Red yeast rice and coenzyme Q<sub>10</sub> as safe alternatives to surmount atorvastatin-induced myopathy in hyperlipidemic rats
Bibliographic record
Abstract
Statins are the first line treatment for the management of hyperlipidemia. However, the primary adverse effect limiting their use is myopathy. This study examines the efficacy and safety of red yeast rice (RYR), a source of natural statins, as compared with atorvastatin, which is the most widely used synthetic statin. Statin interference with the endogenous synthesis of coenzyme Q10 (CoQ10) prompted the hypothesis that its deficiency may be implicated in the pathogenesis of statin-associated myopathy. Hence, the effects of combination of CoQ10 with either statin have been evaluated. Rats were rendered hyperlipidemic through feeding them a high-fat diet for 90 days, during the last 30 days of the diet they were treated daily with either atorvastatin, RYR, CoQ10, or combined regimens. Lipid profile, liver function tests, and creatine kinase were monitored after 15 and 30 days of drug treatments. Heart contents of CoQ9 and CoQ10 were assessed and histopathological examination of the liver and aortic wall was performed. RYR and CoQ10 had the advantage over atorvastatin in that they lower cholesterol without elevating creatine kinase, a hallmark of myopathy. RYR maintained normal levels of heart ubiquinones, which are essential components for energy production in muscles. In conclusion, RYR and CoQ10 may offer alternatives to overcome atorvastatin-associated myopathy.
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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 | 0.000 |
| 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".