Diesel exhaust in asthmatics modulates leukotriene E4 and airflow through epigenetic changes
Bibliographic record
Abstract
Rationale/Objectives : Exposure to diesel exhaust (DE) is associated with acute health effects in asthmatics but underlying mechanisms have not been clearly delineated. Methods/Measurements : Randomized crossover, double-blind experiment; 13 partici-pants exposed on 2 different days to filtered air (FA) or DE (300 μg PM 2.5 /m 3 ). Forced expiratory volume in 1–second (FEV 1 ) and urine for leukotriene E4 (uLTE 4 ) were collected at baseline and 6 and 30 hours post-baseline. Blood was sampled at baseline and 6 hours post-baseline and profiled for 734 microRNAs using the Nanostring platform. Results: An increase in LTE 4 (from 0h to 6h) was associated with a decline in FEV 1 over the same timeframe upon exposure to FA (p=0.01) but not DE (p=0.55). Exposure interacts (p=0.007) upon the relationship between LTE 4 and FEV 1 . From the 101 miRNAs that were above background, 27 miRNAs target genes in the cysteinyl leukotriene pathway. The association between the change in FEV 1 and the change in miR-128 and miR-342-3p levels each varied by exposure (interaction p<0.05). The association between the change in LTE 4 and the change in miR-340 and miR-142-5p each varied by exposure (interaction p<0.05). Conclusion: DEP exposure in asthmatics alters urinary cysteinyl leukotriene (CYSLT; e.g. LTE 4 ) and airflow by modulating changes in specific miRNA. The miRNA implicated in this study (miR 128, 342-3p, 340, and 142-5p) are associated with important leukotriene pathway genes, e.g. ALOX5, P2RY2 and CYSLT receptor 2, OXGR1 and P2RY13 respectively. DE exposure appears to modulate leukotriene levels and airflow through changes in microRNA. Funding: WorkSafeBC, CIHR, BC Lung Association, AllerGen NCE.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".