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Related Concept Videos

Pulmonary Tuberculosis IV01:26

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Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
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Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
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Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
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Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
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Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
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Machine-Learning-Derived, Mechanistically Informed Transcriptomic Signature to Diagnose Active Tuberculosis and Guide

Asif Hassan Syed1, Nashwan Alromema1, Hatem A Almazarqi2

  • 1Department of Computer Science, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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A new four-gene signature accurately distinguishes active tuberculosis (TB) from latent TB infection (LTBI). This transcriptomic biomarker offers a non-sputum diagnostic and reveals host pathways for targeted TB treatment.

Keywords:
diagnostic biomarkershost-directed therapymachine learningtranscriptomicstuberculosis

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Area of Science:

  • Infectious Diseases
  • Genomics
  • Biomarker Discovery

Background:

  • Differentiating active tuberculosis (TB) from latent TB infection (LTBI) is a significant diagnostic challenge.
  • Current biomarkers offer limited insight into TB pathogenesis for guiding treatment decisions.
  • A need exists for novel diagnostic tools and therapeutic targets in TB management.

Purpose of the Study:

  • To develop a novel two-mode biomarker signature for differentiating active TB from LTBI.
  • To identify host pathways involved in TB pathogenesis for potential therapeutic interventions.
  • To create a transcriptomic diagnostic tool aligned with World Health Organization (WHO) performance targets.

Main Methods:

  • Multicohort transcriptomic analysis incorporating stringent machine learning pipelines.
  • Feature selection using ANOVA, Boruta-XGBoost, and LASSO regression to identify key genes.
  • Development of an ensemble stacking classifier (Random Forest and XGBoost) for diagnostic performance evaluation.

Main Results:

  • A four-gene signature (TAP2, SORT1, WARS, ANKRD22) was identified, significantly upregulated in active TB.
  • The signature achieved high diagnostic accuracy (ROC-AUC = 0.991) in stratifying infection phases, validated in an independent cohort.
  • Genes mapped to core dysregulated host pathways: antigen presentation, lipid trafficking, interferon response, and inflammasome signaling.

Conclusions:

  • The developed biomarker signature provides a highly specific, non-sputum diagnostic for active TB.
  • The signature offers a mechanistic map of host pathways, identifying potential targets for therapeutic intervention.
  • This transcriptomic discovery facilitates the clinical implementation of new strategies against TB.