Development of Natural Products for Lowering Blood Lipids
Bibliographic record
Abstract
Although many natural products are available for lowering blood lipids, their efficacies are relatively low. Developing new products or improving the efficacy of current products is in demand. Recently, a great interest has arisen in the lipid‐lowering effect of plant alkaloid, berberine (BBR). Plant stanols (PS) are another class of natural compounds that are widely used to lower blood cholesterol. Because PS and BBR reduce cholesterol through distinct mechanisms, it is interesting to know how BBR and PS affect blood cholesterol when provided in combination. We have conducted several studies in hamsters and rats to determine the effect of BBR and PS alone and in combination on plasma cholesterol levels. The results demonstrated that the combination of BBR and PS substantially improved the cholesterol‐lowering efficacy through a synergistic inhibition on intestinal cholesterol absorption. Gene expression of intestinal ABCG5 and ABCG8 were also synergistically lowed by the combination of BBR and PS, indicating that the inhibition of cholesterol absorption by BBR and PS was independent from sterol transporters, ABCG5 and ABCG8. In addition, BBR and PS synergistically reduced plasma triacylglycerides. These findings suggest that the combination of BBR and plant stanols is a promising approach to the development of new natural product that can efficaciously lower both cholesterol and triacylglycerides.
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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) | 0.000 | 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) | 0.003 | 0.001 |
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".