Melatonin synergistically enhances protective effect of atorvastatin against gentamicin-induced nephrotoxicity in rat kidney
Bibliographic record
Abstract
The risk of serious side-effects such as nephrotoxicity is the principal limitation of gentamicin (GEN) therapeutic efficacy. Oxidative stress is considered to be an important mediator of GEN-induced nephrotoxicity. The present study was designed to evaluate the efficacy of the combination of melatonin (MT) plus atorvastatin (ATO) against GEN-induced nephrotoxicity in rats. We utilized 30 male Wistar albino rats allocated in 5 groups, each containing 6 rats: control, GEN (100 mg/kg/day), ATO (10 mg/kg/day) + GEN, MT (20 mg/kg/day) + GEN, and ATO (10 mg/kg/day) plus MT (20 mg/kg/day) + GEN. Kidney weight, serum creatinine and urea concentration, renal ROS, MDA, GSH levels, SOD, and CAT activity were determined. GEN-induced nephrotoxicity was evidenced by marked elevations in serum urea and creatinine, kidney weight, renal ROS, and MDA levels and reduction in renal GSH level, SOD and CAT activity. MT pretreatment significantly lowered the elevated serum creatinine concentration, kidney weight, renal ROS and MDA levels. However ATO could not reduce these parameters, but similarly to MT, it was able to enhance the renal GSH level, CAT and SOD activity. In addition, a combination therapy of MT plus ATO enhanced the beneficial effects of ATO, while not changing the effects of MT effects or even improving them. The present study indicates that a combination therapy of MT plus ATO can attenuate renal injury in rats treated with GEN, possibly by reducing oxidative stress, and it seems that MT can enhance the beneficial effects of ATO.
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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 | 0.001 |
| 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".