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Record W2909473101 · doi:10.1097/mol.0000000000000574

Pathways of smooth muscle foam cell formation in atherosclerosis

2019· review· en· W2909473101 on OpenAlexafffund
Collin Pryma, Carleena Ortega, Joshua A. Dubland, Gordon A. Francis

Bibliographic record

VenueCurrent Opinion in Lipidology · 2019
Typereview
Languageen
FieldImmunology and Microbiology
TopicAtherosclerosis and Cardiovascular Diseases
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsFoam cellScavenger receptorSmooth muscleCell biologyCellCell typePopulationChemistryCholesterolBiologyBiochemistryMedicineLipoproteinEndocrinology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Smooth muscle cells (SMCs) are the major cell type in human atherosclerosis-prone arteries and take up excess lipids, thereby contributing to luminal occlusion. Here we provide a focused review on pathways by which smooth muscle cells (SMCs) can become foam cells in atherosclerosis. RECENT FINDINGS: A synthesis of recent and older investigations provides key mechanistic insights into SMC foam cell formation. LDL and other apoB-containing lipoproteins are modified by a diverse array of oxidative, enzymatic, and nonenzymatic processes present in the arterial intima. These modifications of LDL all promote the aggregation of LDL (agLDL), a key finding from analysis of arterial lesion particles. Scavenger receptor and phagocytic capacity of SMCs can vary greatly, perhaps related to differences in SMC phenotype or in-vitro cell culture environments, and can be increased with exposure to cytokines, growth factors, and cholesterol. Macrophages promote the formation of SMC foam cells in direct or indirect co-culture models. SUMMARY: SMCs contribute significantly to the foam cell population in atherosclerosis. Further investigation and identification of key mechanisms of SMC foam cell formation will help drive new therapeutics to reduce 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.152
GPT teacher head0.336
Teacher spread0.185 · 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

Citations68
Published2019
Admission routes2
Has abstractyes

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