Bibliographic record
Abstract
BACKGROUND: Proprotein convertase subtilisin/kexin type 9 (PCSK9), which promotes degradation of hepatic low density lipoprotein receptor (LDLR), has a role in plasma cholesterol metabolism. Its gene is associated with the development of familial hypercholesterolemia. mRNA silencing or inhibition of PCSK9-induced degradation of LDLR may be used to treat this disease. OBJECTIVE/METHODS: To summarize approaches proposed to reduce the levels of PCSK9 and/or its activity. RESULTS/CONCLUSIONS: mRNA knockdown approaches include the use of antisense oligonucleotides either as soluble phosphorothioates or locked nucleic acids and lipidoid nanoparticles embedded with small interfering RNAs. Passive immunization is also an option. Other strategies include inhibition of the zymogen activation of proPCSK9, or the interaction of PCSK9 with the EGF-A domain of the LDLR. The N-terminal prosegment and the C-terminal Cys-His rich domain (CHRD), are alternative targets. Annexin A2 specifically binds the CHRD and inhibits PCSK9 function, and Annexin A2 peptide mimics could pave the way for the development of novel PCSK9-inhibitory compounds.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".