Urinary Eicosanoid Levels Reflect Allergen and Diesel Exhaust Coexposure and Are Linked to Impaired Lung Function
Bibliographic record
Abstract
Eicosanoids are potent regulators of homeostasis and inflammation. Co-exposure to allergen and diesel exhaust (DE) have been shown to lead to eosinophilic inflammation, impaired airflow, and increased airway responsiveness. It is not clear whether eicosanoids mediate the mechanism by which these exposures impair lung function. We conducted a randomized, double-blinded, and four-arm crossover study. Fourteen allergen-sensitized participants were exposed to four conditions: negative control; allergen-alone exposure; DE and allergen coexposure; coexposure with particle-reducing technology applied. Quantitative metabolic profiling of urinary eicosanoids was performed using LC-MS/MS. As expected, allergen inhalation increased eicosanoids. The prostacyclin metabolite 2,3-dinor-6-keto-PGF 1α (PGF 1α, prostaglandin F 1α ) increased with coexposure, but particle depletion suppressed this pathway. Individuals with a high genetic risk score demonstrated a greater increase in isoprostane metabolites following coexposure. Causal mediation analyses showed that allergen induced airflow impairment was mediated via leukotriene E 4 and tetranor-prostaglandin D metabolite. Overall, DE exposure did not augment the allergen’s effect on urinary eicosanoids, except insofar as variant genotypes conferred susceptibility to the addition of DE in terms of isoprostane metabolites. These findings will add to the body of previous controlled human exposure studies and provide greater insight into how complex environmental exposures in urban air may influence individuals with sensitivity to aeroallergens.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".