Berberine Decreases Plasma Lipids and Liver Weight in Hypercholesterolemic Rats
Bibliographic record
Abstract
Although several studies in humans and animals have reported the efficacy of berberine (BBR) on lipid metabolism, the dose response has not been well established. We determined the effect of BBR at different doses on plasma lipids and liver weight in rats. Male Sprague‐Dawley rats were divided into five groups and fed a normal control diet (NC), an atherogenic diet containing 2% cholesterol and 28% fat (AT), the AT diet supplemented with BBR at a dose of 50 mg/kg body weight (BW)/d (ATB50), 100 mg/kg BW/d (ATB100), and 150 mg/kg BW/d (ATB150), respectively, for 8 wk. Results showed that the AT diet increased plasma cholesterol to 2.5 fold of the NC. BBR reduced plasma total cholesterol and nonHDL‐cholesterol as compared with the AT, with maximal reduction being achieved by ATB100. BBR reduced plasma triacylglycerides up to 25% but did not reach significance due to large variations observed within the groups. The AT diet increased relative liver weight to body weight to 2.5‐fold of the NC group and by contrast, BBR decreased the relative liver weight in a dose dependent pattern. The AT diet increased body weight relative to the NC, but this effect was combated by BBR supplementation. In summary, BBR is effective in reducing plasma cholesterol levels and liver weight, and tended to lower plasma triacylglycerides and body weight in hypercholesterolemic rats. Supported by a CIHR grant.
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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) | 0.002 | 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".