Effects of Controlled Diesel Exhaust and Allergen Exposure on microRNA and Gene Expression in Humans. Modulation of Lung Inflammatory Markers Associated with Asthma
Bibliographic record
Abstract
Abstract Rationale Air pollution may contribute to the development of allergic diseases, including asthma, by enhancing immune responses to allergen, but the effects on microRNA, messenger RNA, and inflammatory markers remain unexplored. Objectives To determine if acute exposure to diesel exhaust and/or allergen alters expression of microRNA and genes with effects on inflammatory markers associated with asthma. Methods Fifteen atopic participants were exposed in a crossover trial to inhaled filtered air or diesel exhaust (DE; 300 μg particulate matter with aerodynamic diameter less than or equal to 2.5 μm/m3), followed by saline-controlled, segmental allergen challenge. Lung inflammatory markers, gene expression, and microRNAs were measured in bronchial brushings, bronchial wash, or bronchoalveolar lavage 48 hours after exposure. Results DE + saline and DE + allergen significantly modulated the highest number of microRNAs and messenger RNAs. Allergen exposure significantly modulated microRNAs, including miR-324-5p, miR-132-3p, and miR-183-5p, and genes, including cyclin-dependent kinase inhibitor 1C (CDKN1A) and nuclear factor κB inhibitor α (NFKBIA), but DE did not significantly modify this effect. Inflammatory markers, including bronchoalveolar lavage eosinophil cell percentages and eosinophil cationic protein, were significantly increased, whereas bronchoalveolar lavage epithelial cell percentages were reduced after allergen exposure. Conclusions Expression of genes and microRNAs associated with bronchial immune responses were significantly modulated by allergen and DE. Clinical trial registered with www.clinicaltrials.gov (NCT01699204).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".