Airway eosinophilia in response to allergen is augmented by diesel exhaust
Bibliographic record
Abstract
Rationale: Mechanisms of association between traffic-related air pollution and asthma remain unclear. Specifically, there is limited evidence in animal models and human nasal models that diesel exhaust (DE) augments the response to allergen, but this has not been shown in vivo in the human lung. Methods: We recruited 10 volunteers and determined their specific sensitization by skin prick testing. Each subject participated in a blinded crossover experiment between two conditions (filtered air (FA) and 300µg PM2.5/m3 of DE), randomized and counter-balanced to order (Fig.1). Each subject was exposed to each condition, with a 4-week washout period between exposures. One hour following exposure, diluent-controlled segmental allergen challenge was performed using 5mL of saline and allergen extract (in a concentration 10-fold lower than the lowest concentration producing a positive (≥3mm) wheal to skin prick) in the lingular and right middle lobe segments. Two days post-exposure initiation, bronchoalveolar lavages (BAL) is collected in both the allergen-affected and control regions. BAL eosinophils were assessed by differential cell counts on cytospins. Eosinophilic cationic protein (ECP) was assessed by ELISA. Pairwise t-tests were performed to compare effects of contrasting conditions (DES = diesel exhaust + saline; DEA = diesel exhaust + allergen; FAA = filtered air + allergen). Results: BAL eosinophilia was significantly higher in DEA vs FAA (p = 0.007) and DEA vs DES (p= 0.01 [n = 8]). BAL ECP was higher in DEA vs DES (p = 0.001; n = 10). Conclusions: DE has a synergistic effect on airway eosinophilia in response to allergen in previously-sensitized individuals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".