Bibliographic record
Abstract
This article reviews the discovery of PCSK9, its structure-function characteristics, and its presently known and proposed novel biological functions. The major critical function of PCSK9 deduced from human and mouse studies, as well as cellular and structural analyses, is its role in increasing the levels of circulating low-density lipoprotein (LDL)-cholesterol (LDLc), via its ability to enhance the sorting and escort of the cell surface LDL receptor (LDLR) to lysosomes. This implicates the binding of the catalytic domain of PCSK9 to the EGF-A domain of the LDLR. This also requires the presence of the C-terminal Cys/His-rich domain, its binding to the secreted cytosolic cyclase associated protein 1, and possibly another membrane-bound "protein X". Curiously, in PCSK9-deficient mice, an alternative to the downregulation of the surface levels of the LDLR by PCSK9 is taking place in the liver of female mice in a 17β-estradiol-dependent manner by still an unknown mechanism. Recent studies have extended our understanding of the biological functions of PCSK9, namely its implication in septic shock, vascular inflammation, viral infections (Dengue; SARS-CoV-2) or immune checkpoint modulation in cancer via the regulation of the cell surface levels of the T-cell receptor and MHC-I, which govern the antitumoral activity of CD8+ T cells. Because PCSK9 inhibition may be advantageous in these processes, the availability of injectable safe PCSK9 inhibitors that reduces by 50% to 60% LDLc above the effect of statins is highly valuable. Indeed, injectable PCSK9 monoclonal antibody or small interfering RNA could be added to current immunotherapies in cancer/metastasis.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".