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Record W4283157381 · doi:10.3389/fphys.2022.921597

Smooth Muscle Cell—Macrophage Interactions Leading to Foam Cell Formation in Atherosclerosis: Location, Location, Location

2022· review· en· W4283157381 on OpenAlexafffund
Pinhao Xiang, Valentin Blanchard, Gordon A. Francis

Bibliographic record

VenueFrontiers in Physiology · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicAtherosclerosis and Cardiovascular Diseases
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsFoam cellMacrophageInflammationCellCell typePopulationCell biologySmooth muscleVascular smooth musclePathologyMedicineBiologyImmunologyInternal medicineIn vitroBiochemistry

Abstract

fetched live from OpenAlex

Cholesterol-overloaded cells or "foam cells" in the artery wall are the biochemical hallmark of atherosclerosis, and are responsible for much of the growth, inflammation and susceptibility to rupture of atherosclerotic lesions. While it has previously been thought that macrophages are the main contributor to the foam cell population, recent evidence indicates arterial smooth muscle cells (SMCs) are the source of the majority of foam cells in both human and murine atherosclerosis. This review outlines the timeline, site of appearance and proximity of SMCs and macrophages with lipids in human and mouse atherosclerosis, and likely interactions between SMCs and macrophages that promote foam cell formation and removal by both cell types. An understanding of these SMC-macrophage interactions in foam cell formation and regression is expected to provide new therapeutic targets to reduce the burden of atherosclerosis for the prevention of coronary heart disease, stroke and peripheral vascular 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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.261
Teacher spread0.238 · 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

Citations47
Published2022
Admission routes2
Has abstractyes

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