MicroRNA-34a protects myocardial cells against ischemia–reperfusion injury through inhibiting autophagy via regulating TNFα expression
Bibliographic record
Abstract
BACKGROUND: ischemia-reperfusion (I/R) is a consequence of restored blood supply after myocardial infarction. Myocardial I/R injury can be alleviated by reducing autophagy in heart tissue. MicroRNA-34a (miR-34a) has been shown to regulate autophagy in a renal model of I/R, but it is not known whether it can protect cardiac tissues from I/R injury. This study investigated how miR-34a protects myocardial cells from I/R injury by inhibiting autophagy via regulation of tumor necrosis factor α (TNFα). METHODS: we constructed an I/R model in vivo using Langendorff perfusion, and we constructed an in vivo model by treating neonatal rat cardiomyocytes (NRCMs) with hypoxia-reoxygenation (H/R method). Transfected adenoviral-overexpressed miR-34a mimics and controlled NRCMs after H/R. We analyzed cell viability using the MTT assay and a cell counting kit-8 (CCK-8) assay. Changes in the rate of apoptosis were detected by flow cytometry. We investigated the effect mechanisms of miR-34a with Western blot and luciferase assays. RESULTS: miR-34a expression decreased after in vivo reperfusion of the myocardial cells and heart tissues of neonatal rats. MiR-34a reduced apoptosis of the NRCMs and autophagy levels, simultaneously, after H/R injury. Further, miR-34a decreased the expression of Lc3-II and p62, indicating that miR-34a reduces myocardial I/R injury by decreasing TNFα expression. CONCLUSION: miR-34a can inhibit autophagy levels after I/R by targeting TNFα, thereby reducing myocardial injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".