Inhaled diesel exhaust alters the allergen-induced bronchial secretome in humans
Bibliographic record
Abstract
Diesel exhaust (DE) is a paradigm for traffic-related air pollution. Human adaptation to DE is poorly understood and currently based on oversimplified models. DE promotes allergic responses, but protein expression changes mediated by this interaction have not been systematically investigated. The aim of this study was to define the effect of inhaled DE on allergen-induced proteins in the lung. We performed a randomised and blinded controlled human crossover exposure study. Participants inhaled filtered air or DE; thereafter, contralateral lung segments were challenged with allergen or saline. Using label-free quantitative proteomics, we comprehensively defined DE-mediated alteration of allergen-driven secreted proteins (secretome) in bronchoalveolar lavage. We further examined expression of proteins selected from the secretome data in independent validation experiments using Western blots, ELISA and immunohistochemistry. We identified protein changes unique to co-exposure (DE+allergen), undetected with mono-exposures (DE or allergen alone). Validation studies confirmed that specific proteins ( e.g. the antimicrobial peptide cystatin-SA) were significantly enhanced with DE+allergen compared to either mono-exposure. This study demonstrates that common environmental co-exposures can uniquely alter protein responses in the lungs, illuminating biology that mono-exposures cannot. This study highlights the value of complex human in vivo models in detailing airway responses to inhaled pollution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 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".