ArticleTuberculosis (Edinburgh, Scotland)2024
Predictive biomarkers for latent Mycobacterium tuberculosis infection.
Article in Tuberculosis (Edinburgh, Scotland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Transforming tuberculosis diagnosis with clinical metagenomics: progress and roadblocks.Journal of clinical microbiology · 2025Review
- Technologies in Biomarker Discovery for Animal Diseases: Mechanisms, Classification, and Diagnostic Applications.Animals : an open access journal from MDPI · 2025Review
- The Evolving Landscape of Host Biomarkers for Diagnosis and Monitoring of Tuberculosis.Biomedicines · 2025Review
- Artificial Intelligence in Bacterial Infections Control: A Scoping Review.Antibiotics (Basel, Switzerland) · 2025Review
- An integrative and comprehensive analysis of blood transcriptomes combined with machine learning models reveals key signatures for tuberculosis diagnosis and risk stratification.Frontiers in microbiology · 2025Article
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Authors and funding
5 authors.
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Abstract
Tuberculosis is a leading cause of infectious death worldwide, with almost a fourth of the world's population latently infected with its causative agent, Mycobacterium tuberculosis. Current diagnostic methods are insufficient to differentiate between healthy and latently infected populations. Here, we used a machine learning approach to analyze publicly available proteomic data from saliva and serum in Ethiopia's healthy, latent TB (LTBI) and active TB (ATBI) people. Our analysis discovered a profile of six proteins, Mast Cell Expressed Membrane Protein-1, Hemopexin, Lamin A/C, Small Proline Rich Protein 2F, Immunoglobulin Kappa Variable 4-1, and Voltage Dependent Anion Channel 2 that can precisely differentiate between the healthy and latently infected populations. This data suggests that a combination of six host proteins can serve as accurate biomarkers to diagnose latent infection. This is important for populations living in high-risk areas as it may help in the surveillance and prevention of severe disease.
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