Preventative role of interleukin-17 producing regulatory T helper type 17 (Treg17) cells in type 1 diabetes in non-obese diabetic mice
Bibliographic record
Abstract
T helper type 17 (Th17) cells have been shown to be pathogenic in autoimmune diseases; however, their role in type 1 diabetes (T1D) remains inconclusive. We have found that Th17 differentiation of CD4(+) T cells from BDC2·5 T cell receptor transgenic non-obese diabetic (NOD) mice can be driven by interleukin (IL)-23+IL-6 to produce large amounts of IL-22, and these cells induce T1D in young NOD mice upon adoptive transfer. Conversely, polarizing these cells with transforming growth factor (TGF)-β+IL-6 led to non-diabetogenic regulatory Th17 (Treg 17) cells that express high levels of aryl hydrocarbon receptor (AhR) and IL-10 but produced much reduced levels of IL-22. The diabetogenic potential of these Th17 subsets was assessed by adoptive transfer studies in young NOD mice and not NOD.severe combined immunodeficient (SCID) mice to prevent possible transdifferentiation of these cells in vivo. Based upon our results, we suggest that both pathogenic Th17 cells and non-pathogenic regulatory Treg 17 cells can be generated from CD4(+) T cells under appropriate polarization conditions. This may explain the contradictory role of Th17 cells in T1D. The IL-17 producing Treg 17 cells offer a novel regulatory T cell population for the modulation of autoimmunity.
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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 | 0.001 |
| 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".