Impact of Exposure to Diesel Exhaust on Inflammation Markers and Proteases in Former Smokers with Chronic Obstructive Pulmonary Disease: A Randomized, Double-blinded, Crossover Study
Bibliographic record
Abstract
Abstract Rationale There is growing evidence that chronic obstructive pulmonary disease (COPD) can be caused and exacerbated by air pollution exposure. Objectives To document the impact of short-term air pollution exposure on inflammation markers, proteases, and antiproteases in the lower airways of older adults with and without COPD. Methods Thirty participants (10 ex-smokers with mild to moderate COPD and 20 healthy participants [9 ex-smokers and 11 never-smokers]), with an average age of 60 years, completed this double-blinded, controlled, human crossover exposure study. Each participant was exposed to filtered air (control) and diesel exhaust (DE), in washout-separated 2-hour periods, in a randomly assigned order. Bronchoscopy was performed 24 hours after exposure to collect lavage. Cell counts were performed on blood and airway samples. ELISAs were performed to measure acute inflammatory proteins, matrix proteinases, and antiproteases in the airway and blood samples. Measurements and Main Results In former smokers with COPD, but not in the other participants, exposure to DE increased serum amyloid A (effect estimate, 1.67; 95% confidence interval [CI], 1.21–2.30; P = 0.04) and matrix metalloproteinase 10 (effect estimate, 2.61; 95% CI, 1.38–4.91; P = 0.04) in BAL. Circulating lymphocytes were increased after DE exposure (0.14 [95% CI, 0.05–0.24] cells × 109/L; P = 0.03), irrespective of COPD status. Conclusions A controlled human crossover study of DE exposure reveals that former smokers with COPD may be susceptible to an inflammatory response compared with ex-smokers without COPD or never-smoking healthy control participants. Clinical trial registered with www.clinicaltrials.gov (NCT 02236039).
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 | 0.005 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".