Resveratrol ameliorates cisplatin-induced oxidative injury in New Zealand rabbits
Bibliographic record
Abstract
This study investigated the preventive role of resveratrol in cisplatin-induced nephrotoxicity. The study used groups of New Zealand rabbits that were treated as follows: group C (cisplatin treated), group R (resveratrol treated), group R+C (resveratrol + cisplatin treatment), and group E (control group). Kidney levels of glutathione were significantly lower in group C than in groups E and R, whereas glutathione levels in group R+C were found to be similar to the control values. Malondialdehyde levels in group C were significantly higher than in groups E and R. However, malondialdehyde levels in group R+C were similar to group E. Kidney levels of nitric oxide were significantly higher in the cisplatin group than in the control, whereas nitric oxide levels were at basal values in group R+C. Cisplatin treatment significantly reduced kidney levels of glutathione peroxidase, superoxide dismutase, and catalase activity compared with those of group E, whereas resveratrol treatment significantly increased levels of glutathione peroxidase, superoxide dismutase, and catalase activity in group R+C. However, cisplatin injection did not affect mRNA levels of glutathione peroxidase, superoxide dismutase, or catalase enzymes. Histopathological and immunohistochemical analyses indicated that cisplatin caused kidney damage, which was mostly prevented by resveratrol treatment. In conclusion, resveratrol ameliorates cisplatin-induced oxidative injury in the kidney of rabbit.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".