Metformin attenuates ischemia/reperfusion-induced apoptosis of cardiac cells by downregulation of p53/microRNA-34a via activation of SIRT1
Bibliographic record
Abstract
Metformin has been demonstrated to be beneficial for the treatment of an impaired myocardium as a result of ischemia/reperfusion (I/R) injury, and miR-34a may be involved in this process. The aim of the present study was to determine the mechanisms by which metformin attenuated myocardial I/R injury-induced apoptosis. In the in vivo I/R model using Sprague-Dawley rats, metformin reduced the area of damaged myocardium and serum creatine MB isoform (CKMB) activity resulting in protection of the myocardium. Metformin also reduced apoptosis and the expression of apoptosis associated proteins, including caspase 3 and cleaved caspase, and decreased the expression of miR-34a, which is upregulated during I/R injury, which in turn resulted in corresponding changes in expression of Bcl-2, a direct target of miR-34a both in vitro and in vivo. To further examine the role of miR-34a in this process, H9C2 cells were transfected by a miR-34a mimic and inhibitor. Overexpression of miR-34a increased apoptosis in H9C2 cells induced by oxygen-glucose deprivation/recovery and knockdown of miR-34a expression-reduced apoptosis under the same conditions. Therefore, the effect of metformin on miR-34a in vitro were assessed. Metformin decreased the deacetylation activity of silent information regulator 1 resulting in reduced Ac-p53 levels, which reduced the levels of pri-miR-34a, and thus in turn reduced miR-34a levels. To confirm these results clinically, 90 patients with ST-segment elevation myocardial infarction following percutaneous coronary intervention were recruited. Patients who took metformin regularly before infarction had lower miR-34a levels and lower serum CKMB activity. Metformin also improved the sum ST-segment recovery following I/R injury. In conclusion, metformin may be helpful in the treatment of myocardial I/R.
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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) | 0.001 | 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".