Modulation of glucocorticoid-induced GAD expression in pancreatic beta-cells by transcriptional activation of the GAD67 promoter and its possible effect on the development of diabetes.
Bibliographic record
Abstract
GAD is a pancreatic -cell autoantigen in humans and nonobese diabetic (NOD) mice.Modulation of GAD expression in pancreatic -cells has been suggested to be associated with the development of autoimmune diabetes.Hormonal changes through environmental stimuli are considered to influence the expression of the disease.We determined whether steroid hormones would modulate the expression of GAD in pancreatic -cells.We treated NOD mouse -cells (MIN6N8a cells) with various steroids, including testosterone, estradiol, progesterone, and cortisol, and examined the expression of GAD67 mRNA.We found that only cortisol enhanced the expression of GAD67, whereas the other steroid hormones had no effect.When we treated MIN6N8a cells with a synthetic glucocorticoid, dexamethasone, we found that GAD67 mRNA expression was stimulated in a dose-and time-dependent manner.Cells treated with 100 nmol/l dexamethasone for 6 h showed a 10-fold increase in the expression of GAD67 mRNA and an increase in GAD67 protein.The upregulation of GAD67 expression in -cells by dexamethasone was found to be due to the transcriptional activation of the GAD67 promoter.We then examined whether dexamethasone would influence the development of diabetes in NOD mice.Injection of dexamethasone into neonatal NOD mice resulted in a significant increase in the expression of GAD67 mRNA in pancreatic -cells and the development of insulitis and diabetes.We conclude that glucocorticoid hormones can modulate GAD expression by the transcriptional activation of the GAD promoter and may influence the development of autoimmune diabetes in NOD mice.
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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) | 0.001 | 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".