Alpha-lipoic acid enhances ischemic postconditioning-mediated improvement of myocardial infarction and apoptosis in diabetic rats with ischemia/reperfusion injury
Bibliographic record
Abstract
This work evaluated the combined effects of alpha-lipoic acid (ALA) and ischemic postconditioning (Post) on myocardial infarction and cell death in rats with chronic type-II diabetes following ischemia/reperfusion injury. The rats received a high-fat diet and were given one intraperitoneal injection of 35 mg/kg streptozotocin to induce chronic diabetes. They were then pretreated with ALA (100 mg/kg/day, orally) for 5 weeks before undergoing ischemia/reperfusion (I/R) insult. The hearts experienced 35 min regional ischemia through ligating the left anterior descending coronary artery, followed by 60 min reperfusion. The Post protocol involved 6 cycles of a 10/10 s algorithm, applied during the early stage of reperfusion. The use of Post alone did not significantly alter lactate dehydrogenase and infarct size levels, while ALA showed positive effects. Similar findings were observed for apoptotic changes with single treatments. However, the concurrent administration of ALA and Post significantly reduced the protein expressions of Bax, Bax/Bcl2, and cleaved caspase-3 while increasing Bcl2 expression. Additionally, the histopathological findings of the combined therapy were superior to those of single treatments. The concomitant use of ALA and Post effectively inhibited apoptosis, leading to cardiac recovery after I/R injury in diabetic conditions. This strategy could improve outcomes for preserving diabetic hearts following I/R insults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".