Translational Mini-Review Series on Th17 Cells: Function and regulation of human T helper 17 cells in health and disease
Bibliographic record
Abstract
Abstract OTHER ARTICLES PUBLISHED IN THIS MINI-REVIEW SERIES ON Th17 CELLS Induction of interleukin-17 production by regulatory T cells. Clin Exp Immunol 2009; doi:10.1111/j.1365-2249.2009.04038.x Are T helper 17 cells really pathogenic in autoimmunity? Clin Exp Immunol 2009; doi:10.1111/j.1365-2249.2009.04039.x CD4+ T helper cells: functional plasticity and differential sensitivity to regulatory T cell-mediated regulation. Clin Exp Immunol 2009; doi:10.1111/j.1365-2249.2009.04040.x Development of mouse and human T helper 17 cells. Clin Exp Immunol 2009; doi:10.1111/j.1365-2249.2009.04041.x Summary T helper (Th) cell have a central role in modulating immune responses. While Th1 and Th2 cells have long been known to regulate cellular and humoral immunity, Th17 cells have been identified only recently as a Th lineage that regulates inflammation via production of distinct cytokines such as interleukin (IL)-17. There is growing evidence that Th17 cells are pathological in many human diseases, leading to intense interest in defining their origins, functions and developing strategies to block their pathological effects. The cytokines that regulate Th17 differentiation have been the focus of much debate, due primarily to inconsistent findings from studies in humans. Evidence from human disease suggests that their in vivo development is driven by specialized antigen-presenting cells. Knowledge of how Th17 cells interact with other immune cells is limited, but recent data suggest that Th17 cells may not be subject to strict cellular regulation by T regulatory cells. Notably, Th17 cells and T regulatory cells appear to share common developmental pathways and both cell types retain significant plasticity. Herein, we will discuss the molecular and cellular regulation of Th17 cells with an emphasis on studies in humans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.033 |
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".