Chelating effects of carnosine in ameliorating nickel-induced nephrotoxicity in rats
Bibliographic record
Abstract
The kidney is one of the main organs affected by nickel toxicity. We investigated the protective effects of carnosine on nickel-induced oxidative stress in kidney of rats. Animals received NiSO4 (20 mg·kg−1·day−1 intragastrically) and (or) carnosine (10 mg·kg−1·day−1 intragastrically) for 21 days and then were evaluated for biochemical, molecular, and histopathological alterations. Nickel caused an increase in renal levels of malondialdehyde and a decrease in reduced glutathione, catalase, and superoxide dismutase levels and total antioxidant capacity. Carnosine prevented the prooxidant and antioxidant imbalance induced by nickel. Nickel-treated rats showed an increase in serum creatinine, urea, and uric acid with a concomitant decrease in albumin. Nickel markedly accumulated in kidney of exposed rats, but its concentration was effectively reduced by carnosine treatment. Carnosine corrected the biochemical abnormalities and the elevated renal TNF-α and IL-6 levels in the nickel-treated group. It also attenuated nickel-induced abnormalities in renal architecture. Although carnosine showed antioxidant and anti-inflammatory effects in renal tissue of nickel-exposed rats, we cannot clearly attribute the protective effect of carnosine to these effects. Instead, the beneficial effect of carnosine observed in the current study may be due to chelation between nickel and carnosine. Thus, carnosine may represent a therapeutic option to protect against nickel-induced nephrotoxicity that deserves further consideration and examination.
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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 | 0.001 | 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".