The effect of dapagliflozin on myocardial ischemia–reperfusion injury in diabetic rats
Bibliographic record
Abstract
The incidence of ischemic heart disease is 2–3 times higher in diabetic patients. However, the effect of dapagliflozin on ischemia–reperfusion myocardial injury in diabetic rats has not been studied. We examined the effects of dapagliflozin on myocardial IR injury in streptozotocin–nicotinamide-induced diabetic rats. Rats were divided into four groups ( n = 7 in each group): control, control-dapagliflozin, diabetes, and diabetes-dapagliflozin. Dapagliflozin (1.5 mg/kg/day) was administered concomitantly in drinking water for 2 months. The hearts were perfused in a Langendorff's apparatus at 2 months and assessed before (baseline) and after myocardial IR for the following parameters: left ventricular developed pressure (LVDP), minimum and maximum rates of pressure change in the left ventricle (±d P/d t), endothelial nitric oxide (NO) synthase (eNOS) and inducible NO synthase (iNOS) mRNA expressions, creatine kinase MB (CK-MB) and troponin imyocardial enzyme extravasation, and lactate dehydrogenase. The recovery of LVDP and ±d P/d t in diabetic rats was lower than that in controls but near normal after dapagliflozin treatment. Diabetic rats had decreased eNOS expression and increased iNOS expression at baseline and after IR, whereas dapagliflozin normalized these parameters after IR. Compared with controls, cardiac NOx levels were initially lower in diabetic patients but higher after IR. Baseline MDA levels were higher in diabetic rats after IR, whereas cardiac NOx levels decreased after treatment with dapagliflozin. Dapagliflozin protects the diabetic rat heart from ischemia–reperfusion myocardial injury by regulating the expression of eNOS and iNOS and inhibiting cardiac lipid peroxidation.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".