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Record W4282829495 · doi:10.1097/mol.0000000000000823

Understanding the ins and outs of lipoprotein (a) metabolism

2022· review· en· W4282829495 on OpenAlexafffund
Michael B. Boffa, Marlys L. Koschinsky

Bibliographic record

VenueCurrent Opinion in Lipidology · 2022
Typereview
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsWestern UniversityRobarts Clinical Trials
FundersCanadian Institutes of Health Research
KeywordsMetabolismMedicineEndocrinology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review summarizes our current understanding of the processes of apolipoprotein(a) secretion, assembly of the Lp(a) particle and removal of Lp(a) from the circulation. We also identify existing knowledge gaps that need to be addressed in future studies. RECENT FINDINGS: The Lp(a) particle is assembled in two steps: a noncovalent, lysine-dependent interaction of apo(a) with apoB-100 inside hepatocytes, followed by extracellular covalent association between these two molecules to form circulating apo(a).The production rate of Lp(a) is primarily responsible for the observed inverse correlation between apo(a) isoform size and Lp(a) levels, with a contribution of catabolism restricted to larger Lp(a) isoforms.Factors that affect apoB-100 secretion from hepatocytes also affect apo(a) secretion.The identification of key hepatic receptors involved in Lp(a) clearance in vivo remains unclear, with a role for the LDL receptor seemingly restricted to conditions wherein LDL concentrations are low, Lp(a) is highly elevated and LDL receptor number is maximally upregulated. SUMMARY: The key role for production rate of Lp(a) [including secretion and assembly of the Lp(a) particle] rather than its catabolic rate suggests that the most fruitful therapies for Lp(a) reduction should focus on approaches that inhibit production of the particle rather than its removal from circulation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.384
GPT teacher head0.425
Teacher spread0.041 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations30
Published2022
Admission routes2
Has abstractyes

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