Sex Bias in IL-10 Production by Type I Regulatory T Cells Regulated by PI3K/mTOR Signaling
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS) is more prevalent in women, with a female-to-male ratio of 3:1. The molecular mechanisms driving this sex difference are still mostly unknown. MS results from immune dysfunction, with an imbalance in effector and regulatory T cells. Among the latter, Type I regulatory T cells (Tr1) are dysfunctional in people with MS (pwMS), secreting less IL-10, a potent anti-inflammatory cytokine, than in healthy donors. Our objectives were to explore the effect of biological sex on Tr1 cell differentiation in healthy donors and pwMS. METHODS: T cells were isolated from peripheral blood mononuclear cells, and Tr1 differentiation was induced by costimulation with the complement regulator CD46 or IL-27. The frequency of Tr1 cells and their production of IL-10 and IFN-γ were examined. The impact of the PI3K/mTOR pathway on male and female Tr1 cells was also studied. RESULTS: = 0.0043), while increasing IL-10 levels in the CD46 pathway in both sexes (8 men and 11 women). DISCUSSION: We demonstrate that sex influences IL-10 production by Tr1 cells via the PI3K pathway, potentially contributing to the greater susceptibility of women to MS. Furthermore, our data suggest that targeting PI3Kδ may represent a novel therapeutic strategy to boost IL-10 production in female pwMS.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".