Mycobacteria-Specific Cytokine Responses Detect Tuberculosis Infection and Distinguish Latent from Active Tuberculosis
Bibliographic record
Abstract
RATIONALE: Current immunodiagnostic tests for tuberculosis (TB), including the tuberculin skin test and IFN-γ release assay (IGRA), have significant limitations, which include their inability to distinguish between latent TB infection (LTBI) and active TB, a distinction critical for clinical management. OBJECTIVES: To identify mycobacteria-specific cytokine biomarkers that characterize TB infection, determine their diagnostic performance characteristics, and establish whether these biomarkers can distinguish between LTBI and active TB. METHODS: A total of 149 children investigated for TB infection were recruited; all participants underwent a tuberculin skin test and QuantiFERON-TB Gold assay. In parallel, whole-blood assays using early secretory antigenic target-6, culture filtrate protein-10, and PPD as stimulatory antigens were undertaken, and cytokine responses were determined by xMAP multiplex assays. MEASUREMENTS AND MAIN RESULTS: IFN-γ, interferon-inducible protein-10 (IP-10), tumor necrosis factor (TNF)-α, IL-1ra, IL-2, IL-13, and MIP-1β (macrophage inflammatory protein-1β) responses were significantly higher in LTBI and active TB cases than in TB-uninfected individuals, irrespective of the stimulant. Receiver operating characteristic analyses showed that IP-10, TNF-α, and IL-2 responses achieved high sensitivity and specificity for the distinction between TB-uninfected and TB-infected individuals. TNF-α, IL-1ra, and IL-10 responses had the greatest ability to distinguish between LTBI and active TB cases; the combinations of TNF-α/IL-1ra and TNF-α/IL-10 achieved correct classification of 95.5% and 100% of cases, respectively. CONCLUSIONS: We identified several mycobacteria-specific cytokine biomarkers with the potential to be exploited for immunodiagnosis. Incorporation of these biomarkers into future immunodiagnostic assays for TB could result in substantial gains in sensitivity and allow the distinction between LTBI and active TB based on a blood test alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".