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Record W2163792483 · doi:10.1161/circresaha.114.300645

MicroRNA Control of High-Density Lipoprotein Metabolism and Function

2014· review· en· W2163792483 on OpenAlexafffund
Katey J. Rayner, Kathryn J. Moore

Bibliographic record

VenueCirculation Research · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Ottawa
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsmicroRNALipoproteinFunction (biology)BiologyBiogenesisCell biologyHigh-density lipoproteinLow-density lipoproteinGene expressionSubclassRegulation of gene expressionGeneBioinformaticsComputational biologyCholesterolGeneticsBiochemistry

Abstract

fetched live from OpenAlex

Recent discoveries of microRNAs (miRNAs) that control high-density lipoprotein abundance and function have expanded our knowledge of the mechanisms regulating this important lipoprotein subclass. miRNAs have been shown to regulate gene networks that control high-density lipoprotein biogenesis and uptake, as well as discrete steps in the reverse cholesterol transport pathway. Furthermore, high-density lipoprotein itself has been shown to transport miRNAs selectively in health and disease, offering new possibilities of how this lipoprotein may alter gene expression in distal target cells and tissues. Collectively, these discoveries offer new insights into the mechanisms governing high-density lipoprotein metabolism and function and open new avenues for the development of therapeutics for the treatment of cardiovascular disease.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.040
GPT teacher head0.341
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), 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

Citations87
Published2014
Admission routes2
Has abstractyes

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