Exposure to ambient particles accelerates monocyte release from bone marrow in atherosclerotic rabbits
Bibliographic record
Abstract
Exposure to air pollution [particulate matter, particles <10 microm (PM(10))] causes a systemic inflammatory response that includes stimulation of the bone marrow (BM) and progression of atherosclerosis. Monocytes are known to play a key role in atherogenesis by migration into subendothelial lesions where they appear as foam cells. The present study was designed to quantify the BM monocyte response in Watanabe heritable hyperlipidemic (WHHL) rabbits after PM(10) exposure. WHHL rabbits were given twice weekly intrapharyngeal instillations of 5 mg of PM(10) for 4 wk to a total of 40 mg and compared with control WHHL or New Zealand White (NZW) rabbits. The thymidine analog 5'-bromo-2'-deoxyuridine was used to label dividing cells in the BM and a monoclonal antibody to identify monocytes in peripheral blood. The transit time of monocytes through the BM was faster in WHHL than in NZW rabbits (30.4 +/- 1.9 h vs. 35.2 +/- 0.9 h, WHHL vs. NZW; P < 0.05). PM(10) instillation exposure increased circulating band cell counts, caused rapid release of monocytes from the BM, and further shortened their transit time through the BM to 23.2 +/- 1.6 h (P < 0.05). The percentage of alveolar macrophages containing particles in the lung correlated with the BM transit time of monocytes (r(2) = 0.45, P <0.05). We conclude that atherosclerosis increases the release of monocytes from the BM, and PM(10) exposure accelerates this process in relation to the amount of particles phagocytosed by alveolar macrophages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".