Does posttreatment thymoquinone reverse high-dose atorvastatin-induced hepatic oxidative injury in rats?
Bibliographic record
Abstract
Atorvastatin (ATO) was commonly used to lower blood cholesterol, but it caused harmful effects to organs, including the liver. Thymoquinone (TQ), a prominent constituent of Nigella sativa, has antioxidant, antiinflammatory, antiapoptotic, antimicrobial, and anticancer activity. The current study investigated the mechanism of ATO-induced hepatotoxicity, whether posttreatment TQ could reverse ATO-induced hepatic injury, and the mechanism of action of TQ as a hepatoprotective agent. Forty adult male Sprague Dawley rats were divided into four equal groups: control, TQ-treated, ATO-treated, and combined ATO/TQ-treated. Rats were treated for 8 weeks and 10 days and euthanized by cervical dislocation 3 days after the last treatment. Blood samples and livers were tested for liver enzymes, oxidative stress, and apoptosis markers and used for histopathological and ultrastructural examination. The ATO-treated group showed an increase in liver enzymes, decreases in reduced glutathione and catalase, and increases in the malondialdehyde lipid peroxidation marker, protein carbonylation, and caspase 3 activity. Posttreatment TQ in the ATO/TQ-treated group seemed to reverse these changes. Histopathological and ultrastructural examination supported these data. Results from the current study suggested that posttreatment TQ may reverse oxidative stress injury in rat liver produced by ATO, suggesting a potential clinical application of using TQ to prevent ATO-induced hepatic injury.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".