Berberine significantly reduces plasma total cholesterol and nonhigh density lipoprotein cholesterol levels in Golden Syrian hamsters
Bibliographic record
Abstract
At present, people are actively seeking efficacious natural products to help reduce or maintain their cholesterol levels within a healthy range. In this regard, the plant alkaloid berberine has received a renewed interest for its significant lipid‐lowering capability and for its novel mechanism of action to reduce plasma cholesterol. However, only a few studies have been carried out to date and it is important to look further into the efficacy and safety, as well as examine the mechanisms by which this natural compound lowers both plasma cholesterol and triglyceride levels. The present study examined the effect and safety of dietary supplementation with berberine on plasma cholesterol and triglyceride levels in an animal model. Male Golden Syrian hamsters were fed with either a semi‐synthetic research diet containing 0.15% cholesterol and 5% fat or the control diet with addition of 100 mg/kg/d of berberine. After 4 wk, plasma total cholesterol and non‐HDL cholesterol levels were reduced by 22% ( P < 0.0001) and 28% ( P < 0.0001). Although a trend of triglyceride reduction was observed after berberine supplementation, it was not significantly different from that of the control group. Histopathological examinations in the major organs did not reveal any significant pathological changes due to berberine supplementation. It may be concluded that berberine is a potent natural agent to reduce plasma cholesterol levels without apparent side effects in hamsters.
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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".