Effect of chronic rosiglitazone, metformin and glyburide treatment on β‐cell mass, function and insulin sensitivity in mZDF rats
Bibliographic record
Abstract
Here we investigate the effect of rosiglitazone (RSG), metformin (MET) and glyburide (GLIB) on plasma glucose levels, beta-cell mass, function and insulin sensitivity in 10-week-old diabetic male Zucker diabetic fatty (mZDF) rats using quantitative morphometry and a mathematical model beta-cell mass, insulin and glucose kinetics (betaIG). At treatment start, 10-week-old diabetic mZDF rats were severely hyperglycaemic and had very low beta-cell function (insulin secretory capacity). RSG treatment significantly lowered plasma glucose levels in 67% of the mZDF rats. MET was effective at lowering plasma glucose levels in 33% of the mZDF rats, while GLIB was completely ineffective at lowering blood glucose levels in 10-week-old mZDF rats. RSG treatment prevented the fall in beta-cell mass after 6-8 weeks of treatment accompanied by a significant decrease in beta-cell death while MET treatment had no effect on beta-cell mass. RSG treatment increased insulin sensitivity 10-fold, increased beta-cell function fivefold and modestly increased beta-cell mass 1.4-fold. MET treatment increased insulin sensitivity fourfold, with no significant effect on beta-cell function or mass. Although RSG treatment was highly successful in lowering plasma glucose levels, the 33% of mZDF rats that did not respond to the treatment had significantly lower beta-cell function prior to treatment start compared with the responder group. Thus, the low level of beta-cell function at treatment start may explain why none of these agents were completely effective at lowering blood glucose levels in 10-week-old diabetic mZDF rats. Nevertheless, these data suggest that the preservation of beta-cell mass and improvement in beta-cell function play a role in the overall beneficial effect of RSG in 10-week-old diabetic mZDF rats.
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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 | 0.000 | 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".