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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