Squalene synthase: a critical enzyme in the cholesterol biosynthesis pathway
Bibliographic record
Abstract
High levels of plasma low-density lipoprotein cholesterol (LDL-C) are a significant risk factor for heart disease. Statins (3-hydroxy-3-methyl-glutaryl-CoA reductase inhibitors) have been extensively used to treat high-plasma LDL-C levels and are effective in preventing heart disease. However, statins can be associated with adverse side effects in some patients and do not work effectively in others. As an alternative to statins, the development of cholesterol-lowering agents that directly inhibit squalene synthase have shown promise. Clinical studies have shown that squalene synthase inhibitors are effective in lowering plasma levels of total cholesterol and LDL-C. Squalene synthase plays an important role in the cholesterol biosynthesis pathway as it is responsible for the flow of metabolites into either the sterol or the non-sterol branches of the pathway. In addition, variants of the squalene synthase gene appear to modulate plasma cholesterol levels in human populations and therefore may be linked to cardiovascular disease. In this review, we examine squalene synthase and the gene that codes for it (farnesyldiphosphate farnesyltransferase 1). In particular, we investigate their role in the regulation of cellular and plasma cholesterol levels, including data that suggest that squalene synthase may be involved in the etiology of hypercholesterolemia.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".