Resveratrol Protects Against Methotrexate-Induced Hepatic Injury in Rats
Bibliographic record
Abstract
PURPOSE: The aim of this study to investigate the possible protective effect of resveratrol on some liver and serum/plasma parameters in methotrexate induced toxicity in rats. Methotrexate is used widely to treat various neoplastic diseases such as acute lymphoblastic leukemia, lymphoma, solid cancers, and autoimmune diseases. We hypothesized that resveratrol has a potential to decrease the oxidant damage in MTX-induced hepatic injury. METHODS: Following a single dose of methotrexate (20 mg/kg, i.p.), either saline or resveratrol (10 mg/kg, orally) was administered for 5 days. After decapitation of the rats, trunk blood was obtained and the liver was removed to measure malondialdehyde and glutathione levels, myeloperoxidase and thromboplastic activities and collagen content. Aspartate aminotransferase, alanine aminotransferase and lactate dehydrogenase activity were measured in the serum samples, while TNF-alpha and total antioxidant capacity were assayed in plasma samples. RESULTS: Our results showed that MTX administration increased the hepatic malondialdehyde levels, myeloperoxidase and thromboplastic activities and collagen contents and decreased glutathione, while these alterations were reversed in resveratrol-treated group. Elevated aspartate aminotransferase and alanine aminotransferase activities and TNF-alpha level observed following MTX treatment was depressed with resveratrol. CONCLUSIONS: The present study showed that resveratrol protects against methotrexate-induced hepatic injury and may be of therapeutic potential in alleviating the systemic side effects of chemotherapeutics.
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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 | 0.000 |
| 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) | 0.002 | 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".