ArticleScientific reports2021
Risk assessment of latent tuberculosis infection through a multiplexed cytokine biosensor assay and machine learning feature selection.
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 20 papers, 1 of them a synthesis that pooled it.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Biomarker tests of progression from tuberculosis infection to disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Evanescent wave-based optical biosensors for innovations, medical application and future perspectives.Journal of advanced research · 2026Review
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Transforming cytokine diagnostics: AI, multiplexing, and point-of-care biosensing technologies.Mikrochimica acta · 2025Review
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Screening for latent tuberculosis infection in patients with chronic kidney disease: a review of evidence and current practice in the UK.Clinical kidney journal · 2025Review
- Hypercoagulability in Tuberculosis: Pathophysiological Mechanisms, Associated Risks, and Advances in Management-A Narrative Review.Journal of clinical medicine · 2025Review
- Multiplexed cytokine profiling identifies diagnostic signatures for latent tuberculosis and reactivation risk stratification.PloS one · 2025Article
- Article
- Blood and Site of Disease Inflammatory Profiles Differ in Patients With Pericardial Tuberculosis and Human Immunodeficiency Virus Type 1.Open forum infectious diseases · 2023Article
- Article
- Challenges and the Way forward in Diagnosis and Treatment of Tuberculosis Infection.Tropical medicine and infectious disease · 2023Review
- Plasma CXCL8 and MCP-1 as surrogate plasma biomarkers of latent tuberculosis infection among household contacts-A cross-sectional study.PLOS global public health · 2023Article
- Review
- Rapid detection of an Ebola biomarker with optical microring resonators.Cell reports methods · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Immune responses in COVID-19 and tuberculosis coinfection: A scoping review.Frontiers in immunology · 2022Article
- Feasibility of novel approaches to detect viableFrontiers in medicine · 2022Review
- Standardized Whole Blood Assay and Bead-Based Cytokine Profiling Reveal Commonalities and Diversity of the Response to Bacteria and TLR Ligands in Cattle.Frontiers in immunology · 2022Article
- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
Accurate detection and risk stratification of latent tuberculosis infection (LTBI) remains a major clinical and public health problem. We hypothesize that multiparameter strategies that probe immune responses to Mycobacterium tuberculosis can provide new diagnostic insights into not only the status of LTBI infection, but also the risk of reactivation. After the initial proof-of-concept study, we developed a 13-plex immunoassay panel to profile cytokine release from peripheral blood mononuclear cells stimulated separately with Mtb-relevant and non-specific antigens to identify putative biomarker signatures. We sequentially enrolled 65 subjects with various risk of TB exposure, including 32 subjects with diagnosis of LTBI. Random Forest feature selection and statistical data reduction methods were applied to determine cytokine levels across different normalized stimulation conditions. Receiver Operator Characteristic (ROC) analysis for full and reduced feature sets revealed differences in biomarkers signatures for LTBI status and reactivation risk designations. The reduced set for increased risk included IP-10, IL-2, IFN-γ, TNF-α, IL-15, IL-17, CCL3, and CCL8 under varying normalized stimulation conditions. ROC curves determined predictive accuracies of > 80% for both LTBI diagnosis and increased risk designations. Our study findings suggest that a multiparameter diagnostic approach to detect normalized cytokine biomarker signatures might improve risk stratification in LTBI.
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Registered trials
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.