Bibliographic record
Abstract
Since the discovery of proprotein convertase subtilisin kexin 9 (PCSK9) in 2003, this PC has attracted a lot of attention from the scientific community and pharmaceutical companies. Secreted into the plasma by the liver, the proteinase K-like serine protease PCSK9 binds the low-density lipoprotein (LDL) receptor at the surface of hepatocytes, thereby preventing its recycling and enhancing its degradation in endosomes/lysosomes, resulting in reduced LDL-cholesterol clearance. Surprisingly, in a nonenzymatic fashion, PCSK9 enhances the intracellular degradation of all its target proteins. Rare gain-of-function PCSK9 variants lead to higher levels of LDL-cholesterol and increased risk of cardiovascular disease; more common loss-of-function PCSK9 variants are associated with reductions in both LDL-cholesterol and risk of cardiovascular disease. It took 9 years to elaborate powerful new PCSK9-based therapeutic approaches to reduce circulating levels of LDL-cholesterol. Presently, PCSK9 monoclonal antibodies that inhibit its function on the LDL receptor are evaluated in phase III clinical trials. This review will address the biochemical, genetic, and clinical aspects associated with PCSK9's biology and pathophysiology in cells, rodent and human, with emphasis on the clinical benefits of silencing the expression/activity of PCSK9 as a new modality in the treatment of hypercholesterolemia and associated pathologies.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".