Characterization of Proliferating Lesion‐Resident Cells During All Stages of Atherosclerotic Growth
Bibliographic record
Abstract
BACKGROUND: Monocyte recruitment leads to accumulation of macrophage foam cells and contributes to atherosclerotic lesion growth. Recent studies have reported that lesion-resident macrophages can proliferate and represent a major cellular component during lesion development. This study was designed to assess whether the rate of macrophage proliferation changes during well-established stages of lesion growth and to characterize other populations of proliferating cells within these lesions. METHODS AND RESULTS: Using murine models of atherosclerosis (Apoe(-/-) and LDLr(-/-) mice) and human coronary artery lesions, in situ proliferation of lesion-resident cells at different stages of growth was assessed by staining for Ki67 and bromodeoxyuridine (BrdU). In early lesions, close to half of all actively growing macrophages were proliferating in situ. BrdU pulse labeling allowed for accurate identification of in situ proliferating macrophages compared to those derived from monocyte recruitment. Local macrophage proliferation declined as lesions advanced. Interestingly, intimal inflammatory cell infiltrates containing proliferating T lymphocytes were identified during the active phase of lesion growth and correlated with apoptotic cell death. Inflammatory cell infiltrates were completely resolved in advanced lesions and replaced with the necrotic core. CONCLUSIONS: Our findings indicate that atherosclerotic lesions contain locally proliferating macrophages primarily during early and intermediate stages of lesion growth. Furthermore, T-lymphocyte-enriched inflammatory cell infiltrates represent a novel subset of proliferating cells within the atherosclerotic lesion that correlate with apoptosis and precede the necrotic core. These findings have novel implications in understanding the pathogenesis of atherosclerosis and may implicate proliferating T lymphocytes as a contributing factor to lesion progression and stability.
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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".