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Record W2333022264 · doi:10.1097/mol.0000000000000279

So Much Cholesterol

2016· review· en· W2333022264 on OpenAlexafffund
Joshua A. Dubland, Gordon A. Francis

Bibliographic record

VenueCurrent Opinion in Lipidology · 2016
Typereview
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
FundersCanadian Institutes of Health Research
KeywordsCholesterolFoam cellProinflammatory cytokineArteryCoronary artery diseaseEffluxCoronary atherosclerosisMacrophageCoronary arteriesSmooth muscleBiologyLipoproteinChemistryPathologyInternal medicineMedicineEndocrinologyImmunologyInflammationBiochemistryIn vitro

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Smooth muscle cells (SMCs) form the thickened intimal layer in atherosclerosis-prone arteries in early life, and provide the initial site for retention and uptake of atherogenic lipoproteins. Here we review current knowledge regarding the importance of SMCs in the deposition of cholesterol in atherosclerotic plaque. RECENT FINDINGS: SMCs were found to comprise at least 50% of total foam cells in human coronary artery atherosclerosis, and exhibit a selective loss of expression of the cholesterol efflux promoter ATP-binding cassette transporter A1. Cholesterol loading induced a loss of SMC gene expression and an increase in macrophage and proinflammatory marker expression by cultured mouse and human arterial SMCs, with reversal of these effects upon removal of the excess cholesterol. Mice engineered to track all cells of SMC lineage indicated that, at most, SMCs make up about one-third of total cells in atherosclerotic plaque in these animals. SUMMARY: SMCs appear to be the origin of the majority of foam cells in human atherosclerotic plaque. Recent studies suggest a renaissance of research on the role of SMCs in atherosclerosis is needed to make the next leap forward in the prevention and treatment of this 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.154
GPT teacher head0.443
Teacher spread0.290 · 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.

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

Citations80
Published2016
Admission routes2
Has abstractyes

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