Rosiglitazone prevents diabetes by increasing beta‐cell mass in an animal model of type 2 diabetes characterized by reduced beta‐cell mass at birth
Bibliographic record
Abstract
AIM: Interventions that preserve or increase beta-cell mass may also prevent type 2 diabetes. Rosiglitazone prevents diabetes in people with high glucose levels who have impaired glucose tolerance and/or impaired fasting glucose. The effect of this drug on both glucose levels and beta-cell mass was studied in a rat model of diabetes, characterized by reduced beta-cell mass at birth with normoglycaemia, and progression to dysglycaemia with age. METHODS: Female Wistar rats were given either saline (vehicle) or nicotine during pregnancy and lactation. Offspring of saline-exposed dams were given vehicle and offspring of nicotine-exposed dams were randomized to receive either vehicle or rosiglitazone starting at weaning. Beta-cell mass, proliferation and apoptosis were determined at birth and at 4 and 26 weeks of age. Glucose homeostasis was examined following sequential oral glucose tolerance tests (OGTT). RESULTS: Rosiglitazone treatment prevented the development of dysglycaemia in nicotine-exposed animals. The ability of rosiglitazone to preserve normoglycaemia appeared to be because of its ability to increase beta-cell mass through a combination of enhanced beta-cell proliferation and decreased beta-cell apoptosis. CONCLUSIONS: These results suggest that if rosiglitazone administration is started prior to the onset of glucometabolic abnormalities, it prevents the onset of dysglycaemia by partially restoring beta-cell mass in animals with reduced beta-cell mass at birth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".