Lycopene has reduced renal damage histopathologically and biochemically in experimental renal ischemia-reperfusion injury
Bibliographic record
Abstract
BACKGROUND: The present study aimed to investigate whether the inflammatory and antioxidant lycopene has a therapeutic effect against renal ischemia/reperfusion (I/R) injury. MATERIALS AND METHODS: In this study, 24 Wistar-Albino rats, weighing from 200 to 250 g, were divided into four groups. All rats underwent median laparotomy under anesthesia. No procedures were performed in the control group (Group C), whereas 100 mg/kg lycopene was administered by gavage in the lycopene group (Group L). The arteries of both kidneys were clamped for 45 min in the ischemia group (Group I), whereas 100 mg/kg lycopene was administered by gavage 30 min before clamping renal arteries, and ischemia was performed in the treatment group (Group T) rats. For all rats, blood samples and renal tissues were collected at 6 h of reperfusion. Samples were used to examine serum BUN, creatinine, MDA and GSH levels, and the renal tissues were used to examine MDA and GSH levels, and renal histopathologies. RESULTS: The treatment group had statistically significant lower serum MDA levels, histopathological tubular vacuolization, loss of brush border and tubular dilatation (p < 0.05), whereas serum BUN, creatinine, tissue MDA, and tissue and serum GSH levels were improved in favor of the treatment group, even though it was not statistically significant (p > 0.05). CONCLUSION: The present study demonstrated that lycopene, which was administered prior to renal I/R injury, prevented renal damage through biochemical and histopathological parameters.
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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".