO-LBCL. https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-t003.jpg Table 3. Univariate analysis for overall survival. https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-g002.jpg Figure 2. The overall survival according to the CD20 (A), the CD79a (B), the CD138 (C), the age (D), https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-g003.jpg Figure 3. Echocardiographic assessment of pericardial effusion. (A) Initial echocardiogram (October https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-g004.jpg Figure 4. Pathological analysis of the pericardial effusion. Cytological smears show diffuse, scatte https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-g005.jpg Figure 5. Comparison of PET-CT scans. A comparison of cardiac imaging from November 23, 2022, and Ma https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-g006.jpg Figure 6. Pathological findings of the pleural effusion. The smear shows a diffuse population of sca https://www.frontiersin.org/files/Articles/1724247/fonc-15-1724247-HTML/image_m/fonc-15-1724247-g007.jpg Figure 7. Serial bilateral chest CT imaging of pleural effusion. (A) February 2025: Image obtained a https://www.frontiersin.org/journals/oral-health/articles/10.3389/froh.2025.1664019/full https://www.frontiersin.org/files/Articles/1664019/froh-06-1664019-HTML/image_m/froh-06-1664019-g001.jpg Figure 1. Methodology workflow. https://www.frontiersin.org/files/Articles/1664019/froh-06-1664019-HTML/image_m/froh-06-1664019-g002.jpg Figure 2. Search strategy and records retrieved from pubMed (n = 231). https://www.frontiersin.org/files/Articles/1664019/froh-06-1664019-HTML/image_m/froh-06-1664019-g003.jpg Figure 3. Search strategy and records retrieved from Web of science (n = 271). https://www.frontiersin.org/files/Articles/1664019/froh-06-1664019-HTML/image_m/froh-06-1664019-g004.jpg Figure 4. Search strategy and records retrieved from CINAHL (n = 0). https://www.frontiersin.org/files/Articles/1664019/froh-06-1664019-HTML/image_m/froh-06-1664019-g005.jpg Figure 5. Search strategy and records retrieved from google scholar (n = 24). https://www.frontiersin.org/files/Articles/1664019/froh-06-1664019-HTML/image_m/froh-06-1664019-g006.jpg Figure 6. PRISMA 2020 flowchart. https://www.frontiersin.org/files/Articles/1664019/froh-06-1664019-HTML/image_m/froh-06-1664019-t001.jpg Table 1. Synthesis of studies on global policies in reducing ECC. https://www.frontiersin.org/files/Articles/1664019/froh-06-1664019-HTML/image_m/froh-06-1664019-g007.jpg Figure 7. Evidence mapping of global policy approaches to ECC by income group and policy type. https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1803680/full https://www.frontiersin.org/files/Articles/1803680/fmed-13-1803680-HTML/image_m/fmed-13-1803680-t001.jpg Table 1. Demographic and educational characteristics of participating undergraduate medical students https://www.frontiersin.org/files/Articles/1803680/fmed-13-1803680-HTML/image_m/fmed-13-1803680-g001.jpg Figure 1. Comparison of knowledge score and attitude subscales scores across sociodemographic and re https://www.frontiersin.org/files/Articles/1803680/fmed-13-1803680-HTML/image_m/fmed-13-1803680-t002.jpg Table 2. Comparison of knowledge, attitude subscale, and barrier burden scores based on university a https://www.frontiersin.org/files/Articles/1803680/fmed-13-1803680-HTML/image_m/fmed-13-1803680-t003.jpg Table 3. Comparison of knowledge, attitude subscale, and barrier burden scores based on academic yea https://www.frontiersin.org/files/Articles/1803680/fmed-13-1803680-HTML/image_m/fmed-13-1803680-t004.jpg Table 4. Comparison of prior research participation (yes/no) across demographic and educational char https://www.frontiersin.org/files/Articles/1803680/fmed-13-1803680-HTML/image_m/fmed-13-1803680-t005.jpg Table 5. Comparison of prior workshops enrollment (yes/no) across demographic and educational charac https://www.frontiersin.org/files/Articles/1803680/fmed-13-1803680-HTML/image_m/fmed-13-1803680-t006.jpg Table 6. Multivariable logistic regression analysis of factors associated with previous participatio https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1615256/full https://www.frontiersin.org/files/Articles/1615256/fonc-15-1615256-HTML/image_m/fonc-15-1615256-g001.jpg Figure 1. Immunohistochemical specimens of the cervical lymph node puncture pathology and the prosta https://www.frontiersin.org/files/Articles/1615256/fonc-15-1615256-HTML/image_m/fonc-15-1615256-g002.jpg Figure 2. Whole-body computed tomography scan. (A) Computed tomography scan on March 16, 2023, showi https://www.frontiersin.org/files/Articles/1615256/fonc-15-1615256-HTML/image_m/fonc-15-1615256-g003.jpg Figure 3. Pelvic magnetic resonance imaging. (A–C) Prostate magnetic resonance imaging findings on J https://www.frontiersin.org/files/Articles/1615256/fonc-15-1615256-HTML/image_m/fonc-15-1615256-g004.jpg Figure 4. Disease timeline chart of the patient. https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1705370/full https://www.frontiersin.org/files/Articles/1705370/fcimb-15-1705370-HTML/image_m/fcimb-15-1705370-t001.jpg Table 1. Molecular characteristics of the six E. cloacae complex isolates. https://www.frontiersin.org/files/Articles/1705370/fcimb-15-1705370-HTML/image_m/fcimb-15-1705370-t002.jpg Table 2. Analysis of the genetic characteristics of E. cloacae strains x9, F12, and x230151, and com https://www.frontiersin.org/files/Articles/1705370/fcimb-15-1705370-HTML/image_m/fcimb-15-1705370-t003.jpg Table 3. Gene expression of the acrAB-tolC from E. cloacae isolates x9, F12 and x230151. https://www.frontiersin.org/files/Articles/1705370/fcimb-15-1705370-HTML/image_m/fcimb-15-1705370-t004.jpg Table 4. Characterization of the E. cloacae DNA sequences harboring the NDM gene. https://www.frontiersin.org/files/Articles/1705370/fcimb-15-1705370-HTML/image_m/fcimb-15-1705370-g001.jpg Figure 1. Comparative plots of the complete x9_p1 plasmid against pCRE40_1, pSL131_IncA/C-IncX3, and https://www.frontiersin.org/files/Articles/1705370/fcimb-15-1705370-HTML/image_m/fcimb-15-1705370-g002.jpg Figure 2. Whole-plasmid comparisons of F12_p2 with pEA49-KPC, p72_4 and F5111. Comparative diagrams https://www.frontiersin.org/files/Articles/1705370/fcimb-15-1705370-HTML/image_m/fcimb-15-1705370-g003.jpg Figure 3. Comparative plots of the complete x230151_p2 plasmid against CRECL11 plasmid unnamed 1, pE https://www.frontiersin.org/files/Articles/1705370/fcimb-15-1705370-HTML/image_m/fcimb-15-1705370-g004.jpg Figure 4. Comparative analysis of the blaNDM-1-bleMBL-harboring plasmids x9_p1, F12_p2, and x230151_ https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2025.1654503/full https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-g001.jpg Figure 1. CONSORT flow-diagram. Per-protocol analyses included Menopause Rating Scale, Perceived Str https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-t001.jpg Table 1. Demography and baseline profile of participants in ITT dataset (n = 135). https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-t002.jpg Table 2. MRS scale and MEN-QoL scale at baseline and during the study period in the PP dataset (n =  https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-g002.jpg Figure 2. Mean scores of MRS. (A) Somato-vegetative domain score, (B) psychological domain score, (C https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-g003.jpg Figure 3. Mean scores of MENQOL assessment. (A) Vasomotor domain score, (B) psychological domain sco https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-t003.jpg Table 3. POMS scale during the study period in the PP dataset (n = 125). https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-g004.jpg Figure 4. Mean scores of POMS assessment. (A) Tension domain score, (B) anger domain score, (C) depr https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-t004.jpg Table 4. PSS score, Hot flashes, and mood improvement at baseline and during the study period in the https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-g005.jpg Figure 5. Mean scores of (A) PSS total score, (B) hot flashes improvement and (C) mood improvement. https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-t005.jpg Table 5. Serum hormonal levels at baseline and week 8 in the PP dataset (n = 125). https://www.frontiersin.org/files/Articles/1654503/frph-07-1654503-HTML/image_m/frph-07-1654503-t006.jpg Table 6. Laboratory parameters at baseline and week 8 in the safety dataset. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1802286/full https://www.frontiersin.org/files/Articles/1802286/fdgth-08-1802286-HTML/image_m/fdgth-08-1802286-t001.jpg Table 1. Conceptual distinctions between engagement, motivation, and sustained attention in technolo https://www.frontiersin.org/files/Articles/1802286/fdgth-08-1802286-HTML/image_m/fdgth-08-1802286-g001.jpg Figure 1. Inferential ladder for interpreting technology-related effects in ASD. Commonly reported b https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1759338/full https://www.frontiersin.org/files/Articles/1759338/fvets-13-1759338-HTML/image_m/fvets-13-1759338-g001.jpg Figure 1. Classic gross and histologic features of rhesus macaques with and without CE. (A–C) Demons https://www.frontiersin.org/files/Articles/1759338/fvets-13-1759338-HTML/image_m/fvets-13-1759338-t001.jpg Table 1. Comparison of diagnostic features of post-infectious irritable bowel syndrome and chronic e https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1824547/full https://www.frontiersin.org/files/Articles/1824547/fvets-13-1824547-HTML-r1/image_m/fvets-13-1824547-g001.jpg Figure 1. Experimental design. Day 0 corresponds to the day of treatment administration in the diagr https://www.frontiersin.org/files/Articles/1824547/fvets-13-1824547-HTML-r1/image_m/fvets-13-1824547-g002.jpg Figure 2. Evolution of body weight (mean ± SE) in lambs from Day −4 to Day 31 relative to weaning. B https://www.frontiersin.org/files/Articles/1824547/fvets-13-1824547-HTML-r1/image_m/fvets-13-1824547-g003.jpg Figure 3. Behavioral responses to weaning (mean ± SE) from Day −3 to Day 3 relative to weaning: (A) https://www.frontiersin.org/files/Articles/1824547/fvets-13-1824547-HTML-r1/image_m/fvets-13-1824547-g004.jpg Figure 4. Feeding-related behaviors (mean ± SE) from Day −3 to Day 3 relative to weaning: (A) Eating https://www.frontiersin.org/files/Articles/1824547/fvets-13-1824547-HTML-r1/image_m/fvets-13-1824547-g005.jpg Figure 5. Stress-related behaviors (mean ± SE) from Day −3 to Day 3 relative to weaning: (A) Bleatin https://www.frontiersin.org/files/Articles/1824547/fvets-13-1824547-HTML-r1/image_m/fvets-13-1824547-t001.jpg Table 1. Mean ± standard error (SE) of biochemical parameters, and E coli counts in feces, in the th https://www.frontiersin.org/files/Articles/1824547/fvets-13-1824547-HTML-r1/image_m/fvets-13-1824547-t002.jpg Table 2. Mean ± standard error (SE) of hematological parameters, in the three groups of lambs studie https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1622800/full https://www.frontiersin.org/files/Articles/1622800/fvets-12-1622800-HTML/image_m/fvets-12-1622800-t001.jpg Table 1. Summary statistics for all dogs evaluated for iron EDTA ingestion, as well as dogs categori https://www.frontiersin.org/files/Articles/1622800/fvets-12-1622800-HTML/image_m/fvets-12-1622800-t002.jpg Table 2. Clinical signs reported before presentation and in-hospital among 61 dogs evaluated for iro https://www.frontiersin.org/files/Articles/1622800/fvets-12-1622800-HTML/image_m/fvets-12-1622800-t003.jpg Table 3. Numbers and proportions of dogs receiving different types of medications following iron EDT https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1575633/full https://www.frontiersin.org/files/Articles/1575633/fdgth-07-1575633-HTML/image_m/fdgth-07-1575633-t001.jpg Table 1. Socio-demographic characteristics of the participants. https://www.frontiersin.org/files/Articles/1575633/fdgth-07-1575633-HTML/image_m/fdgth-07-1575633-g001.jpg Figure 1. Healthcare providers' awareness of the use of artificial intelligence in healthcare. https://www.frontiersin.org/files/Articles/1575633/fdgth-07-1575633-HTML/image_m/fdgth-07-1575633-g002.jpg Figure 2. Healthcare providers' knowledge of the use of artificial intelligence in healthcare. https://www.frontiersin.org/files/Articles/1575633/fdgth-07-1575633-HTML/image_m/fdgth-07-1575633-g003.jpg Figure 3. Healthcare providers' perception of the benefits of artificial intelligence in healthcare. https://www.frontiersin.org/files/Articles/1575633/fdgth-07-1575633-HTML/image_m/fdgth-07-1575633-g004.jpg Figure 4. Healthcare providers' readiness to adopt artificial intelligence in healthcare. https://www.frontiersin.org/files/Articles/1575633/fdgth-07-1575633-HTML/image_m/fdgth-07-1575633-t002.jpg Table 2. Independent analysis of factors associated with readiness to adopt AI. https://www.frontiersin.org/files/Articles/1575633/fdgth-07-1575633-HTML/image_m/fdgth-07-1575633-t003.jpg Table 3. Multivariate analysis of factors associated with readiness to adopt artificial intelligence https://www.frontiersin.org/files/Articles/1575633/fdgth-07-1575633-HTML/image_m/fdgth-07-1575633-t004.jpg Table 4. Challenges perceived by healthcare providers with the use of AI in healthcare. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1638923/full https://www.frontiersin.org/files/Articles/1638923/fphar-16-1638923-HTML-r1/image_m/fphar-16-1638923-g001.jpg Figure 1. Flow chart of study selection. https://www.frontiersin.org/files/Articles/1638923/fphar-16-1638923-HTML-r1/image_m/fphar-16-1638923-t001.jpg Table 1. Studies included in the meta-analysis and reported events (the number of events was obtaine https://www.frontiersin.org/files/Articles/1638923/fphar-16-1638923-HTML-r1/image_m/fphar-16-1638923-g002.jpg Figure 2. Forest plot illustrating the atrial fibrillation (A), the cerebral ischemic events (B) and https://www.frontiersin.org/files/Articles/1638923/fphar-16-1638923-HTML-r1/image_m/fphar-16-1638923-g003.jpg Figure 3. Groups analyzed in the FAERS analysis. HF, heart failure; IHD, ischemic heart disease; IHD https://www.frontiersin.org/files/Articles/1638923/fphar-16-1638923-HTML-r1/image_m/fphar-16-1638923-g004.jpg Figure 4. Forest plot of the Information Component (IC) of ivabradine, beta-blockers, and single bet https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1725818/full https://www.frontiersin.org/files/Articles/1725818/fpsyg-16-1725818-HTML/image_m/fpsyg-16-1725818-g001.jpg Figure 1. Diagram of the scaffolding performed by the mediator in the dialogical interactions with t https://www.frontiersin.org/files/Articles/1725818/fpsyg-16-1725818-HTML/image_m/fpsyg-16-1725818-t001.jpg Table 1. Number of sessions that each group underwent in each experimental condition. https://www.frontiersin.org/files/Articles/1725818/fpsyg-16-1725818-HTML/image_m/fpsyg-16-1725818-g002.jpg Figure 2. Percentage of intervals with distancing occurrences and narrative functions adjusted score https://www.frontiersin.org/files/Articles/1725818/fpsyg-16-1725818-HTML/image_m/fpsyg-16-1725818-t002.jpg Table 2. LuDiCa effect size data on narrative functions and distancing by group. https://www.frontiersin.org/files/Articles/1725818/fpsyg-16-1725818-HTML/image_m/fpsyg-16-1725818-g003.jpg Figure 3. Percentage of intervals with prompt + SR and complete occurrences. The sessions indicated https://www.frontiersin.org/files/Articles/1725818/fpsyg-16-1725818-HTML/image_m/fpsyg-16-1725818-t003.jpg Table 3. LuDiCa effect size data on Prompt + SR and complete by group. https://www.frontiersin.org/files/Articles/1725818/fpsyg-16-1725818-HTML/image_m/fpsyg-16-1725818-g004.jpg Figure 4. Percentage of intervals with occurrences of initiations and enthusiasm. The sessions indic https://www.frontiersin.org/files/Articles/1725818/fpsyg-16-1725818-HTML/image_m/fpsyg-16-1725818-t004.jpg Table 4. LuDiCa effect size data on Initiations and Enthusiasm by group. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1752716/full https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g001.jpg Figure 1. Frequency distribution of photosynthesis ranging from 10 to 37 μmol m−2 s−1 across diverse https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g002.jpg Figure 2. Photosynthesis rate variation across rice subspecies. The x-axis represents six rice subsp https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g003.jpg Figure 3. Photosynthetic response of various accessions to increasing light intensity. Zhe733 and 31 https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g004.jpg Figure 4. Rice accessions (x-axis) analyzed based on photosynthetic capacity parameters (y-axis) der https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g005.jpg Figure 5. NPQ induction and relaxation curves of various accessions carried out in a period of 30 mi https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g006.jpg Figure 6. Population structured analysis of URMC diversity panel. Bar plot of accession membership c https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-t001.jpg Table 1. Significantly associated SNPs for photosynthesis identified using GWASs. https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g007.jpg Figure 7. Manhattan plot of SNPs associated with photosynthesis rates (μmol m−2 s−1) using the BLINK https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g008.jpg Figure 8. Quantile–quantile (Q-Q) plot for SNPs associated with photosynthesis rates (μmol m−2 s−1) https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-t002.jpg Table 2. Potential candidate genes identified based on flanking region of QTNs and ortholog analysis https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g009.jpg Figure 9. Gene haplotype analysis was performed based on GWAS results, followed by downstream analys https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g010.jpg Figure 10. Gene network analysis of selected genes based on haplotype and ortholog analysis. A total https://www.frontiersin.org/files/Articles/1752716/fpls-17-1752716-HTML/image_m/fpls-17-1752716-g011.jpg Figure 11. The Venn diagram illustrates the overlap of SNPs among GWAS and the genomic prediction mo https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1748196/full https://www.frontiersin.org/files/Articles/1748196/fnut-13-1748196-HTML-r1/image_m/fnut-13-1748196-g001.jpg Figure 1. Fatty acid metabolism of omega-6 and omega-3 PUFA. LA, linoleic acid, 18:2ω6; GLA, gamma-l https://www.frontiersin.org/files/Articles/1748196/fnut-13-1748196-HTML-r1/image_m/fnut-13-1748196-g002.jpg Figure 2. Transdiagnostic commonalities of mental disorders. The figure summarizes shared challenges https://www.frontiersin.org/files/Articles/1748196/fnut-13-1748196-HTML-r1/image_m/fnut-13-1748196-t001.jpg Table 1. Fatty acid composition in schizophrenia. https://www.frontiersin.org/files/Articles/1748196/fnut-13-1748196-HTML-r1/image_m/fnut-13-1748196-t002.jpg Table 2. Fatty acid composition in depression. https://www.frontiersin.org/files/Articles/1748196/fnut-13-1748196-HTML-r1/image_m/fnut-13-1748196-t003.jpg Table 3. Fatty acid composition in bipolar disorder. https://www.frontiersin.org/files/Articles/1748196/fnut-13-1748196-HTML-r1/image_m/fnut-13-1748196-t004.jpg Table 4. Fatty acid composition in anxiety disorders and PTSD. https://www.frontiersin.org/files/Articles/1748196/fnut-13-1748196-HTML-r1/image_m/fnut-13-1748196-t005.jpg Table 5. Fatty acid composition in anorexia nervosa in women. https://www.frontiersin.org/files/Articles/1748196/fnut-13-1748196-HTML-r1/image_m/fnut-13-1748196-g003.jpg Figure 3. Possible causes of PUFA abnormalities. LA, linoleic acid, 18:2ω6; ALA, alpha-linolenacid, https://www.frontiersin.org/files/Articles/1748196/fnut-13-1748196-HTML-r1/image_m/fnut-13-1748196-g004.jpg Figure 4. Mechanisms of action of EPA and DHA. NTM, neurotransmitter; BDNF, brain-derived neurotroph https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1779894/full https://www.frontiersin.org/files/Articles/1779894/fonc-16-1779894-HTML/image_m/fonc-16-1779894-t001.jpg Table 1. Clinical characteristics of PTC patients harboring NTRK fusions. https://www.frontiersin.org/files/Articles/1779894/fonc-16-1779894-HTML/image_m/fonc-16-1779894-t002.jpg Table 2. Comparison of clinical and pathological features in PTC patients with NTRK1 versus NTRK3 fu https://www.frontiersin.org/files/Articles/1779894/fonc-16-1779894-HTML/image_m/fonc-16-1779894-t003.jpg Table 3. Correlation of clinical characteristics with LLNM in NTRK-fusion PTCs. https://www.frontiersin.org/files/Articles/1779894/fonc-16-1779894-HTML/image_m/fonc-16-1779894-t004.jpg Table 4. Comparison of clinical and pathological characteristics in PTC patients with NTRK-fusion ve https://www.frontiersin.org/files/Articles/1779894/fonc-16-1779894-HTML/image_m/fonc-16-1779894-g001.jpg Figure 1. Ultrasonographic and pathological findings of NTRK-fusion PTCs. (A-C) a hobnail variant PT https://www.frontiersin.org/files/Articles/1779894/fonc-16-1779894-HTML/image_m/fonc-16-1779894-t005.jpg Table 5. Sonographic features of NTRK-fusion and BRAFV600E PTCs. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1820331/full https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-t001.jpg Table 1. Eligibility criteria. https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-g001.jpg Figure 1. Flow diagram of the study selection process. https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-t002.jpg Table 2. Characteristics of participants, intervention protocols, and outcome measurements in the in https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-g002.jpg Figure 2. RoB 2 assessments. https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-g003.jpg Figure 3. Risk of overall bias. https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-t003.jpg Table 3. GRADE analysis. https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-t004.jpg Table 4. Pooled effects of unilateral versus bilateral plyometric training on physical fitness outco https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-g004.jpg Figure 4. Effects of unilateral versus bilateral plyometric training on jump in adolescent team-spor https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-g005.jpg Figure 5. Effects of unilateral versus bilateral plyometric training on sprint in adolescent team-sp https://www.frontiersin.org/files/Articles/1820331/fphys-17-1820331-HTML/image_m/fphys-17-1820331-g006.jpg Figure 6. Effects of unilateral versus bilateral plyometric training on change-of-direction in adole https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1755669/full https://www.frontiersin.org/files/Articles/1755669/fimmu-17-1755669-HTML/image_m/fimmu-17-1755669-t001.jpg Table 1. Distribution of patient characteristics (n=13). https://www.frontiersin.org/files/Articles/1755669/fimmu-17-1755669-HTML/image_m/fimmu-17-1755669-g001.jpg Figure 1. Characteristics of tumor response. (A) Kaplan-Meier plot of PFS; (B) Kaplan-Meier plot of https://www.frontiersin.org/files/Articles/1755669/fimmu-17-1755669-HTML/image_m/fimmu-17-1755669-t002.jpg Table 2. Treatment-Related adverse events in the SHR-1210 plus apatinib combination study. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2026.1801044/full https://www.frontiersin.org/files/Articles/1801044/fneur-17-1801044-HTML/image_m/fneur-17-1801044-g001.jpg Figure 1. The ‘performance ceiling’ in concussion prognosis (2018–2025). A comparison of prognostic https://www.frontiersin.org/files/Articles/1801044/fneur-17-1801044-HTML/image_m/fneur-17-1801044-t001.jpg Table 1. Summary of machine learning models for concussion prognosis categorized by data modality. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1651717/full https://www.frontiersin.org/files/Articles/1651717/fmicb-16-1651717-HTML-r1/image_m/fmicb-16-1651717-g001.jpg Figure 1. Root-associated microbial composition of alfalfa across six regions. (a) Bacterial phylum- https://www.frontiersin.org/files/Articles/1651717/fmicb-16-1651717-HTML-r1/image_m/fmicb-16-1651717-g002.jpg Figure 2. LEfSe analysis of rhizosphere microbial communities in alfalfa across different regions (p https://www.frontiersin.org/files/Articles/1651717/fmicb-16-1651717-HTML-r1/image_m/fmicb-16-1651717-g003.jpg Figure 3. Analysis of rhizosphere microbial communities in 6 regions. (a) α diversity analysis of ba https://www.frontiersin.org/files/Articles/1651717/fmicb-16-1651717-HTML-r1/image_m/fmicb-16-1651717-g004.jpg Figure 4. Microbial co-occurrence networks for six sample sites (HLA, ZLT, ZRHC, HLB, ZRHA, WLT). Pa https://www.frontiersin.org/files/Articles/1651717/fmicb-16-1651717-HTML-r1/image_m/fmicb-16-1651717-g005.jpg Figure 5. Multi-scale driving mechanism of soil physicochemical properties on rhizosphere microbial https://www.frontiersin.org/files/Articles/1651717/fmicb-16-1651717-HTML-r1/image_m/fmicb-16-1651717-g006.jpg Figure 6. Functional prediction of bacterial and fungal communities in alfalfa rhizosphere soils. (a https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1815807/full https://www.frontiersin.org/files/Articles/1815807/fimmu-17-1815807-HTML/image_m/fimmu-17-1815807-g001.jpg Figure 1. Structural characterization of rBlo t 2 isoforms. (A) 15% SDS-PAGE analysis of representat https://www.frontiersin.org/files/Articles/1815807/fimmu-17-1815807-HTML/image_m/fimmu-17-1815807-g002.jpg Figure 2. Endolysosomal degradation and LPS-binding activity of rBlo t 2. (A) Both rBlo t 2 isoforms https://www.frontiersin.org/files/Articles/1815807/fimmu-17-1815807-HTML/image_m/fimmu-17-1815807-g003.jpg Figure 3. Immunological characterization of rBlo t 2 isoforms. (A) Initial Western blot analysis of https://www.frontiersin.org/files/Articles/1815807/fimmu-17-1815807-HTML/image_m/fimmu-17-1815807-g004.jpg Figure 4. rBlo t 2.2 reactivity in different allergic diseases phenotypes. (A) Brazilian sera reacti https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1782545/full https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g001.jpg Figure 1. RCTD integrates results of SC and ST (A). UMAP plot of 127943 cells from single-cell sampl https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g002.jpg Figure 2. The construction and comparative analysis of spatial niches. (A) Visualization of spatial https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g003.jpg Figure 3. The tumor niche uncovers key cells and genes involved in tumor progression (A). Box line p https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g004.jpg Figure 4. The tumor niche uncovers key cells and genes involved in tumor progression (A). Box plots https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g005.jpg Figure 5. Cell communication and spatial localization reveal that CYP27A1+TAMs function through T ce https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g006.jpg Figure 6. Immunotherapy enhances the function of CYP27A1+TAMs through LXR upregulation (A). Heatmap https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g007.jpg Figure 7. External Cohort Validation of CYP27A1+TAMs Function (A). UMAP plot of 201,003 cells from t https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g008.jpg Figure 8. Construction of CMRS model through 101 machine learning approaches (A). Venn diagram showi https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g009.jpg Figure 9. Prognostic evaluation and external validation of the CMRS model (A–D). Kaplan-Meier surviv https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g010.jpg Figure 10. In vivo experiments demonstrate that macrophage-specific expression of CYP27A1 suppresses https://www.frontiersin.org/files/Articles/1782545/fimmu-17-1782545-HTML-r1/image_m/fimmu-17-1782545-g011.jpg Figure 11. Morphological validation of CYP27A1+TAMs functions within the TIME (A–D). H&E staining an https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1680624/full https://www.frontiersin.org/files/Articles/1680624/fonc-15-1680624-HTML/image_m/fonc-15-1680624-t001.jpg Table 1. Baseline clinical characteristics of small cell lung cancer patients in the training and ex https://www.frontiersin.org/files/Articles/1680624/fonc-15-1680624-HTML/image_m/fonc-15-1680624-t002.jpg Table 2. Results of univariate and multivariate Cox regression analyses for overall survival. https://www.frontiersin.org/files/Articles/1680624/fonc-15-1680624-HTML/image_m/fonc-15-1680624-g001.jpg Figure 1. Forest plot and nomogram for overall survival prediction. (A) Multivariate Cox regression https://www.frontiersin.org/files/Articles/1680624/fonc-15-1680624-HTML/image_m/fonc-15-1680624-g002.jpg Figure 2. Comprehensive evaluation of the nomogram model’s predictive performance for OS. (A) Time-d https://www.frontiersin.org/files/Articles/1680624/fonc-15-1680624-HTML/image_m/fonc-15-1680624-g003.jpg Figure 3. Risk stratification and survival analysis. (A) Survival status plot: patients are ranked b https://www.frontiersin.org/files/Articles/1680624/fonc-15-1680624-HTML/image_m/fonc-15-1680624-g004.jpg Figure 4. Subgroup analysis and ROC curves of individual indicators. (A, B) Overall survival curves https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2025.1716229/full https://www.frontiersin.org/files/Articles/1716229/fncel-19-1716229-HTML/image_m/fncel-19-1716229-t001.jpg Table 1. Fluorochrome-conjugated antibodies used in flow cytometry. https://www.frontiersin.org/files/Articles/1716229/fncel-19-1716229-HTML/image_m/fncel-19-1716229-t002.jpg Table 2. Primer sequences of mouse genes for RT-PCR. https://www.frontiersin.org/files/Articles/1716229/fncel-19-1716229-HTML/image_m/fncel-19-1716229-g001.jpg Figure 1. The effect of low-dose long-term aspirin on laser-induced choroidal neovascularization. (A https://www.frontiersin.org/files/Articles/1716229/fncel-19-1716229-HTML/image_m/fncel-19-1716229-g002.jpg Figure 2. The effect of low-dose long-term aspirin on circulating immune cell constitution and activ https://www.frontiersin.org/files/Articles/1716229/fncel-19-1716229-HTML/image_m/fncel-19-1716229-g003.jpg Figure 3. The effect of low-dose aspirin on cytokine gene expression in bone marrow-derived macropha https://www.frontiersin.org/files/Articles/1716229/fncel-19-1716229-HTML/image_m/fncel-19-1716229-g004.jpg Figure 4. The effect of low-dose aspirin on the expression and secretion of TSP-1 in bone marrow-der https://www.frontiersin.org/files/Articles/1716229/fncel-19-1716229-HTML/image_m/fncel-19-1716229-g005.jpg Figure 5. The effect of low-dose long-term aspirin on laser-induced choroidal neovascularization (CN https://www.frontiersin.org/files/Articles/1716229/fncel-19-1716229-HTML/image_m/fncel-19-1716229-t003.jpg Table 3. Demographic and clinical characteristics of nAMD patients and controls. https://www.frontiersin.org/files/Articles/1716229/fncel-19-1716229-HTML/image_m/fncel-19-1716229-g006.jpg Figure 6. The serum levels of TSP-1 and VEGF in nAMD patients with and without aspirin use. Serum wa https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1797084/full https://www.frontiersin.org/files/Articles/1797084/fpubh-14-1797084-HTML/image_m/fpubh-14-1797084-g001.jpg Figure 1. Organization of rehabilitation services within the WHO framework. https://www.frontiersin.org/files/Articles/1797084/fpubh-14-1797084-HTML/image_m/fpubh-14-1797084-t001.jpg Table 1. Comparative overview of rehabilitation financing models. https://www.frontiersin.org/files/Articles/1797084/fpubh-14-1797084-HTML/image_m/fpubh-14-1797084-t002.jpg Table 2. Comparison across three different models for rehabilitation delivery in Europe. https://www.frontiersin.org/files/Articles/1797084/fpubh-14-1797084-HTML/image_m/fpubh-14-1797084-t003.jpg Table 3. Organization of healthcare professions in Italy. https://www.frontiersin.org/files/Articles/1797084/fpubh-14-1797084-HTML/image_m/fpubh-14-1797084-t004.jpg Table 4. Policy recommendations for strengthening the rehabilitation workforce in Italy. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1778211/full https://www.frontiersin.org/files/Articles/1778211/fimmu-17-1778211-HTML/image_m/fimmu-17-1778211-g001.jpg Figure 1. (A-D) oculocutaneous albinism, with evidence of hypopigmentation of hair, eyelashes, and e https://www.frontiersin.org/files/Articles/1778211/fimmu-17-1778211-HTML/image_m/fimmu-17-1778211-g002.jpg Figure 2. Timeline of primary diagnostic and therapeutic steps. HPS1/HPS-1, Hermansky-Pudlak syndrom https://www.frontiersin.org/files/Articles/1778211/fimmu-17-1778211-HTML/image_m/fimmu-17-1778211-g003.jpg Figure 3. HRCT of the chest showing radiological progression from 2021 to 2024, with increased groun https://www.frontiersin.org/files/Articles/1778211/fimmu-17-1778211-HTML/image_m/fimmu-17-1778211-g004.jpg Figure 4. (A, B) whole blood analysis by TEM, showing platelets with rarefaction up to the disappear https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2026.1769892/full https://www.frontiersin.org/files/Articles/1769892/fcimb-16-1769892-HTML/image_m/fcimb-16-1769892-t001.jpg Table 1. Distribution of demographical and clinical characteristics of the cohorts. https://www.frontiersin.org/files/Articles/1769892/fcimb-16-1769892-HTML/image_m/fcimb-16-1769892-g001.jpg Figure 1. Comparison of bacterial and fungal gut microbiota profiles between paediatric and adult ul https://www.frontiersin.org/files/Articles/1769892/fcimb-16-1769892-HTML/image_m/fcimb-16-1769892-g002.jpg Figure 2. Machine learning classification performance based on bacterial microbiota, fungal mycobiot https://www.frontiersin.org/files/Articles/1769892/fcimb-16-1769892-HTML/image_m/fcimb-16-1769892-g003.jpg Figure 3. Inter-kingdom correlation networks in paediatric and adult ulcerative colitis (UC) patient https://www.frontiersin.org/files/Articles/1769892/fcimb-16-1769892-HTML/image_m/fcimb-16-1769892-g004.jpg Figure 4. Distribution of disease activity, microbial ecological indices, and immune markers across https://www.frontiersin.org/files/Articles/1769892/fcimb-16-1769892-HTML/image_m/fcimb-16-1769892-t002.jpg Table 2. Lysozyme, sIgA and F. prausnitzii faecal levels in paediatric and adult UC patients. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1800364/full https://www.frontiersin.org/files/Articles/1800364/fnut-13-1800364-HTML/image_m/fnut-13-1800364-g001.jpg Figure 1. Experimental protocol. Participants arrived fasted (4–6 h), provided a urine sample, and h https://www.frontiersin.org/files/Articles/1800364/fnut-13-1800364-HTML/image_m/fnut-13-1800364-g002.jpg Figure 2. Mean global symptom score (Mean score ± SE) across exercise moments (baseline, pre-exercis https://www.frontiersin.org/files/Articles/1800364/fnut-13-1800364-HTML/image_m/fnut-13-1800364-t001.jpg Table 1. Gastrointestinal symptom scores by region and time point. https://www.frontiersin.org/files/Articles/1800364/fnut-13-1800364-HTML/image_m/fnut-13-1800364-t002.jpg Table 2. Prevalence of individual gastrointestinal and systemic symptoms by exercise modality. https://www.frontiersin.org/files/Articles/1800364/fnut-13-1800364-HTML/image_m/fnut-13-1800364-g003.jpg Figure 3. Hedonic scores (mean ± SE) for palatability at pre-exercise, 30 min, and end of exercise. https://www.frontiersin.org/files/Articles/1800364/fnut-13-1800364-HTML/image_m/fnut-13-1800364-t003.jpg Table 3. Descriptive statistics of physiological variables by condition. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1742014/full https://www.frontiersin.org/files/Articles/1742014/feduc-11-1742014-HTML/image_m/feduc-11-1742014-t001.jpg Table 1. Characteristics of study participants. https://www.frontiersin.org/files/Articles/1742014/feduc-11-1742014-HTML/image_m/feduc-11-1742014-t002.jpg Table 2. Descriptive statistics in Mexico and Peru. https://www.frontiersin.org/files/Articles/1742014/feduc-11-1742014-HTML/image_m/feduc-11-1742014-t003.jpg Table 3. Fit indices of the BPNSFS measurement models. https://www.frontiersin.org/files/Articles/1742014/feduc-11-1742014-HTML/image_m/feduc-11-1742014-t004.jpg Table 4. Statistical indices of two-factor models. https://www.frontiersin.org/files/Articles/1742014/feduc-11-1742014-HTML/image_m/feduc-11-1742014-t005.jpg Table 5. Path coefficients from second-orders models. https://www.frontiersin.org/files/Articles/1742014/feduc-11-1742014-HTML/image_m/feduc-11-1742014-t006.jpg Table 6. Oblique model – Mexico and Peru. https://www.frontiersin.org/files/Articles/1742014/feduc-11-1742014-HTML/image_m/feduc-11-1742014-t007.jpg Table 7. ESEM model. https://www.frontiersin.org/files/Articles/1742014/feduc-11-1742014-HTML/image_m/feduc-11-1742014-t008.jpg Table 8. Measurement invariance Among Peruvian and Mexican adolescents. https://www.frontiersin.org/files/Articles/1742014/feduc-11-1742014-HTML/image_m/feduc-11-1742014-t009.jpg Table 9. Internal consistency of BPNSFS. https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2026.1791429/full https://www.frontiersin.org/files/Articles/1791429/fsoc-11-1791429-HTML/image_m/fsoc-11-1791429-t001.jpg Table 1. Content validity of the QUPTW items. https://www.frontiersin.org/files/Articles/1791429/fsoc-11-1791429-HTML/image_m/fsoc-11-1791429-t002.jpg Table 2. Descriptive measures and correlation matrix. https://www.frontiersin.org/files/Articles/1791429/fsoc-11-1791429-HTML/image_m/fsoc-11-1791429-g001.jpg Figure 1. Factor structure of the QUPTW. https://www.frontiersin.org/files/Articles/1791429/fsoc-11-1791429-HTML/image_m/fsoc-11-1791429-t003.jpg Table 3. Invariance analysis of the QUPTW by sex. https://www.frontiersin.org/files/Articles/1791429/fsoc-11-1791429-HTML/image_m/fsoc-11-1791429-g002.jpg Figure 2. Structural model of the relationship between use of public transportation, anger, depressi https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1719776/full https://www.frontiersin.org/files/Articles/1719776/fvets-13-1719776-HTML-r2/image_m/fvets-13-1719776-t001.jpg Table 1. Hominis-type MICE-CDSs detected using ICEB-2PG45 as a reference (n = 80/124). https://www.frontiersin.org/files/Articles/1719776/fvets-13-1719776-HTML-r2/image_m/fvets-13-1719776-t002.jpg Table 2. Spiroplasma-type MICE-CDSs detected using ICEB-1PG45 as a reference (n = 124/124). https://www.frontiersin.org/files/Articles/1719776/fvets-13-1719776-HTML-r2/image_m/fvets-13-1719776-t003.jpg Table 3. Comparative results of the screening of cMICE by qPCR and cPCR. https://www.frontiersin.org/files/Articles/1719776/fvets-13-1719776-HTML-r2/image_m/fvets-13-1719776-g001.jpg Figure 1. Relative expression ratio (RQ, linear scale) of cMICE under different culture conditions/s https://www.frontiersin.org/files/Articles/1719776/fvets-13-1719776-HTML-r2/image_m/fvets-13-1719776-t004.jpg Table 4. Proportion of positive M. bovis cMICE in total samples and as stratified by cattle type fro https://www.frontiersin.org/files/Articles/1719776/fvets-13-1719776-HTML-r2/image_m/fvets-13-1719776-t005.jpg Table 5. Mycoplasmopsis bovis cMICE in total samples, stratified by cattle type from various sources https://www.frontiersin.org/files/Articles/1719776/fvets-13-1719776-HTML-r2/image_m/fvets-13-1719776-t006.jpg Table 6. Conjugation frequencies between M. bovis 643G and M. bovis I100P with orbital shaking. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1646709/full https://www.frontiersin.org/files/Articles/1646709/fneur-16-1646709-HTML/image_m/fneur-16-1646709-t001.jpg Table 1. States included in United States Census Bureau Regions. https://www.frontiersin.org/files/Articles/1646709/fneur-16-1646709-HTML/image_m/fneur-16-1646709-t002.jpg Table 2. Total NSAH deaths stratified by sex and race. https://www.frontiersin.org/files/Articles/1646709/fneur-16-1646709-HTML/image_m/fneur-16-1646709-g001.jpg Figure 1. Multiple Joinpoint model of overall NSAH mortality between 1999 and 2022. https://www.frontiersin.org/files/Articles/1646709/fneur-16-1646709-HTML/image_m/fneur-16-1646709-g002.jpg Figure 2. Multiple Joinpoint model of NSAH mortality stratified by sex between 1999 and 2022. https://www.frontiersin.org/files/Articles/1646709/fneur-16-1646709-HTML/image_m/fneur-16-1646709-g003.jpg Figure 3. Multiple Joinpoint model of NSAH mortality stratified by race between 1999 and 2022. https://www.frontiersin.org/files/Articles/1646709/fneur-16-1646709-HTML/image_m/fneur-16-1646709-g004.jpg Figure 4. Multiple Joinpoint model of NSAH mortality stratified by US Census Region between 1999 and https://www.frontiersin.org/files/Articles/1646709/fneur-16-1646709-HTML/image_m/fneur-16-1646709-g005.jpg Figure 5. Multiple Joinpoint model of NSAH mortality stratified by rural vs. urban classification be https://www.frontiersin.org/files/Articles/1646709/fneur-16-1646709-HTML/image_m/fneur-16-1646709-g006.jpg Figure 6. Multiple Joinpoint model of NSAH mortality stratified by 10-year age groups between 1999 a https://www.frontiersin.org/journals/urology/articles/10.3389/fruro.2025.1647133/full https://www.frontiersin.org/files/Articles/1647133/fruro-05-1647133-HTML/image_m/fruro-05-1647133-t001.jpg Table 1. Clinical and demographic characteristics of 12,300 patients with upper urinary tract urothe https://www.frontiersin.org/files/Articles/1647133/fruro-05-1647133-HTML/image_m/fruro-05-1647133-t002.jpg Table 2. Median two, five, and ten-year survival estimates of 12,300 patients with upper urinary tra https://www.frontiersin.org/files/Articles/1647133/fruro-05-1647133-HTML/image_m/fruro-05-1647133-t003.jpg Table 3. Multivariable cox regression model of 12,300 patients with upper urinary tract urothelial c https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1678264/full https://www.frontiersin.org/files/Articles/1678264/fcvm-12-1678264-HTML-r1/image_m/fcvm-12-1678264-t001.jpg Table 1. Baseline demographic characteristics. https://www.frontiersin.org/files/Articles/1678264/fcvm-12-1678264-HTML-r1/image_m/fcvm-12-1678264-t002.jpg Table 2. Baseline clinical variables. https://www.frontiersin.org/files/Articles/1678264/fcvm-12-1678264-HTML-r1/image_m/fcvm-12-1678264-t003.jpg Table 3. Means of FVL and Non-FVL groups before and after propensity score stratification. https://www.frontiersin.org/files/Articles/1678264/fcvm-12-1678264-HTML-r1/image_m/fcvm-12-1678264-t004.jpg Table 4. In-hospital mortality outcomes. https://www.frontiersin.org/files/Articles/1678264/fcvm-12-1678264-HTML-r1/image_m/fcvm-12-1678264-t005.jpg Table 5. Length of stay outcomes. https://www.frontiersin.org/files/Articles/1678264/fcvm-12-1678264-HTML-r1/image_m/fcvm-12-1678264-t006.jpg Table 6. Total cost outcomes. https://www.frontiersin.org/journals/gastroenterology/articles/10.3389/fgstr.2026.1681142/full https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-t001.jpg Table 1. Liver and IHBD cancer-related age-adjusted mortality rate per 100,000 people; overall and s https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-g001.jpg Figure 1. Liver and IHBD cancer-related age-adjusted mortality rate per 100,000 people; overall and https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-g002.jpg Figure 2. Joinpoint model of Liver and IHBD cancer-related AAMR per 100,000 people overall and strat https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-t002.jpg Table 2. Liver and IHBD cancer-related age-adjusted mortality rate per 100,000 people stratified by https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-g003.jpg Figure 3. Liver and IHBD cancer-related age-adjusted mortality rate per 100,000 people; overall and https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-g004.jpg Figure 4. Joinpoint model of Liver and IHBD cancer-related AAMR per 100,000 people overall and strat https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-t003.jpg Table 3. Liver and IHBD cancer-related age-adjusted mortality rate per 100,000 people stratified by https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-g005.jpg Figure 5. Liver and IHBD cancer-related age-adjusted mortality rate per 100,000 people; overall and https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-g006.jpg Figure 6. Joinpoint model of Liver and IHBD cancer-related AAMR per 100,000 people overall and strat https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-t004.jpg Table 4. Liver and IHBD cancer-related age-adjusted mortality rate per 100,000 people stratified by https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-g007.jpg Figure 7. Liver and IHBD cancer-related age-adjusted mortality rate per 100,000 people; overall and https://www.frontiersin.org/files/Articles/1681142/fgstr-05-1681142-HTML/image_m/fgstr-05-1681142-g008.jpg Figure 8. Joinpoint model of Liver and IHBD cancer-related AAMR per 100,000 people overall and strat https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1683346/full https://www.frontiersin.org/files/Articles/1683346/fped-13-1683346-HTML/image_m/fped-13-1683346-g001.jpg Figure 1. Extreme prematurity related mortality trends overall and stratified by sex, 1999–2023. https://www.frontiersin.org/files/Articles/1683346/fped-13-1683346-HTML/image_m/fped-13-1683346-g002.jpg Figure 2. Extreme prematurity related mortality trends stratified by race and ethnicity, 1999–2023. https://www.frontiersin.org/files/Articles/1683346/fped-13-1683346-HTML/image_m/fped-13-1683346-g003.jpg Figure 3. Extreme prematurity related mortality trends stratified by region, 1999–2023. https://www.frontiersin.org/files/Articles/1683346/fped-13-1683346-HTML/image_m/fped-13-1683346-g004.jpg Figure 4. Extreme prematurity related mortality trends stratified by rural vs. urban locality, 1999– https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2026.1686158/full https://www.frontiersin.org/files/Articles/1686158/fnut-13-1686158-HTML-r1/image_m/fnut-13-1686158-g001.jpg Figure 1. Inclusion and exclusion criteria flow chart. Figure was designed by authors. https://www.frontiersin.org/files/Articles/1686158/fnut-13-1686158-HTML-r1/image_m/fnut-13-1686158-g002.jpg Figure 2. PRISMA diagram. Figure was developed using the PRISMA 2020 Guideline (21). https://www.frontiersin.org/files/Articles/1686158/fnut-13-1686158-HTML-r1/image_m/fnut-13-1686158-t001.jpg Table 1. Descriptions of included studies. https://www.frontiersin.org/files/Articles/1686158/fnut-13-1686158-HTML-r1/image_m/fnut-13-1686158-t002.jpg Table 2. Key findings from included studies. https://www.frontiersin.org/files/Articles/1686158/fnut-13-1686158-HTML-r1/image_m/fnut-13-1686158-g003.jpg Figure 3. Location of included articles stratified by country. *Suriname, Guyana, and French Guiana https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1691932/full https://www.frontiersin.org/files/Articles/1691932/fonc-15-1691932-HTML/image_m/fonc-15-1691932-g001.jpg Figure 1. Overall AAMRs of ovarian cancer. https://www.frontiersin.org/files/Articles/1691932/fonc-15-1691932-HTML/image_m/fonc-15-1691932-g002.jpg Figure 2. AAMRs of ovarian cancer by race. https://www.frontiersin.org/files/Articles/1691932/fonc-15-1691932-HTML/image_m/fonc-15-1691932-g003.jpg Figure 3. AAMRs of ovarian cancer by region. https://www.frontiersin.org/files/Articles/1691932/fonc-15-1691932-HTML/image_m/fonc-15-1691932-g004.jpg Figure 4. AAMRs of ovarian cancer: urban vs. rural. https://www.frontiersin.org/files/Articles/1691932/fonc-15-1691932-HTML/image_m/fonc-15-1691932-g005.jpg Figure 5. Crude mortality rates of 10-year age groups. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1713388/full https://www.frontiersin.org/files/Articles/1713388/fneur-16-1713388-HTML/image_m/fneur-16-1713388-t001.jpg Table 1. Baseline demographic characteristics. https://www.frontiersin.org/files/Articles/1713388/fneur-16-1713388-HTML/image_m/fneur-16-1713388-t002.jpg Table 2. Baseline clinical characteristics. https://www.frontiersin.org/files/Articles/1713388/fneur-16-1713388-HTML/image_m/fneur-16-1713388-t003.jpg Table 3. Multivariate regression analysis of PLEX and IVIG treatment outcomes compared to neither tr https://www.frontiersin.org/files/Articles/1713388/fneur-16-1713388-HTML/image_m/fneur-16-1713388-t004.jpg Table 4. Multivariate regression analysis of PLEX outcomes compared to IVIG outcomes with propensity https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1715138/full https://www.frontiersin.org/files/Articles/1715138/fpubh-13-1715138-HTML/image_m/fpubh-13-1715138-g001.jpg Figure 1. Joinpoint model of newborns affected by premature rupture of membranes crude mortality rat https://www.frontiersin.org/files/Articles/1715138/fpubh-13-1715138-HTML/image_m/fpubh-13-1715138-g002.jpg Figure 2. Joinpoint model of newborns affected by premature rupture of membranes crude mortality rat https://www.frontiersin.org/files/Articles/1715138/fpubh-13-1715138-HTML/image_m/fpubh-13-1715138-g003.jpg Figure 3. Joinpoint model of newborns affected by premature rupture of membranes crude mortality rat https://www.frontiersin.org/files/Articles/1715138/fpubh-13-1715138-HTML/image_m/fpubh-13-1715138-g004.jpg Figure 4. Joinpoint model of newborns affected by premature rupture of membranes crude mortality rat https://www.frontiersin.org/files/Articles/1715138/fpubh-13-1715138-HTML/image_m/fpubh-13-1715138-g005.jpg Figure 5. Joinpoint model of newborns affected by premature rupture of membranes crude mortality rat https://www.frontiersin.org/files/Articles/1715138/fpubh-13-1715138-HTML/image_m/fpubh-13-1715138-g006.jpg Figure 6. Joinpoint model of newborns affected by premature rupture of membranes crude mortality rat https://www.frontiersin.org/files/Articles/1715138/fpubh-13-1715138-HTML/image_m/fpubh-13-1715138-g007.jpg Figure 7. Joinpoint model of newborns affected by premature rupture of membranes crude mortality rat https://www.frontiersin.org/files/Articles/1715138/fpubh-13-1715138-HTML/image_m/fpubh-13-1715138-g008.jpg Figure 8. Joinpoint model of newborns affected by premature rupture of membranes crude mortality rat https://www.frontiersin.org/files/Articles/1715138/fpubh-13-1715138-HTML/image_m/fpubh-13-1715138-g009.jpg Figure 9. Joinpoint model of newborns affected by premature rupture of membranes crude mortality rat https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1696511/full https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t001.jpg Table 1. Baseline characteristics of the study population. https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t002.jpg Table 2. Comparison of the three quantitative software packages in the evaluation of left ventricula https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-g001.jpg Figure 1. Pairwise comparisons of EF (A), EDV (B), ESV (C), EDVI (D), ESVI (E), BW (F), and SD (G) v https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t003.jpg Table 3. Comparison of EF, EDV, and ESV values derived from 4DM, QGS, ECTb, and echocardiography. https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t004.jpg Table 4. Comparison of the left ventricular function and synchronicity parameters obtained by differ https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t005.jpg Table 5. Age-related differences in left ventricular function and synchronicity parameters obtained https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t006.jpg Table 6. Comparison of left ventricular function and synchronicity parameters obtained using QGS bet https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t007.jpg Table 7. Multiple linear regression analysis of EF, volume, and volume index obtained using QGS. https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t008.jpg Table 8. Clinical reference limits for left ventricular function parameters when using the three sof https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t009.jpg Table 9. Clinical reference limits for left ventricular synchronicity parameters when using the thre https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t010.jpg Table 10. Comparison of left ventricular function and synchronicity parameters between the sexes aft https://www.frontiersin.org/files/Articles/1696511/fcvm-13-1696511-HTML/image_m/fcvm-13-1696511-t011.jpg Table 11. Comparison of left ventricular synchronicity parameters with other studies. https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1722882/full https://www.frontiersin.org/files/Articles/1722882/fvets-12-1722882-HTML/image_m/fvets-12-1722882-t001.jpg Table 1. Comparison of presumptive zygote developmental stages (%) in vitrified and non-vitrified ca https://www.frontiersin.org/files/Articles/1722882/fvets-12-1722882-HTML/image_m/fvets-12-1722882-g001.jpg Figure 1. Fertilization outcomes of presumptive zygote derived from non-vitrified cattle oocytes in https://www.frontiersin.org/files/Articles/1722882/fvets-12-1722882-HTML/image_m/fvets-12-1722882-g002.jpg Figure 2. Post-warming fertilization outcomes of vitrified cattle oocytes in different media. The pe https://www.frontiersin.org/files/Articles/1722882/fvets-12-1722882-HTML/image_m/fvets-12-1722882-g003.jpg Figure 3. Morphological assessment of fertilization status in cattle oocytes after 18 h of in vitro https://www.frontiersin.org/files/Articles/1722882/fvets-12-1722882-HTML/image_m/fvets-12-1722882-t002.jpg Table 2. Comparison of embryonic development stages between non-vitrified and vitrified cattle oocyt https://www.frontiersin.org/files/Articles/1722882/fvets-12-1722882-HTML/image_m/fvets-12-1722882-g004.jpg Figure 4. (A) In vitro cleavage of non-vitrified (pre-warmed) presumptive zygotes, (B) In vitro clea https://www.frontiersin.org/files/Articles/1722882/fvets-12-1722882-HTML/image_m/fvets-12-1722882-g005.jpg Figure 5. Post-warming development of vitrified bovine oocytes in different culture media. The perce https://www.frontiersin.org/files/Articles/1722882/fvets-12-1722882-HTML/image_m/fvets-12-1722882-g006.jpg Figure 6. Developmental competence of presumptive zygotes derived from non-vitrified cattle oocytes https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1698795/full https://www.frontiersin.org/files/Articles/1698795/fsurg-12-1698795-HTML/image_m/fsurg-12-1698795-g001.jpg Figure 1. (A) Primary low-grade myofibroblastic sarcoma of the breast: The excised surface of the br https://www.frontiersin.org/files/Articles/1698795/fsurg-12-1698795-HTML/image_m/fsurg-12-1698795-g002.jpg Figure 2. Chest CT showing an apical 1.5-cm mass (yellow arrow) and enlarged lymph nodes in the righ https://www.frontiersin.org/files/Articles/1698795/fsurg-12-1698795-HTML/image_m/fsurg-12-1698795-g003.jpg Figure 3. Metastatic low-grade myofibroblastic sarcoma in the lung. Immunohistochemical staining for https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1694670/full https://www.frontiersin.org/files/Articles/1694670/fcimb-15-1694670-HTML/image_m/fcimb-15-1694670-g001.jpg Figure 1. Flowchart of the study. https://www.frontiersin.org/files/Articles/1694670/fcimb-15-1694670-HTML/image_m/fcimb-15-1694670-g002.jpg Figure 2. Epidemiological characteristics of pertussis. (A) Distribution of pertussis cases (red) an https://www.frontiersin.org/files/Articles/1694670/fcimb-15-1694670-HTML/image_m/fcimb-15-1694670-t001.jpg Table 1. Comparison of demographic and clinical features of B. pertussis (+) and B. pertussis (−) gr https://www.frontiersin.org/files/Articles/1694670/fcimb-15-1694670-HTML/image_m/fcimb-15-1694670-g003.jpg Figure 3. Comparison of peripheral blood cell parameters between B. pertussis (+) and B. pertussis ( https://www.frontiersin.org/files/Articles/1694670/fcimb-15-1694670-HTML/image_m/fcimb-15-1694670-g004.jpg Figure 4. Comparison of inflammatory indicators between B. pertussis (+) and (B) pertussis (−) group https://www.frontiersin.org/files/Articles/1694670/fcimb-15-1694670-HTML/image_m/fcimb-15-1694670-g005.jpg Figure 5. Sensitivity, specificity, and odds ratio of different clinical features in diagnosis of pe https://www.frontiersin.org/files/Articles/1694670/fcimb-15-1694670-HTML/image_m/fcimb-15-1694670-g006.jpg Figure 6. Performance of different criteria alone or in combination with other factors in pertussis https://www.frontiersin.org/files/Articles/1694670/fcimb-15-1694670-HTML/image_m/fcimb-15-1694670-g007.jpg Figure 7. Univariable logistic regression of leukocyte, neutrophils, lymphocyte, and PCT. The ROC of https://www.frontiersin.org/files/Articles/1694670/fcimb-15-1694670-HTML/image_m/fcimb-15-1694670-g008.jpg Figure 8. Multivariable logistic regression and univariable logistic regression of indicated factors https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1723551/full https://www.frontiersin.org/files/Articles/1723551/fcimb-15-1723551-HTML/image_m/fcimb-15-1723551-g001.jpg Figure 1. Neighbor-joining tree based on partial rpoB gene showing the phylogenetic relationship of https://www.frontiersin.org/files/Articles/1723551/fcimb-15-1723551-HTML/image_m/fcimb-15-1723551-t001.jpg Table 1. Comparative genomic analysis and genomic characteristics of some Corynebacterium kroppenste https://www.frontiersin.org/files/Articles/1723551/fcimb-15-1723551-HTML/image_m/fcimb-15-1723551-g002.jpg Figure 2. Phylogenetic analysis of 53 Corynebacterium kroppenstedtii Complex strains (A) SNP phyloge https://www.frontiersin.org/files/Articles/1723551/fcimb-15-1723551-HTML/image_m/fcimb-15-1723551-t002.jpg Table 2. Phenotypic comparison of C. parakroppenstedtii, C. pseudokroppenstedtii, and related specie https://www.frontiersin.org/files/Articles/1723551/fcimb-15-1723551-HTML/image_m/fcimb-15-1723551-t003.jpg Table 3. Antimicrobial susceptibilities of Corynebacterium parakroppenstedtii and Corynebacterium ps https://www.frontiersin.org/files/Articles/1723551/fcimb-15-1723551-HTML/image_m/fcimb-15-1723551-t004.jpg Table 4. Demographics and clinical characteristics of patients infected by Corynebacterium parakropp https://www.frontiersin.org/journals/pain-research/articles/10.3389/fpain.2026.1760721/full https://www.frontiersin.org/files/Articles/1760721/fpain-07-1760721-HTML/image_m/fpain-07-1760721-g001.jpg Figure 1. PRISMA flowchart. https://www.frontiersin.org/files/Articles/1760721/fpain-07-1760721-HTML/image_m/fpain-07-1760721-t001.jpg Table 1. Study characteristics of the included studies. https://www.frontiersin.org/files/Articles/1760721/fpain-07-1760721-HTML/image_m/fpain-07-1760721-g002.jpg Figure 2. Evaluation of the different risk factors for bias from the studies included in this system https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1771008/full https://www.frontiersin.org/files/Articles/1771008/fmed-13-1771008-HTML-r1/image_m/fmed-13-1771008-t001.jpg Table 1. Study characteristics of the included studies. https://www.frontiersin.org/files/Articles/1771008/fmed-13-1771008-HTML-r1/image_m/fmed-13-1771008-g001.jpg Figure 1. PRISMA flowchart. https://www.frontiersin.org/files/Articles/1771008/fmed-13-1771008-HTML-r1/image_m/fmed-13-1771008-g002.jpg Figure 2. Evaluation of the different risk factors for bias from the studies included in this system https://www.frontiersin.org/journals/medical-technology/articles/10.3389/fmedt.2026.1717535/full https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-t001.jpg Table 1. Genetic ancestry-specific distribution of study subjects. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g001.jpg Figure 1. Age and sex-based representation in the study group. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g002.jpg Figure 2. Study design flow chart. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g003.jpg Figure 3. A screenshot from cliniface showing the facial landmarks (19 medial and 25 bilateral) as y https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-t002.jpg Table 2. Change in the number of human phenotype ontology (HPO) terms identified by cliniface before https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g004.jpg Figure 4. Male growth curve for palpebral fissure length. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g005.jpg Figure 5. Female growth curve for palpebral fissure length. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g006.jpg Figure 6. Male growth curve for bizygomatic width. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g007.jpg Figure 7. Female growth curve for bizygomatic width. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g008.jpg Figure 8. Comparison of growth curves for bizygomatic width comparing this work's estimated populati https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g009.jpg Figure 9. Comparison of growth curves for palpebral fissure length between different genetic ancestr https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g010.jpg Figure 10. Nasal protrusion in males and females of Chinese vs. European genetic ancestry. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g011.jpg Figure 11. Nasal bridge length in males and females of Chinese vs. European genetic ancestry. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g012.jpg Figure 12. Innercanthal width in males and females of Chinese vs. European genetic ancestry. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g013.jpg Figure 13. Composite image from cliniface showing how a selected 3D facial measurement (nasal protru https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g014.jpg Figure 14. Facial height in males and females of Chinese vs. European genetic ancestry. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g015.jpg Figure 15. Labial fissure width in males and females of Chinese vs. European genetic ancestry. https://www.frontiersin.org/files/Articles/1717535/fmedt-08-1717535-HTML/image_m/fmedt-08-1717535-g016.jpg Figure 16. Philtral length in males and females of Chinese vs. European genetic ancestry. https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1686128/full https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-t001.jpg Table 1. Eligibility criteria using the Population, Intervention, Comparisons, Outcomes, Time, and S https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-g001.jpg Figure 1. A schematic diagram of GRADE’s process used for the inclusion of study outcomes in the sys https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-g002.jpg Figure 2. PRISMA flow diagram for pharmacological interventions and therapeutic exercises for rheuma https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-t002.jpg Table 2. Summary characteristics of studies included in the review, 2004–2024. https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-g003.jpg Figure 3. Forest plot showing ACR 20 at 24 months. The overall effect shows that the intervention (D https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-g004.jpg Figure 4. Forest plot showing ACR 50 at 24 months. The overall pooled Risk Ratio is 2.46 (95% CI, 1. https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-g005.jpg Figure 5. Forest plot showing ACR 70 at 24 months. At near-remission (ACR 70), the pooled Risk Ratio https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-g006.jpg Figure 6. Forest plot showing SMD of HAQ-DI at 24 weeks. The overall effect shows a non-significant, https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-g007.jpg Figure 7. Forest plot showing SMD of DAS at 24 weeks. The overall effect shows a small, non-signific https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-t003.jpg Table 3. Summary of meta-analysis findings. https://www.frontiersin.org/files/Articles/1686128/fphar-16-1686128-HTML-r1/image_m/fphar-16-1686128-t004.jpg Table 4. GRADE evidence profile: pain, physical function, quality of life, health-related quality of https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1661072/full https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-t001.jpg Table 1. The fundamental information and biochemical parameters of observation and controls. https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-g001.jpg Figure 1. Comparison of serum biochemical indicators between the observation group (people with over https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-g002.jpg Figure 2. Vitamin D3 intervention improved lipid deposition in rats on a high fat diet. (A) Oil red https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-g003.jpg Figure 3. Effects of Vitamin D3 on levels of serum lipids, inflammatory factors, 25(OH)D3 and IL-27 https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-g004.jpg Figure 4. Effects of vitamin D3 on mRNA and protein levels of IL-27/P38MAPK/PGC-1α pathway in high-f https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-g005.jpg Figure 5. Calcitriol reduces lipid deposition and increases the levels of IL-27 in 3T3-L1 cell. (A) https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-g006.jpg Figure 6. Effects of vitamin D3 on the IL-27/P38MAPK/PGC-1α pathway at mRNA and protein levels in 3T https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-g007.jpg Figure 7. IL-27 siRNA inhibited the effect of Vitamin D3. (A–F) The mRNA expression of VDR, IL-27R, https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-g008.jpg Figure 8. PGC-1α siRNA inhibited the effect of Vitamin D3. (A–F) The mRNA expression of VDR, IL-27R, https://www.frontiersin.org/files/Articles/1661072/fnut-12-1661072-HTML-r1/image_m/fnut-12-1661072-g009.jpg Figure 9. White fat beige machine drawing. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1785362/full https://www.frontiersin.org/files/Articles/1785362/fpsyg-17-1785362-HTML/image_m/fpsyg-17-1785362-g001.jpg Figure 1. Research model. https://www.frontiersin.org/files/Articles/1785362/fpsyg-17-1785362-HTML/image_m/fpsyg-17-1785362-t001.jpg Table 1. Confirmatory factor analysis and reliability and validity indicators. https://www.frontiersin.org/files/Articles/1785362/fpsyg-17-1785362-HTML/image_m/fpsyg-17-1785362-t002.jpg Table 2. Model fitting index. https://www.frontiersin.org/files/Articles/1785362/fpsyg-17-1785362-HTML/image_m/fpsyg-17-1785362-t003.jpg Table 3. Correlation coefficient of main variables. https://www.frontiersin.org/files/Articles/1785362/fpsyg-17-1785362-HTML/image_m/fpsyg-17-1785362-t004.jpg Table 4. Summary of regression results. https://www.frontiersin.org/files/Articles/1785362/fpsyg-17-1785362-HTML/image_m/fpsyg-17-1785362-t005.jpg Table 5. Instantaneous indirect effect. https://www.frontiersin.org/files/Articles/1785362/fpsyg-17-1785362-HTML/image_m/fpsyg-17-1785362-g002.jpg Figure 2. The moderating effect. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1539550/full https://www.frontiersin.org/files/Articles/1539550/fendo-16-1539550-HTML/image_m/fendo-16-1539550-g001.jpg Figure 1. Flow chart of the screening process for the selection of the study population. https://www.frontiersin.org/files/Articles/1539550/fendo-16-1539550-HTML/image_m/fendo-16-1539550-t001.jpg Table 1. Baseline characteristics of participants according to Non-COPD/ COPD from the U.S. National https://www.frontiersin.org/files/Articles/1539550/fendo-16-1539550-HTML/image_m/fendo-16-1539550-t002.jpg Table 2. Association of LC9 Scores with COPD Risk. https://www.frontiersin.org/files/Articles/1539550/fendo-16-1539550-HTML/image_m/fendo-16-1539550-g002.jpg Figure 2. Nonlinear associations between the LC9 LE8 and LS7 scoring systems and the risk of COPD in https://www.frontiersin.org/files/Articles/1539550/fendo-16-1539550-HTML/image_m/fendo-16-1539550-t003.jpg Table 3. Association of LC9 Scores with COPD Risk: Subgroup Analysis. https://www.frontiersin.org/files/Articles/1539550/fendo-16-1539550-HTML/image_m/fendo-16-1539550-g003.jpg Figure 3. ROC curves for "LC9", "LE8" and "LS7" scoring systems. The figure displays AUC with 95% co https://www.frontiersin.org/files/Articles/1539550/fendo-16-1539550-HTML/image_m/fendo-16-1539550-g004.jpg Figure 4. The mediation pathway analysis of the association between LC9 score and the risk of COPD t https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2026.1730470/full https://www.frontiersin.org/files/Articles/1730470/fped-14-1730470-HTML/image_m/fped-14-1730470-g001.jpg Figure 1. CONSORT study flow diagram. https://www.frontiersin.org/files/Articles/1730470/fped-14-1730470-HTML/image_m/fped-14-1730470-g002.jpg Figure 2. The study protocol and ultrasound lung examination region are shown. From left to right, t https://www.frontiersin.org/files/Articles/1730470/fped-14-1730470-HTML/image_m/fped-14-1730470-t001.jpg Table 1. Baseline characteristics and clinical data of neonatal patients under general anaesthesia. https://www.frontiersin.org/files/Articles/1730470/fped-14-1730470-HTML/image_m/fped-14-1730470-t002.jpg Table 2. Comparison of the incidence of significant atelectasis and lung ultrasound scores between t https://www.frontiersin.org/files/Articles/1730470/fped-14-1730470-HTML/image_m/fped-14-1730470-g003.jpg Figure 3. Comparison of intraoperative blood gas analysis, respiratory parameters and vital signs. ( https://www.frontiersin.org/files/Articles/1730470/fped-14-1730470-HTML/image_m/fped-14-1730470-g004.jpg Figure 4. The circulatory changes in the LPV group during lung RMs. (A) Circulatory changes during t