Update on the Benefits and Mechanisms of Action of the Bioactive Vegetal Alkaloid Berberine on Lipid Metabolism and Homeostasis
Bibliographic record
Abstract
Elevation of circulating levels of blood cholesterol, especially LDL cholesterol, and/or the decrease of HDL cholesterol levels have long been recognized as primary risk factors for developing atherosclerosis that leads to cardiovascular and cerebrovascular disease. Hypertriglyceridemia is an independent risk factor that is known to contribute to the development of atherosclerosis. Thus, various interventional efforts aimed at reducing hypercholesterolemia and hypertriglyceridemia have been practiced clinically for decades to reduce morbidity and mortality risk associated with deleterious cardiovascular and cerebrovascular events. As such, many drugs have been developed and clinically used to treat hypocholesteremia and/or hypertriglyceridemia; however, dietary approaches including supplements along with changes in nutrition and lifestyle have become increasingly attractive and acceptable methods used to control borderline or moderately increased levels of blood cholesterol and triacylglycerols. In this regard, the use of a plant/herbal bioactive compound, berberine (BBR), has recently been studied extensively in terms of its efficacy as well as its mechanisms of action and safety as an alternative intervention that beneficially modulates blood lipids. The aim of this review is to provide a comprehensive update on BBR research, new concepts and directions in terms of product development and current challenges, and future prospects of using BBR to manage diseases and complications associated with dyslipidemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".