AMBIENT AIR PARTICULATES STIMULATE ALVEOLAR MACROPHAGES OF SMOKERS TO PROMOTE DIFFERENTIATION OF MYELOID PRECURSOR CELLS
Bibliographic record
Abstract
Studies from our laboratory have shown that exposure to air pollution particles smaller than 10 microm (PM10) induced a systemic inflammatory response that includes the release of granulocytes from the bone marrow. In the present study we tested the hypothesis that mediators released from human alveolar macrophages (AM) exposed to PM10 accelerate the maturation of granulocyte precursors. Human myeloid precursor cells (HL60 cells) were incubated with the supernatant from AM exposed to PM10. Phagocytosis of PM10 by AM resulted in the production of cytokines, particularly interleukin-6 (IL-6) and granulocyte-macrophage colony-stimulating factor (GM-CSF) (P < .05). The supernatant from AM exposed to PM10 did not influence myeloid cell proliferation but promoted cell differentiation as measured by surface GD11b and CD14 expressions compared to control supernatant (P < .05). This effect of exposed-AM supernatants on myeloid cell differentiation was blocked by anti-IL-6 monoclonal antibodies (CD11b and CD14; P < .05) and anti-GM-CSF monoclonal antibodies (CD14, P < .01). We conclude that human AM exposed to PM10 produce mediators, particularly IL-6 and GM-CSF that promote the differentiation of bone marrow myeloid cells and we speculate that these cytokines are involved in the release of granulocytes from the bone marrow associated with exposure to air pollution particulates.
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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 | 0.000 | 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) | 0.002 | 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".