Sitagliptin protects diabetic rats with acute myocardial infarction through induction of angiogenesis: role of IGF-1 and VEGF
Bibliographic record
Abstract
Angiogenesis is regulated in a tissue-specific manner in all patients, especially those with diabetes. In this study, we describe a novel molecular pathway of angiogenesis regulation in diabetic rats with myocardial infarction (MI) and examine the cardioprotective effects of different doses of sitagliptin. Male rats were divided into 5 groups: normal vehicle group, diabetic group, diabetic + MI, diabetic + MI + 5 mg/kg sitagliptin, and diabetic + MI + 10 mg/kg sitagliptin. Isoproterenol in diabetic rats resulted in significant (p < 0.05) disturbance to the electrocardiogram, cardiac histopathological manifestations, and an increase in inflammatory markers compared with the vehicle and diabetic groups. Treatment with sitagliptin improved the electrocardiogram and histopathological sections, upregulated vascular endothelial growth factor (VEGF) and transmembrane phosphoglycoprotein protein (CD34) in cardiac tissues, and increased serum insulin-like growth factor 1 (IGF-1) and decreased cardiac tissue homogenate for interleukin 6 (IL-6) and cyclooxygenase 2 (COX-2). A relationship was found between serum IGF-1 and cardiac VEGF and CD34 accompanied by an improvement in cardiac function of diabetic rats with MI. Therefore, the observed effects of sitagliptin occurred at least partly through an improvement in angiogenesis and the mitigation of inflammation. Consequently, these data suggest that sitagliptin may contribute, in a dose-dependent manner, to protection against acute MI in diabetic individuals.
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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) | 0.000 | 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) | 0.001 | 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".