Regulation of Hepatic Lipid Metabolism by a Natural Health Product
Bibliographic record
Abstract
Hypercholesterolemia is a major risk factor for cardiovascular disease. Current treatment for lipid lowering is provided primarily by the statin drugs, which block HMG‐CoA reductase, the rate‐limiting enzyme of cholesterol synthesis, and improve clearance of plasma LDL‐cholesterol. However, many patients cannot tolerate the statin dosages recommended to reach their target lipid levels, due to side effects such as muscle pain and decline in liver function. Berberine, an herbal product used in traditional Chinese medicine, has great potential as a new regulator of lipid metabolism. The objective of this study was to determine whether berberine had an effect on HMG‐CoA reductase and to elucidate the underlying mechanism by which it might act. HMG‐CoA reductase activity was decreased in HepG2 cells after treatment with berberine. The inhibitory effect of berberine on HMG‐CoA reductase was due to post‐translational modification of the enzyme. Western immunoblotting analysis revealed that berberine treatment resulted in a significant increase in the phosphorylation of HMG‐CoA reductase, leading to inactivation of the enzyme. Cells that were treated with berberine exhibited a decrease in cholesterol storage as a result of reduced HMG‐CoA reductase activity. The molecular mechanism by which berberine regulated posttranslational modification of HMG‐CoA reductase was also investigated. Funding: NSERC, CIHR, HSF and MHRC.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".