Particle depletion of diesel exhaust restores allergen-induced lung-protective surfactant protein D in human lungs
Bibliographic record
Abstract
Rationale Exposure to air pollution is linked with increased asthma morbidity and mortality. To understand pathological processes linking air pollution and allergen exposures to asthma pathophysiology, we investigated the effect of coexposure to diesel exhaust (DE) and aeroallergen on immune regulatory proteins in human airways. Methods Fourteen allergen-sensitised participants completed this randomised, double-blinded, cross-over, controlled exposure study. Each participant underwent four exposures (allergen-alone exposure, DE and allergen coexposure, particle-depleted DE (PDDE) and allergen coexposure, and sham exposure) on different order-randomised dates, each separated by a 4-week washout. Serum and bronchoalveolar lavage (BAL) were assayed for pattern recognition molecules, cytokines, chemokines and inflammatory mediators. Results In human airways, allergen-alone exposure led to accumulation of surfactant protein D (SPD; p=0.02). Coexposure to allergen and DE did not elicit the same increase of SPD as did allergen alone; diesel particulate reduction restored allergen-induced SPD accumulation. Soluble receptor for advanced glycation end products was higher with particle reduction than without it. In the systemic circulation, there was a transient increase in SPD and club cell protein 16 (CC16) 4 hours after allergen alone. CC16 was augmented by PDDE, but not DE. % eosinophils in BAL (p<0.005), eotaxin-3 (p<0.0001), interleukin 5 (IL-5; p<0.0001) and thymus and activation regulated chemokine (p=0.0001) were each increased in BAL by allergen. IL-5, SPD and % eosinophils in BAL were correlated with decreased FEV1. Conclusion Short-term coexposure to aeroallergen and DE alters immune regulatory proteins in lungs; surfactant levels are dependent on particle depletion. Trial registration number 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 | 0.000 | 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".