Acute air pollution exposure alters neutrophils in never-smokers and at-risk humans
Bibliographic record
Abstract
Outdoor air pollution exposure increases chronic obstructive pulmonary disease (COPD) hospitalisations, and may contribute to COPD development. The mechanisms of harm, and the extent to which at-risk populations are more susceptible are not fully understood. Neutrophils are recruited to the lung following diesel exhaust exposure, a model of traffic-related air pollution (TRAP), but their functional role in this response is unknown. The purpose of this controlled human-exposure crossover study was to assess the effects of acute diesel exhaust exposure on neutrophil function in never-smokers and at-risk populations, with support from additionalin vitrostudies. 18 participants, including never-smokers (n=7), ex-smokers (n=4) and mild–moderate COPD patients (n=7), were exposed to diesel exhaust and filtered air for 2 h on separate occasions, and neutrophil function in blood (0 h and 24 h post-exposure) and bronchoalveolar lavage (24 h post-exposure) was assessed. Compared to filtered air, diesel exhaust exposure reduced the proportion of circulating band cells at 0 h, which was exaggerated in COPD patients. Diesel exhaust exposure increased the amount of neutrophil extracellular traps (NETs) in the lung across participants. COPD patients had increased peripheral neutrophil activation following diesel exhaust exposure.In vitro, suspended diesel exhaust particles increased the amount of NETs measured in isolated neutrophils. We propose NET formation as a possible mechanism through which TRAP exposure affects airway pathophysiology. In addition, COPD patients may be more prone to an activated inflammatory state following exposure. This is the first controlled human TRAP exposure study directly comparing at-risk phenotypes (COPD and ex-smokers) with lower-risk (never-smokers) participants, elucidating the human susceptibility spectrum.
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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 | 0.001 |
| 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 | 0.001 | 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".