Comparison of interferon-γ-, interleukin (IL)-17- and IL-22-expressing CD4 T cells, IL-22-expressing granulocytes and proinflammatory cytokines during latent and active tuberculosis infection
Bibliographic record
Abstract
In this study, we investigated the role and expression of T helper type 17 (Th17) cells and Th17 cytokines in human tuberculosis. We show that the basal proportion of interferon (IFN)-γ-, interleukin (IL)-17- and IL-22-expressing CD4(+) T cells and IL-22-expressing granulocytes in peripheral blood were significantly lower in latently infected healthy individuals and active tuberculosis patients compared to healthy controls. In contrast, CD4(+) T cells expressing IL-17, IL-22 and IFN-γ were increased significantly following mycobacterial antigens stimulation in both latent and actively infected patients. Interestingly, proinflammatory IFN-γ and tumour necrosis factor (TNF)-α were increased following antigen stimulation in latent infection. Similarly, IL-1β, IL-4, IL-8, IL-22 and TNF-α were increased in the serum of latently infected individuals, whereas IL-6 and TNF-α were increased significantly in actively infected patients. Overall, we observed differential induction of IL-17-, IL-22- and IFN-γ-expressing CD4(+) T cells, IL-22-expressing granulocytes and proinflammatory cytokines in circulation and following antigenic stimulation in latent and active tuberculosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".