Tuberculosis (TB) remains a global health challenge, necessitating the development of precise diagnostic tools to discriminate between active TB (ATB) and latent TB infection (LTBI). TB manifests in two primary states including ATB and LTBI. ATB arises when Mycobacterium tuberculosis proliferates, prompting symptomatic expression. Clinical manifestations include coughing (sometimes hemoptysis), chest pain, weakness, weight loss, fever, and night sweats. Airborne transmission ensues, highlighting the urgency of diagnosis and multi-drug antibiotic treatment. Conversely, LTBI denotes asymptomatic individuals with a positive TB test but absent clinical disease. LTBI, while non-contagious, poses a risk of progression to ATB, particularly in immunocompromised individuals. Identifying LTBI permits targeted antibiotic therapy, mitigating progression risks. Accurate discrimination between these two statuses is pivotal for effective management, encompassing containment of transmission, tailored treatment strategies, and informed public health interventions. Such precision facilitates comprehensive TB control efforts, optimizing individual outcomes and population health. This Research Topic endeavours to explore the intricate immunological characteristics that could distinguish these two states, aiming to identify reliable biomarkers and introduce novel diagnostic approaches.
The present investigation will delve into the host immune response, specifically focusing on the dynamic immune cell response and cytokine profiles associated with ATB and LTBI. By deciphering these immunological signatures, we aim to unveil potential biomarkers and techniques that exhibit adequate capacity in differentiating between these two TB statuses. Additionally, this Research Topic will critically evaluate existing diagnostic methods and highlight their strengths and limitations, paving the way for introducing innovative approaches. Cutting-edge technologies such as advanced single cell-based sequencing assays, multiplexed cytometric and serological panels, as well as emerging algorithms derived from machine learning will be explored to enhance the accuracy and efficiency of TB diagnostics.
The goal of this collection is to contribute to the development of a comprehensive diagnostic toolkit that enables clinicians to discriminate between ATB and LTBI with heightened precision. Such advancements in diagnostic strategies hold the promise of facilitating timely and targeted interventions, thereby aiding in the global effort to control and eliminate TB.
Scope:
1. Novel approaches or biomarkers for the discrimination ATB from LTBI:
Delve into the forefront of TB research, where scientists are pioneering innovative methods and biomarkers to distinguish between ATB and LTBI. Explore the latest advancements in diagnostic techniques. This illuminates the promising avenues for more accurate and timely diagnosis, which is critical for effective TB management and control.
2. Cutting-edge technologies targeting the prediction of the transition between ATB and LTBI:
Witness the integration of cutting-edge technologies in the quest to predict the transition between ATB and LTBI. Discover how advances in genomics, transcriptomics, proteomics, and machine learning are revolutionizing our understanding of TB dynamics. Explore predictive models that leverage big data analytics and artificial intelligence to forecast individuals at risk of progressing from latent infection to active disease. This unveils the potential of precision medicine approaches to personalize TB prevention strategies and optimize healthcare resources.
3. Transformation and application of multidisciplinary integration in TB diagnostics:
Enter the era of multidisciplinary collaboration, where experts from diverse fields converge to tackle the complex challenges of TB diagnostics. Uncover how integration across disciplines such as microbiology, immunology, bioinformatics, and engineering is driving innovation in TB diagnostic technologies. Explore hybrid approaches that combine traditional laboratory methods with cutting-edge techniques and point-of-care devices. This manuscript showcases the transformative power of multidisciplinary integration in accelerating the development and deployment of next-generation TB diagnostic tools.
4. Powerful and high-standard verification of existing methods for diagnosing TB infection:
Explore the rigorous validation and verification processes essential for ensuring the accuracy and reliability of existing TB diagnostic methods. Delve into the meticulous evaluation of conventional techniques such as microscopy, culture, and nucleic acid amplification tests to establish their sensitivity, specificity, and reproducibility. Discover how advancements in quality assurance and standardization protocols are raising the bar for TB diagnostic testing globally. This highlights the importance of robust validation studies in underpinning evidence-based TB control strategies and improving patient outcomes.
Tuberculosis (TB) remains a global health challenge, necessitating the development of precise diagnostic tools to discriminate between active TB (ATB) and latent TB infection (LTBI). TB manifests in two primary states including ATB and LTBI. ATB arises when Mycobacterium tuberculosis proliferates, prompting symptomatic expression. Clinical manifestations include coughing (sometimes hemoptysis), chest pain, weakness, weight loss, fever, and night sweats. Airborne transmission ensues, highlighting the urgency of diagnosis and multi-drug antibiotic treatment. Conversely, LTBI denotes asymptomatic individuals with a positive TB test but absent clinical disease. LTBI, while non-contagious, poses a risk of progression to ATB, particularly in immunocompromised individuals. Identifying LTBI permits targeted antibiotic therapy, mitigating progression risks. Accurate discrimination between these two statuses is pivotal for effective management, encompassing containment of transmission, tailored treatment strategies, and informed public health interventions. Such precision facilitates comprehensive TB control efforts, optimizing individual outcomes and population health. This Research Topic endeavours to explore the intricate immunological characteristics that could distinguish these two states, aiming to identify reliable biomarkers and introduce novel diagnostic approaches.
The present investigation will delve into the host immune response, specifically focusing on the dynamic immune cell response and cytokine profiles associated with ATB and LTBI. By deciphering these immunological signatures, we aim to unveil potential biomarkers and techniques that exhibit adequate capacity in differentiating between these two TB statuses. Additionally, this Research Topic will critically evaluate existing diagnostic methods and highlight their strengths and limitations, paving the way for introducing innovative approaches. Cutting-edge technologies such as advanced single cell-based sequencing assays, multiplexed cytometric and serological panels, as well as emerging algorithms derived from machine learning will be explored to enhance the accuracy and efficiency of TB diagnostics.
The goal of this collection is to contribute to the development of a comprehensive diagnostic toolkit that enables clinicians to discriminate between ATB and LTBI with heightened precision. Such advancements in diagnostic strategies hold the promise of facilitating timely and targeted interventions, thereby aiding in the global effort to control and eliminate TB.
Scope:
1. Novel approaches or biomarkers for the discrimination ATB from LTBI:
Delve into the forefront of TB research, where scientists are pioneering innovative methods and biomarkers to distinguish between ATB and LTBI. Explore the latest advancements in diagnostic techniques. This illuminates the promising avenues for more accurate and timely diagnosis, which is critical for effective TB management and control.
2. Cutting-edge technologies targeting the prediction of the transition between ATB and LTBI:
Witness the integration of cutting-edge technologies in the quest to predict the transition between ATB and LTBI. Discover how advances in genomics, transcriptomics, proteomics, and machine learning are revolutionizing our understanding of TB dynamics. Explore predictive models that leverage big data analytics and artificial intelligence to forecast individuals at risk of progressing from latent infection to active disease. This unveils the potential of precision medicine approaches to personalize TB prevention strategies and optimize healthcare resources.
3. Transformation and application of multidisciplinary integration in TB diagnostics:
Enter the era of multidisciplinary collaboration, where experts from diverse fields converge to tackle the complex challenges of TB diagnostics. Uncover how integration across disciplines such as microbiology, immunology, bioinformatics, and engineering is driving innovation in TB diagnostic technologies. Explore hybrid approaches that combine traditional laboratory methods with cutting-edge techniques and point-of-care devices. This manuscript showcases the transformative power of multidisciplinary integration in accelerating the development and deployment of next-generation TB diagnostic tools.
4. Powerful and high-standard verification of existing methods for diagnosing TB infection:
Explore the rigorous validation and verification processes essential for ensuring the accuracy and reliability of existing TB diagnostic methods. Delve into the meticulous evaluation of conventional techniques such as microscopy, culture, and nucleic acid amplification tests to establish their sensitivity, specificity, and reproducibility. Discover how advancements in quality assurance and standardization protocols are raising the bar for TB diagnostic testing globally. This highlights the importance of robust validation studies in underpinning evidence-based TB control strategies and improving patient outcomes.