Contribution of Intimal Smooth Muscle Cells to Cholesterol Accumulation and Macrophage-Like Cells in Human Atherosclerosis
Bibliographic record
Abstract
BACKGROUND: Intimal smooth muscle cells (SMCs) contribute to the foam cell population in arterial plaque, and express lower levels of the cholesterol exporter ATP-binding cassette transporter A1 (ABCA1) in comparison with medial arterial SMCs. The relative contribution of SMCs to the total foam cell population and their expression of ABCA1 in comparison with intimal monocyte-derived macrophages, however, are unknown. Although the expression of macrophage markers by SMCs following lipid loading has been described, the relevance of this phenotypic switch by SMCs in human coronary atherosclerosis has not been determined. METHODS AND RESULTS: Human coronary artery sections from hearts explanted at the time of transplantation were processed to clearly delineate intracellular and extracellular lipids and allow costaining for cell-specific markers. Costaining for oil red O and the SMC-specific marker SM α-actin of foam cell-rich lesions revealed that 50±7% (average±standard error of the mean, n=14 subjects) of total foam cells were SMC derived. ABCA1 expression by intimal SMCs was significantly reduced between early and advanced atherosclerotic lesions, with no loss in ABCA1 expression by myeloid lineage cells. Costaining with the macrophage marker CD68 and SM α-actin revealed that 40±6% (n=15) of CD68-positive cells originated as SMCs in advanced human coronary atherosclerosis. CONCLUSIONS: These findings suggest SMCs contain a much larger burden of the excess cholesterol in human coronary atherosclerosis than previously known, in part, because of their relative inability to release excess cholesterol via ABCA1 in comparison with myeloid lineage cells. Our results also indicate that many cells identified as monocyte-derived macrophages in human atherosclerosis are in fact SMC derived.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".