Infarct size is increased in female post-MI rats treated with rapamycin
Bibliographic record
Abstract
Rapamycin represents a recognized drug-based therapeutic approach to treat cardiovascular disease. However, at least in the female heart, rapamycin may suppress the recruitment of putative signalling events conferring cardioprotection. The present study tested the hypothesis that rapamycin-sensitive signalling events contributed to the cardioprotective phenotype of the female rat heart after an ischemic insult. Rapamycin (1.5 mg/kg) was administered to adult female Sprague-Dawley rats 24 h after complete coronary artery ligation and continued for 6 days. Rapamycin abrogated p70S6K phosphorylation in the left ventricle of sham rats and the noninfarcted left ventricle (NILV) of 1-week postmyocardial-infarcted (MI) rats. Scar weight (MI 0.028 +/- 0.006, MI+rapamycin 0.064 +/- 0.004 g) and surface area (MI 0.37 +/- 0.08, MI+rapamycin 0.74 +/- 0.03 cm2) were significantly larger in rapamycin-treated post-MI rats. In the NILV of post-MI female rats, rapamycin inhibited the upregulation of eNOS. Furthermore, the increased expression of collagen and TGF-beta3 mRNAs in the NILV were attenuated in rapamycin-treated post-MI rats, whereas scar healing was unaffected. The present study has demonstrated that rapamycin-sensitive signalling events were implicated in scar formation and reactive fibrosis. Rapamycin-mediated suppression of eNOS and TGF-beta3 mRNA in post-MI female rats may have directly contributed to the larger infarct and attenuation of the reactive fibrotic response, respectively.
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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 | 0.001 | 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) | 0.004 | 0.001 |
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".