Hepatoprotection by carotenoids in isoniazid–rifampicin induced hepatic injury in rats
Bibliographic record
Abstract
This study evaluates the hepatoprotective effect of carotenoids against isoniazid (INH) and rifampicin (RIF). Thirty-six adult rats were divided into the following 4 groups: (1) control group treated with normal saline; (2) INH + RIF group treated with 50 mg·(kg body mass)-1·day-1 of INH and RIF each; (3) INH + RIF+ carotenoids group treated with 50 mg·(kg body mass)-1·day-1 of INH and RIF each and 10 mg·(kg body mass)-1·day-1 of carotenoids; and (4) carotenoids group treated with 10 mg·(kg body mass)-1·day-1 of carotenoids for 28 days intragastrically. Oxidative stress and antioxidant levels in liver and blood, liver histology and change in transaminases were measured in all the above-mentioned groups. There was an increase in lipid peroxidation with a reduction in thiols, catalase, and superoxide dismutase (SOD) in the liver and blood of rats accompanied by an increase in transaminases, bilirubin, and alkaline phosphatase. Treatment with carotenoids along with INH + RIF partially reversed lipid peroxidation, thiols, catalase, and SOD in the liver and blood of rats. Elevated levels of the enzymes in serum were also reversed partially by this treatment. The degree of necrosis, portal triaditis, and inflammation were also lowered in the carotenoids group. In conclusion, carotenoids supplementation in INH + RIF treated rats showed partial protection.
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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 | 0.000 | 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".