Particulate matter air pollution exposure promotes recruitment of monocytes into atherosclerotic plaques
Bibliographic record
Abstract
Epidemiologic studies have shown an association between exposure to ambient particulate air pollution <10 microm in diameter (PM(10)) and increased cardiovascular morbidity and mortality. We previously showed that PM(10) exposure causes progression of atherosclerosis in coronary arteries. We postulate that the recruitment of monocytes from the circulation into atherosclerotic lesions is a key step in this PM(10)-induced acceleration of atherosclerosis. The study objective was to quantify the recruitment of circulating monocytes into vessel walls and the progression of atherosclerotic plaques induced by exposure to PM(10). Female Watanabe heritable hyperlipidemic rabbits, which naturally develop systemic atherosclerosis, were exposed to PM(10) (EHC-93) or vehicle by intratracheal instillation twice a week for 4 wk. Monocytes, labeled with 5-bromo-2'-deoxyuridine (BrdU) in donors, were transfused to recipient rabbits as whole blood, and the recruitment of BrdU-labeled cells into vessel walls and plaques in recipients was measured by quantitative histological methodology. Exposure to PM(10) caused progression of atherosclerotic lesions in thoracic and abdominal aorta. It also decreased circulating monocyte counts, decreased circulating monocytes expressing high levels of CD31 (platelet endothelial cell adhesion molecule-1) and CD49d (very late antigen-4 alpha-chain), and increased expression of CD54 (ICAM-1) and CD106 (VCAM-1) in plaques. Exposure to PM(10) increased the number of BrdU-labeled monocytes adherent to endothelium over plaques and increased the migration of BrdU-labeled monocytes into plaques and smooth muscle underneath plaques. We conclude that exposure to ambient air pollution particles promotes the recruitment of circulating monocytes into atherosclerotic plaques and speculate that this is a critically important step in the PM(10)-induced progression of atherosclerosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".