PPAR-alpha activation as a preconditioning-like intervention in rats in vivo confers myocardial protection against acute ischaemia–reperfusion injury: involvement of PI3K–Akt
Bibliographic record
Abstract
Peroxisome proliferator-activated receptors (PPAR) regulate the expression of genes involved in lipid metabolism, energy production, and inflammation. Their role in ischaemia-reperfusion (I/R) is less clear, although research indicates involvement of PPARs in some forms of preconditioning. This study aimed to explore the effects of PPAR-α activation on the I/R injury and potential cardioprotective downstream mechanisms involved. Langendorff-perfused hearts of rats pretreated with the selective PPAR-α agonist WY-14643 (WY, pirinixic acid; 3 mg·(kg body mass)·day(-1); 5 days) were subjected to 30 min ischaemia - 2 h reperfusion with or without the phosphatidylinositol 3-kinase (PI3K)-Akt inhibitor wortmannin for the evaluation of functional (left ventricular developed pressure, LVDP) recovery, infarct size (IS), and reperfusion-induced arrhythmias. A 2-fold increase in baseline PPAR-α mRNA levels (qPCR) in the WY-treated group and higher post-I/R PPAR-α levels compared with those in untreated controls were accompanied by similar changes in the expression of PPAR-α target genes PDK4 and mCPT-1, regulating glucose and fatty acid metabolism, and by enhanced Akt phosphorylation. Post-ischaemic LVDP restoration in WY-treated hearts reached 60% ± 9% of the pre-ischaemic values compared with 24% ± 3% in the control hearts (P < 0.05), coupled with reduced IS and incidence of ventricular fibrillation that was blunted by wortmannin. Results indicate that PPAR-α up-regulation may confer preconditioning-like protection via metabolic effects. Downstream mechanisms of PPAR-α-mediated cardioprotection may involve PI3K-Akt activation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".