Protective effects of combined β-caryophyllene and silymarin against ketoprofen-induced hepatotoxicity in rats
Bibliographic record
Abstract
Ketoprofen (Ket), widely utilized in treatment of many inflammatory disorders, is found to induce liver toxicity especially with overdose. This study aimed to evaluate the possible protective effects of concomitant β-caryophyllene (Cary) and silymarin (Sily) against Ket-induced hepatotoxicity in rats. Forty adult male albino rats were divided into 5 groups (each n = 8): the control group received distilled water for 6 weeks; the Ket-treated group received distilled water for 5 weeks and Ket in a dose of 8 mg·kg(-1)·day(-1) p.o. for the 6th week; the Cary + Ket treated group received Cary in a dose of 200 mg·kg(-1)·day(-1) orally for 6 weeks and Ket for the 6th week; the Sily + Ket treated group received Sily in the dose of 150 mg·kg(-1)·day(-1) for 6 weeks and Ket for the 6th week; and the Cary + Sily + Ket treated group received Sily and Cary for 6 weeks and Ket for the 6th week. At end of the experiment, serum ALT, AST, and albumin and liver total antioxidant capacity (t.TAC) and malondialdehyde (t.MDA) were measured in all rats. Ket increased serum ALT and AST and t.MDA and decreased t.TAC. Cary and Sily improved these changes. Combined Cary and Sily restored these liver changes to nearly normal. Combined Cary and Sily is hepatoprotective, with the ability to scavenge oxidants against Ket-induced hepatotoxicity in rats.
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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 | 0.000 | 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".