Nephroprotective and antioxidant effect of green tea (<i>Camellia sinensis</i>) against nicotine-induced nephrotoxicity in rats and characterization of its bioactive compounds by HPLC–DAD
Bibliographic record
Abstract
Nicotine is a potential inducer of oxidative stress, through which it can damage numerous biological molecules. Natural antioxidants that prevent or slow the progression and severity of nicotine toxicity may have a significant health impact. The purpose of this study, conducted on Wistar rats, was to evaluate the beneficial effects of green tea (Camellia sinensis) extract on nicotine treatment-induced damage on kidney. Our results showed that nicotine significantly (p < 0.01) increased serum and kidney malondialdehyde, the serum contents of urea, creatinine, and uric acid. In addition, nicotine intoxication significantly (p < 0.01) decreased the levels of vitamins E and C in serum and kidney tissue as well as the activities of superoxide dismutase, catalase, and glutathione peroxidase. Interestingly, animals that were pretreated with green tea, prior to nicotine administration, showed a significant nephroprotection, revealed by a significant reduction-induced oxidative damage for all tested markers. The nephroprotective activity of green tea is mediated, at least in part, by the antioxidant effect of its constituents.
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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 | 0.000 |
| 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".