Anti-apoptotic effects of myocardin-related transcription factor-A on rat cardiomyocytes following hypoxia-induced injury
Bibliographic record
Abstract
Myocardin-related transcription factor-A (MRTF-A) can transduce both biomechanical and humoral signals, which can positively modulate cardiac damage induced by acute myocardial infarction. However, the molecular mechanism that underlies the contribution that MRTF-A provides to the myocardium is not completely understood. The objective of this study was to investigate the effects of MRTF-A on myocardium apoptosis and its mechanisms. Our experiment results showed that MRTF-A expression increased and Bcl-2 expression reduced during myocardial ischemia-reperfusion in rat. Meanwhile, primary cardiomyocytes were pretreated with wild-type MRTF-A or siRNA of MRTF-A before exposure to hypoxia. We found that overexpression of MRTF-A in myocardial cells inhibited apoptosis and the release of cytochrome c. MRTF-A enhanced Bcl-2, which contributes to MRTF-A interaction with Bcl-2 in the nuclei of cardiomyocytes. MRTF-A upregulation expression of Bcl-2 in cardiomyocytes induced by hypoxia was inhibited by PD98059, an ERK1/2 inhibitor. In conclusions, MRTF-A improved myocardial cell survival in a cardiomyocyte model of hypoxia-induced injury; this effect was correlated with the upregulation of anti-apoptotic gene Bcl-2 through the activation of ERK1/2.
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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 | 0.000 |
| 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".