Regulation of Cholesterol Biosynthesis in Hepatocytes
Bibliographic record
Abstract
Hypercholesterolemia, a condition of high cholesterol levels in the circulation, poses a major risk for developing cardiovascular disease, such as atherosclerosis. A common method of reducing plasma cholesterol levels relies on the administration of drugs that limit cholesterol synthesis or uptake, many of which have undesirable side effects. Thus, some patients are turning to an alternative treatment, namely natural health products. Natural health products are often equally or even more effective at treating illness than synthetic drugs and may produce fewer side effects. The goal of this study was to identify a natural health product that regulates hepatic cholesterol synthesis by inhibiting HMG-CoA reductase, the enzyme which catalyzes the rate-limiting step of the cholesterol synthesis pathway. Several natural compounds were screened using the human hepatoma cell line HepG2. One compound, berberine, showed great potential as a regulator of cholesterol synthesis and so became the subject of this investigation. Berberine inhibited HMG-CoA reductase activity and decreased cellular accumulation of cholesterol. Berberine was shown to regulate HMG-CoA reductase through activation of metabolic regulator AMP-activated protein kinase, which modifies HMG-CoA reductase post-translationally and thereby decreases its activity. In conclusion, this study demonstrates that the natural health product berberine decreases cholesterol synthesis by activating a cellular signalling pathway to bring about post-translational modification of HMG-CoA reductase, and in doing so, inhibits this enzyme. This novel mechanism supports berberine’s potential for a cholesterol-lowering therapy and its role in reducing the risk for cardiovascular disease.
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 | 0.001 |
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".