Impact of Diesel Exhaust Exposure on Plasma and Bronchial Alveolar Lavage Exposome in Human Participants
Bibliographic record
Abstract
Abstract Rationale: Exposure to diesel exhaust (DE), a form of traffic-related air pollution, can cause pulmonary inflammation and oxidative stress. Pulmonary inflammation and oxidative stress encompass the production of inflammatory mediators that may enter the bloodstream, producing systemic effects. Untargeted molecular profiling, referred to as Exposomics, of bronchoalveolar lavage (BAL) and plasma may provide valuable insights into the lung and systemic effects of DE exposure. This study utilizes a controlled human exposure study to evaluate concurrent changes in the exposome between the airways and systemic circulation associated with acute DE exposure. Methods: We analyzed samples from twenty-nine research participants with and without chronic obstructive pulmonary disease (COPD) (aged 40-80; 11 females and 18 males) who participated in a randomized, double-blinded, controlled exposure crossover study. Participants were exposed to DE (standardized to 300 µg/m3 of particulate matter ≤2.5 µm) and filtered air (FA; as a control) for 2 hours, during separate visits separated by a ≥4-week washout period. BAL and plasma were collected at baseline and after exposure. Endogenous metabolites and xenobiotics small molecules were analyzed using liquid chromatography high-resolution mass spectrometry (LC-HRMS) on an Orbitrap. Using a machine learning approach, structural annotation was conducted combining spectral library database matching (MassBank EU) with molecular formula and analogue prediction (SIRIUS and MS2Query). Weighted correlation networks were generated using the R package WGCNA (v1.73) to discern clusters of metabolite features associated with exposure. Results: In BAL, there were 9178 features common in both FA and DE groups, along with 474 features exclusive to FA and 733 features exclusive to DE. In plasma, there were 21130 features common in both FA and DE groups, along with 2508 features exclusive to FA and 1957 features exclusive to DE. Between both BAL and plasma, weighted correlation networks revealed several clusters of features that were negatively correlated with DE exposure among several participants. Conclusion: In summary, we analyzed data from bronchoalveolar lavage (BAL) and plasma exosomes following controlled exposure to DE, a well-established model of traffic-related air pollution. Our findings revealed alterations in molecular features in response to DE exposure. There were unique molecular features detected upon DE exposure, and clusters of features that were negatively correlated with DE exposure in BAL and plasma. Further analysis will focus on bioinformatically integrating the BAL and plasma exposome to reveal the non-invasive markers of DE exposure in plasma that can be studied to gain insight into longer-term exposures.
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 | 0.002 |
| 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) | 0.003 | 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".