Molecular Mechanisms for Gender Differences in Susceptibility to T Cell-Mediated Autoimmune Diabetes in Nonobese Diabetic Mice
Bibliographic record
Abstract
Nonobese diabetic (NOD) mice spontaneously develop diabetes with a strong female prevalence; however, the mechanisms for this gender difference in susceptibility to T cell-mediated autoimmune diabetes are poorly understood. This investigation was initiated to find mechanisms by which sex hormones might affect the development of autoimmune diabetes in NOD mice. We examined the expression of IFN-gamma, a characteristic Th1 cytokine, and IL-4, a characteristic Th2 cytokine, in islet infiltrates of female and male NOD mice at various ages. We found that the most significant difference in cytokine production between sexes was during the early stages of insulitis at 4 wk of age. IFN-gamma was significantly higher in young females, whereas IL-4 was higher in young males. CD4(+) T cells isolated from lymph nodes of female mice and activated with anti-CD3 and anti-CD28 Abs produced more IFN-gamma, but less IL-4, as compared with males. Treatment of CD4(+) T cells with estrogen significantly increased, whereas testosterone treatment decreased the IL-12-induced production of IFN-gamma. We then examined whether the change in IL-12-induced IFN-gamma production by treatment with sex hormones was due to the regulation of STAT4 activation. We found that estrogen treatment increased the phosphorylation of STAT4 in IL-12-stimulated T cells. We conclude that the increased susceptibility of female NOD mice to the development of autoimmune diabetes could be due to the enhancement of the Th1 immune response through the increase of IL-12-induced STAT4 activation by estrogen.
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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".