ArticleScientific reports2021
Microarray-based selection of a serum biomarker panel that can discriminate between latent and active pulmonary TB.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods.Diseases (Basel, Switzerland) · 2026Review
- Diagnostic value of miR-155, miR-29a and lncRNAs (NEAT1, GAS5 and COX2) in HIV, tuberculosis, and HIV/TB co-infection.Scientific reports · 2026Article
- Tuberculosis diagnosis using artificial intelligence: current trends and future prospects.Frontiers in medicine · 2025Review
- Evaluation of serological assays for the diagnosis of childhood tuberculosis disease: a study protocol.BMC infectious diseases · 2024Article
- From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.Military Medical Research · 2023Review
- Review
- Challenges and the Way forward in Diagnosis and Treatment of Tuberculosis Infection.Tropical medicine and infectious disease · 2023Review
- Pediatric Tuberculosis Diagnostics: Present and Future.Journal of the Pediatric Infectious Diseases Society · 2022Article
- New developments in tuberculosis diagnosis and treatment.Breathe (Sheffield, England) · 2022Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacterial culture of M. tuberculosis (MTB), the causative agent of tuberculosis (TB), from clinical specimens is the gold standard for laboratory diagnosis of TB, but is slow and culture-negative TB cases are common. Alternative immune-based and molecular approaches have been developed, but cannot discriminate between active TB (ATB) and latent TB (LTBI). Here, to identify biomarkers that can discriminate between ATB and LTBI/healthy individuals (HC), we profiled 116 serum samples (HC, LTBI and ATB) using a protein microarray containing 257 MTB secreted proteins, identifying 23 antibodies against MTB antigens that were present at significantly higher levels in patients with ATB than in those with LTBI and HC (Fold change > 1.2; p < 0.05). A 4-protein biomarker panel (Rv0934, Rv3881c, Rv1860 and Rv1827), optimized using SAM and ROC analysis, had a sensitivity of 67.3% and specificity of 91.2% for distinguishing ATB from LTBI, and 71.2% sensitivity and 96.3% specificity for distinguishing ATB from HC. Validation of the four candidate biomarkers in ELISA assays using 440 serum samples gave consistent results. The promising sensitivity and specificity of this biomarker panel suggest it merits further investigation for its potential as a diagnostic for discriminating between latent and active TB.
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