Cardioprotective effects of liraglutide pretreatment on isoprenaline-induced myocardial injury in rats
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) increases the risk of cardiovascular disease, especially myocardial injury. Due to their hypoglycemic effects, glucagon-like peptide-1 receptor agonists (GLP-1RAs) are efficiently used for T2DM management. GLP-1RAs also have anti-inflammatory and antioxidative effects and can improve cardiac function. The aim of this study was to investigate the cardioprotective effects of liraglutide, a GLP-1RA, on isoprenaline-induced myocardial injury in rats. The study included four groups of animals. They were pretreated with saline for 10 days + saline on days 9 and 10 (control), saline for 10 days + isoprenaline on days 9 and 10 (isoprenaline group), liraglutide for 10 days + saline on days 9 and 10 (liraglutide group), and liraglutide for 10 days, and on days 9 and 10 isoprenaline was administered. This study evaluated ECG, myocardial injury markers, oxidative stress markers, and pathohistological changes. The results showed that liraglutide mitigated the isoprenaline-induced cardiac dysfunction recorded by ECG. Liraglutide reduced serum markers of myocardial injury such as high-sensitive troponin I, aspartate aminotransferase, alanine aminotransferase, reduced thiobarbituric acid reactive substances, increased catalase and superoxide dismutase activity, increased reduced glutathione level, and improved lipid profile. Liraglutide induced antioxidative protection and alleviated isoprenaline-induced myocardial injury.
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 | 0.001 |
| 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) | 0.001 | 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".