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        <title>Frontiers in Medical Engineering | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/medical-engineering</link>
        <description>RSS Feed for Frontiers in Medical Engineering | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-12T07:45:27.913+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2026.1774073</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2026.1774073</link>
        <title><![CDATA[Quality control in multiple-breath washout measurements: an automated, model-based approach quantifies effects on primary outcomes]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Florian Wyler</author><author>Silvio Borer</author><author>Marion Curdy</author><author>Xenia Bovermann</author><author>Bettina Sarah Frauchiger</author><author>Philipp Latzin</author>
        <description><![CDATA[Quality control (QC) is an important but challenging step in the correct interpretation of multiple-breath washout (MBW) tests, as irregular behaviors are frequent, particularly among young and ill patients, and can lead to biased outcomes. We developed a model-based, automated QC algorithm to estimate the effect of irregular behaviors on MBW outcomes. It compares results between simulated measurements containing either ideal target behaviors or the observed irregular behaviors of the measurement. Differences in primary outcomes - lung clearance index (LCI), functional residual capacity - between simulations served as behavior-specific QC outcomes. We validated the automated QC algorithm against a dataset of 2,471 measurements of 87 children with cystic fibrosis (CF). We compared the results of automated QC with the ones performed by experienced raters in a second dataset of 100 measurements of healthy children and children with CF. The test could be applied successfully to all measurements. We found that the impact of QC-related factors on MBW outcomes explained around 45% of the within-visit variability of LCI in children with CF. The largest effect on LCI was caused by leaks or trapped gas (in 31% of measurements), changes in end-expiratory lung volume (23%), and variations in breath size around the end-of-test (19%). For the second dataset, the agreement between automated QC and QC by experienced raters was moderate but comparable to QC performed by two experienced raters. Our novel automated QC tool estimates the accuracy of MBW outcomes, providing a fast, reproducible and impact-based method to perform QC for MBW measurements.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2026.1734520</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2026.1734520</link>
        <title><![CDATA[Enhancing precision in breast conserving surgery: introduction to emerging imaging technologies]]></title>
        <pubdate>2026-03-26T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>Elisa Wylleman</author><author>James Michaelson</author><author>Daniel Richard Leff</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2026.1548013</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2026.1548013</link>
        <title><![CDATA[Impact energy during cadaveric transforaminal lumbar interbody fusion (TLIF) is replicated by a modular benchtop device]]></title>
        <pubdate>2026-02-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Caitlin Luke</author><author>Micah Foster</author><author>Alexis Graham</author><author>Tanner Jones</author><author>Halleigh Faulkner</author><author>Alyna-Marie Janus</author><author>Clark Hensley</author><author>Jerald Redmond</author><author>MeLeah A. Henson</author><author>Lauren B. Priddy</author><author>Matthew W. Priddy</author>
        <description><![CDATA[In the treatment of intervertebral disc pathologies, lumbar interbody fusion (LIF) involves the removal and replacement of the degenerative disc with an interbody fusion device (IFD). Leveraging a benchtop impaction device for repeatable replication of cadaveric IFD insertion would alleviate the challenges of relying solely on cadaveric models and accelerate the establishment of use condition parameters for refinement of IFD design and LIF procedures. Using a custom benchtop device, the objective of this work was to determine the benchtop testing conditions which mimic the impact energy absorption during cadaveric transforaminal lumbar interbody fusion (TLIF). From initial experiments with 1.0 lb (0.454 kg) drop weights of steel, zinc, and aluminum, we determined aluminum facilitated an impact duration closest to that of historical cadaveric TLIF data. Thus, subsequent testing utilized the aluminum drop weight impacting Ti-6Al-4V IFDs (12 mm height x 24 mm length). A lateral compressive load of 200 N was applied using springs with a stiffness of 63.22 lb/in (11.07 N/mm). Drop heights from which the aluminum weight was dropped ranged from 60 cm to 120 cm in increments of 10 cm. Data were collected by an impact force sensor on the IFD insertion instrument, button compression sensors on the compression platens, and a laser displacement sensor below the IFD. The impact waveform for benchtop and cadaveric data was characterized by four waveform characteristics: impact duration, area under the impulse curve, peak force, and initial impact slope of the waveform. Area under the curve represented energy absorption from the insertion instrument to the IFD. The cadaveric impact duration was replicated by all drop height groups on the benchtop device, and the peak impact force was replicated by the 90 cm, 110 cm, and 120 cm groups. The area under the impulse curve was replicated by the 110 cm and 120 cm groups. No benchtop group replicated the initial impact slope of the waveform seen in cadaveric data. In conclusion, a benchtop impaction device was validated for replication of energy absorption during insertion of IFDs in cadaveric TLIF procedures. Ultimately, this work will accelerate advancements in IFD design and failure analysis, enhancing the repeatability of TLIF surgical techniques.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1476892</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1476892</link>
        <title><![CDATA[Bio-sampling alternative diagnostic imaging for telemedicine: a feasibility study of an AI-based throat camera for influenza]]></title>
        <pubdate>2026-02-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Masayoshi Nageishi</author><author>Shingo Kano</author>
        <description><![CDATA[IntroductionTelemedicine has progressed rapidly alongside advances in information and diagnostic technologies, providing benefits in accessibility, convenience, cost efficiency, and infection control. However, reliance on biological sample–based diagnostics, such as blood and urine tests, has limited the scope of fully remote diagnosis, confining many services to hybrid or in-clinic settings. Although home-based biological testing has partially addressed these constraints, fundamental barriers to complete remote diagnostic workflows remain.MethodsThis study evaluates the feasibility of bio-sampling alternative diagnostic imaging (BADI), an artificial intelligence–based diagnostic approach recently introduced in influenza testing. We developed a conceptual framework to compare conventional in-clinic testing, home-based biological testing, and BADI-based diagnostics across key dimensions relevant to remote medical implementation.ResultsThe analysis identified four essential conditions for the transition of BADI-type diagnostics to remote healthcare delivery: (1) sufficient availability and accessibility for patients, (2) assurance of diagnostic accuracy and clinical reliability, (3) high usability and interpretive clarity for both patients and healthcare providers, and (4) seamless integration into medical systems with immediate linkage to appropriate treatment.ConclusionBADI demonstrates significant potential to overcome the limitations of biological sample–dependent diagnostics in telemedicine. Addressing the identified conditions is critical for enabling safe, scalable, and effective remote diagnosis and for advancing fully remote healthcare models.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1703555</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1703555</link>
        <title><![CDATA[Mechanically adaptive hydrogels for bone tissue engineering: from classification and biomechanics to modeling and translational applications]]></title>
        <pubdate>2025-12-18T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ioana-Cristina Băncilă</author>
        <description><![CDATA[Hydrogels are key materials in bone tissue engineering due to their high water content, biocompatibility, and tunable mechanics. Mechanically adaptive hydrogels, a class of smart biomaterials, can dynamically adjust stiffness and viscoelasticity in response to environmental cues, closely mimicking bone extracellular matrix behavior. This review critically synthesizes the hydrogel types, biomechanical properties, and scaffold fabrication strategies, with a focus on mechanically responsive systems. Finite element modeling (FEM) is highlighted as a predictive tool for scaffold design, while bone-on-chip (BoC) platforms provide physiologically relevant in vitro evaluation. Recent advances in composite hydrogels, reinforcement methods, and multi-scale modeling are analysed to identify gaps in standardization, mechanical mapping and biological outcomes. By linking mechanical adaptability to clinical scenarios such as craniofacial reconstruction, spinal fusion, and osteochondral repair, this review provides a concise framework for the rational design and translation and future research in mechanically adaptive hydrogels in bone regeneration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1628589</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1628589</link>
        <title><![CDATA[A review on the use of radionuclide imaging techniques to detect margin positivity in intraoperative specimens during breast-conserving surgery]]></title>
        <pubdate>2025-10-14T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Aaditya Sinha</author><author>Arnie Purushotham</author>
        <description><![CDATA[IntroductionAchieving negative margins during breast-conserving surgery (BCS) for breast cancer is critical to reduce re-excision rates and minimise local recurrence. Intraoperative imaging techniques using radiotracers such as 18F-fluorodeoxyglucose (18F-FDG) offer a promising solution. When administered intravenously, 18F-FDG accumulates preferentially in malignant tissues due to their elevated glycolytic activity, enabling molecular imaging of tumour margins. Technologies such as Cerenkov Luminescence Imaging (CLI), Flexible Auto-Radiography (FAR), and intraoperative PET/CT systems have emerged as tools to visualise radiotracer distribution in excised breast tissue, offering real-time insight into margin status.Materials and methodologyCLI operates on the principle of detecting visible light photons generated by positrons from 18F-FDG travelling faster than light in tissue. FAR captures beta particles via a scintillating film to yield high-resolution surface maps of tracer activity. These modalities were evaluated both independently and in combination (CLI-FAR) using the LightPath® system, while the XEOS AURA 10 system was utilised for intraoperative PET/CT imaging. A series of feasibility studies and interventional trials assessed their diagnostic performance in real-time margin assessment during BCS.ResultsGrootendorst et al. (J. Nucl. Med., 2017, 58(6), 891–898) demonstrated that CLI achieved 89% sensitivity and 95% specificity in identifying positive margins in a cohort of 12 patients. Jurrius et al. (EJNMMI Res., 2021, 11(1)) reported 81.7% sensitivity and 46.2% specificity with FAR in 66 patients. The CLI-FAR technique, by Sinha et al. (Radiol. Adv., 2024, 1(2)), yielded 76.9% sensitivity and 97.8% specificity, reducing re-excision rates by 69%. PET/CT-based intraoperative imaging using the AURA 10 device, as evaluated by Crem et al. (ESMO Open, 2024, 9), achieved 91% sensitivity and 94% specificity, while Göker et al. (Acta Chir. Belg., 2020, 120(5), 366–374) reported 79% sensitivity and 72% specificity using micro-PET/CT. Radiation exposure to surgical staff across studies remained low (15–38 µSv), and imaging added minimal time to operative workflows.ConclusionRadionuclide-based intraoperative specimen imaging offers a viable, real-time solution for margin assessment in BCS. Techniques such as CLI, FAR, and intraoperative PET/CT demonstrate a strong correlation with histopathology, with the potential to significantly reduce re-excision rates. Challenges remain in imaging larger specimens and tumours with low metabolic activity. However, integrating these technologies into surgical practice presents a transformative opportunity for precision-guided oncologic surgery.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1571528</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1571528</link>
        <title><![CDATA[The role of micro-CT in breast cancer management: a systematic review on the clinical applications of micro-CT in breast cancer and a diagnostic accuracy meta-analysis on intraoperative margin assessment]]></title>
        <pubdate>2025-09-25T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Seyed Mostafa Meshkati Yazd</author><author>Mahtab Vasigh</author><author>Andreas S. Papazoglou</author><author>Alexandros C. Liatsos</author><author>Keivan Ranjbar</author><author>Austin D. Williams</author><author>Richard J. Bleicher</author><author>Orestis L. Katsamenis</author><author>Mathew L. Pierotti</author><author>Christian X. Cruz Pico</author><author>Stamatios Theocharis</author><author>Fani Tsolaki</author><author>Daniel R. Leff</author><author>James S. Michaelson</author>
        <description><![CDATA[BackgroundThere is a growing interest in exploring intraoperative methods for margin assessment of malignant breast specimens in breast-conserving surgeries (BCS). Micro-computed tomography (micro-CT) has already exhibited clinical value, yielding high-resolution three-dimensional (3D) volumetric images. Against this background, this study aimed to systematically evaluate the role of micro-CT in intraoperative margin assessment (IMA) in BCS.MethodsA systematic literature review has been conducted in Scopus, EMBASE, and PubMed up to 10 December 2024. Studies reporting the diagnostic indices of micro-CT for IMA compared to histopathologic results were utilized for a diagnostic accuracy meta-analysis.ResultsEight out of the initially retrieved 2,921 studies evaluated the role of micro-CT in IMA and were eligible for calculating the pooled diagnostic indices. In those studies, 988 specimens/margins were scanned, and the scanning time ranged from 4 to 30 min. The pooled diagnostic indices were: a sensitivity of 0.63 (95% CI: 0.45–0.79), a specificity of 0.78 (95% CI: 0.68–0.85), and an accuracy of 0.77 (95% CI: 0.71–0.84) for micro-CT based IMA compared to the gold-standard histopathological assessment.ConclusionThis study demonstrates that micro-CT imaging is a promising IMA technique for BCS by providing high-resolution 3D images. These images can be acquired within a few minutes, allowing surgeons to assess margin status intra-operatively, and identify more than 70% of positive margins where reoperation rates are likely to decrease. Although these findings are encouraging, their clinical translation is still under investigation, and adequately empowered clinical trials are warranted to investigate the re-excision and local recurrence rates after micro-CT IMA assessment.Systematic Review Registrationhttps://osf.io/342h8.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1608247</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1608247</link>
        <title><![CDATA[X-ray phase contrast for intra-operative specimen imaging in breast conserving surgery and other areas]]></title>
        <pubdate>2025-09-17T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Alessandro Olivo</author><author>Daniel R. Leff</author>
        <description><![CDATA[Current platforms for cancer surgery are inherently imprecise and this is manifest in high rates of incomplete excision and reoperative intervention. A prominent example is breast conserving surgery where intra-operative determination of margin involvement is challenging leading to high national average rates of positive resection margins needing revisional procedures. To meet these demands of improved precision it is valuable to image the resected tissue in real time in such a way that enables tissue characterization. A plethora of imaging methods have been proposed, with X-ray micro-CT appearing as one of the most promising due to its ability to scan the entire resection in 3D, as opposed to 2D imaging methods and/or approaches that only allow sampling the tissue at specific locations with limited field-of-view. A key, well-known limitation is the limited soft tissue sensitivity of X-rays, which has recently been overcome through the advent of X-ray phase contrast imaging (XPCI). The introduction of XPCI methods working with conventional sources (as opposed to specialized facilities such as synchrotrons) has spawn a series of exciting studies aiming at translating XPCI into clinical applications, which have recently extended into the realm of intra-operative imaging for breast conserving surgery and other areas. This article briefly introduces the XPCI technology, then reviews its existing applications in intra-operative imaging.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1607453</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1607453</link>
        <title><![CDATA[Confocal fluorescence microscopy for real-time breast cancer diagnosis: current advances and future perspectives]]></title>
        <pubdate>2025-09-12T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Khushi Vyas</author><author>Ahmed Ezzat</author><author>Nicholas Holford</author><author>Rathi Ramakrishnan</author><author>Daniel R. Leff</author>
        <description><![CDATA[BackgroundConfocal fluorescence microscopy (CFM) is a powerful optical biopsy technique which captures cellular resolution images of the tissue surface without the need for tissue fixation or sectioning. The evolution of CFM with miniaturization and fibre-based optics now allows rapid capture of wide field images with microscopic resolution. For in-situ diagnostics, there is growing evidence that CFM systems could rapidly and accurately identify breast cancer with clinically actionable results.Review FocusThis comprehensive review discusses different technological advances in CFM systems and explores emerging trends in Artificial Intelligence (AI) and robotic integration in breast cancer imaging. The review further discusses the clinical implications of these technologies, including their potential to reduce re-excision rates following breast conserving surgery (BCS) and improve surgical workflow efficiency.MethodsA comprehensive literature review using PubMed, Embase and Web of Science databases was conducted by three reviewers independently covering studies published from January 2013 to December 2024. We included studies that provided human tissue data (preclinical and clinical) relevant to breast cancer imaging, focusing on the technological features, intra-operative usability, and ease of use of different bench-top and fibre-based CFM systems. Research focusing on future trends and emerging challenges in standardizing imaging protocols for breast cancer CFM imaging and automating diagnostic workflows were also considered.Results and conclusionOf 1382 articles identified from database screening, 28 fulfilled the inclusion criteria. Only 10 clinical studies reported statistical differentiation among specimens. Bench-top CFM systems demonstrated high-resolution imaging with accuracy ranging 83%–99.6% making them effective for detailed tissue analysis. However, their size and operational complexity limit their use during live surgery. In contrast, fibre-based CFM systems offer miniaturized flexible micro-endoscopes that enable real-time, in-situ imaging with accuracy upto 94% demonstrating suitability for intra-operative diagnosis. Notably, fibre-bundle based Cellvizio® confocal laser endomicroscopy (CLE) system and line-scan CLE system can identify breast pathology but data is lacking on intra-operative diagnostic accuracy for margin assessment on wide local excision specimens. New developments like the commercial Histolog® Confocal Microscopy system (SamanTree Medical SA, Lausanne, Switzerland) has potential to identify missed tumour margins in up to 75% of cases, enhancing the accuracy of margin assessments.While these technologies are promising, several obstacles must be overcome before CFM can be widely adopted in routine surgical practice. Additionally, AI- powered automation in CFM, although promising, requires large-scale validation to ensure accurate real-time tissue classification. Integrating robotics and AI-enhanced CFM could greatly improve real-time surgical decision-making, minimizing interpretation errors and enhancing workflow efficiency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1606951</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1606951</link>
        <title><![CDATA[Finite element analysis of cardiovascular stent frames: identifying appropriate mesh discretization]]></title>
        <pubdate>2025-07-02T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Brandon A. Lurie</author><author>Stuart Kari</author><author>Marc Horner</author><author>Christopher A. Basciano</author><author>Santhosh Kumar Murugadass</author><author>Nuno Rebelo</author>
        <description><![CDATA[Engineers use finite element analysis (FEA) to predict the deformations, strains, stresses, and resistive forces of metallic stent frames under in vivo, in vitro, and manufacturing-induced loading conditions. The discretization of the geometric model influences the simulation predictions, with the error generally reducing with mesh refinement. This improved accuracy comes with the trade-off of requiring more resources. Since FEA influences decisions that carry patient and business risk, engineers must balance the computational cost against numerical accuracy. This paper explores a methodology for selecting the mesh discretization for a computational model of an implantable stent frame based on discretization error, computational cost, and the risk associated with using the model to inform a specific decision. The methodology includes estimating the exact solution for the numerical model, calculating the discretization error and computational cost for various mesh discretization options, and considering the error and cost when selecting one of the options. The method was applied to a laser-cut nitinol stent model for four different finite element solvers to demonstrate its real-world applicability and that it is agnostic to solver type and developer. We were able to estimate the exact solution to the numerical model with a 95% confidence interval using submodeling, a geometry representative of the full stent frame, and four systematically refined meshes. The selection of the mesh discretization is subjective, with the importance of each model’s computational cost dependent on the number of simulations, resource availability, and risk. Three real-world implantable medical device examples of using FEA to inform a project decision are presented, each with a mesh discretization option suggested and rationalized based on the discretization error and computational costs. FEA’s important role in developing implantable stent frames and providing evidence of their safety to decision makers and regulatory bodies underscores the need for a method to select a suitable mesh discretization. The methodology explored in this paper calculates the error in the model’s prediction due to discretization and the computational cost. A project team can use this information and the risk associated with using the model to select and rationalize a specific mesh discretization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1560136</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1560136</link>
        <title><![CDATA[Chest wall restriction device for modeling respiratory challenges and dysfunction]]></title>
        <pubdate>2025-05-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Victoria Ribeiro Rodrigues</author><author>Lizuannette Mejia</author><author>Rafael G. Zucchi</author><author>Paul W. Davenport</author><author>Nicholas J. Napoli</author>
        <description><![CDATA[Breathing relies on unrestricted movement of the chest wall to maintain O2 and CO2 balance. Understanding the effects of chest and abdominal restrictions on respiratory function is critical for studying conditions such as respiratory diseases, extreme environments, and load-induced impairments. However, existing methods to simulate these restrictions are limited, lacking the ability to provide both static and dynamic conditions or precise load control. To address these gaps, we developed a novel chest wall and abdomen restriction device capable of independently applying and measuring static and dynamic loads with adjustable and reproducible force levels. Separate bands for the chest and abdomen enable targeted restrictions. In static conditions, the bands are immobilized, preventing any movement of the chest and abdomen. In dynamic conditions, constant force springs provide resistance, allowing movement when sufficient force is applied. Integrated sensors quantify applied loads and respiratory mechanics. To validate the device, healthy participants underwent pulmonary function testing under baseline, static, and dynamic restriction conditions. Significant reductions in forced expiratory volume (FEV1) and forced vital capacity (FVC) were observed under restrictions compared to baseline. Other respiratory metrics also differed significantly, highlighting distinct effects of static and dynamic restrictions. Pressure variability tests confirmed reproducibility and adjustability of loads, while displacement data from linear variable differential transducers (LVDTs) validated the device’s ability to distinguish static and dynamic effects. This device addresses prior limitations by enabling precise, reproducible loading and independent control of chest and abdominal restrictions, supporting research into respiratory diseases, extreme environments, and respiratory mechanics. Our results demonstrate its potential to advance respiratory function research and expand clinical and experimental applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1547895</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1547895</link>
        <title><![CDATA[In Vivo quantification of 4D modeling and remodeling in trabecular and cortical bone microstructure]]></title>
        <pubdate>2025-03-21T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Peter T. Shyu</author><author>Samuel T. Robinson</author><author>X. Edward Guo</author>
        <description><![CDATA[Bone is constantly adapting each of its microstructural compartments by modeling and remodeling. These adaptations are delineated by whether bone formation and resorption are coupled in space and time. Time-lapse microCT imaging has become a valuable technique for characterizing bone dynamics in 3D. Our previous study used longitudinal microCT imaging to quantify modeling and remodeling across the bone microstructure in response to PTH treatment and mechanical loading. Here, we detail our technique of voxel-tracking to specifically identify time-dependent modeling and remodeling by examining the sequence of formation and resorption events in trabecular and cortical bone. We apply this technique to WT and SOST KO littermate mice under long-term mechanical loading and quantify site-specific bone volume changes. Loading particularly affected WT trabecular and periosteal bone by increasing anabolic modeling and remodeling while decreasing catabolic modeling. Under load-controlled loading, these effects were reduced in SOST KO mice. Endosteal bone was less responsive to loading for both genotypes, with subtler and more time-dependent responses resulting in a load-dependent increase in WT catabolic modeling. Thus, we present a technique that directly assesses longitudinal 3D bone modeling and remodeling across the bone microstructure.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1397406</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1397406</link>
        <title><![CDATA[Whole secretome of mesenchymal stem cells is fully incorporated in lipid bicontinuous cubic phases]]></title>
        <pubdate>2025-03-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sandra Barata-Antunes</author><author>Rui A. Sousa</author><author>António J. Salgado</author><author>Bruno F. B. Silva</author>
        <description><![CDATA[Lipid bicontinuous cubic phases are precursors to cubosomes–a promising type of nanoparticle for the delivery of multicomponent biomolecular mixtures for applications in health such as regenerative medicine and wound healing. In this study, we showed that the secretome of mesenchymal stem cells (MSCs), a complex mixture of growth factors, cytokines, extracellular vesicles, and other cell-secreted molecules with therapeutic potential, can be fully incorporated into the bicontinuous cubic phases of phytantriol and monoolein. When the secretome was added to dry lipid films, the resulting partial phase diagrams of these lipid-secretome systems, although more complex, resemble those of their lipid-water analogs. Remarkably, visual inspections and Small-Angle X-ray Scattering (SAXS) studies showed composition regions of homogeneous solid-like lipid mesophases without excess liquid phase-separation. This indicates that the diverse secretome components, even with their varied sizes and structures, are fully integrated into the cubic phases. SAXS showed patterns dominated by bicontinuous cubic phases with structural parameters close to the lipid-water systems. This suggests that water-soluble proteins likely localize within the water channels of the bicontinuous cubic phase, which must exhibit flexibility to accommodate proteins of diverse sizes, likely through the formation of locally disordered channels. Extracellular vesicles and associated membrane proteins, on the other hand, are likely fusing with and integrating into the cubic membranes. These findings underscore the potential of such liquid crystalline materials as matrices for the entire secretome, paving the way for future secretome-based cell-free therapeutics such as tissue regeneration, neuroprotective and anti-inflammatory treatments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2024.1455116</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2024.1455116</link>
        <title><![CDATA[Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs]]></title>
        <pubdate>2024-12-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Benjamin Caillet</author><author>Gilbert Maître</author><author>Steve Devènes</author><author>Darren Hight</author><author>Alessandro Mirra</author><author>Olivier L. Levionnois</author><author>Alena Simalatsar</author>
        <description><![CDATA[We here present a deep-learning approach for computing depth of anesthesia (DoA) for pigs undergoing general anesthesia with propofol, integrated into a novel general anesthesia specialized MatLab-based graphical user interface (GAM-GUI) toolbox. This toolbox permits the collection of EEG signals from a BIOPAC MP160 device in real-time. They are analyzed using classical signal processing algorithms combined with pharmacokinetic and pharmacodynamic (PK/PD) predictions of anesthetic concentrations and their effects on DoA and the prediction of DoA using a novel deep learning-based algorithm. Integrating the DoA estimation algorithm into a supporting toolbox allows for the clinical validation of the prediction and its immediate application in veterinary practice. This novel, artificial-intelligence-driven, user-defined, open-access software tool offers a valuable resource for both researchers and clinicians in conducting EEG analysis in real-time and offline settings in pigs and, potentially, other animal species. Its open-source nature differentiates it from proprietary platforms like Sedline and BIS, providing greater flexibility and accessibility.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2024.1463793</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2024.1463793</link>
        <title><![CDATA[3D scanner measuring preterm infants’ head circumference and cranial volume: validation in a simulated care setting]]></title>
        <pubdate>2024-11-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ronald van Gils</author><author>Onno Helder</author><author>René Kornelisse</author><author>Irwin Reiss</author><author>Jenny Dankelman</author>
        <description><![CDATA[IntroductionWeekly head circumference (HC) measurements using a measuring tape is the current standard for longitudinal brain growth monitoring of preterm infants. The MONITOR3D (M3D) 3D scanner has been developed to measure both HC and cranial volume (CrV) of preterm infants within incubators. The M3D’s usability, accuracy and precision were validated in a simulated setting in a neonatal intensive care unit (NICU).Materials and methodsDuring a simulated routine care moment, NICU nurses conducted M3D scans of a preterm doll simulating an extreme low birthweight preterm (ELBW; BW < 1,000 g) infant, followed by manual HC measurements using a measuring tape. Usability was quantified by percentage of successful HC and CrV measurements from scans. HC and CrV were calculated by marking anatomical landmarks on the 3D image. Measurements were compared to the real, ground truth (GT) values of the doll’s head, defined by an accurate medical scanner. Measurement accuracy was assessed using mean or median absolute measurement error (ME), and precision by the spread of ME, represented by the 95% interval of the ME range. ME intervals were compared with preterm weekly growth increases to assess clinical usability.ResultsRegarding usability, 56 M3D scan sessions resulted in 25 successful (44.6%) HC and CrV measurements, with incomplete 3D data being the primary cause of unsuccessful scans. Accuracy of the measuring tape for HC was 0.2 cm (proportional 0.9% of GT), and precision was 1.6 cm (6.3%). M3D’s accuracy of HC was 0.4 cm (1.5%), and precision was 0.7 cm (2.9%). For CrV, M3D’s accuracy was 8.0 mL (3.8%) and precision 22.6 mL (10.8%).ConclusionThe M3D scanner is suitable for measuring HC and CrV in ELBW infants. However, current scan success rate is too low for practical usability. The M3D’s accuracy and precision are clinically sufficient, while the precision of the current measuring tape method is inadequate for preterm infants. This makes the M3D a promising alternative for HC, offering less disturbance to the infant. In the future, the M3D technique could facilitate the creation of CrV growth reference charts for ELBW infants, enhancing the accuracy of clinical growth monitoring for preterm infants.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2024.1484232</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2024.1484232</link>
        <title><![CDATA[Gluing osteochondral fragments: development of a novel strategy for dual adhesive application in a preclinical model]]></title>
        <pubdate>2024-11-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alicja J. Bojan</author><author>Peyman Karami</author><author>Philip Procter</author><author>Dominique P. Pioletti</author>
        <description><![CDATA[This study proposes a novel dual adhesive approach for fixing osteochondral fractures, aiming to address the limitations of current fixation methods by incorporating both a bone adhesive (phosphoserine modified calcium phosphate cement PM-CPC) and a cartilage adhesive (methacrylated phosphoserine-containing gelatin MePGa hydrogel). The feasibility and efficacy of this approach were investigated using an ex vivo bovine knee model. Results indicate successful gluing of osteochondral cylinders with both adhesives, with no significant difference in adhesion strength between the groups (adhesion strength mean of 1211.6 kPa, SD 602.4 kPa, and mean of 1299.6 kPa, SD 850.9 kPa for groups 1 and 2 respectively). Importantly, the inclusion of the hydrogel component in the dual adhesive system aims to enhance cartilage repair potential, complementing the mechanical support provided by the bone adhesive. Each adhesive offers distinctive benefits: PM-CPC for mechanical support and bone repair, and MePGa hydrogel for cartilage repair. The study demonstrates the potential of the dual adhesive strategy for osteochondral repair, though further refinement and in vivo validation are needed.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2024.1491942</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2024.1491942</link>
        <title><![CDATA[Thermomechanobiology as a new research field in soft tissues]]></title>
        <pubdate>2024-11-06T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Dominique P. Pioletti</author>
        <description><![CDATA[During intense galloping, the difference in temperature between the external and the central part of an equine superficial digital flexor tendon can be as high as 7°C. Thirty minutes of jogging modifies the temperature in human knee cartilage from 32°C to 37°C. Intrinsic dissipative phenomena related to the viscoelastic behavior of soft tissues have been identified to be primarily responsible for the observed temperature increase, a situation referred to as self-heating in mechanics. While a 5°C increase may be considered negligible from a mechanical point of view in the cartilage at first sight, it can have a significant biological impact. It has been recently proposed that self-heating and the resulting increase of temperature in cartilage following mechanical stimulation can be necessary for its maintenance. This new concept complements the general acceptance that mechanobiology is central to the homeostasis of musculoskeletal tissues. In most biomechanical and biological studies on cartilage or other soft tissues, the temperature is set at 37°C and considered constant, despite human knee cartilage at rest being around 32°C, for example. Therefore, there is a deficit of information on the role and effect of physiological temperature variation induced through mechanical loading in soft tissues, opening a new research avenue that we coin thermomechanobiology.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2024.1434753</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2024.1434753</link>
        <title><![CDATA[Identifying neurophysiological correlates of stress]]></title>
        <pubdate>2024-10-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dingyi Pei</author><author>Shravika Tirumala</author><author>Kyaw T. Tun</author><author>Akshara Ajendla</author><author>Ramana Vinjamuri</author>
        <description><![CDATA[Stress has been recognized as a pivotal indicator which can lead to severe mental disorders. Persistent exposure to stress will increase the risk for various physical and mental health problems. Early and reliable detection of stress-related status is critical for promoting wellbeing and developing effective interventions. This study attempted multi-type and multi-level stress detection by fusing features extracted from multiple physiological signals including electroencephalography (EEG) and peripheral physiological signals. Eleven healthy individuals participated in validated stress-inducing protocols designed to induce social and mental stress and discriminant multi-level and multi-type stress. A range of machine learning methods were applied and evaluated on physiological signals of various durations. An average accuracy of 98.1% and 97.8% was achieved in identifying stress type and stress level respectively, using 4-s neurophysiological signals. These findings have promising implications for enhancing the precision and practicality of real-time stress monitoring applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2024.1419786</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2024.1419786</link>
        <title><![CDATA[A stochastic model-based control methodology for glycemic management in the intensive care unit]]></title>
        <pubdate>2024-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Melike Sirlanci</author><author>George Hripcsak</author><author>Cecilia C. Low Wang</author><author>J. N. Stroh</author><author>Yanran Wang</author><author>Tellen D. Bennett</author><author>Andrew M. Stuart</author><author>David J. Albers</author>
        <description><![CDATA[Introduction: Intensive care unit (ICU) patients exhibit erratic blood glucose (BG) fluctuations, including hypoglycemic and hyperglycemic episodes, and require exogenous insulin delivery to keep their BG in healthy ranges. Glycemic control via glycemic management (GM) is associated with reduced mortality and morbidity in the ICU, but GM increases the cognitive load on clinicians. The availability of robust, accurate, and actionable clinical decision support (CDS) tools reduces this burden and assists in the decision-making process to improve health outcomes. Clinicians currently follow GM protocol flow charts for patient intravenous insulin delivery rate computations.Methods: We present a mechanistic model-based control algorithm that estimates the optimal intravenous insulin rate to keep BG within a target range; the goal is to develop this approach for eventual use within CDS systems. In this control framework, we employed a stochastic model representing BG dynamics in the ICU setting and used the linear quadratic Gaussian control methodology to develop a controller.Results: We designed two experiments, one using virtual (simulated) patients and one using a real-world retrospective dataset. Using these, we evaluated the safety and efficacy of this model-based glycemic control methodology. The presented controller avoids hypoglycemia and hyperglycemia in virtual patients, maintaining BG levels in the target range more consistently than two existing GM protocols. Moreover, this methodology could theoretically prevent a large proportion of hypoglycemic and hyperglycemic events recorded in a real-world retrospective dataset.Discussion: The current version of the methodology shows potential usefulness in GM of ICU patients. However, it is limited to a subgroup of the ICU patient population, who are fed through and enteral tube and delivered intravenous insulin. After extending to a broader ICU patient population who can consume oral nutrition and are delivered subcutaneous insulin for GM, the methodology could be tested with pilot studies and clinical trials for eventual use as a CDS tool.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2024.1393224</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2024.1393224</link>
        <title><![CDATA[Measure of the prediction capability of EEG features for depth of anesthesia in pigs]]></title>
        <pubdate>2024-07-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Benjamin Caillet</author><author>Gilbert Maître</author><author>Alessandro Mirra</author><author>Olivier L. Levionnois</author><author>Alena Simalatsar</author>
        <description><![CDATA[Introduction: In the medical and veterinary fields, understanding the significance of physiological signals for assessing patient state, diagnosis, and treatment outcomes is paramount. There are, in the domain of machine learning (ML), very many methods capable of performing automatic feature selection. We here explore how such methods can be applied to select features from electroencephalogram (EEG) signals to allow the prediction of depth of anesthesia (DoA) in pigs receiving propofol.Methods: We evaluated numerous ML methods and observed that these algorithms can be classified into groups based on similarities in selected feature sets explainable by the mathematical bases behind those approaches. We limit our discussion to the group of methods that have at their core the computation of variances, such as Pearson’s and Spearman’s correlations, principal component analysis (PCA), and ReliefF algorithms.Results: Our analysis has shown that from an extensive list of time and frequency domain EEG features, the best predictors of DoA were spectral power (SP), and its density ratio applied specifically to high-frequency intervals (beta and gamma ranges), as well as burst suppression ratio, spectral edge frequency and entropy applied to the whole spectrum of frequencies.Discussion: We have also observed that data resolution plays an essential role not only in feature importance but may impact prediction stability. Therefore, when selecting the SP features, one might prioritize SP features over spectral bands larger than 1 Hz, especially for frequencies above 14 Hz.]]></description>
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