Particle Depletion Does Not Remediate Acute Effects of Traffic-related Air Pollution and Allergen. A Randomized, Double-Blind Crossover Study
Bibliographic record
Abstract
Abstract Rationale Diesel exhaust (DE), an established model of traffic-related air pollution, contributes significantly to the global burden of asthma and may augment the effects of allergen inhalation. Newer diesel particulate-filtering technologies may increase NO2 emissions, raising questions regarding their effectiveness in reducing harm from associated engine output. Objectives To assess the effects of DE and allergen coexposure on lung function, airway responsiveness, and circulating leukocytes, and determine whether DE particle depletion remediates these effects. Methods In this randomized, double-blind crossover study, 14 allergen-sensitized participants (9 with airway hyperresponsiveness) underwent inhaled allergen challenge after 2-hour exposures to DE, particle-depleted DE (PDDE), or filtered air. The control condition was inhaled saline after filtered air. Blood sampling and spirometry were performed before and up to 48 hours after exposures. Airway responsiveness was evaluated at 24 hours. Measurements and Main Results PDDE plus allergen coexposure impaired lung function more than DE plus allergen, particularly in those genetically at risk. DE plus allergen and PDDE plus allergen each increased airway responsiveness in normally responsive participants. DE plus allergen increased blood neutrophils and was associated with persistent eosinophilia at 48 hours. DE and PDDE each increased total peripheral leukocyte counts in a manner affected by participant genotypes. Changes in peripheral leukocytes correlated with lung function decline. Conclusions Coexposure to DE and allergen impaired lung function, which was worse after particle depletion (which increased NO2). Thus, particulates are not necessarily the sole or main culprit responsible for all harmful effects of DE. Policies and technologies aimed at protecting public health should be scrutinized in that regard. Clinical trial registered with www.clinicaltrials.gov (NCT02017431).
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".