Myocardial and metabolic dysfunction in type 2 diabetic rats: impact of ghrelin
Bibliographic record
Abstract
Diabetes mellitus (DM) is commonly associated with metabolic and cardiac dysfunctions. The aim of this study was to examine the effect of ghrelin on metabolic and cardiac dysfunctions in a type-2 diabetes mellitus (T2DM) rat model. For this, 48 male adult Sprague-Dawley rats were divided equally into 4 groups: Group I, fed normal chow, served as normal control group; Groups II-IV, were fed a high-fat diet for 2 weeks followed by injection of streptozotocin (STZ) (35 mg/kg body mass) to create a model of T2DM; Group II, were not treated; Group III, were treated with the vehicle (saline); Group IV, were treated with ghrelin (40 µg/kg body mass) twice daily for 10 days. The untreated diabetic rats showed a significant increase in serum fasting blood glucose, insulin homeostasis model assessment (HOMA) index, triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C), total serum cholesterol (TC), and body mass, with a decrease in high-density lipoprotein cholesterol (HDL-C) (p < 0.05). Hearts isolated from diabetic rats showed a significant increase in myocardial fat content, a significant decrease in GLUT4, and an increase in acyl-CoA oxidase enzyme mRNA (p < 0.05). Ghrelin administration for 10 days caused a significant improvement in lipid profile, HOMA index, and body mass, and significantly corrected the myocardial mass, significantly reduced the fat content of the myocardium, significantly increased GLUT4, and decreased acyl CoA oxidase mRNA (p < 0.05). Thus, ghrelin improves both the metabolic functions and the disturbed energy metabolism in the cardiac muscle of obese diabetic 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 | 0.000 | 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) | 0.002 | 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".