The phenotypic distribution and functional profile of tuberculin‐specific CD4 T‐cells characterizes different stages of TB infection
Bibliographic record
Abstract
BACKGROUND: Recent publications have suggested that altered proportions of functional CD4 T-cell subsets correlate with active pulmonary TB. Also, CD27-expression on tuberculin-activated IFN-γ(+) CD4 T-cells is known to differ significantly between patients with active pulmonary TB and healthy TB-unexposed BCG vaccinees. Here, we explore links between CD4 T-cell phenotype, multiple functional subsets, and control of TB. METHODS: We examined ex-vivo overnight tuberculin activated CD4 T-cells in regards to CD27-expression and the activation markers, CD154 upregulation, IFN-γ, TNF-α, IL-2, and degranulation in 44 individuals, including cases of clinically active pulmonary TB, and hospital staff with prolonged TB exposure, some of whom had latent TB. RESULTS: Active pulmonary TB generally showed an excess of TNF-α(+) subsets over IFN-γ(+) subsets, paralleled by decreased CD27 expression on activated IFN-γ(+) or CD154(+) CD4 T-cells. The single subset distinguishing best between active pulmonary TB and high TB exposure was CD154(+) /TNF-α(+) / IFN-γ(-) /IL-2(-) /degranulation(-) (AUROC 0.90). The ratio between the frequencies of TNF-α(+) /IFN-γ(+) CD4 T-cells was an effective alternative parameter (AUROC 0.87). CONCLUSIONS: Functional subsets and phenotype of tuberculin induced CD4 T-cells differ between stages of TB infection. Predominance of TNF-α(+) CD4 T-cells in active infection suggests an increased effort of the immune system to contain disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".