Interplay between CD45RA+ regulatory T cells and TNF- in the regulation of human Th17 differentiation
Bibliographic record
Abstract
The balance between effector CD4(+) T cells secreting IL-17 (T(h)17) and regulatory T cells (Treg) plays an important role in autoimmune disorders that include rheumatoid arthritis (RA) and Crohn's disease. Tumor necrosis factor (TNF)-alpha is a key pro-inflammatory cytokine that contributes to disease pathogenesis. We investigated the interplay between CD45RA(+) Treg and TNF-alpha in the regulation of human T(h)17 differentiation. We found that CD45RA(+) Treg promoted while TNF-alpha inhibited naive CD4(+) T-cell differentiation into IL-17 and CCL20 co-expressing T(h)17 cells without influencing their IL-22 release. Unexpectedly, CD45RA(+) Treg depletion abrogated TNF-alpha suppressive function. Finally, dendritic cell-derived TNF-alpha suppressed the development of IL-17(+)CCL20(+) expressing T(h)17 cells. In conclusion, CD45RA(+) Treg positively governs human T(h)17 development, which is impaired by TNF-alpha. We propose that TNF-alpha may represent a negative feedback mechanism to control IL-17/CCL20- but not IL-22-associated autoimmune pathologies.
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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 | 0.001 | 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".