Low-fat cheese ameliorates glucose intolerance and normalizes insulin secretion in a rat model of type 2 diabetes by promoting β-cell recovery
Bibliographic record
Abstract
We aimed to determine if cheese could reduce glucose intolerance in aged rats with overt type 2 diabetes (T2D). Male Sprague-Dawley rats treated with high-fat diet (HFD) and streptozotocin (STZ) to elicit T2D were hyperglycemic. One week after STZ injection, low-fat (LOW) or regular-fat (REG) cheese was provided for 5 weeks and compared with T2D and low-fat diet reference (REF) groups. Food intake and weight gain were similar in all groups. Oral glucose tolerance tests revealed glucose intolerance in T2D rats that was partially ameliorated by LOW but not REG. Insulin secretion during the oral glucose tolerance test was impaired in T2D and REG at 10 min ( p < 0.05) but the iAUC was highly variable in all groups and statistical differences were not detected ( p > 0.05). β-cell mass and pancreatic insulin content in T2D and REG were 50% lower than REF ( p < 0.05), whereas LOW was not significantly different. Although isolated islets from all groups responded to glucose, the absolute amount of insulin secreted by T2D and REG was markedly reduced compared with REF, while LOW islets had relatively normal secretion. In conclusion, LOW but not REG cheese enhanced β-cell recovery from HFD/STZ treatment that led to amelioration of glucose tolerance within 5 weeks.
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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 | 0.001 |
| 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".