Antigen-Specific T-Cell Activation Distinguishes between Recent and Remote Tuberculosis Infection
Bibliographic record
Abstract
Abstract Rationale Current diagnostic tests fail to identify individuals at higher risk of progression to tuberculosis disease, such as those with recent Mycobacterium tuberculosis infection, who should be prioritized for targeted preventive treatment. Objectives To define a blood-based biomarker, measured with a simple flow cytometry assay, that can stratify different stages of tuberculosis infection to infer risk of disease. Methods South African adolescents were serially tested with QuantiFERON-TB Gold to define recent (QuantiFERON-TB conversion <6 mo) and persistent (QuantiFERON-TB+ for >1 yr) infection. We defined the ΔHLA-DR median fluorescence intensity biomarker as the difference in HLA-DR expression between IFN-γ+ TNF+ Mycobacterium tuberculosis–specific T cells and total CD3+ T cells. Biomarker performance was assessed by blinded prediction in untouched test cohorts with recent versus persistent infection or tuberculosis disease and by unblinded analysis of asymptomatic adolescents with tuberculosis infection who remained healthy (nonprogressors) or who progressed to microbiologically confirmed disease (progressors). Measurements and Main Results In the test cohorts, frequencies of Mycobacterium tuberculosis–specific T cells differentiated between QuantiFERON-TB− (n = 25) and QuantiFERON-TB+ (n = 47) individuals (area under the receiver operating characteristic curve, 0.94; 95% confidence interval, 0.87–1.00). ΔHLA-DR significantly discriminated between recent (n = 20) and persistent (n = 22) QuantiFERON-TB+ (0.91; 0.83–1.00); persistent QuantiFERON-TB+ and newly diagnosed tuberculosis (n = 19; 0.99; 0.96–1.00); and tuberculosis progressors (n = 22) and nonprogressors (n = 34; 0.75; 0.63–0.87). However, ΔHLA-DR median fluorescent intensity could not discriminate between recent QuantiFERON-TB+ and tuberculosis (0.67; 0.50–0.84). Conclusions The ΔHLA-DR biomarker can identify individuals with recent QuantiFERON-TB conversion and those with disease progression, allowing targeted provision of preventive treatment to those at highest risk of tuberculosis. Further validation studies of this novel immune biomarker in various settings and populations at risk are warranted.
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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 | 0.005 |
| 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 | 0.001 |
| 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".