Diesel exhaust augments allergen-induced lower airway inflammation in allergic individuals: a controlled human exposure study
Bibliographic record
Abstract
RATIONALE: Traffic-related air pollution has been shown to augment allergy and airway disease. However, the enhancement of allergenic effects by diesel exhaust in particular is unproven in vivo in the human lung, and underlying details of this apparent synergy are poorly understood. OBJECTIVE: To test the hypothesis that a 2 h inhalation of diesel exhaust augments lower airway inflammation and immune cell activation following segmental allergen challenge in atopic subjects. METHODS: 18 blinded atopic volunteers were exposed to filtered air or 300 µg PM(2.5)/m(3) of diesel exhaust in random fashion. 1 h post-exposure, diluent-controlled segmental allergen challenge was performed; 2 days later, samples from the challenged segments were obtained by bronchoscopic lavage. Samples were analysed for markers and modifiers of allergic inflammation (eosinophils, Th2 cytokines) and adaptive immune cell activation. Mixed effects models with ordinal contrasts compared effects of single and combined exposures on these end points. RESULTS: Diesel exhaust augmented the allergen-induced increase in airway eosinophils, interleukin 5 (IL-5) and eosinophil cationic protein (ECP) and the GSTT1 null genotype was significantly associated with the augmented IL-5 response. Diesel exhaust alone also augmented markers of non-allergic inflammation and monocyte chemotactic protein (MCP)-1 and suppressed activity of macrophages and myeloid dendritic cells. CONCLUSION: Inhalation of diesel exhaust at environmentally relevant concentrations augments allergen-induced allergic inflammation in the lower airways of atopic individuals and the GSTT1 genotype enhances this response. Allergic individuals are a susceptible population to the deleterious airway effects of diesel exhaust. TRIAL REGISTRATION NUMBER: NCT01792232.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".