Pathways of smooth muscle foam cell formation in atherosclerosis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".