New developments in proprotein convertase subtilisin–kexin 9's biology and clinical implications
Bibliographic record
Abstract
PURPOSE OF REVIEW: High levels of LDL-cholesterol (LDL-C) are directly associated with devastating cardiovascular complications. Statins downregulate cholesterol synthesis and upregulate hepatic mRNA levels of LDL receptor (LDLR) and proprotein convertase subtilisin-kexin 9 (PCSK9), a validated enhancer of LDLR protein degradation. Herein, we summarize recent discoveries of the biological properties of PCSK9 in both health and disease states. RECENT FINDINGS: PCSK9 downregulation of the LDLR protein likely explains the observed protective effect of the loss of PCSK9 in reducing lipoprotein(a) and incidence of septic shock. Injectable inhibitory PCSK9 monoclonal antibodies are now prescribed to hypercholesterolemic patients that do not reach target levels of LDL-C with available drugs. PCSK9 also reduces the levels of other receptors, for example, VLDL receptor (VLDLR), ApoER2, CD36, and CD81. The efficacy of the upregulation of LDLR and VLDLR cell surface levels in the absence of PCSK9 is both tissue and sex dependent. As LDLR, CD81, and VLDLR are hepatitis C receptors, PCSK9 may protect against certain viral infections. SUMMARY: New functions of PCSK9 and other receptor targets are beginning to emerge to explain the observed changes in LDL-C and triglycerides. The effect of PCSK9 loss-of-function on glucose metabolism, factors that regulate the expression of PCSK9, and the roles of PCSK9 in other tissues, for example, intestine, kidney, and brain require further investigations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.002 |
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".