Astaxanthin prevents nephrotoxicity through <i>Nrf2/HO-1</i> pathway
Bibliographic record
Abstract
Renal toxicity is one of the side effects of methotrexate (MTX). Therefore, this study explored the use of astaxanthin (AST), as a natural carotenoid, against MTX-induced nephrotoxicity emphasizing the changes in oxidative stress and the expression of nuclear factor erythroid 2-related factor 2/heme oxygenase 1 ( Nrf2/HO-1). During the 10 days of the experiment, male Wistar rats in different groups received MTX (10 mg/kg) on days 6, 8, and 10 and three doses of AST (25, 50, and 75 mg/kg) during the entire course. Renal failure caused by MTX was observed in significant histopathological changes and a significant increase in serum levels of creatinine, urea, and uric acid ( p < 0.05). Oxidative change induced by MTX injection was also observed by remarkably increasing the tissue level of malondialdehyde (MDA) and decreasing the activity of superoxide dismutase (SOD) and catalase ( p < 0.001). AST decreases the adverse effects of MTX by upregulating the expression of Nrf2/HO-1 genes ( p < 0.01) and decreasing the tissue level of MDA ( p < 0.01). Also, AST significantly reduced the amount of creatinine, urea, and uric acid in the serum and improved the activity of SOD and catalase in the kidney tissue ( p < 0.05). Thus, AST may protect the kidney against oxidative stress caused by MTX.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".