Impact of particulate matter (PM) exposure on lung macrophage phenotype and phagocytic activity
Bibliographic record
Abstract
Background: Epidemiologic studies have shown that particulate matter (PM10) increase pulmonary morbidity and mortality. Lung macrophages (LM) are key first-line immune effector cells phagocytosing and processing inhaled PM10. LM phenotypes (M1 which are more pro-inflammatory and M2 more anti-inflammatory promoting tissue repair) role and responses in processing PM is still unclear. The goal of this study was to evaluate the phagocytic function of different macrophage phenotypes following exposure to urban air pollution particles. Methods: PMA was use to differentiate THP-1 monocytes into macrophages (M0) and further into M1 mac9s with INF-λ and LPS and M2 mac9s using IL-4 and IL-13. Surface marker labelling (CD68, CD206, CD163 and CD80) and flow cytometry were used to quantify different populations. Urban PM10 (EHC93) phagocytosis were quantify and mediator production measure. The impact of PM10 on phagocytosis of Staph Aureus were measured. Results: PM10 exposure increase M1 (13 to 19 to 28% with 0, 0.03mg/ml and 0.05mg/ml PM) and decrease M2 differentiation in a dose depended manner. PM10 were phagocytosed the best by M1 mac9ss with less phagocytosis by M2 mac9s (22.4±3.2vs 10.5±2.1%, p<0.05). M1 mac9s produce IL-1β, IL-6 and TNF-α, while M2 mac9s produce IL-10, IL-1RA and CCL-22 in a dose dependent manner. PM10 reduces M1 mac9s phagocytosis of Staph Aureas (76±8%vs48±9%vs41±5% with 0, 0.03mg/ml and 0.05mg/ml PM respectively, p<0.001) in a dose dependent manner with no effect on M2 macrophages. Conclusions: Urban particle exposure skewed macrophages to a M1 phenotype but also suppress their phagocytosis of bacteria, suggesting it promote vulnerability to lung infection/colonization.
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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) | 0.003 | 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".