Modulation of autoimmune diseases by interleukin (IL)-17 producing regulatory T helper (Th17) cells.
Bibliographic record
Abstract
Following the discovery of interleukin (IL)-17 producing T helper (Th17) cells as a distinct lineage of CD4+ T helper cells it became clear that these cells play an important role in the host defense against extracellular fungal and bacterial pathogens and participate in the pathogenesis of multiple inflammatory and autoimmune disorders. Depending on the microenvironment, Th17 cells can alter their differentiation programme ultimately giving rise to either protective or pro-inflammatory pathogenic cells. We found that besides the conventional in vitro protocol for Th17 differentiation by transforming growth factor-beta (TGF-β) plus IL-6 cytokines, a combination of IL-23 plus IL-6 can also induce Th17 cells. The Th17 cells induced by IL-23 plus IL-6 (termed as effector Th17, Teff17 cells) are pathogenic upon adoptive transfer into non-obese diabetic (NOD) mice contributing to the development of type 1 diabetes (T1D) while cells induced by TGF-β plus IL-6 (termed as regulatory T cells, Treg17 cells) are non pathogenic and regulatory, and suppressed the pathogenic T cells in T1D. These cells differentially expressed a number of cytokines where Teff17 cells exhibited an increase in granulocyte-macrophage colony-stimulating factor (GM-CSF) and IL-22 whereas Treg17 cells demonstrated increased expression of IL-21 and immunosuppressive cytokine IL-10. Differentiation of Th17 cells is controlled by a transcription factor, RORγT although these cells also express variable levels of T-bet and FoxP3 transcription factors. This points to a dual functional role of Th17 subsets in autoimmune diseases particularly T1D. We suggest that similar to conventional regulatory T cells (Treg), induction of regulatory Treg17 cells could play an important role in modulating and preventing certain autoimmune diseases.
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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 | 0.000 | 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) | 0.000 | 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".