Rosiglitazone protects against beta cell destruction in ZDF (Type 2) diabetic rats
Bibliographic record
Abstract
The progression of type 2 diabetes involves insulin resistance, impairment in insulin secretion, and eventual loss of β‐cell mass. Current therapies largely address insulin sensitivity and secretion but it is also important that therapies be found which protect against β‐cell loss. Finegood et al (2000) have reported that rosiglitazone (RSG) treatment largely prevents loss of β‐cell mass. We have confirmed this observation and present new information on islet morphology during this process. Examination of pancreas sections from 6 week‐old ZDF fa/fa rats, by means of double immunofluorescence staining for insulin and glucagon, revealed the normal oval morphology of islets with a mantle of α‐cells at the periphery and β‐cells in the central region. Six weeks of treatment with RSG (10 μmol/kg BW/day) maintained this structure whereas, in untreated rats, the islets had lost their normal architecture. After another 6 weeks, atrophy of the islets, fibrosis and loss of insulin‐positive cells was observed in the untreated rats whereas in the treated group, the islets were larger with numerous insulin‐positive cells. However, at this time point, islets from both groups showed diffusion into exocrine tissue and loss of the α‐cell mantle. The appearance of signs of endoplasmic reticulum stress during the loss of islet integrity was also protected against by RSG. (Supported by CIHR and Canadian Diabetes Association).
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".