ArticlePloS one2018
Identification of plasma biomarkers for discrimination between tuberculosis infection/disease and pulmonary non tuberculosis disease.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 6 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 6 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Diagnostic accuracy ofFrontiers in immunology · 2026Pooled it
- Monokine induced by gamma interferon for detecting pulmonary tuberculosis: A diagnostic meta-analysis.Medicine · 2020Pooled it
- Is interleukin-2 an optimal marker for diagnosing tuberculosis infection? A systematic review and meta-analysis.Annals of medicine · 2020Pooled it
- Pooled it
- Gaps in Study Design for Immune Parameter Research for Latent Tuberculosis Infection: A Systematic Review.Journal of immunology research · 2020Pooled it
- Accumulate evidence for IP-10 in diagnosing pulmonary tuberculosis.BMC infectious diseases · 2019Pooled it
- HLA-E-restricted SARS-CoV-2 epitopes drive CD8Journal of translational medicine · 2026Article
- Usefulness of the combined IFN-γ and IL-2 release test in distinguishing between pulmonary tuberculosis and non-pulmonary tuberculosis.BMC infectious diseases · 2026Article
- Development of a score derived from full blood count parameters to differentiate individuals with tuberculosis disease from those with tuberculosis infection.Clinical and experimental immunology · 2026Article
- Machine learning-based feature selection suggests circulating proteins as best at predicting and diagnosing tuberculosis inFrontiers in cellular and infection microbiology · 2026Article
- The Diagnostic Value of Interleukin-2 and Interferon-γ Induced by Fusion Protein (ESAT-6/CFP-10/Rv1985c) for Active Mycobacterium tuberculosis Infection.Journal of clinical laboratory analysis · 2025Article
- Multiplexed cytokine profiling identifies diagnostic signatures for latent tuberculosis and reactivation risk stratification.PloS one · 2025Article
- Machine learning-driven discovery of NETs-associated diagnostic biomarkers and molecular subtypes in tuberculosis.Frontiers in cellular and infection microbiology · 2025Article
- Candidate serum protein biomarkers for active pulmonary tuberculosis diagnosis in tuberculosis endemic settings.BMC infectious diseases · 2024Article
- Quantum Dot-Based Nanosensors for In Vitro Detection ofNanomaterials (Basel, Switzerland) · 2024Review
- Integrating pathogen- and host-derived blood biomarkers for enhanced tuberculosis diagnosis: a comprehensive review.Frontiers in immunology · 2024Review
- Development and Evaluation of a Promising Biomarker for Diagnosis of Latent and Active Tuberculosis Infection.Infectious diseases & immunity · 2024Article
- Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis.Frontiers in cellular and infection microbiology · 2024Article
- Article
- Antibodies as key mediators of protection againstFrontiers in immunology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We used the Luminex Bead Array Multiplex Immunoassay to measure cytokines, chemokines and growth factors responses to the same antigens used for RD1-based Interferon γ Release Assay (IGRA) test. Seventy-nine individuals, 27 active TB, 32 latent infection subsets, 20 individuals derivative purified protein (PPD) negative (subjects that do not have any indurative cutaneous reaction after 72 hrs of intradermal injection of PPD) and with other pulmonary disease were retrospectively studied. Forty-eight analytes were evaluated by Luminex Assay in plasma obtained from whole blood stimulated cells. The diagnostic accuracies of the markers detected were evaluated by ROC curve analysis and by the combination of multiple biomarkers to improve the potential to discriminate between infection/disease and non infection. Among 48 cytokines, 13 analytes, namely IL-3, IL-12-p40, LIF, IFNα2, IL-2ra, IL-13, b-NGF, SCF, TNF-β, TRAIL, IL-2, IFN-γ, IP-10, and MIG, were significantly higher in the active TB and LTBI groups, compared to NON-TB patients, while MIF was significantly lower in active TB patients compared to NON-TB and LTBI groups. The diagnostic accuracies of the markers detected in the culture supernatants evaluated by ROC curve analysis revealed that 11 analytes (IL2, IP10, IFN-γ, IL13, MIG, SCF, b-NGF, IL12-p40, TRAIL, IL2 Ra, LIF) discriminated between NON-TB and LTBI groups, with AUC for all analytes ≥0.73, while 14 analytes (IL2, IP10, IFN-γ, MIG, SCF, b-NGF, IL12-p40, TRAIL, IL2Ra, MIF, TNF-β, IL3, IFN-α2, LIF) discriminated between NON-TB and active TB groups, with AUC ≥0.78, that is a moderate, value in terms of accuracy of a diagnostic test. Finally, the combinations of seven biomarkers resulted in the accurate prediction of 88.89% of active TB patients, 82.35% of subjects with latent infection and 90% of non-TB patients, respectively. Taken together, our data suggest that combinations of whole blood Mycobacterium tuberculosis (Mtb) antigen dependent cytokines production could be useful as biomarkers to determine tuberculosis disease states when compared to non TB cohort.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.