Protective role of nebivolol via AKT1/Hif-1α/eNOS signaling pathway: nephrotoxicity caused by methotrexate in a rat model
Bibliographic record
Abstract
Methotrexate (MTX) is an antineoplastic and anti-inflammatory agent, which is used in severe diseases. Its use should be limited due to side effects such as nephrotoxicity, myelotoxicity, and hepatotoxicity. Nebivolol (NBV), which is a beta-blocker used in the treatment of hypertension, also contributes to vasodilation in tissues by activating the endothelial nitric oxide synthase (eNOS) enzyme. The purpose of this study is to research the effect of NBV on MTX-induced nephrotoxicity through the AKT1/hypoxia-inducible factor 1-alpha (Hif-1α)/eNOS signaling pathway. The rats were randomly divided into three groups of eight each. The groups were control, MTX, and MTX + NBV. A single dose of 20 mg/kg MTX was given intraperitoneally to the rats on the first day of the study and 10 mg/kg NBV was given orally to the treatment group for 7 days. At the end of the study, rats' blood and kidney tissues were taken for histopathological, immunohistochemical, and biochemical examinations. MTX administration significantly decreased the expression levels of AKT1, eNOS, and Hif-1α compared with the control group ( p < 0.001 for all), and NBV treatment increased these values compared with the MTX group ( p < 0.001 for all). In conclusion, NBV treatment ameliorated the MTX-induced nephrotoxicity via AKT1/Hif-1α/eNOS signaling pathway.
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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 | 0.000 | 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".