Preconditioning with atorvastatin against renal ischemia–reperfusion injury in nondiabetic versus diabetic rats
Bibliographic record
Abstract
Acute renal failure complicates renal ischemia-reperfusion (I/R) owing to reactive oxygen species production. Atorvastatin (ATO) has anti-inflammatory and antioxidant properties. The current study investigated whether ATO alleviated damage induced by renal I/R injury in nondiabetic versus diabetic rat models. Thirty-six rats were equally divided into 6 groups: group A1 (nondiabetic sham), group A2 (nondiabetic I/R), group A3 (nondiabetic ATO + I/R), group B1 (diabetic sham), group B2 (diabetic I/R), and group B3 (diabetic ATO + I/R). All groups experienced 45 min of bilateral renal ischemia followed by 24 h of reperfusion. Groups A3 and B3 were treated with single intraperitoneal doses of ATO (10 mg/kg) 30 min before ischemia. Histological analysis of kidney tissues, kidney function tests, and analyses of caspase-3 and CD44 expression and oxidative stress markers were performed to assess tubular injury. Histological analysis revealed marked tubular damage in groups A2 and B2 but improvement in groups A3 and B3. Improvements were also found in groups A3 and B3 for caspase-3 and CD44 expression, kidney function tests, and oxidative stress markers. Our results suggest ATO may ameliorate renal I/R injury differently between nondiabetic and diabetic rats.
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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 | 0.000 | 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".