Smooth Muscle Cells Contribute the Majority of Foam Cells in ApoE (Apolipoprotein E)-Deficient Mouse Atherosclerosis
Bibliographic record
Abstract
Objective— Smooth muscle cells (SMCs) are the most abundant cells in human atherosclerotic lesions and are suggested to contribute at least 50% of atheroma foam cells. In mice, SMCs contribute fewer total lesional cells. The purpose of this study was to determine the contribution of SMCs to total foam cells in apolipoprotein E-deficient (ApoE −/− ) mice, and the utility of these mice to model human SMC foam cell biology and interventions. Approach and Results— Using flow cytometry, foam cells in the aortic arch of ApoE −/− mice were characterized based on the expression of leukocyte-specific markers. Nonleukocyte foam cells increased from 37% of total foam cells in 27-week-old to 75% in 57-week-old male ApoE −/− mice fed a chow diet and were ≈70% in male and female ApoE −/− mice following 6 weeks of Western diet feeding. A similar contribution to total foam cells by SMCs was found using SMC-lineage tracing ApoE −/− mice fed the Western diet for 6 or 12 weeks. Nonleukocyte foam cells contributed a similar percentage of total atheroma cholesterol and exhibited lower expression of the cholesterol exporter ABCA1 (ATP-binding cassette transporter A1) when compared with leukocyte-derived foam cells. Conclusions— Consistent with previous studies of human atheromas, we present evidence that SMCs contribute the majority of atheroma foam cells in ApoE −/− mice fed a Western diet and a chow diet for longer periods. Reduced expression of ABCA1, also seen in human intimal SMCs, suggests a common mechanism for formation of SMC foam cells across species, and represents a novel target to enhance atherosclerosis regression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".