Kolaviron attenuates diclofenac-induced nephrotoxicity in male Wistar rats
Bibliographic record
Abstract
The beneficial effects of kolaviron, a natural biflavonoid from the seeds of Garcinia kola, have been attributed to its antioxidant and anti-inflammatory activities. This study was designed to investigate the renoprotective effect of kolaviron in rat model of diclofenac (DFC)-induced acute renal failure. Thirty-five male Wistar rats were divided into 7 groups of 5 rats each as follows: a control group that received propylene glycol orally and treatment groups that received DFC, DFC recovery, DFC followed by kolaviron at 3 different doses, and kolaviron only. DFC-treated rats showed sluggishness, illness, and anorexia. Their urine contained appreciable protein, glucose, and ketone bodies. Histopathological examination of their kidneys revealed profound acute tubular necrosis. DFC treatment significantly increased levels of plasma creatinine, urea, sodium, chloride, potassium ions, and increased renal tissue activities of superoxide dismutase, catalase, levels of malondialdehyde, and hydrogen peroxide. Fractional excretion of sodium and potassium and renal tissue levels of reduced glutathione and prostaglandin E2 (PGE2) decreased significantly in DFC-treated groups. However, kolaviron administration significantly reduced the toxic effect of DFC on PGE2 release; plasma levels of creatinine, urea, glucose, and electrolytes; and significantly attenuated renal tubular and oxidative damages. Furthermore, the effects of DFC administration on food consumption, water intake, urine output and urine protein, glucose, ketone bodies, and electrolytes were significantly attenuated in animals treated with kolaviron. The results suggested that kolaviron ameliorated DFC-induced kidney injury in Wistar rats by decreasing renal oxidative damage and restoration of renal PGE2 release back to the basal levels.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".