Cardioprotective effect of pioglitazone in diabetic and non-diabetic rats subjected to acute myocardial infarction involves suppression of AGE-RAGE axis and inhibition of apoptosis
Bibliographic record
Abstract
Insulin resistance increases risk of cardiovascular diseases. This work investigated the protective effect of pioglitazone on myocardial infarction (MI) in non-diabetic and diabetic rats, focusing on its role on advanced glycated endproducts (AGEs) and cardiac apoptotic machinery. Male rats were divided into 2 experiments: experiment I and II (non-diabetic and diabetic rats) were assigned as saline, MI (isoproterenol, 85 mg/kg, daily), and MI+pioglitazone (5, 10, and 20 mg/kg). Injection of isoproterenol in diabetic rats produced greater ECG disturbances compared to non-diabetic rats. Treatment with pioglitazone (5 mg/kg) reduced the infarct size and improved some ECG findings. Pioglitazone (10 mg/kg) enhanced ECG findings, improved the histopathological picture and downregulated apoptosis in cardiac tissues. Whereas the higher dose of pioglitazone (20 mg/kg) did not improve most of the measured parameters but rather worsened some of them, such as proapoptotic markers. Importantly, a positive correlation was found between serum AGEs and cardiac AGE receptors (RAGEs) versus caspase 3 expression in the two experiments. Therefore, the current effect of pioglitazone was, at least in part, mediated through downregulation of AGE-RAGE axis and inhibition of apoptosis. Consequently, these data suggest that pioglitazone, at optimized doses, may have utility in protection from acute MI.
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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) | 0.000 | 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".